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WO2002008931A1 - Procede et appareil pour activites d'ajustement optimal dans des periodes de consommation au ralenti - Google Patents

Procede et appareil pour activites d'ajustement optimal dans des periodes de consommation au ralenti Download PDF

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Publication number
WO2002008931A1
WO2002008931A1 PCT/US2001/041171 US0141171W WO0208931A1 WO 2002008931 A1 WO2002008931 A1 WO 2002008931A1 US 0141171 W US0141171 W US 0141171W WO 0208931 A1 WO0208931 A1 WO 0208931A1
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WIPO (PCT)
Prior art keywords
user
activities
activity
time
presentation
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PCT/US2001/041171
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English (en)
Inventor
James T. Brady
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Ten Square Inc
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Ten Square Inc
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Priority to AU2001276043A priority Critical patent/AU2001276043A1/en
Publication of WO2002008931A1 publication Critical patent/WO2002008931A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M11/00—Telephonic communication systems specially adapted for combination with other electrical systems
    • H04M11/06—Simultaneous speech and data transmission, e.g. telegraphic transmission over the same conductors
    • H04M11/064—Data transmission during pauses in telephone conversation

Definitions

  • This invention relates to a method of selecting a set of activities, from a larger possible set of activities, to present to a customer given: the customer's history, a store's history, and the time and day of the customer's activity; such that the maximum value is extracted from the available customer idle time, without exceeding the idle time, in a specified percent of cases.
  • activities include: viewing ads, selecting coupons, buying products, looking at public service announcements, etc.
  • This invention relates generally to point-of-service computer systems of the type used in multilane retail stores, gas stations and banks to record transactions. Specifically, the invention relates to point-of-service systems that can handle the presentation of visual or audio information to the customer and allow that customer to respond to that information. Point-of-service systems have mechanisms for inputting a code recorded on a credit card, debit card, loyalty card, driver's license and/or acquiring via some other means the identification of a customer being serviced. Such mechanisms include magnetic track readers (commonly called
  • the customer is idle for a period of time (idle time).
  • the customer waits for the checkout clerk to ring up the purchases.
  • the customer waits for the attendant or pump to fill the tank. This is wasted time. In general, neither the customer nor the store benefits from the activities of the customer during this time.
  • a mechanism for making use of this time can allow the retailer to gain additional revenue and profit.
  • This invention allows these objectives to be met by optimizing the value of the activities presented to the customer.
  • the method of the invention involves the use of information about the characteristics of the customer in a specific venue or a similar venue, at a time of day, and date; the use of profiles regarding specific consumers and generic consumers; information about a current retail transaction; and using that information in whole or in part to optimize the customer idle time.
  • An optimization takes into effect the value of a proposed use of the time the customer is idle (idle time) during the checkout or delivery processes, and the desire of the owner of the venue to avoid extending the primary transaction being performed.
  • the primary transaction might be the purchase of groceries, drugs, gas, etc.
  • the value of the activities can be represented in the form of revenue to the presenting retailer, a service company managing the interactions, a target retailer or product manufacturer and the utility to the customer.
  • a number of means are provided to: identify the customer; identify the venue; identify a start of idle time; present information; to select activities such as ads, coupon offers, product offerings and service offerings; to fit alternative activities to the idle time; evaluate the alternative activities; and to select between the alternative activities based on the value of the alternative dialogs.
  • the invention performs the steps of: determining a likely speed of the customers response to various presentations, determining the current system response time, determining the probability distribution for the time to compete activities, collecting the value of the possible activities, estimating the probability distribution for the time the customer will be idle at the point-of-service terminal; and selecting the best set and ordering of activities that will fit within the expected idle time, while meeting a criteria on the probability of continuing the activity beyond the expected idle time.
  • FIG. 1 is an overview of the problem solved by the invention.
  • Fig. 2 is a block diagram of a computer system, which is adapted to perform the method of the invention.
  • Fig. 3 is a block diagram of a computer system called the Point-of-service Terminal.
  • Fig. 4 is a block diagram of a computer system called the Store Server.
  • Fig. 5 is a diagram of a tree structure that represents a customer activity.
  • Fig. 6 is a data table that describes the customer.
  • Fig. 7 is a data table that describes a Customer Store Profile.
  • Fig. 8 is a data table that describes Credit, Debit and Loyalty Cards.
  • Fig. 9 is a data table that describes a Store Definition Table.
  • Fig. 10 is a data table that describes the Idle Time Message.
  • Fig. 11 is a Point-of-service Terminal Table.
  • Fig. 12 is a data table that describes a Store Profile Table.
  • Fig. 13 is a data table that describes a Customer Session.
  • Fig. 14 is a list of proposed Activities.
  • Fig. 15 Is the Activity Table.
  • Fig. 16 is an Activity Work Table.
  • Fig. 17 is an activity diagram that describes a Customer Session.
  • Fig. 18 is an activity diagram that describes usage of Customer Idle Time.
  • Fig. 19 is an activity diagram that describes a Customer Activity Cycle.
  • Fig. 20 is an activity diagram that describes Customer Action Cycle.
  • Fig. 21 is a flow chart for Activity Selection.
  • Fig. 22 is a flow chart describing Value Packing.
  • the invention is described in terms of a multilane store (that is, a store with multiple checkout counters), but applies to other retail, wholesale and financial institutions.
  • Fig. 1 describes the characteristics of Idle Time 4 and Activities' 8, 14, and 18 activity time.
  • Idle Time 4 is composed of Base Idle Time 2 and Idle Time Variation 4, which can be represented by a probability distribution giving the probability that Idle Time 4 will complete in a specified time.
  • Activities 8, 14, and 18 (instances of Activity 200 described and illustrated below with reference to Fig. 5) have similar variations and representations.
  • the variations described as items 4, 8, 14 and 18 are shown as having a finite limit, but in practice the tails on these variations can be quite long.
  • the method described is concerned with establishing the probability that an Activity 8, 14 or 18 will complete in Idle Time 4. In the case of Activity 8, the probability is high that it will complete.
  • Activity 8 completes prior to the end of the Idle Time 4, then the idle time probability distribution used for Idle Time 4 has to be renormalized to account for the portion used by Activity 8. After renormalization, Activity 18 can be tested to see if the probability it will complete is sufficient. If so, it is presented and the process continues. Otherwise,
  • Activity 14 would be tested in the same manner. In the course of this process, none, all or any combination of the activities could be presented to the customer prior to the end of Idle Time 4.
  • An important aspect of this invention is the order the Activities 8, 14 and 18 are examined. This is determined by the optimization used. In the case of the embodiment described, it is the time-value of the activities, although many other optimizations are possible. Thus in this example Activity 8 has the highest time- value, Activity 18 the next highest time-value and Activity 14 the lowest. It should be noted that the time- value of an Activity 8, 14, or 18 is dependent on the current state of the system presenting Activity 200 as described below with reference to
  • FIG. 2 is a block diagram of a network of computers adapted to perform the method of the invention.
  • a Central Server 20 is coupled by a wide area network (WAN) 22 to one or more Store Servers 24.
  • WAN wide area network
  • Store Server 24 is connected via Local Area Network (LAN) 26 to one or more Point-of-service Terminal 28. Normal Point-of-service Terminal activities are conducted between the Store Server 24 and Point-of-service Terminal 28. These activities are well documented elsewhere and the details are not important to this invention.
  • Store Server 24 selects activities to be presented via Point-of-service Terminal 28 and receives responses from Point- of-service Terminal 28 as the customer interacts with the activities.
  • Central Server 20 acts as a consolidation point for gathering the information from multiple Store Servers 24. The arrangement of the system and the distribution of function, as described in this embodiment, are one of many possible alternatives.
  • Fig. 3 illustrates further details of Point-of-service Terminal 28.
  • Point-of- service Terminal 28 performs the functions of presenting information to the customer and receiving the response to that presentation.
  • the configuration shown is representative of Point-of-service Terminal 28, but does not include all possible input and output devices.
  • Microprocessor 40 is a conventional microprocessor with a Bus 38 that connects it to Processor Memory 32.
  • Microprocessor 40 contains a Clock 42 that provides time in a form that includes the Month, Day-of- week, Hour, Minute.
  • Processor Memory 32 contains Application 34 that performs the following functions: presentation of Activities 200, collection of responses from the customer during the presentation of an Activity 200. This information is stored as Application Data 36 into Processor Memory 32 and is transferred to and from Store Server 24 via LAN 26 as needed.
  • Input Device 30 is normally a keypad or touch screen, but could be any other form of input device such as an audio response unit. Input Device 30 is used to collect responses from the customer. It is attached to Microprocessor 40 via attachment 50, which is commonly a standard interface like the RS-232 standard.
  • Display Screen 52 is a visual output device such as a Cathode Ray Tube (CRT), a flat panel liquid crystal display (LCD) or similar device. Other types of output devices, such as audio output units, can be used for this function.
  • Display Screen 52 presents Activities 200 to the customer or other users of the system. It is attached to Microprocessor 40 by Attachment 48.
  • Magnetic Stripe Reader 54 is an input device attached to Microprocessor 40 via Attachment 50. It is used to collect customer information from credit, debit, loyalty and other ID cards having a magnetic stripe.
  • Point-of-service Terminal 28 could be performed by PC, client and server machines having additional capabilities. This includes being package with the Store Server 24 or Central Server 20. Point-of-service Terminal 28 responds to the control messages from the Store Server 24, and does not require long-term storage of Application Data 36, but can take advantage of nonvolatile storage when it is present.
  • Store Server 24 is a representation of a typical Store Server 24 and can be configured with the appropriate Applications 76 in Program Memory 74 and structures in Data Memory 94 to perform Store Server 24 function.
  • Fig. 4 is shown configured as a Store Server 24.
  • Store Server 24 functions can be packaged in numerous ways, including in a single server, in a plurality of servers, or integrated with Central Server 20 functions.
  • CPU 70 is a conventional microprocessor with a Bus 114 that connects it to Disk Drive(s) 90, a LAN Adapter 92, a WAN Adapter 112, a Program Memory 74 and a Data Memory 94.
  • CPU 70 contains a Clock 72 that provides time in a form that includes the Month, Day-of-week, Hour, Minute, Seconds and fractions of Seconds.
  • This Data Memory 94 typically contains the following: Customer Table 220 (see Fig. 6), Customer Store Profile Table 250 (see Fig. 7), Credit Debit Loyalty Card Table 330 (see Fig. 8), Store Definition Table 340 (see Fig. 9), Idle
  • Application 78 acquires information from Central Server 20 via WAN
  • Adapter 112 and WAN 22 to build data structures in Data Memory 94 and to update Disk Drive(s) 90. It then uses that information to communicate with Point- of-service Terminal 28 vial Local Area Network Adapter 92 and LAN 26, entering into a dialog with a customer presenting Customer Activities 200 to the customer and receiving responses. Customer Activities 200 are selected from Activity Table 700 based on the identification or non-identification of the customer. This is done using Credit, Debit and Loyalty Card Table 330 and Customer Table 220 to convert the external customer identifier into a Customer ID 336 and subsequently using it to access either Customer Store Profile Table 250 (identified customer) or Store Profile Table 510 (non-identified customer) respectively. Once the profile is selected, its information is used, in conjunction with Store Definition Table 340's
  • Idle Time Fit criteria 348 to select Customer Activity 200 from Activity Table 700. These are then presented to the customer using Point-of-service Terminal 28.
  • Fig. 5 is a diagram of a structure that represents a Customer Activity 200.
  • Customer Activity 200 is composed of a number of screen presentations. The presentations start with a Screen 201 having Screen ID 202. Associated with
  • Screen 201 is a list of Screen ID's 204, 206 and 208 that are individually associated with a Selection ID 203, 205, and 207.
  • the selections are activated by input devices composing the Point-of-service Terminal 28 as specified by the various Selection IDs.
  • Screen ID 1 204 maps to Screen 210, Screen ID 2 206 to Screen 212, and Screen ID 2 208 to Screen 214.
  • Dialog Block 202 is a table with a list of the allowable Selection ID's (in this case: 203, 205, and 207) and Screen IDs (in this case: 204, 206 and 208), of the screen that is to be displayed if that selection is made. Screens 201, 210, 212, 214 etc. are chained together using the various Screen IDs. As the Screen ID is used for the linkage, any meshed structure of Customer Activity Blocks is possible.
  • Selection ID indicates a response by the customer to the Screen displayed.
  • the response may indicate the customer's acceptance of an offer, rejection of an offer, the desire to page forward or backward in the dialog, or other meaning consistent with content of the dialog being presented.
  • the details of such dialog management are well understood by anyone skilled in the state of the art.
  • Customer Activity 200 dialogs can be constructed to any length by replicating the structure in Screen
  • Fig. 6 illustrates Customer Table 220 that contains a Customer ID 222 which is the ID provided to the retailer by the customer in the course of the primary transaction being performed. It may be a credit card number or other ID, or no ID (anonymous ID). It also contains Date of Creation 224, and Date of Profile Update 226. This table is used by Store Server 24 Application 76 to control the currency of the various profiles.
  • Fig. 7 is the Customer Store Profile 250 contains the information needed by
  • Customer Store Server 24 to interact with an identified customer. Such customers have used the same identification a sufficient number of times to merit the Central Server 20's generation of a Customer Store Profile Table 250. Customer Store Profile Table 250 is transferred by Central Server 20 to the Store Server 24 via WAN 22 and Wide Area Network Adapter 112 where it is stored in Data Memory 94 and
  • Disk Drive(s) 90 It is composed of the following fields: Customer ID 252 that corresponds to Customer ID 222 and is the internal identification used in the system for identified customers. Store ID 254 is a unique identification for each store participating in the system.
  • Think Time Distribution Parameters 256 are the parameters for the probability distribution that fits the prior experiences with the customer. These describe the variability of the customer's Think Time 1042 (see Fig. 20). The probability distribution used is determined by the Idle Time Distribution Type 506 (see Fig. 11). Agility Coefficient 258 describes the quickness of the customer in using the system. The following fields are repeated for each day-part of each day of the week.
  • Idle Time Distribution Parameters 266 are the parameters for the probability distribution used to describe Idle Time 1010 (see Fig. 17). In this embodiment, the gamma distribution is used. Its parameters are alpha and beta, which are used in Fig. 22, Value Packing to calculate the probability that a Customer Activity 200 will complete prior to the end of the Idle Time 1010. 1st most likely Activity ID/ Activity Type 270 and Probability of Completion of 1st most likely Activity ID/Activity Type 272 describe either the Activity ID 702 or the Activity Type 704 that has the highest probability to be completed in this period.
  • Fig. 8 is the Credit, Debit and Loyalty Card Table 330.
  • the table associates Card ID 332 with a Customer Name 334 and Customer ID 336, which is assigned by Store Server 24 when the customer is initially added to the system.
  • Fig. 9 is the Store Definition Table 340.
  • the table contains a Store ID 342 which uniquely identifies the store; Store Type 343 which is used, optionally, to build Customer Store Profile Tables 250 that span instances of the same Store Type 343 or to build Store Profile Tables that span instances of the same Store
  • Idle Time Fit Criteria 344 which specifies the acceptable probability of overrunning the Idle Time 1010.
  • Store Definition Table 340 is built by Central Server 20 as stores are entered into the system.
  • Fig. 10 is the Idle Time Message 360. These signals are messages generated by Store Server 24 when it detects the start or stop of Idle Time 1010.
  • Idle Time Message Two pieces of information are required in a Fig. 10, Idle Time Message.
  • Message ID 362 specifies whether the signal is a 'Start of Idle Time' or an 'End of Idle Time'.
  • Point-of-service Terminal ID 364 associates that signal with a specific checkout counter. This is then associated to a specific customer via Customer Session Table's 600 Point-of-service Terminal 602 field (see Fig. 13).
  • Fig. 11 is Point-of-service Terminal Table 500, which associates a Screen ID 504 with a Point-of-service Terminal ID 502. That screen is presented at End of Session 1016 at Point-of-service Terminal 28 specified by Point-of-service Terminal ID 502.
  • Idle Time Distribution Type 506 specifies the probability distribution that is to be used for activity at a specific Point-of-service Terminal 28.
  • Idle Time Distribution Type 506, with Idle Time Distribution Parameters 226 or 532 constitute the complete definition of the probability distribution for the current instance of Idle Time 1010.
  • Fig. 12 is Store Profile Table 510. It is supplied from Central Server 20 as part of a periodic download process.
  • Store ID 512 is a unique identifier assigned by Central Server 20 when the store is added to the system.
  • Point-of-service Terminal ID 514 identifies Point-of-service Terminal 28 in the store for that specific profile. For example, the Express Checkout Lane in a store would have a different Store Profile Table 510 entry than the normal Checkout Lanes.
  • Customer Type 516 is an identifier for a group of customers having similar characteristics that merit a unique profile.
  • the customer information used to develop the Store Profile Table 510 are developed from the customer experience from the store profiled or from venues that are similar to said store.
  • Think Time Distribution Parameter 520 is the parameters for the probability distribution that fits the prior experiences with the anonymous customers. These describe the variability of the customers' Think Time 1042 (see Fig. 20). The probability distribution used is determined by the Idle Time Distribution Type 506.
  • Agility Coefficient 524 describes the quickness of the average customer in
  • Idle Time Distribution Parameters 532 are the parameters for the probability distribution used to describe Idle Time 1010. In this embodiment, the gamma distribution is used. Its parameters are alpha and beta, which are used in Fig. 22, Value Packing to calculate the probability that a Customer Activity 200 will complete prior to the end of the Idle Time 1010. 1st most likely Activity ID/Activity Type 538 and Probability of Completion of 1st most likely Activity
  • ID/ Activity Type 540 describe either the Activity ID 702 or the Activity Type 704 (see Fig. 14) that has the highest probability to be completed in this period.
  • 2nd most likely Activity ID/ Activity Type 542 and Probability of Completion of 2nd most likely Activity ID/Activity Type 544, and 3rd most likely Activity ID/ Activity Type 546 and Probability of Completion of 3rd most likely Activity ID/ Activity Type 548 behave in the same manner. It should be noted that this list could be of any length. These parameters are used in Fig. 22, block 2008 to build Proposed Activity List 800.
  • Fig. 13 is the Customer Session Table 600.
  • Customer Session 1000 (see Fig. 17) is recorded in the Customer Session Table 600, at the point the customer is identified during Checkout Idle Time 1010.
  • Customer Session Table 600 contains:
  • Point-of-service Terminal 602 which identifies the checkout counter, gas pump, or other terminal used by the customer; Session Number 604, which is assigned by Store Server 24, and is assigned when the customer is identified; and Customer ID 606, which is provided by the customer, generally by passing an identification card Magnetic Stripe Reader 28, or is identified as an anonymous customer, if no identification is used.
  • Customer Store Profile Table 250 is located by using Customer ID 606 to find the entry, and the time from Store Server 24's internal Clock 72 is used to locate the Customer Store Profile Table 250 's Day-of-week 262 and Day-part 264, which are to be used. In a similar manner, the anonymous customer is processed using the Store Profile Table 510.
  • Fig. 14 is the Activity Table 700 that has all the activities offered in the store. These activities are describe in the form of outputs to the customer in the form of screen, video, and audio presentations; and inputs in the form of touchscreen, key pad, voice, card readers and other devices at the point of sale.
  • An Activity 200 is a dialog composed of said inputs and outputs that is presenting ads, information messages, coupon offers, offers to buy, etc. to the customer. It contains: Activity ID 702 which uniquely identifies the activity; Activity Type 704, which specifies the general characteristics of the activity, Activity Value 706, which specifies the economic value of the activity; Activity Time Distribution Type 708, which specifies the type of probability distribution used to describe the activity; and Activity Time Distribution Parameters, which complete a normalized description of the time to complete the activity.
  • Fig. 15 is Proposed Activity List 800, which are the candidate activities to be presented to a customer.
  • Proposed Activity List 800's entries are copied from Activity Table 700, after being selected using the Customer Store Profile Table
  • Activity Time Distribution Parameters 710 is adjusted as in Fig. 22, and placed in Adjusted Activity Time Distribution Parameters 812.
  • Time- Value 814 is calculated by finding the mean of the distribution specified by Adjusted Activity Time Distribution Parameters 812 and dividing it into Activity Value 808.
  • Proposed Activity List 800 is built in Fig. 22 and block 2008 and used in Fig. 21 as part of the activity selection process. In the process of building the list, Activity Time Distribution Parameters 710 are adjusted using System Response
  • Fig. 16 is the Store Configuration Table 910, which is composed of Store ID 912 and Next Session Number 914.
  • Next Session Number 914 is initialized with the value of zero. It is incremented by one each time a Session Number 604 in the Fig. 13, Customer Session Table 600 is assigned. When the field overflows, it is reset to zero. The field size is picked such that Session Numbers 914 assigned in one 24-hour period are unique.
  • System Response Time 916 is calculated dynamically as shown in Fig. 16, System Response Time Calculation. Percent on
  • Time Completion 918 is the overrun criteria for the store.
  • the Store Configuration Table 910 is built by Store Server 24 at system bring up. This table is in Store Server 24 and has one entry for the store.
  • ACTIVITY DIAGRAMS Fig. 17, Customer Session 1000 is a diagram that describes a typical Customer Session in a supermarket. Customer Session 1000 will be explained in the context of a grocery store, but is applicable, with minor variations, to most Point-of-service venues. This diagram illustrates the normal flow of a customer in a store. The first customer activity is to Shop 1002. When the shopping list has been filled, the customer enters a Wait for Checkout 1004.
  • Fig. 18 is a diagram that describes how the Idle Time 1010 is used.
  • the system presents a series of Customer Activities 200 (Activity 1 , Activity 2 through
  • Activity n These activities are selected based on the time available, the time required to present the activity and the value of the activity.
  • the selection of the Customer Activities 200 is described in Fig. 21 and Fig. 22.
  • Fig. 19 is a diagram that describes the Activity Cycle 1030.
  • the Fig. 19 Activity Cycle is composed of one or more Action Cycles 1040 (see Fig. 20).
  • Actions 1040 involve the presentation of a Screen 201 with a set of selections that constitute the customer's response to the screen.
  • Activity Cycle 1030 starts with the 'Start of Idle Time' in the Fig. 10, Idle Time Message and ends with the 'End of Idle Time' in the related Fig. 10, Idle Time Message.
  • Fig. 20 is a diagram that describes Action Cycle 1040. The Action Cycle
  • 1040 is viewed from the customer's perspective in that it starts with a screen displayed on the Display Screen 52.
  • the customer reads and thinks about the information presented during Think Time 1042. Once a decision is made, the customer responds during Enter Response 1044.
  • the system evaluates the response that is in the form of a Selection ID, and then at the end of System
  • FLOW CHARTS Fig. 21 is a flow chart for Activity Selection, which starts at block 1900.
  • Block 1900 calculates the Idle Time Probability Distribution for the remainder of the Idle Time 1010, by accessing the Idle Time Distribution Parameters 270 or 532, for an identified customer or non-identified customer, respectively, and the Idle Time Distribution Type 506 from Point-of-service Terminal Table 500.
  • Block 1902 accesses Activity Time Distribution Type 710, and Adjusted
  • Activity Time Distribution Parameters 712 from Proposed Activity List 800. Then it access System Response Time 916 from Store Configuration Table 910 and either Think Time Distribution Parameters 256 (identified customer) or 520 (anonymous customer). These are used to produce an adjusted activity time distribution for the Customer Activity 200. Then it passes control to block 1904.
  • Block 1904 uses the scaled idle time distribution from block 1900 and the adjusted activity time distribution from block 1902 and calculates the probability that the Customer Activity 200 will complete prior to the end of Idle Time 1010. This can be done by using integral calculus. Then it passes control to block 1906. Block 1906 compares the probability produced in block 1904 with the Idle
  • Block 1908 sets up to access the next Customer Activity 200 in the Proposed Activity List 800 by deleting the top Customer Activity 200 in the Proposed Activity List 800 and then passes control to block 1910.
  • Block 1910 checks Proposed Activity List 800, for a Customer Activity 200. If so control passes to block 1902, otherwise control passes to block 1916.
  • Block 1912 sets up the return and indicates the selected Customer Activity 200. It then returns to the process that called Activity Selection.
  • Block 1916 sets up the return and indicates that no activity met the criteria, and returns to the process that called Activity Selection.
  • Fig. 22 is a flow chart describing Value Packing. It starts with block 2002, which determines if the customer is anonymous, or not. If anonymous, control passes to block 2004, which accesses the Customer Store Profile Table 250.
  • Block 2008 accesses the Idle Time Distribution Parameters 256 (identified customer) or 532 (anonymous customer). It then builds the Proposed Activity List 800 from the activity information Customer Store Profile Table 250 (identified customer) or Store Profile Table 510 (anonymous customer), and the Activity Table 700 (see Fig. 13). Additional Customer Activities 200 can be added to the list based on other criteria.
  • Time- Value 814 is calculated as specified in Fig. 15. The Time-Value 814 can be calculated by dividing the Activity Value 706 by the mean of the adjusted activity time probability distribution, which, optionally, can be multiplied by the Probability of Completion 272, 276, 280, 540, 544, and 548.
  • the Proposed Activity List 800 is then sorted into descending sequence on Time- Value 814. Control then passes to block 2010.
  • Block 2010 calls Activity Selection (Fig. 21) using Proposed Activity List 800 as a parameter of the call. Upon return from Activity Selection (Fig. 21), control passes to block 2012.
  • Block 2012 determines if an activity was selected. If so control passes to block 2014, otherwise the process is ended.
  • Block 2014 presents the selected Customer Activity 200 to the customer and if the selected Customer Activity 200 cannot be repeated, it is deleted from the
  • Block 2016 checks to see if Idle Time 1010 has ended. If so the processing of the customer ends, otherwise control passes to block 2010. TYPICAL HARDWARE AND SOFTWARE INFRASTRUCTURE
  • This invention is based on conventional point-of-service systems. Many manufacturers supply point-of-service equipment, and the invention is not limited to any particular manufacture's equipment.
  • the invention can be implemented using IBM's 3680 Programmable Store System.
  • the Store Server 24 being an IBM
  • Point-of-service Terminal 28 being IBM 3683s.
  • IBM supplies software (IBM programmable store system MICRCODE) used in controlling operations in the hardware described and a program product (Subsystem Program Preparation Support II (SPPS II)) for writing server and terminal programs.
  • SPPS II includes languages, macroinstructions and a terminal display language. Communication between the terminals and the store server is done by using a LAN 26 and a program written in IBM 3650 Programmable Store System Microcode.
  • Other application programs that form the infrastructure for the invention are SDM (Store Data Management) and HCP (Host Communication Program), both sold by IBM.
  • the system notes that by the absence of an identification. Typically the customer is paying by cash or check and has not used a loyalty card to initiate the session. The session starts with the customer making a selection from the initial screen presented. This might be the customer indicating that 'he or she is going to pay in cash. At that time, the system accesses the profile for the anonymous customer. This is called the store profile table. This profile has been built from the activities of the customers in this store or similar stores. The profile contains the expected idle time probability distribution and the parameters necessary to calculate a specific instance of an idle time probability distribution for use during the session.
  • activity time distribution parameters are used in conjunction with the candidate activities' activity time distribution type, and activity time distribution parameters to produce an activity time probability distribution for all instances of the candidate activities.
  • activity time probability distribution As each activity time probability distribution is produced, it is evaluated for the expected idle time for that activity. Say an activity presented an advertisement to the customer without any action expected on the part of the customer. In this case, the activity would be presented for a fixed period of time.
  • the activity profile would indicate it was a point probability distribution with a mean of 1.
  • the system would know the standard time that ads are presented, say 15 seconds, and the estimated activity time would be 15 seconds.
  • a second activity presents an opportunity to request a coupon for a product.
  • the anonymous profile indicates that the agility coefficient is 3, indicating a moderately fast customer. The coefficient would be multiplied by the beta parameter of the gamma probability distribution
  • the beta is then adjusted based on the current system response time.
  • the system response time is converted into a multiplier based on a well know non-linear mapping. If the system response time were 0.1 seconds the multiplier would be 1.0; if it were 1.5 second the multiplier would be 3.6. Once the multiplier is determined the prior beta value would be multiplied by it. to produce the expected activity time's gamma probability distribution, and its expected mean would be calculated.
  • a third activity presents a opportunity to purchase a product and requires a number of interactions to configure the product.
  • a cup of coffee The customer would be presented the offer, select the: brand of coffee, the size of the cup, how much cream, and how much sugar; and then signal the final purchase. This could take up to six screens and 6 customer selections.
  • expected activity time would be the same as the previous example but the gamma probability distribution would have much higher values due to the longer time such an activity would take.
  • each activity would have its time-value calculated by multiplying the activity value by the probability of completion of that activity (from the anonymous profile), and dividing that product by the expected activity time. This computation produces a figure of merit for the activity.
  • the highest value figure of merit is selected as the first candidate activity.
  • the candidate activity's probability distribution and the idle time probability distribution would be evaluated to calculate the probability that the activity would complete within the idle time. If this probability met the percent on time completion criteria, the activity would be scheduled. Otherwise, activities would be evaluated in descending order based on the figure of merit until an activity was scheduled or the candidate list is exhausted. In this example, we will assume that at least one activity is scheduled.
  • the system adjusts the idle time distribution based on the actual time that all the previous activity(ies) consumed. Then the candidate evaluation process in the prior paragraph is repeated, starting with the next activity in the candidate list.
  • customer store profile is used instead of the anonymous profile. Because the profile is for a specific customer or a very limited set of customers (say husband and wife with the same credit card number), the profile has much more accurate profile information which will increase the value of the activities presented, reduce the probability of exceeding the percent on time completion criteria, and increasing the probability that an activity will be completed by the customer.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention porte sur un appareil et un procédé correspondant permettant de présenter à un consommateur des formes variées d'informations (produit, marque) dans des périodes de consommation au ralenti. Un signal indiquant le début de la période au ralenti (2) est utilisé pour déclencher la présentation au consommateur de différentes offres. Ceci s'effectue dans une série d'interactions entre une interface informatique et un consommateur. Les interactions sont planifiées en fonction des informations antérieurement acquises concernant le consommateur, la transaction courante, le lieu de la transaction, l'heure du jour, la date de transaction et le temps estimé disponible. Ce procédé permet à l'appareil d'utiliser des informations concernant le consommateur et au système de sélectionner et présenter des activités (6, 12, 16) au consommateur. Les informations accumulées concernant les transactions du consommateur sont analysées et utilisées pour améliorer l'efficacité de l'interaction du client avec le système dans des lieux de présentation identiques.
PCT/US2001/041171 2000-07-24 2001-06-27 Procede et appareil pour activites d'ajustement optimal dans des periodes de consommation au ralenti Ceased WO2002008931A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2001276043A AU2001276043A1 (en) 2000-07-24 2001-06-27 Method and apparatus for optimal fitting activities into customer idle time

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US62457700A 2000-07-24 2000-07-24
US09/624,577 2000-07-24

Publications (1)

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WO2002008931A1 true WO2002008931A1 (fr) 2002-01-31

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Country Link
AU (1) AU2001276043A1 (fr)
TW (1) TW535080B (fr)
WO (1) WO2002008931A1 (fr)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10355477A1 (de) * 2003-11-27 2005-06-23 Daimlerchrysler Ag Verfahren und Vorrichtung zur aktiven Pausengestaltung
US7470700B2 (en) 2003-08-13 2008-12-30 Takeda Pharmaceutical Company Limited Dipeptidyl peptidase inhibitors
US7507552B1 (en) 2004-04-16 2009-03-24 Takeda San Diego, Inc. Crystallization of histone deacetylase 2
US8084605B2 (en) 2006-11-29 2011-12-27 Kelly Ron C Polymorphs of succinate salt of 2-[6-(3-amino-piperidin-1-yl)-3-methyl-2,4-dioxo-3,4-dihydro-2H-pyrimidin-1-ylmethy]-4-fluor-benzonitrile and methods of use therefor
US8222411B2 (en) 2005-09-16 2012-07-17 Takeda Pharmaceutical Company Limited Dipeptidyl peptidase inhibitors
US8906901B2 (en) 2005-09-14 2014-12-09 Takeda Pharmaceutical Company Limited Administration of dipeptidyl peptidase inhibitors

Citations (2)

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Publication number Priority date Publication date Assignee Title
US5623404A (en) * 1994-03-18 1997-04-22 Minnesota Mining And Manufacturing Company System and method for producing schedules of resource requests having uncertain durations
US5883819A (en) * 1996-12-31 1999-03-16 Northern Telecom Limited Method and system for quality of service assessment for multimedia traffic under aggregate traffic conditions

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5623404A (en) * 1994-03-18 1997-04-22 Minnesota Mining And Manufacturing Company System and method for producing schedules of resource requests having uncertain durations
US5883819A (en) * 1996-12-31 1999-03-16 Northern Telecom Limited Method and system for quality of service assessment for multimedia traffic under aggregate traffic conditions

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7470700B2 (en) 2003-08-13 2008-12-30 Takeda Pharmaceutical Company Limited Dipeptidyl peptidase inhibitors
US7579357B2 (en) 2003-08-13 2009-08-25 Takeda Pharmaceutical Company Limited Dipeptidyl peptidase inhibitors
DE10355477A1 (de) * 2003-11-27 2005-06-23 Daimlerchrysler Ag Verfahren und Vorrichtung zur aktiven Pausengestaltung
US7507552B1 (en) 2004-04-16 2009-03-24 Takeda San Diego, Inc. Crystallization of histone deacetylase 2
US8906901B2 (en) 2005-09-14 2014-12-09 Takeda Pharmaceutical Company Limited Administration of dipeptidyl peptidase inhibitors
US8222411B2 (en) 2005-09-16 2012-07-17 Takeda Pharmaceutical Company Limited Dipeptidyl peptidase inhibitors
US8084605B2 (en) 2006-11-29 2011-12-27 Kelly Ron C Polymorphs of succinate salt of 2-[6-(3-amino-piperidin-1-yl)-3-methyl-2,4-dioxo-3,4-dihydro-2H-pyrimidin-1-ylmethy]-4-fluor-benzonitrile and methods of use therefor

Also Published As

Publication number Publication date
AU2001276043A1 (en) 2002-02-05
TW535080B (en) 2003-06-01

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