HK1111495A - System and method for location based social networking - Google Patents

Description

System and method for location-based social networking

Technical Field

The field of the invention relates generally to systems and methods for generating and collecting profile information about people and entities and matching and filtering the people and entities based on the profile information.

Background

The social networking system may use the profile to connect people who may wish to know each other. The idea of connecting strangers or friends that might not otherwise be known is powerful. However, the value of these systems may be limited by the underlying methods used to match (basic preference characteristics such as common business relationships, social relationships, relatives, consistent physical characteristics, or self-declared preferences for food, clothing, leisure activities, sports, entertainment, music, art, etc.).

The main problem with such basic social networking systems is the lack of verifiability and authenticity of the matching criteria, resulting in an excessive number of low quality matches. When people (or connections to entities) are known through such matching criteria, too many low quality matches may result in a loss of confidence in the overall system, overall low availability, and trust issues.

Another problem is that such systems force the user to do tedious work similar to filling out a questionnaire to create a self-generated profile by entering personal information. This can create two problems: the inconvenience of participants and the lack of criteria that everyone can trust. First, many people are busy or lazy. Any system that relies on its user to create and update multivariate profiles is inherently deficient. Many people will have their profiles stale. Secondly, people have different standards when they relate to their own declared information. I may think i am a wine expert, but by definition i am a novice. In addition, the information I provides in creating my profile may not be useful in distinguishing I from other users in the system. For example, i may mention in my self-generated profile that i am a Red Sox fan. However, this information may not be useful to distinguish I from the thousands of other Red Sox fans in Boston. Subtleties are lost. For example, i may be a faithful fan and want to know someone else who has season tickets like i. In other words, the rating information may be important, but in self-generated profile formation it is sometimes either lost or mischaracterized.

What is desired, therefore, is an improved system and method for adding accountability and criteria to a user profile, ideally one that does not burden the user with the cumbersome task of setting up and maintaining a profile. What is also desired is an improved system and method for location-based and context-based user matching and filtering. It is also desirable to allow people to match not only with other people, but also with "entities" such as restaurants, bars, organizations, groups, stores, and even cities.

Disclosure of Invention

The technical scheme of the invention relates to a system and a method for social networking. Location-related data and other behavioral and exogenously generated characteristics are used to replace or supplement self-generated profiles to enhance the quality and credibility of matches made with the system and to assist in the input of profile information.

Drawings

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIG. 1 is a block diagram of a mobile device that may be used with embodiments of the present invention.

Fig. 2 is a block diagram illustrating direct information exchange between mobile devices according to an example embodiment of the present invention.

Fig. 3 is a block diagram illustrating a network system according to an example embodiment of the present invention.

FIG. 4 is a logic diagram of a user profile according to an example embodiment of the invention.

FIG. 5 is a logic diagram of a target profile in accordance with an example embodiment of the present invention.

Fig. 6 is a flowchart illustrating a method for matching users according to an exemplary embodiment of the present invention.

Detailed Description

Example embodiments of the present invention provide a system and method for collecting and generating profile information for mobile device users and for matching users based on those profiles. In particular, the mobile device may generate or receive location-based information to augment both the generation of profile information and the use of that information to match users in different contexts. In particular, both the profile and the manner in which it is used may vary based on the location of the user and the programmable filters and settings established by the user. For example, a user at a nightclub on a saturday night may be notified of nearby friends or other users who have common friends or other characteristics that indicate a matching or ancillary social gathering. On the other hand, when the user is at a trade show on a weekday, different profile information and filters may be used. The environment may be easily set by a user (by manually selecting "work mode", "social mode", etc.) and/or may be automatically set by the device based on time, place, or other parameters. To this end, the device may use some programming logic to automatically determine the environment, including using statistics to guess, for example, that a user is likely to be in "social mode" given a combination of location, time, and other factors, thus saving the user the trouble of having to periodically adjust his device settings.

Profile information may be generated from behavioral characteristics of the user, externally generated characteristics, and user-specified information. In addition, the profile may include a unique identifier and one or more pseudonyms or temporary identifiers for privacy purposes. The profiles may then be used to suggest matches between users or to provide the users with icebreakers (topics suitable for initiating conversations).

Data fields in the user profile may be tagged to indicate the type of data (behavioral, extrinsic, user-specified, or other) and the quality of the data for matching purposes. The quality factor may include an indication of the relevance of particular data to the user and a confidence in the accuracy of the information. For example, data may be collected based on the location visited by the user. However, if location tracking is disabled most of the time, data about a small set of locations visited by the user may not provide meaningful information about the user's behavior. Additionally, the trustworthiness of the data may depend on the source of the data (e.g., whether it is user-specified or obtained from an outside source) or whether the data is validated by a reliable source. For example, my user profile may indicate that me and Joe Smith are friends. This information may provide a useful way to match people with common friends. However, Joe may not consider I friends at all. Thus, he may be misled about his real friend to know me, think me is a friend of Joe, and later just find Joe hardly to know me. This confidence problem may often arise without some means of qualifying the reliability of the data for matching. Without other indicia of trustworthiness of the data, the usefulness of the information may be compromised, raising suspicion of the entire matching system. Given that matching systems wish to instill trust in the connection, this can lead to abuse in which people no longer feel cheated, no longer trust the system, but merely find that they have been associated based on cheating. On the other hand, if Joe acknowledges, validates, or even rates our relationships, or (another way to do the same) if my behavior profile indicates (through GPS tracking or other means) that i actually spent much time with Joe, or if i regularly communicated with him through phone or email, the trustworthiness of this information (and generally to the system) can be enhanced. Supplementing the user-submitted information with behavioral information derived from observed behaviors can therefore significantly increase the trustworthiness of the data used to make the matches, and thus can improve the overall user experience.

FIG. 1 is a block diagram of an example mobile device 100 that may be used with embodiments of the present invention. The mobile device may be, for example, a Personal Digital Assistant (PDA), a cellular telephone, a laptop computer, a pager, or other communication device. The example device 100 includes a central processing unit 102, a memory 106, a Network Interface Card (NIC)110, a Global Positioning System (GPS)112, and a bus 104 for communication between these components. The memory 106 may store profile information 108, including profile information about the user of the mobile device 100, settings and filters for matching target profiles of other users and using those profiles. The memory 106 may store the profiles in a relational database, flat file system, or other database file format. Storage of this information on the device 100 allows matching to occur on an ad hoc basis when other devices are encountered (e.g., via bluetooth or other communication interface), regardless of whether a connection to a particular network or server exists. For example, fig. 2 shows two mobile devices 202 and 204 using direct communication to exchange selected profile information (e.g., which may include target profiles indicating potential matches). Either device may determine whether there is a match and send a notification to the other user.

The profile information 108 may be used by application software stored in memory 106 and may be processed on the CPU 102. All or a portion of this information may also be stored on a separate network server, which may generate, maintain, and process the user profile. NIC110 or other network interface provides access to an external network to allow communication with a network server and/or other mobile devices.

In some embodiments, the user may participate by using a passive identifier such as a card in his wallet. If the system can track the user only by his physical attributes (e.g. with a casino-type (casino-style) camera connected to a computer running the facial recognition software) or by a biometric identifier, the user can actually participate without having to do or carry anything; because the system "looks" at him, his profile can be updated and retrieved by the system as needed. In some embodiments, the devices and apparatuses intended for use with the system may be replaced by chips and devices implanted in the human body, biometric codes, and other tracking technologies. Thus, embodiments of the invention are not limited to smart phones and other mobile devices, but may be modified to include other implementation techniques.

The GPS 112 generates location data that can be used to generate profile information and match users. Other location-based information may be generated or received instead of or in addition to location information generated from the global positioning system. In some embodiments, the location information may be generated based on the cellular telephone network (e.g., based on the cell in which the telephone is located or by more sophisticated triangulation techniques), or by determining the proximity of devices or network access points for which location data is available. In other words, a device without location data may know its location at least approximately if it can communicate with a device that knows its location either directly or indirectly (e.g., a device that relies on GPS but is indoors and cannot see the sky may be daisy-chained to convey location information from nearby devices that can see the sky). Additionally, a particular venue may broadcast or provide information (e.g., via bluetooth, Wi-Fi, or other mechanisms) about a particular location, event, or activity. When a user enters or leaves a night club, amusement park, sports arena, concert hall, or other venue, a message may be sent to the mobile communication device to indicate a location or other information about the event or activity. Different messages may be sent for different parts of the venue so that a device of a user, for example, at a multi-screen movie theater, may know which movie the user is watching. Local triangulation systems or mechanisms for determining the relative position of users may also be used to identify specific "micro-locations" or to help users find each other within a venue, which may be useful especially when the users do not know each other, for example in matching between strangers occurring in a crowded bar (micro-location methods may include the strength and direction of the received signal between two users, thus working like a compass, with arrows pointing to the target user and a "cold and hot" meter indicating the distance). The identities of other users and devices within a specified range may be collected and time stamped for the user's profile, including safeguards for privacy (e.g., recording and time stamping of certain meta-information (meta-information) about people in your vicinity, rather than names). Additionally, if other users and devices contain location information (e.g., GPS location, location entered by the user, etc.), the information may be associated with the profile even if the user's device is unable to generate the information.

Data relating to people at the same location may be used to enhance the user's profile. For example, if the user goes to a bar consisting of people aged 20 and 30 who work in the financial department, this can be used to "teach" the system about the user, which can announce the matching selection. Similarly, information known about people visiting a given location can be used to build a profile of the venue itself; thus, depending on the time of day or whether a match is being played at Fenway Park on the day, the bar may recite "entity profiles" as motorcycle enthusiasts, Red Sox enthusiasts, or a place of aggregation of both. When an entity has profile information associated with it, then this information can be accessed in advance by anyone who wants to go to the bar. Profile matching algorithms can be used to predict whether you will be compatible with regular patrons at the bar. A feedback system using behavioral information, such as whether the user repeatedly frequents a particular bar or a particular "type" of bar and/or user-declared information (e.g., when the user explicitly rates his experience at a given location), may be employed. The profiles of the entities and their approvers can interact dynamically, building on each other and evolving over time as warranted.

The process of selecting places where time is spent, such as bars, night clubs, schools, restaurants, country clubs, vacation resorts, companies (where you may be employed), etc., is what happens today without any help from technology: people go and return where they like these places. Or they go to these places based on friend recommendations. Embodiments of the present invention serve to reduce some time and labor, and at the same time introduce statistical analysis elements into the human process that are inefficient and characterized by trial-and-error. This is not intended to obliterate the occasional character found by humans or overwhelm subjective recommendations; but rather as a useful supplemental guidance system.

Fig. 3 illustrates a network 300 according to an example embodiment of the invention. Network 300 includes a network server 302 and a database storage system 304. Database storage system 304 stores profile information about system users. Database storage system 304 may store the profiles in a relational database, flat file system, or other database, or other file format. The network server 302 collects information from the mobile devices and other sources to generate profile information. For example, web server 302 may collect information about email account usage in communication with other users of the system. The network server 302 also collects information about the user's location and process profile to match the user based on the information. While some or all of this information may be stored on individual devices and allow ad hoc matching, the use of a network server allows information about a venue to be collected regardless of whether a particular user is at the location. For example, the server may determine the number of users at a particular night club and the number of potentially matching users or whether a specified friend is at the location. The user may request this information in advance to decide whether to go to a particular location. Aggregated (or individual) profile information may also be provided for estimating advertisement opportunities at a particular venue (e.g., determining which advertisements to display on a monitor of a sporting event depending on the user's profile at the event) or for other purposes. The web server 302 may also be used to collect historical information about the venue and the people who are there. Such information may be useful to people interested in knowing patterns, such as who frequently visits the venue and when. The user may want to know whether, for example, a person who has a common friend with him is often going to a given club. Similarly, if he is planning an illegal rendezvous point, he may want to verify that no people he knows or regular helpers are now at the venue, or may even appear there (the likelihood is determined by statistical analysis and cross-matching of his acquaintance's profile with that of the entity).

In an example embodiment, the server 302 is connected to the Internet 306 for communication with other devices. In other embodiments, the server may be directly connected to a wireless network, cellular telephone system, or other network. In this example embodiment, mobile devices 310, 314, 316, 324, 326, and 328 may connect to the Internet (or other network) through various methods to allow communication with server 302 and other mobile devices. For example, the device 310 may be synchronized with a personal computer 308 that provides a connection to the Internet. Data may be exchanged with the server via personal computer 308. In addition, whether or not personal computer 308 is connected to a particular mobile device, data for a user profile may be entered through it. Similarly, the data of the user profile may come from a (networked) device (330, 332), such as a DVR, a smart card, a digital reader, etc. Mobile devices 314 and 316 can also connect to the network through wireless server 312, with wireless server 312 providing a connection to the internet. For example, wireless "hot spots" are increasingly being provided at coffee shops, libraries, and other locations, and may be provided at night clubs, sporting events, or other locations accessible to system users. Mobile devices 324, 326 and 328 may also be connected to server 302 through a cellular telephone network. The mobile telecommunications switching offices 320 and 322 may use cellular telecommunications protocols (e.g., CDMA, GSM, TDMA, or other protocols) to communicate with the mobile devices 324, 326, and 328. Mobile telecommunications switching offices 320 and 322 may connect to the internet 306 through local office 318.

FIG. 4 is a block diagram of a user profile 400 according to an example embodiment of the invention. While FIG. 4 illustrates a data structure for an example user profile, it is to be understood that other databases, data structures, and formats may be used to store and associate the desired data in the user profile. The mobile device 100 and/or the network server 302 may be used to generate and update the user profile 400. The user profile may also be entered from other systems.

The example user profile 400 may be stored in a relational database and may have associated tables for storing profile data 402 and settings 406. The data table 402 may also include entries for various characteristics stored as part of the profile. Each property may be stored as a row in the data table 402. The data table 402 may contain an entry user type 450 indicating whether the profile is for a person or for an entity (e.g., a restaurant, an entertainment venue, etc.). For example, an entry may include a field identifier 404 identifying the entry, a data value 406, a data type 408, quality indicators such as relevance 410 and trustworthiness 412, and a pointer or other link to applicable settings 414 in a settings table 416. The settings table 416 includes settings and parameters that control how the entries in the data table 402 are used for matching and other purposes. The settings table 416 includes user settings 418 and default and automatic settings 426 established by the system (e.g., application software on the mobile device 100 or the web server 302). Regardless of whether a data entry may be used alone or must be aggregated 422 with other specified information before it can be used, a user may provide settings that control the availability 420 of data entries for various purposes, as well as other privacy settings 424. Default settings for each of these options may be established and stored in table 426. In addition, the automatic settings may be stored by the system in table 426 as an option that cannot be changed by the user.

The following is a more detailed description of data table 402 and examples of the types of entries that may be stored in the table. The data entry includes a type 408 indicating how the data was collected or generated. The data types may include behavioral characteristics collected by the system based on user actions, extrinsic information collected from sources other than the user, and user-provided information. The data entry also includes an indication of the quality and usefulness of the information. In the example shown in FIG. 4, relevance 410 and confidence 412 may be associated with data stored in a table. Relevance 410 and trustworthiness 412 may be indicated by a numerical ranking based on how the information is collected, generated, and/or validated. For example, the correlation 410 may indicate whether behavioral characteristics or composite data are generated from a large sample size. For example, if the data entry is based on the user's location, it may only be generated when the user's location can be tracked (which may be controlled by settings 416 for the device and may be limited by the range and availability of the location system). If the user's location is widely tracked, the data entry may have more relevance as an indicator of user behavior. For example, a match may be made between two people drinking coffee in the same cafe each morning. This information can be used at least to act as an icebreaker. Confidence level 412 may also be indicated. In contrast to user-asserted data, a high level of trustworthiness may be associated with data entries resulting from behavior tracked by the system, data provided in the form of security tokens, or information validated or rated by another source.

The use of behavioral information in the user profile and externally generated information can greatly enhance the quality and type of matches that can be made by the system. The following are example behavioral characteristics and externally generated characteristics that may be used to enhance the location-dependent matching engine included as part of application software on the server 302 and/or mobile device in accordance with embodiments of the present invention.

Telephone and email use. Rather than relying on a flat address book or requiring the user to sort everyone in the address book, the web server 302 or mobile device 100 can monitor the actual phone, SMS, and/or email usage (and/or any other communication device or account or connected device, including a PC) to infer who the user really knows and some information about the nature of the relationship (business versus social); a system that collects all of this in an automated fashion would be richer than any system that requires constant manual input. If I converse with John Burns for one hour each day, it can be inferred that I is well done with him. Thus, i want to know if John Burns happens to be in the mall that i just entered (and vice versa), but i do not have to care about someone who happens to be in my address book but with whom i have a five minute conversation just a few years ago. (alternatively, there may be situations where users seek looser and more distant contacts, as these sometimes provide more value, as there are more than close relationships, and they spread the user's network farther, which may be particularly useful in seeking work, finding sales prospects, etc. there is a theory that many distant contacts are actually more valuable in business than a smaller set of very close contacts.) this actual usage information is more content-rich than a flat contact when it is used for matching. It also increases the degree of interaction: since i never conversed on the phone with Gweneth Paltrow, i cannot claim that she is a friend even if she is for some reason on my address book. Interaction and authenticity are important factors for secondary matching (i.e. two people touching through a common acquaintance) because it is important to touch a person through a real medium if you want to take advantage of (leveraging) the instant trust that occurs when two strangers find that they know someone together. It should be noted that the behavior information may be combined with information entered by the user to obtain richer content. For example, when a user talks to someone on a telephone, a record of such a conversation is automatically recorded. The record may then be enriched with comments from users annotating and rating the conversation and the contact.

This data may be entered into the user profile 400 by either the network server 302 or the mobile device 100 (or a mobile device connected to a PC). For example, the mobile device 100 may be a cellular telephone having an address book in memory 106. The cellular telephone may track the frequency of calls and the amount of time spent calling for each person in the address book. Additionally, frequently dialed numbers (or numbers of people who frequently call your) may be automatically added to the address book and this data may be associated with the phone number even if other information is not available. A laptop computer or mobile email device (which may be the same or a different device than a cellular telephone) may track the number of messages sent and received from various email addresses. This information may be tracked separately or associated with the address book. The network server 302 and database storage system 304 may allow multiple mobile (or non-mobile) devices to register to the same user profile. In this way, the use of e-mail and the use of phone calls may be associated with a particular individual in the user's address book. The contacts (or particular email addresses or phone numbers) with which there is a communication may be added as entries in the data table 402 of the user profile. For example, a contact such as "John Bums" may be added with a field 404 (which may be linked to other contact information for this person through a relational database). The value 406 may be a number representing the frequency and duration of the communication. The types 408 will be classified according to behavior. The correlation 410 may be a number based on whether the sample size is large (whether tracking is on and how long it takes) and whether both email accounts and phone accounts are registered with the system for tracking. Confidence 412 may be a number indicating whether the communication relies on a complete contact record (or just an email address or phone number), whether John Burns is also a registered user, whether he identifies the user as a friend and/or whether his contact information or communication usage information matches information in a particular user profile, whether there is a history of physical proximity (physical proximity), or other factors indicating the reliability of the data.

In physical proximity. As with conventional phone interactions between people, physical proximity may indicate a relationship and its closeness. A bluetooth enabled mobile device may, for example, automatically record the time two (or more) people spend together; this may be used to "authenticate" the relationship. This may include "time-shifted" approaches, that is, showing that two people may be at the same location but not at the same time; this may also be supplementary information useful in establishing relationships (e.g., they live in the same dormitory or regularly go to the same library, even though not always at the same time). The physical proximity may be used as a separate data entry or aggregated with phone and email usage to generate an overall data value that serves as a proxy for the level of relationship with others (whether registered users of the system or not).

Where you go. In addition, the location visited by the user may be used to match or enhance the trustworthiness of other data entries. A field 404 may be added for frequently accessed locations. The value 406 may be the coordinates of a location where the user spends a lot of time. The data may be separated by the date and time that those locations were accessed to distinguish locations accessed during working hours from locations accessed on weekends or at night. The venue may also have wireless capability and provide tags or signs with additional information about the places visited (e.g., concert hall, amusement park, sports events, etc. when a particular artist was performing). In addition to using this message for matching, the user can be provided with it as a potential icebreaker for talking, e.g. letting both know that they are both Elvis Costello fans (based on the fact that they both see him at a concert) — something they may like to discuss as a way to "break ice". It should be added that they can be used for verifiability of data in cases where the tokens are digitally signed or are difficult to forge.

The "where you go" information may also be used by advertisers or other businesses that may want to know more about you for marketing purposes. For example, a token showing you have seen the same movie multiple times may be useful not only for matching purposes (informing similar hobbyists of your interest), but also for advertisers. A spider-man vendor may want to hype a sequel by only sending out a special message or promotional message to those who are certified to see at least the first movie (in the movie theater) three times. This is described in detail below.

The data may also be used to enhance the relevance 410 and trustworthiness 412 of other data entries. For example, if I go to an 81 Red Sox game a year, then it gives I more weight on the Red Sox's interest than a self-generated profile. Moreover, this information does not have to be manually entered by the user. It can be recorded automatically by simply going to the Fenway Park on the day of the race (letting my matching device "know" where i am using a device that tracks location or by interacting with the Fenway Park's transmitter or matching device or with other networked devices of the race). Unlike the subjective, self-generated nature, the fact about my location is objective and verifiable and may assign a higher value to trustworthiness 412 in data table 402. It can be assumed that as these devices and location-based matching systems become popular, spoofing will become an issue threatening the trustworthiness and usability of the system. Using authentication mechanisms like digital signature tokens can "prove" to a person or other interested person (e.g., an advertiser who offers special treatment to the most avid fans) that you are really indicating where you are (or your profile).

Other actions. What cafes you often go to, what gym you go to (and how often), when you wake up, whether you go to church every sunday, what tv shows you record (or watch) on a Digital Video Recorder (DVR), what website you visit (and what you buy, what information field you have on your computer (cookie), or what you do online), and in this regard, any behavior or trackable activity using trackable devices or appliances can prove who you are. As such, they can enhance matching and supplements, or even overwhelm (scroll) what you say about yourself in the profile; because it occurs automatically (passively from the user's perspective), you can "enter" them more easily. These factors may be tracked based on the location/movement of the device and through other devices and systems connected (directly or indirectly) to network server 302 or the mobile device (e.g., a DVR service that tracks recorded and viewed television programs or a cellular telephone network that tracks telephone usage). It may be desirable to turn off monitoring of these kinds of activities to avoid embarrassment and simply protect the privacy of the user, precisely because it is readily available.

A feedback system. The feedback system may also be used to provide information for generating data entries in the user profile 400. What happens when people have the ability to "mark" your with feedback? Is your former girl friend kept away from you? If you have ratings in your profile that are generated about your world, and you cannot edit it, then you can help strangers to evaluate you. Thus, you may result in "better" behavior because you do not want people to destroy your "personality credit rating". Thus, similar to the EBay feedback rating, the EBay feedback rating may be useful for informing a stranger whether someone can be trusted. In addition, it can also be an interesting way to "collect" and blaze "friendly action points". There is a powerful causal (karmic) component for establishing and maintaining positive interpersonal grading. These data entries are of a different type 408 than the behavior data entries described above that are tracked by the system. These entries may be marked as ambient data in general, or more specifically as feedback from another user. The value 406 may be a rating of various categories and may also include specific comments (which may or may not be visible to others depending on the nature of the comment, who is seeking access, and other rules that may apply to the powerful information). Relevance 410 may be based on the amount of feedback received and confidence 412 may depend on the person who gave the feedback (does that person have a positive feedback rating themselves.

A security marking. Restaurants, bars, clubs, siblings, churches, universities, and other places or organizations may give certified (digitally signed) "tokens" that can be added to the user profile. These tokens may create new data entries 402 or may be used to improve the trustworthiness 412 of existing data entries. This will allow people to see if someone is "real": are they really members of the club, are they really belonging to that gym, are they really graduates of Villanova, are they really getting honor retirement from the army, are they really frequent acquirers of the platinum medal of delta? This may be useful in matching situations: when a woman is in a bar and would like to know schoolmates, it may be important for her to determine that they are the true graduates of her mother, not that he says that he is or someone who may spend some time there but who is not graduation.

The person you know. Contact lists can be a powerful way to match users-using a common acquaintance as a medium can be a powerful way to match users. A frequent problem is that the address book is a subset of your acquaintance set. To maximize the effectiveness of the contact list as a matching method, entries (of people you know) can be made based on the normal contact list and observed behavior as described above. These items may also be manually entered by the user, with confidence level 412 increased (or decreased) by behavioral characteristics (as described above) or extrinsic information such as manual feedback from acquaintances who acknowledge the relationship (and their level of closeness) or the presence of corresponding items in the acquaintances' user profile. As described further below, a match or alert may be generated when a friend is nearby (based on location data and user profiles) or when a person with one or more common friends is nearby (or is truly nearby and has common friends). For example, the system may want to alert you that a good friend of your best friend happens to be passing a cafe where you have a meal.

Detailing the topic, other information can be used to enrich your profile. Some examples are: purchases you make with digital currency or credit cards (online or offline); transaction details can be integrated into your profile. The EBay feedback rating may be appended to your profile and the "best and EBay" used (a person may only want to do business with a person, hire a person, or with a person with a high EBay feedback rating). What appointment sites you belong to (and information related to those sites) can be shared with your profile. Even the advertisements you see (online or offline) (and the advertisements to which you respond in some way) are valuable to the seller who wants to sell something to you. More generally, your credit rating, violation records, medical history, driving records, rating and performance ratings, and other "scores" from various real-life activities can be integrated into your profile to assist in matching or for other purposes. (in some cases, a numeric notation may be used for verifiability.) the use of this information in your profile may include whether someone wants to date with you, hire you, credit you, provide you a special promotional message, or show you an advertisement (either online or offline, in the case of a dynamic targeted advertising method such as a variable billboard).

All of this information is powerful in helping people quickly learn and trust strangers. It may also be valuable to businesses because past purchasing behavior may be a strong predictor of future purchases. Some of the same technologies were developed online, such as providing dentist advertisements to people who just made a "dentist in chicago" search on Google. In this case, by combining rich arrays of behavioral data, such as what users search online, what they buy with their credit card or digital currency in a mall near their home, what movies they watch, what restaurants they have at, what television programs they record and/or watch, etc., there is a considerable opportunity for vendors to make one-to-one matches between people and their products. While we are proposing methods of making contact between people, matching between people and "inanimate entities" such as businesses is also an essential component of the present invention. A system that collects and processes behavioral data, including location data, has the ability to send alerts that are essentially sales, which engage users and help businesses. One example is that stores alert you when you enter their competitor's store, perhaps by offering you a discount to entice you to their store. Another example is to let it change dynamically depending on who is looking at the billboard (and what information about it is known based on the user's profile). Another example is to determine a pattern that is useful to the vendor, such as the fact that a person watching the Red Sox game has a meal twice as many times in Dunkin's diet as a person not watching the Red Sox game. In this state, it can be used to better determine the success of an advertising campaign (measured in return on investment). For example, what percentage of people who see a large Adventure (Great Adventure) billboard on a particular roadside (as determined by location tracking data) visited there in six months. The granularity of the behavioral information discussed herein is much larger than anything that has been previously achieved. While many people do not want their behavior monitored and do not want their activities tracked and combined into a profile and shared with advertisers, they may want this information tracked for some of the social benefits outlined herein, such as learning friends or establishing appointments with strangers who fit their target profile. Once this data is collected for one purpose, one can find that there is an incentive to selectively share it with the sales company. Just as gamblers share behavioral information with casinos for complimentary subsidies, and just as travelers share their flight history with airlines to qualify for free flights and preferential treatment, one may be aware of the fact that: because they spend $ 1000 at Prada, every other fashion woman store sends them an "alert" once they enter the mall, offering a special discount for entering their store. Broadly speaking, incentives to share other personal information with vendors may include, but are not limited to, cash payments, better-targeted advertising messages relating to one's interests, special offers from companies desirous of getting their business, and free or sponsored equipment or service fees for interacting with the matching system.

Of course, the user may decide how they want to share their information or whether they want to share their information. In some cases, users may want to share their other private medical records to show that they have recently reviewed and not have any sexually transmitted diseases (signed and date stamped tokens may be issued by a doctor or clinic to authenticate the information). In this case, they may specify that only a small portion of their medical records may be shared, and only in very special cases. It is assumed that there may be a set of rules that entitle the user to control the information as they wish. The following describes a simplified version of how these rules may be applied; it should be appreciated that the rules are simplified herein for practical purposes and that the system may be enhanced with more and/or different user controls.

It is clear that a robust and rich set of data that can be tracked and collected to generate a user profile leads to privacy concerns. To manage the use of data in the user profile, a settings table 416 is provided. Each entry in the table may specify a number of settings and parameters for application to the data entry or data collection. An entry in table 416 may be associated with one or more data entries in data table 402. For example, the user settings 418 may be input by a user via a mobile device or a personal computer. The user may specify an availability 420 setting for indicating whether multiple types or categories of data should be collected or made available for matching purposes. For example, a user may specify that location data may be collected and used based on a particular type of event (nightclub, sporting event, concert, etc.), but that other locations, such as adult nightclubs, should not be tracked or used. The user may also specify that location data may be made available for matching with another user only if certain conditions are met (e.g., alert you with location-based data when you are within 100 feet of another member of your local department (Rotary Club) and 10 feet away from members of a different department of the department). Similarly, rules may be established for providing a photo, an email address, or a phone number to another user, such as a desire to have an interchange of photos. The user may also specify certain times or dates or other conditions that limit matching and notification (e.g., allow matching only on friday and saturday evenings). The user may also specify that a certain level of the collection 422 must be used for matching based on a particular data entry. For example, the user may not want to allow others to match based only on revenue levels entered in the user profile. However, if the match is based on a large amount of aggregated data or a specific type of data with a high confidence, the user may allow revenue to be included in the extracted or aggregated form used by the matching engine (but not provided to other users). Other privacy settings 424 may also be used to limit the collection or use of data, delete it after a certain period of time, or limit its use for matching or disclosure to other users. Just as his behavior is easily monitored for the user, the recording of the behavior should be easily stopped when needed.

The user profile 400 may also include default and automatic settings. For example, table 426 may specify that behavior data types may not be edited by a user, but may be deleted. Just like feedback ranking with EBay, a user can reset all feedback from other users, but cannot simply delete feedback comments he does not like. Similarly, it may be desirable to have the system use an environmental filter. For example, if you are in a place where you are getting many matches, the device you are using (or the server to which it is connected to) can apply a filter to send you an alert only for high quality matches. Similarly, if you are traveling and do not get a "hit," the service may either automatically identify you are in a foreign country by using location detection, or simply lower their threshold by identifying you get few (or no) matches during a given period of time.

In an example embodiment, the user may also specify one or more target profiles (e.g., a 20's individual woman with certain other characteristics) for determining whether there are matching users with a particular profile. In particular, the target profile may be used to identify other users that have matching characteristics and are within a certain distance of the user or within another specified location. FIG. 5 is a logic diagram of a target profile 500 according to an example embodiment of the invention. While FIG. 5 illustrates data structures and field types for an example target profile, it should be understood that other databases, data structures, and formats may be used to store and associate the desired data in the target profile. The mobile device 100 and/or the network server 302 may store one or more target profiles 500 for matching purposes. The target user profile may also be entered from other systems, such as an online dating service.

In addition to assisting manually generated goal profiles, the system can develop global "marriage intelligence" by observing and learning the success and failure among those looking for spouses. For example, the system may "see" such patterns: the person who has taken a walk on their dog is very well with other people who do the same thing, so this behavioral information is used to "target your computer-generated target profile" which is different from the choices you might make for your own. While computers do not necessarily have in-person access to how people "get in" with each other, it may gather this information through direct user feedback, for example, when people get married and communicate this fact as feedback to the system. It may also infer a successful match by, for example, location-based tracking, observing that two people known in january have spent much time together since then. With the richness of data that can be collected and analyzed (extrinsic, self-generated, behavioral, etc.) and with the ever-increasing computing power of computers combined with bayesian algorithms, neural networks, and other artificial intelligence methods, computers can become good at serving people, a very "human-like" skill.

The example target profile 500 may be stored in a relational database and may have associated tables for storing target data ranges 502 and programmable filters 518. The data range table 502 may include a number of entries for characteristics that the system uses to determine whether another user matches the target profile. If there is a match, action may be taken based on the settings 416 (e.g., beep or send a notification, or display information about the matching user or group of users). Each target characteristic may be stored as a row in data range table 502. The entries may correspond to entries for the data table 402 (field 504, value range 506, type 508, relevance 510, and confidence 512) except that a threshold, range, or wildcard value may be used to specify requirements for a user profile that matches the target profile. Additionally, a weight 504 may be assigned to an entry to indicate the importance of the entry in determining whether a match exists. (it should be noted that the same may occur on an ad hoc basis between two devices in a manner that does not require a centralized database.)

Other filters 516 may also be applied to entries for matching purposes. The pointer may link or associate one or more entries in the data range table 502 with the programmable filter 518. Programmable filter 518 may specify other conditions for a general match or for matching a particular entry. For example, the filter may specify whether particular entries must match and/or whether only a certain percentage of the entries must match. The filter may be based on location 520, time 522, context 524, association 526, or other conditions 528. Location 520 may be used to filter users within a certain distance of the user (e.g., within bluetooth range or based on specific location data) or at a specific location or in a specific event or venue. Location 520 may also be used to automatically adjust matching rules (e.g., when the user is far away from home). For example, if a user travels in a foreign country, the rules may only require a match with another user who speaks the same language (or is from the same country). The user may also have a setting 416 indicating an environment 524 (e.g., whether the user is at work, at home, at a social event, or traveling). Depending on the environment 524, one or more different target profiles may be selected and used for matching. Additionally, the criteria for matching may change automatically depending on the context 524 (e.g., the user may be notified of any friends nearby while traveling, but not while working). Time 522 may also be used to automatically adjust the matching criteria (e.g., using different criteria on friday or saturday evenings than on monday mornings).

The system can develop "intelligence" and/or set and "figure out" by looking at other users' rules and settings at a given time so that the user does not have to manually adjust certain environmental factors of his settings. For example, the system may distinguish between a bar and a doctor's office by a "location store". If some users "map out" a location by identifying characteristics of a particular location (or location coupled with time in the case of a multi-purpose facility), this information may be applied to other users who visit the location. In this case, the user can simply set his device to "close the match in the doctor's office" or "close the match everywhere except the bar" and the system can respond to his wishes accordingly.

Similarly, the system can learn by feedback: certain alarms may be repeatedly ignored (snooze), thus indicating that this "category" of alarm is undesirable (whether based on time, place, or other factors). Alternatively, after getting a match alert, the user can send feedback to the system through the user interface of his device to send "please give more things like this". More generally, the system should be trainable to know where home, work, girlfriend's apartment, etc., so it can learn about you and "make sense" of your behavioral patterns.

Associations 526 may be used to change matching rules depending on who you are with (e.g., your wife or your blond) and/or who is nearby (e.g., for more free matching of people who are with or near common friends) and/or who you are in relationship with (e.g., whether you have common friends with a high level of relevance and trustworthiness). The use of secondary contacts for matching may be criteria in the data range table 502 and a filter for determining how to apply other criteria. These entries and filters may be used to notify the user when the next level of contact is within a certain range or distance (e.g., based on the location 570 filter). A venue or event may also be scanned (by making a request to the web server 302) to determine how many friends and/or secondary contacts are at the location. Additionally, if the user's calendar information is incorporated into their profile, this information can be used to predict who may be at a particular location in the future. For example, if you plan to go to miami over the weekend, you may want to scan the city, not only to see who is currently there, but who (among your acquaintances or potential matches) plans to be there when you are there. In this way you can "match" with a suitable blind date flying to miami from a different city.

The user may also make a specific request to "scan" a person at a particular location and with a mobile device (e.g., a woman at the end of a bar) to see if another person has any friends in common, or just to see her public profile. The network server may retrieve the user profile of the mobile device at a specified location or it may obtain it through a direct bluetooth or infrared connection with the mobile device. This is particularly powerful when combined with quality indicia (e.g., relevance 410 and confidence 412) for the matching information, as it promotes the quality of the secondary match and filters out the spoofer. The scanning process may also cause an "icebreaker" topic to be identified, helping to initiate interaction between strangers, as described further below.

You can also use a filter to block certain matches. For example, you may be married, but nevertheless you want to send out a "finger tap" that you would like to romance. In this case, you can use the matching engine to alert you that there is a secondary contact where the medium is your spouse in order to avoid them. "taps" may also be selectively blocked from being sent to any of your spouse's friends, or may be sent only anonymously to people who expressed similar wishes. This may be specified by using a filter based on the association 526 to modify the availability 420 settings for the mobile device.

Similarly, this may apply to people who do not have to be married but who simply want anonymous physical encounters (physicalencoconters), and therefore want to be alerted to others with similar interests. The person seeking these may include the preference in their profile and the system may incorporate this into their matching criteria. Someone seeking such a physical encounter does not have to simply go somewhere where she is physically close to someone with similar wishes (this can now be done with anonymous bluetooth interactions), but can actually decide where to go tonight based on where there is a high density of people with similar preferences. She can do this by scanning a bar, neighborhood, or even city before she leaves home. This "overnight" matching system may also be more effective by combining with other profile data as described above. For example, an individual may have an emotional interest at night only with an individual who spends most of his time outside of her town or vicinity and has a clean health record about sexually transmitted diseases (which may be an incentive that an individual may incorporate medical data into his profile using a digital token issued by his doctor).

Mobile devices may also be used by enterprises and specialized filters may be established for these purposes. Enterprises have a variety of uses for trusted profile data. When you enter a Prada retail store, if the store knows you make one million dollars a year, or you spend $ 1000 a month in competing stores (e.g., because information is transferred or collected from the use of your smart card), it may treat you better than someone who is to not know the purchase history. Similarly, if you walk on the floor of a fair and have a review of work or even past performance in your profile or other desirable characteristics like electrical engineering positions from a certain school, you may be approached by a company that wants to hire you (especially if you indicate in your profile that you are willing to have such an active representation).

In some cases, the transmission of authenticated behavioral information may be beneficial to both the enterprise and the consumer. For example, in the example above, a consumer at Prada may benefit by obtaining VIP treatment and possibly special offers based on a proven history of her past purchases.

In addition, the consumer may be authorized by externally generated data about the enterprise. The restaurant may passively accumulate profile information about her "behavior" because the consumer considers her to be a good prospective customer based on her past purchasing behavior. In this case, the restaurant is an "entity" similar to the human user of the system. Each time someone has a meal at the restaurant, the mobile device may ease the rating of the restaurant — for example, you are eligible to enter a rating of 1-10 when you pay a bill (perhaps with digital currency or a credit card). Your rating will be numerically associated with the restaurant in the user profile 400 for that restaurant (similar to how a former girlfriend might rate you). The next person entering (or walking nearby) the restaurant can see the ratings. This information may be stored in database storage system 304 or may be stored in a locally located tamper-resistant device in or near the restaurant (the restaurant cannot delete or edit the negative rating because it is tamper-resistant).

Several main elements distinguish this from the Zagat style restaurant review. First, it is designed to easily grade restaurants just as you are there and the experience is fresh in your mind. Second, users can get a high quality rating from the device they carry when they need it, i.e. when they are hungry or walk along the street looking for places to have meals (of course, they can also access the same information at other times and places). Just as people match others, a target profile may be established between the user and restaurants so that the mobile device can inform the user of good restaurant matches and provide ratings to help guide the user's decisions.

Another authorized application for consumers is the use of composite ratings. For example, Fenway Park may compile aggregated and anonymous information about who goes to the game. In this case, someone who wants to go to the Red Sox game can find out what "kind" of people to play together. This composite information may be useful in selecting a bar: what its composite statistics of the patrons are is … age, gender ratio, income, ratio of local people to travelers, etc. (all of which will be based on the data entries 402 in the user profile 400 of those in the bar). One may be interested in knowing what the population's composition is at that time, what it is on friday evenings versus saturday evenings, or generally.

The group information need not only be aggregated and anonymous. It can be used to "scan" a bar to see, for example, if anyone you know (or any secondary contact, woman looking for a night, work friend, etc.) is inside. One major benefit is the ability to do this remotely (e.g., by sending a query request to the server 302) before you actually go to the bar, while you are still at home or at work, considering where to go. People may also find it useful to obtain remote composition data about people who live in cities they may want to move to (or similarly about nearby areas), companies they consider working for, golf clubs they want to join, resorts they want to visit, etc.

Embodiments of the present invention also allow for a variety of additional applications.

Competition. It may be interesting when you are competing with friends to see who is encountering more acquaintances. With a location-aware matching engine, you do not have to "meet by chance" a person in person — the matching engine will tell you whether a friend is within a certain specified distance. The authenticity test described herein may improve competition (you may exclude job-related acquaintances from the game or specify only "close friends" calculations based on, for example, a confidence 412 rating).

Causal point (karma point). When two people know with you as an intermediary, you can earn digital cause points (points or tokens in the user profile 400). Collecting causal points may be interesting and may also provide a basis for matching (e.g., filtering out those good matching manufacturers or users with many secondary contacts). Inanimate entities may also earn cause points, such as a coffee shop in the vicinity that are "responsible" for a certain number of matches (i.e., matched people who are regularly at the coffee shop). When two people know with you as an intermediary, you can be warned that this has occurred and that you have earned 10 "cause and effect points".

"Bell ear". In addition to earning causality points, when two people know in the middle of you, you can be notified (by phone, text message, etc.) and invited to join the interaction remotely or in person. Settings 416 on the mobile device 100 may be such that when a match is made between common friends, the device calls or sends a text message or email to the intermediary. In addition, the settings 416 may be used to cause the server 302 to send a message to the intermediary.

A puzzle and a game. Settings and filters allow the use of creative and interactive matching criteria. As described above, people may have public profiles that are visible to some or all other users. The profile can be accessed by a "scan" action, meaning that a person can "point" his device at a person (or her device) and access her public profile. In this public overview, a woman may want to enjoy by challenging the man to answer a puzzle when approaching her or to sing her favorite song, selling her a margarite wine, giving her a certain flower, etc. This may make breaking ice interesting for both parties. These preferences for proximity will be revealed when someone "scans" her look to find a suitable icebreaking topic or searches for a matching "hit" (e.g., a common acquaintance). As a side dot, there may be a record held for each person scan. A woman may wish to know that at a given night, 18 men found she was sufficiently engaging to "scan" her.

And (5) digital waking. Another setting 416 may be used to request notification when certain matches or events occur. A target profile and filter may be established to match users who are most recently at nearby locations (based on a match for the timestamped location entry in the data table 402). For example, the user may be alerted when someone the user knows (as indicated by a data entry in the user profile 400) has passed the user's location within the last 5 minutes. In this case he may call his friend to place a call and possibly arrange to wrap around and meet nearby. The location 520 filter may also be used to relax this criterion for matching depending on where the user is. For example, if a user is traveling in a foreign country, the user may wish to notify him of any friends that are nearby at any time within the last few hours. The user may also want to be notified in the case where a friend was in a previous location that the user visited (e.g., a restaurant that the user had lunch an hour ago).

Fig. 6 is a flowchart illustrating a method for matching users according to an exemplary embodiment of the present invention. Application software on the device 100 or server 302 may process the user profile and the target profile to determine if there is a match and the action to take if there is a match. Any of the profile information and filters described above may be used and any of the actions described above may be taken based on the information in the user profile and the target profile. As shown at 602, the location information may be generated or received by the mobile device 100 and processed locally by the device, or may be sent to the server 302 for processing. The location information may request users in a particular location or event or within a certain range of the user or a specified location. Other criteria-compliant users are detected (either through direct communication as shown in fig. 2 or through the server 302), as shown at 604. Based on the availability settings 420, the device or server then determines whether the user profile (or at least those entries required for matching based on the required target profile and filter) is available for the detected user. If the user profiles are available, they are retrieved and filters are applied to the user profile and the target profile, as shown at 608. The user profile is then compared to the filtered target profile to determine if there is a match, as shown at 610. If there is a match, the settings 614 are checked to determine the appropriate notifications, alarms, and other actions to take. Appropriate notifications, alerts, and actions are then taken by the mobile device 100 and the server 302, as shown at 616. As described above, these actions may include notifying the user of a match, notifying the target of a match, and/or notifying the intermediary that two secondary contacts match.

As described above, the programmable filter 518 may specify whether individual criteria/entries must be met or a percentage of the criteria/entries must be met for an overall match between the criteria and the user profile. In an example embodiment, regular expressions or other logic may be used as part of the programmable filter and/or criteria. A score may be calculated to determine whether a threshold is met for the overall match and used to determine whether to notify the user of other users and user profiles that meet the threshold. There may be more than one threshold or criteria for determining how to notify the user. If another user with a high score is nearby, the mobile device may be caused to sound an audible alert or vibrate and provide an SMS text message with information from the other user's profile. If another user has a lower score that is still high enough for some threshold, a text message may be sent without an audible alert. These are merely examples, and other combinations of notifications and thresholds may be used.

In an example embodiment, some criteria are necessary for matching, such as whether the target is male or female. Other criteria may contribute to a match score that exceeds a certain threshold, such as a revenue level. These criteria may in some cases not contribute to the score or have a fixed number of contributions (e.g., based on weights 514). For example, if the targets share the same secondary contacts, such as a user or other person listed in a contact list associated with the profiles of both the user and the targets, a single weight 514 may be added to the score. In some cases, the criteria may allow the weight 514 or a portion of the weight 514 to be added to the score multiple times based on another user's profile. For example, additional points may be added to the score for each secondary contact in common between the user and the target. The programmable filter 518 may specify a rule whereby points are decremented (eventually equals zero) for each additional second-level contact in common. The same method can be used for other criteria, such as adding more points for higher revenue levels up to some maximum value. Another example uses three levels of contacts (each of the user and the target knows someone who in turn knows a common person, which may be determined based on the address book or other factors as described above). Third-level contacts may contribute some points to the score, but they may contribute fewer points (and have lower weight) than second-level contacts. Any of the other types of criteria and characteristics described above may be included in the score based on whether the logical criteria or rules are satisfied and based on a weight (whether fixed or proportional) that may be added to the score when the criteria or rules are satisfied. A user interface with a dial, slide bar, or other adjustable settings may be used to adjust the weights for different criteria, which may include settings that make the criteria essential for matching, settings (or set criteria) for assigning weights/points that contribute to the score, settings that indicate that a criterion will not be used for matching, and/or settings that indicate that a criterion disqualifies for matching of a goal. The threshold for matching may also be adjusted using a dial, slide bar, or other adjustable setting. If the user gets too many matches or notifications in crowded locations, the user can easily toggle (toggle) a dial, slide bar, or other setting to increase the threshold score needed for a match. If the user is at a location where a match is unlikely to be obtained (e.g., traveling outside town or in a foreign country), the user can easily lower the threshold score required for the match. As described above, the threshold may also be automatically adjusted based on the environment, time of day, location, or other factors determined by the system.

Similarly, scores and thresholds may be used for entities such as businesses, venues, or other objects to determine matches and whether to notify the user. In an example embodiment, similar scores and thresholds may be combined with location-based information to determine whether to serve promotional items, advertisements, or coupons to users. For example, the user may have a credit score or score based on purchases made or other criteria that meets a threshold. When it is determined that a user with a score exceeding a threshold is at a location near a store or business, a particular advertisement or electronic coupon may be transmitted to the user's mobile device. A measurable (scalable) threshold may also be used, where different advertisements or coupons are sent to users based on how much they exceed the threshold score. In some examples, when the user's mobile device is determined to be near (or at) the competitor's business, the directions of the business location or the advertisement or coupon of the business may be sent to the user's mobile device. Whether the user is near a location may be based on a preset radius or distance, or may be weighted differently as part of the score depending on how close the user is to the location.

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims (189)

1. A method for location-based social networking, the method comprising:

providing a profile of a plurality of users, including user-specified information;

automatically determining a location visited by at least a first user without requiring the first user to manually specify the location;

associating information about a location visited by the first user with a profile of the first user;

allowing the second user to specify criteria for matching;

determining information about a current location of the first user;

matching the first user with the criteria based at least in part on information about a location visited by the first user associated with a profile of the first user and based at least in part on information about a current location of the first user; and

providing a notification to the second user.

2. The method of claim 1, wherein the information about the current location of the first user comprises information about proximity of the first user to a location specified by the second user.

3. A method as claimed in any preceding claim, wherein the information about the current location of the first user comprises information about the proximity of the first user to the location of the second user.

4. The method of any one of the preceding claims, wherein the step of determining the current location of the first user comprises using a global positioning system.

5. A method as claimed in any preceding claim, wherein the step of determining the current location of the first user comprises triangulation using mobile communications towers.

6. A method according to any preceding claim, wherein the step of determining the current location of the first user comprises using a daisy chain to devices at known locations.

7. A method as claimed in any preceding claim, wherein the step of determining the current location of the first user comprises receiving a signal by a wireless communications device at the location.

8. A method as claimed in any preceding claim, wherein the step of determining the current location of the first user comprises providing an electronic marker at the location.

9. A method as claimed in any preceding claim, wherein the step of determining the current location of the first user comprises wireless communication between a first mobile device and a second mobile device at the location.

10. The method of claim 9, wherein the first mobile device is associated with a profile of the first user and the second mobile device is associated with a profile of the second user.

11. The method of claim 9, wherein the first mobile device is associated with a profile of the first user and the second mobile device is associated with a profile of the second user.

12. A method as claimed in any preceding claim, wherein the step of determining the location visited by the first user comprises using a global positioning system.

13. A method as claimed in any preceding claim, wherein the step of determining the location visited by the first user comprises triangulation using mobile communications towers.

14. A method according to any preceding claim, wherein the step of determining the location visited by the first user comprises using a daisy chain to devices at known locations.

15. A method as claimed in any preceding claim, wherein the step of determining the location visited by the first user comprises receiving a signal by a wireless communications device at the location.

16. A method as claimed in any preceding claim, wherein the step of determining a location visited by the first user comprises providing an electronic token at the location.

17. A method as claimed in any preceding claim, wherein the step of determining the location visited by the first user comprises wireless communication between a first mobile device and a second mobile device at the location.

18. The method of claim 9, wherein the first mobile device is associated with a profile of the first user and the second mobile device is associated with a profile of another user.

19. A method as claimed in any preceding claim, wherein the information about the location visited by the first user comprises information about at least one other user visiting the location simultaneously.

20. A method as claimed in any preceding claim, wherein the information about locations visited by the first user comprises information about events occurring at a location visited by the first user when a location was visited by the first user.

21. The method of claim 20, wherein the event is a sporting event.

22. The method of any of the preceding claims, further comprising:

automatically determining a location visited by the second user without requiring the second user to manually specify the location; and

associating information about a location visited by the second user with a profile of the second user,

wherein the step of matching the criteria to the first user is based at least in part on information about a location visited by the second user, the location visited by the second user being associated with a profile of the second user.

23. A method for location-based social networking, the method comprising:

providing profiles of a plurality of users;

associating information about a first user's relationship to a third person with a profile of the first user;

allowing the second user to specify criteria for matching;

determining information about the first user's location;

matching the first user with the criteria based at least in part on information about the first user's relationship to the third person and based at least in part on information about the first user's location; and

providing a notification to the second user.

24. The method of claim 23, wherein the second user has a profile with information about the second user's relationship to the third person, and the step of matching the first user with the criteria is based at least in part on the information about the second user's relationship to the third person.

25. The method of claim 23, wherein the information regarding the relationship of the first user to the third person and the first user's profile is based on entries in the first user's address book.

26. The method of claim 23, wherein the information about the relationship of the first user to the third person is based on information about communications between the first user and the third user.

27. The method of claim 26, wherein the information about communications between the first user and the third user comprises information about email communications.

28. The method of claim 27, wherein the information about email communications includes a frequency of the email communications between the first user and the third person.

29. The method of claim 26, wherein the information regarding the communication between the first user and the third user comprises information regarding a telephone call.

30. The method of claim 27, wherein the information about email communications includes a frequency of the phone calls between the first user and the third person.

31. The method of claim 23, wherein the information about the relationship of the first user to the third person is based on confirmation from the third person about the relationship of the first user to the third person.

32. The method of claim 23, wherein the information about the relationship of the first user to the third person is based on feedback from the third person.

33. A method as claimed in any preceding claim, wherein the information about the location of the first user comprises information about the proximity of the first user to a location specified by the second user.

34. A method as claimed in any preceding claim, wherein the information about the location of the first user comprises information about the proximity of the first user to the location of the second user.

35. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises using a global positioning system.

36. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises triangulation using mobile communications towers.

37. A method according to any preceding claim, wherein the step of determining the location of the first user comprises using a daisy chain to devices at known locations.

38. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises receiving a signal by a wireless communications device at the location.

39. A method as claimed in any preceding claim, wherein the step of determining the current location of the first user comprises providing an electronic marker at the location.

40. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises wireless communication between a first mobile device and a second mobile device at the location.

41. The method of claim 46, wherein the first mobile device is associated with a profile of the first user and the second mobile device is associated with a profile of the second user.

42. A method for location-based social networking, the method comprising:

providing a profile of a plurality of users, including user-specified information;

automatically determining behavior information regarding an activity performed by a first user without requiring the first user to manually specify the behavior information;

associating the behavior information with a profile for the first user;

allowing the second user to specify criteria for matching;

determining information about the first user's location;

matching a first user with the criteria based at least in part on behavioral information about an activity performed by the first user and based at least in part on information about a location of the first user; and

providing a notification to the second user.

43. The method of claim 42, wherein the information about the location of the first user comprises information about proximity of the first user to a location specified by the second user.

44. The method of any of the preceding claims, wherein the behavior information is based on a location visited by the first user.

45. The method of any preceding claim, wherein the behavioural information relating to a location visited by the first user comprises information relating to an event occurring at a location visited by the first user when the location was visited by the first user.

46. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises using a global positioning system.

47. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises triangulation using mobile communications towers.

48. A method according to any preceding claim, wherein the step of determining the location of the first user comprises using a daisy chain to devices at known locations.

49. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises receiving a signal by a wireless communications device at the location.

50. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises providing an electronic marker at the location.

51. A method as claimed in any preceding claim, wherein the step of determining the location of the first user comprises wireless communication between a first mobile device and a second mobile device at the location.

52. The method of any preceding claim, wherein the behavioural information comprises information relating to purchases made by the first user.

53. The method of any preceding claim, further comprising associating a television recording device with the profile of the first user, wherein the behavioural information comprises information about television programmes recorded by the user on the recording device.

54. A method as claimed in any preceding claim, further comprising associating a mobile phone with a profile of the first user, wherein the behavioural information comprises information relating to a telephone call made by the user on the phone.

55. A method as claimed in any preceding claim, wherein the behavioural information comprises information about a website visited by the first user.

56. The method of any preceding claim, further comprising associating an email account with the profile of the first user, wherein the behavioural information comprises information about emails sent or received by the user through the email account.

57. The method of any of the preceding claims, further comprising:

automatically determining behavioral information regarding an activity performed by the second user without requiring the second user to manually specify the behavioral information; and

associating the behavior information with a profile of the second user,

wherein the step of matching the first user with the criteria is based at least in part on behavioral information about an activity performed by the second user.

58. The method of any preceding claim, further comprising associating characteristics with the profile of the first user and the trustworthiness of each of the characteristics, wherein the characteristics comprise at least one certified characteristic and at least one unproven characteristic.

59. The method of claim 58, wherein the at least one validated characteristic has a higher level of confidence than the unproven characteristic.

60. A method as claimed in any preceding claim, further comprising associating feedback provided by other users with the profile.

61. The method of claim 59, further comprising allowing the first user to delete all feedback associated with a characteristic rather than selectively deleting portions of feedback associated with the characteristic.

62. A method for location-based social networking, the method comprising:

providing profiles of a plurality of users;

wherein each profile associates a plurality of characteristics with a respective user, including user-specified characteristics and other characteristics not provided by the user;

associating a level of trustworthiness with at least some of the characteristics in the profile of the first user;

allowing the second user to specify criteria for matching;

obtaining information about a location of the first user; and

matching the first user with the criteria based at least in part on a level of trustworthiness associated with a characteristic in the profile of the first user and based at least in part on the location of the first user.

63. The method of claim 62, wherein the characteristics include certified and unproven characteristics and the certified characteristics have a higher level of confidence than the unproven characteristics.

64. The method of claim 63, wherein the verified characteristic comprises information about a relationship with one other user, the relationship verified by the other user.

65. The method of any of the preceding claims, wherein the verified property comprises information verified by a security token.

66. The method of any of the preceding claims, wherein the verified characteristic comprises information about a location visited by the first user.

67. The method of any of the preceding claims, wherein the verified characteristic comprises information about a location that the first user visits simultaneously with at least one other user.

68. The method of any of the preceding claims, wherein the step of matching the first user with the criteria is based at least in part on a location of the second user.

69. A method as claimed in any preceding claim, wherein characteristics not provided by the user have a higher level of confidence than non-validated user-specified characteristics.

70. A method as claimed in any preceding claim, wherein the characteristic comprises a characteristic relating to a relationship of the first user with one other user, and the level of trustworthiness of the characteristic relating to a relationship with one other user is based at least in part on the number of times the first user and the other user are co-located.

71. A method for location-based social networking, the method comprising:

providing profiles of a plurality of users;

wherein each profile associates a plurality of characteristics with a respective user;

associating a relevance level with at least some of the characteristics in the profile of the first user;

allowing the second user to specify criteria for matching;

obtaining information about a location of the first user;

matching the first user with the criteria based at least in part on a relevance level associated with a characteristic in a profile of the first user and based at least in part on a location of the first user.

72. The method of claim 71, wherein the level of correlation of at least some of the characteristics is based on a sample size used to determine the characteristics.

73. A method for location-based social networking, the method comprising:

providing profiles of a plurality of users;

wherein each profile associates a plurality of characteristics with a respective user;

wherein at least some of the characteristics are based on feedback provided by other users;

allowing the second user to specify criteria for matching;

obtaining information about a location of the first user;

matching the first user with the criteria based at least in part on feedback associated with the characteristics in the profile of the first user and based at least in part on the location of the first user.

74. The method of claim 71, further comprising:

allowing the first user to delete all feedback associated with a characteristic.

75. The method of any of the preceding claims, further comprising:

the restriction deletes only selected feedback associated with one characteristic.

76. The method of any of the preceding claims, wherein the feedback comprises a digital rating.

77. The method of any of the above claims, wherein the feedback comprises a comment.

78. A method for location-based social networking, the method comprising:

providing profiles of a plurality of users;

wherein each profile associates a plurality of characteristics with a respective user;

wherein the profile includes feedback provided by other users;

allowing the second user to specify criteria for matching;

obtaining information about a location of the first user;

matching the first user with the criteria based at least in part on characteristics in a profile with the first user and based at least in part on the location of the first user; and

providing feedback to the second user.

79. The method of claim 71, further comprising:

allowing the first user to delete all feedback associated with a characteristic.

80. The method of any of the preceding claims, further comprising:

the restriction deletes only selected feedback associated with one characteristic.

81. The method of any of the preceding claims, wherein the feedback comprises a digital rating.

82. The method of any of the above claims, wherein the feedback comprises a comment.

83. A method for location-based social networking, the method comprising:

providing a profile of a first user, including a plurality of characteristics of the first user;

allowing the second user to specify criteria for matching;

determining information about the first user's location;

matching the first user with the criteria based at least in part on information about the location of the first user; and

providing a notification to the second user, the notification including at least a portion of the first user's profile.

84. The method of claim 83, wherein the notification includes a photograph of the first user.

85. A method according to any preceding claim, further comprising providing a profile of the second user, wherein the profiles of the first and second users both comprise information about a third person, and the notification provides an identity of the third person.

86. The method of any of the above claims, wherein the notification includes a suggestion to be proximate to the first user.

87. A method as claimed in any preceding claim, wherein the suggestion is based on a profile of the first user.

88. A method according to any preceding claim, further comprising providing a profile of the second user, the profile comprising a plurality of characteristics of the second user, wherein the notification comprises information about the same characteristics from the profile of the first user as the characteristics from the profile of the second user.

89. A method as claimed in any preceding claim, wherein the notification comprises an audible alarm on the mobile device.

90. The method of any of the preceding claims, wherein the notification comprises a vibration alarm on the mobile device.

91. A method as claimed in any preceding claim, wherein the notification comprises an SMS or other text message on the mobile device.

92. The method of any of the above claims, wherein the notification includes a suggested topic of ice breaking.

93. A method as claimed in any preceding claim, wherein the notification comprises a suggested topic based on a profile of the first user.

94. The method of any preceding claim, further comprising allowing the second user to select the first user by specifying a location of the first user.

95. A method as claimed in any preceding claim, further comprising allowing the second user to request information about the profile of the first user by indicating a mobile device at the first user or specifying the location of the first user.

96. A method as claimed in any preceding claim, wherein the notification comprises information about a reason for matching the first user with criteria specified by the second user.

97. A method as claimed in any preceding claim, further comprising allowing the first user to specify rules regarding how the first user's profile may be used for matching.

98. The method of any of the preceding claims, wherein the rules specified by the first user include rules based on a location of the first user.

99. The method of any of the preceding claims, wherein the rule automatically modifies a matching criterion based at least in part on a location of the first user.

100. The method of any of the preceding claims, wherein the rules are automatically modified based on the time of day.

101. A method as claimed in any preceding claim, wherein the rules automatically modify the matching criteria based at least in part on the day of the week.

102. The method of any of the preceding claims, wherein the rules automatically modify matching criteria based at least in part on a context determined at least in part by criteria selected from the group consisting of a location of the first user, a time of day, a day of week, and a pattern specified by the first user.

103. A method as claimed in any preceding claim, wherein the rule is based at least in part on an association between a user requesting a match and a third person specified in the profile of the first user.

104. The method of claim 103, wherein the third person is a spouse of the first user.

105. The method of claim 103, wherein the third person is a colleague of the first user.

106. A method as claimed in any preceding claim, wherein the rule requires a common secondary relationship between the profile of the matching user and the profile of the first user to be requested.

107. The method of any of the preceding claims, wherein the rule is based in part on the first user's location and information about that location previously provided by other users.

108. The method of any of the preceding claims, further comprising:

receiving information about success of previous matches between other users; and

generating additional criteria for matching using the information regarding success of previous matches between other users;

wherein the step of matching the first user with the second user-specified criteria is based at least in part on determining whether the profile of the first user meets the additional criteria.

109. The method of claim 108, wherein the step of using information about the success of previous matches between other users to generate further criteria for a match further comprises filtering information about the success of previous matches based on the profile of the second user.

110. The method of any of the above claims, wherein the criteria specified by the second user requires an indication of interest in a specified type of social encounter.

111. The method of any of the above claims, wherein the rule specified by the first user requires an indication of interest in a specified type of social encounter.

112. The method of any of the preceding claims, wherein the criteria specified by the second user requires an indication of interest in work and the notification includes an endorsement by the first user.

113. The method of any of the above claims, wherein the location is an exhibition or a recruitment and the notification includes a proof of employment of the first user.

114. The method of any of the preceding claims, wherein the rule comprises a condition to provide a photograph of the first user, the condition comprising a condition requiring the second user to provide a photograph of the second user.

115. A method as claimed in any preceding claim, wherein the rule specifies characteristics from the first user's profile that can only be used in sets with other characteristics for matching.

116. A method as claimed in any preceding claim, wherein the rule specifies a characteristic that cannot be used for matching.

117. A method as claimed in any preceding claim, wherein the rule specifies a condition that must be satisfied to use a specified characteristic for matching.

118. The method of any of the preceding claims, further comprising:

providing a sensitivity setting that can be adjusted by the second user to adjust the threshold for the criterion that must be met to cause the profile to match.

119. The method of any of the preceding claims, further comprising:

allowing the second user to provide feedback on the success of the match with the first user.

120. The method of any of the preceding claims, further comprising:

automatically providing further criteria for determining a match for the second user based on feedback provided by the second user regarding success of the match with the first user.

121. The method of any of the preceding claims, further comprising determining a location of the second user;

wherein the information regarding the location of the first user includes information regarding whether the first user is at the location of the second user within a specified time period.

122. A method for location-based social networking, the method comprising:

receiving a request for information about a venue;

automatically determining whether any of a plurality of users are located at the venue;

obtaining a profile of a user located at the venue;

in response to the request, providing information based at least in part on a profile of a user located at the venue.

123. The method of any of the preceding claims, wherein the step of determining whether any of the plurality of users is located at the venue comprises using a global positioning system.

124. The method of any of the preceding claims, wherein the step of determining whether any of the plurality of users is located at the venue comprises triangulation using mobile communication towers.

125. A method according to any preceding claim, wherein the step of determining whether any of the plurality of users is located at the site comprises using a daisy chain to devices at known locations.

126. A method as claimed in any preceding claim, wherein the step of determining whether any of the plurality of users is located at the venue comprises receiving a signal by a wireless communications device at the location.

127. A method as claimed in any one of the preceding claims, wherein the step of determining whether any of the plurality of users is located at the venue comprises using an electronic token provided at the location.

128. The method of any of the preceding claims, wherein the step of determining whether any of the plurality of users is located at the venue is based on wireless communication between a first mobile device and a second mobile device at the location.

129. A method as claimed in any preceding claim, wherein the request includes criteria specifying a group of users and the information provided in response to the request includes information indicating whether any of the specified group of users is located at the venue.

130. A method as claimed in any preceding claim, wherein the request comprises criteria for matching a profile and the information provided in response to the request comprises information indicating the number of users at the site having profiles matching the criteria.

131. The method of any preceding claim, further comprising allowing a user to request information about the venue and respond with statistics based on relevant user profiles of users who visited the venue.

132. A method as claimed in any preceding claim, further comprising requesting information about a venue at a future time and responding by providing information survey users with a profile indicating that they will be at the venue at the future time.

133. The method of claim 132 wherein the information is based on calendar entries, tickets, travel plans, or other information about the venue at the future time.

134. A method for location-based social networking, the method comprising:

providing a profile of a first user, the profile including an entry associating the first user with a first mobile device and an entry associating the first user with a third user;

providing a profile of a second user, the profile including an entry associating the second user with a second mobile device and an entry associating the second user with the third user;

receiving location-based information for the first mobile device and the second mobile device; and

matching the first user and the second user based at least in part on the location-based information and entries in the first user profile and the second user profile associated with the third user.

135. The method of claim 134, further comprising:

providing a profile of the third user, the profile including an entry associating the third user with a third mobile device.

136. The method of any of the preceding claims comprising:

sending a notification to the third mobile device indicating that the first user and the second user are matched based at least in part on the association with the third user.

137. The method of any of the preceding claims, further comprising sending an audible alert to the third mobile device.

138. The method of any preceding claim, further comprising initiating a telephone call to the third mobile device.

139. The method of any preceding claim, further comprising adjusting the profile of the third user based on a match between the first user and the second user.

140. A method as claimed in any preceding claim, further comprising rewarding the third user for a point added to a total point associated with the profile of the third user.

141. The method of claim 140, further comprising awarding a prize to the third party based on the total points associated with the profile of the third user.

142. A method for location-based promotion, the method comprising:

providing a profile of a first user, the profile comprising an entry associating the first user with a first mobile device and a plurality of characteristics of the first user;

receiving location-based information for the first mobile device; and

sending a promotional item to the first mobile device based at least in part on the location-based information and the profile of the first user.

143. The method of claim 142, wherein the promotional item comprises an advertisement.

144. A method as claimed in any preceding claim, wherein the promotional item comprises a coupon.

145. The method of any of the above claims, wherein the location-based information of the first mobile device indicates that the first user is within a specified distance from a store, and the promotional item comprises an advertisement for the store.

146. The method of any of the above claims, wherein the location-based information of the first mobile device indicates that the first user is within a specified distance from a store, and the promotional item comprises a coupon for the store.

147. The method of any of the above claims, wherein the location-based information of the first mobile device indicates that the first user is within a specified distance from a store, and the promotional item comprises an orientation of the store.

148. The method of any of the above claims, wherein the location-based information of the first mobile device indicates that the first user is within a specified distance from a store, and the promotional item comprises an advertisement of a competing store.

149. The method of any of the above claims, wherein the location-based information of the first mobile device indicates that the first user is within a specified distance from a store, and the promotional item comprises a coupon for a competing store.

150. The method of any of the above claims, wherein the location-based information of the first mobile device indicates that the first user is within a specified distance from a store, and the promotional item comprises a position of a competing store.

151. The method of any of the preceding claims, further comprising determining the location of the first mobile device with a global positioning system.

152. The method of any preceding claim, further comprising providing an incentive for a user to allow their profile to be used to target advertisements.

153. A method as claimed in any preceding claim, further comprising providing an incentive for a user to provide further information for the user's profile.

154. The method of any preceding claim, further comprising providing an incentive for a user to allow the user's profile to be used to provide information to sales personnel of the store.

155. The method of any of the above claims, wherein behavioral information based on a location visited by the first user is associated with a profile for the first user, and the promotional item is based at least in part on the behavioral information.

156. The method of any of the preceding claims, wherein the behavioral information includes information about purchases made by the first user.

157. The method of any preceding claim, further comprising associating a television recording device with the profile of the first user, wherein the behavioural information comprises information about television programmes recorded by the user on the recording device.

158. The method of any of the preceding claims, wherein the behavior information comprises information about a website visited by the first user.

159. A method according to any preceding claim, further comprising rules allowing the first user to specify how a profile about the first user may be used to send the promotional item.

160. A method according to any preceding claim, wherein the rule specifies characteristics of the first user profile that cannot be used to send the promotional item.

161. A method for location-based promotion, the method comprising:

providing profiles for a plurality of users, wherein each profile is associated with a mobile device of a respective user;

determining whether any of the mobile devices are located at a venue;

obtaining a profile of a user associated with a mobile device located at the venue; and

the obtained profile is used to select or modify an advertisement to be displayed at the venue.

162. A method as claimed in any preceding claim, wherein the step of determining whether any of the plurality of mobile devices is located at the venue comprises using a global positioning system.

163. The method of any of the preceding claims, wherein the step of determining whether any of the plurality of mobile devices is located at the venue comprises triangulation of mobile communication towers.

164. A method as claimed in any preceding claim, wherein the step of determining whether any of the plurality of mobile devices is located at the premises comprises using a daisy chain to devices at known locations.

165. A method as claimed in any preceding claim, wherein the step of determining whether any of the plurality of mobile devices is located at the venue comprises receiving a signal by a wireless communications device at the location.

166. A method as claimed in any preceding claim, wherein the step of determining whether any of the plurality of mobile devices is located at the venue comprises using an electronic token provided at the location.

167. A method as claimed in any preceding claim, wherein the step of determining whether any of the plurality of mobile devices is located at the venue comprises wirelessly communicating between a first mobile device and a second mobile device at the location.

168. The method of any preceding claim, further comprising providing an incentive for a user to allow the user's profile to be used to target advertisements.

169. A method as claimed in any preceding claim, further comprising providing an incentive for a user to provide further information for the user's profile.

170. A method for location-based promotion, the method comprising:

providing profiles for a plurality of users, wherein each profile is associated with a mobile device of a respective user;

determining whether any of the mobile devices are located at a store;

obtaining a profile of a user associated with a mobile device located at the store; and

providing information based on the obtained profile to sales personnel of the store.

171. The method of any of the preceding claims, wherein the step of determining whether any of the plurality of mobile devices is located at the store comprises using a global positioning system.

172. The method of any of the preceding claims, wherein the step of determining whether any of the plurality of mobile devices is located at the store comprises triangulation of mobile communication towers.

173. A method according to any preceding claim, wherein the step of determining whether any of the plurality of mobile devices is located at the store comprises using a daisy chain to devices at known locations.

174. The method of any of the preceding claims, wherein determining whether any of the plurality of mobile devices is located at the store comprises receiving a signal by a wireless communication device at the location.

175. The method of any of the preceding claims, wherein the step of determining whether any of the plurality of mobile devices is located at the store comprises using an electronic token provided at the location.

176. The method of any of the preceding claims, wherein determining whether any of the plurality of mobile devices is located at the store comprises wirelessly communicating between a first mobile device and a second mobile device at the location.

177. The method of any preceding claim, further comprising providing an incentive for a user to allow the user's profile to be used to provide information to sales personnel of the store.

178. A method as claimed in any preceding claim, further comprising providing an incentive for a user to provide further information for the user's profile.

179. A method, comprising:

providing a profile of a plurality of entities, the profile comprising a plurality of characteristics of each entity and at least one location of each entity;

associating a first user with a first mobile device;

allowing the first user to specify criteria for matching;

determining information about the first user's location;

matching at least one of the entities with the criteria based at least in part on the information regarding the location of the first user and the profile of the at least one entity.

180. The method of claim 179, wherein the profile includes feedback from a user.

181. The method of any of the preceding claims, wherein the at least one entity is a restaurant and the profile of the restaurant includes a food type and a review rating.

182. The method of any of the preceding claims, further comprising prompting the first user to make feedback on the mobile device while the mobile device is at the location of the at least one entity.

183. A computer-readable medium including the general outline and instructions for all, or any portion of, the methods set forth in any one of the preceding claims.

184. A mobile device comprising the overview and instructions for all, or any part of, the methods set forth in any of the preceding claims.

185. Any of the preceding claims, wherein the profiles are stored on mobile devices, the information for matching is exchanged directly between mobile devices and one of the mobile devices determines whether there is a match.

186. Any of the preceding claims, wherein the profile is stored on a server and the server determines if there is a match.

187. Any of the above claims, wherein a user is associated with a mobile device and the user's location is based on the location of the mobile device.

188. A computer system having a profile in memory and programmed to execute instructions for all or any portion of the methods set forth in any one of the preceding claims.

189. The computer system of claim 188, further comprising a database, wherein the profile is stored in the database.