US20180353002A1

US20180353002A1 - Customizing beverage profiles for a user

Info

Publication number: US20180353002A1
Application number: US16/105,709
Authority: US
Inventors: John Cronin; Dylan Jonathan WILSON; Steven Matthew PHILBIN; Michael Glynn D'ANDREA
Original assignee: Jooster Ip AG; Vejo Ip AG
Current assignee: Jooster Ip AG; Vejo Ip AG
Priority date: 2016-02-18
Filing date: 2018-08-20
Publication date: 2018-12-13
Also published as: US10464798B2; WO2017143251A1; US10336598B2; US20190106313A1; US20180352999A1; WO2017200613A2; WO2017200613A3; US20180354773A1; US20180352836A1; WO2017143282A1; WO2017143292A1

Abstract

Systems and methods for customizing beverage profiles and associated consumption programs, such as customizing smoothie pods to be used in making smoothies and other beverages, are described. For example, the systems and methods may receive or obtain information associated with a user's previous, current, and/or future activities (e.g., workouts or training sessions), a user's sleep activities, a user's current mental acuity or sharpness, a user's health or fitness, and so on, and determine or create a beverage profile for the user based on the information for the user. Further, the systems and methods may communicate and/or facilitate interactions with various online health or wellness programs.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to the following applications: U.S. Provisional Application No. 62/296,851, filed Feb. 18, 2016; U.S. Provisional Application No. 62/297,009, filed Feb. 18, 2016; U.S. Provisional Application No. 62/296,844, filed Feb. 18, 2016; U.S. Provisional Application No. 62/296,814, filed Feb. 18, 2016; U.S. Provisional Application No. 62/297,716, filed Feb. 19, 2016; U.S. Provisional Application No. 62/297,632, filed Feb. 19, 2016; U.S. Provisional Application No. 62/297,711, filed Feb. 19, 2016; and U.S. Provisional Application No. 62/297,644, filed Feb. 19, 2016, which are hereby incorporated by reference in their entirety.

BACKGROUND

There are numerous retailers, distributors, and companies that attempt to target users with supplements, beverages, and other nutritional foods or drinks. However, most of these products are pre-made and generic to a certain population of users and/or for a certain purpose. For example, companies create sports drinks to assist the performance of a generic user during activities, and retailers sell smoothies that promote certain health benefits to a large population of users.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosed technology will be described and explained through the use of the accompanying drawings.

FIG. 1 is a block diagram illustrating a suitable computing environment for providing beverages having customized beverage profiles to a user.

FIG. 2 is a flow diagram illustrating a method for making a beverage for a user that is based on mental or physical characteristics of a user.

FIG. 3 is a block diagram illustrating a suitable computing environment for providing customized beverages to users based on activities of the users.

FIG. 4A is a flow diagram illustrating a method for preparing a beverage for a user based on activity information associated with the user.

FIG. 4B is a flow diagram illustrating a method for determining a beverage recommendation that is based on user activity information.

FIG. 5 is a display diagram illustrating a user interface that displays recommended beverages to a user based on activities of the user.

FIG. 6 is a block diagram illustrating a suitable computing environment for providing customized beverages to users based on sleep activities of the users.

FIG. 7 is a flow diagram illustrating a method for determining a beverage recommendation for a user based on sleep information associated with the user.

FIG. 8 is a display diagram illustrating a user interface that displays recommended beverages to a user based on sleep information of the user.

FIG. 9 is a block diagram illustrating a suitable computing environment for providing customized beverages to users based on mental acuity information for the users.

FIG. 10 is a flow diagram illustrating a method for determining a beverage recommendation based on user performance on one or more acuity tests before and after consuming a customized beverage.

FIG. 11A is a display diagram illustrating a user interface that present a mental acuity test for a user.

FIG. 11B is a display diagram illustrating a user interface that displays recommended beverages to a user based on test performance for a user.

FIG. 12 is a block diagram illustrating a suitable computing environment for providing customized beverages to users of online wellness programs.

FIG. 13 is a block diagram illustrating components of a beverage network system that interacts online wellness programs.

FIG. 14 is a flow diagram illustrating a method for determining a beverage recommendation for a user of an online wellness program.

FIG. 15 is a flow diagram illustrating a method for determining a nutritional profile for a user.

FIG. 16 is a flow diagram illustrating a method for determining a customized beverage for a user based on the user's nutritional profile.

FIG. 17 is a display diagram illustrating a user interface that presents recommended beverages to a user and facilitates making and ordering of beverages on behalf of the user.

The drawings have not necessarily been drawn to scale. Similarly, some components and/or operations may be separated into different blocks or combined into a single block for the purposes of discussion of some of the embodiments of the present technology. Moreover, while the technology is amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. The intention, however, is not to limit the technology to the particular embodiments described. On the contrary, the technology is intended to cover all modifications, equivalents, and alternatives falling within the scope of the technology as defined by the appended claims.

DETAILED DESCRIPTION

Overview

Systems and methods for customizing beverage profiles and associated consumption programs, such as customizing smoothie pods to be used in making smoothies and other beverages, are described. For example, the systems and methods may receive or obtain information associated with a user's previous, current, and/or future activities (e.g., workouts or training sessions), a user's sleep activities, a user's current mental acuity or sharpness, a user's health or fitness, and so on, and determine or create a beverage profile for the user based on the information for the user.
The systems and methods may make beverages (e.g., smoothies or other drinks) and/or order or create smoothie pods (e.g., containers of ingredients used when making a beverage) having the beverage profile. The systems and methods, therefore, may provide the user with a customized smoothie or other beverage that includes ingredients useful in improving, benefiting, or mitigating the user's health, performance, mental state, and/or other characteristics or states, among other benefits.

Suitable Computing Environment

As described herein, the systems and methods customize beverage profiles for users based on various aspects or characteristics associated with the users, and create or customize smoothie pods having ingredients that include the customized beverage profiles. FIG. 1 is a block diagram illustrating a suitable computing environment 100 for providing beverages having customized beverage profiles to a user.
The computing environment 100 includes a user device 110 (having a user interface 112), such as a mobile device, which may be paired with a user wearable device 115 or peripheral configured to capture and/or measure information associated with the user. A beverage machine 120, such as a machine, device, or refrigerator configured to create beverages from pods or other ingredients sources, may be directly connected to the user device 110 or wearable device 115, or may communicate with the user device 110, the wearable device 115, or other devices, systems, and/or servers over a network 125, such as the Internet.
The beverage machine 120, therefore, may include a communication component 126 that facilitates communicating with various devices over the network 125, a user interface component 122 that renders, displays, and/or presents information to users via a display, such as a graphical user interface (GUI), and/or receives input from users via the display or via various manual controls of the beverage machine 120. The beverage machine 124 also includes a beverage making device 122, such as a blender or other pod-based beverage creating or making devices.
For example, the beverage making device 122 may be configured to extract contents (e.g., ingredients) within a beverage pod, such as a smoothie pod, and mix or combine the extracted contents with various liquids or other mixing substances, such as water, ice, milk, and so on, based on received or stored programs, recipes, and/or instructions. The beverage pods may be pods or cartridges containing specific mixtures of ingredients. For example, a pod may include a mixture of various freeze dried fruits (e.g., freeze dried bananas, strawberries, blueberries, mango, and so on), freeze dried vegetables (e.g., kale, spinach, beets, and so on), additive powders (e.g., protein powders, powdered greens), oils, seeds, supplements, flavors, and so on. In some cases, a pod may include a mixture of many different ingredients. In other cases, the pod may include one or more ingredients.
A beverage profile determination server 130 may support and/or provide a “beverage network” or other cloud-based systems that perform various actions or functions to determine or create beverage profile recommendations for users. For example, the server 130, which may communicate with the beverage machine 120, the user device 110, and/or the wearable device 115 over the network 125, may include various different systems configured to access, receive, obtain, or retrieve certain information about a user (e.g., activity or health information), and generate beverage profiles for beverages targeted to the user based on the information about the users.
Example systems, which are discussed in greater detail herein, include a user activity system 135 configured to generate or determine beverage profiles for beverages based on activity information associated with a user, a user sleep system 140 configured to generate or determine beverage profiles for beverages based on sleep activity associated with the user, and a user acuity system 145 configured to generate or determine beverage profiles for beverages based on mental acuity information measured for a user.
A beverage network system 150 may be part of, or associated with, the server 130 and its various beverage profile recommendation systems. The beverage network system 150 may facilitate interactions between the systems of the server 130 and one or more online health systems 160, such as online wellness programs, online health monitoring systems, medical or doctor partner networks, and so on. Thus, in some embodiments, various third party systems, such as the online system 160, may access the operations of the server 130 via one or more Software As A Service (SaaS) interfaces provided by the beverage network system 150.
Further details regarding the systems, devices, methods, and routines utilized to provide and/or implement various aspects of the computing environment 100 will be described herein. The drawings have not necessarily been drawn to scale. Similarly, some components and/or operations may be separated into different blocks or combined into a single block for the purposes of discussion of some of the embodiments of the present technology. Moreover, while the technology is amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and are described in detail below. The intention, however, is not to limit the technology to the particular embodiments described. On the contrary, the technology is intended to cover all modifications, equivalents, and alternatives falling within the scope of the technology as defined by the appended claims.
FIG. 1 and the discussion herein provide a brief, general description of the suitable computing environment 100 in which the system can be supported and implemented. Although not required, aspects of the system are described in the general context of computer-executable instructions, such as routines executed by a general-purpose computer, e.g., mobile device, a server computer, or personal computer. Those skilled in the relevant art will appreciate that the system can be practiced with other communications, data processing, or computer system configurations, including: Internet appliances, hand-held devices (including tablet computers and/or personal digital assistants (PDAs)), all manner of cellular or mobile phones, multi-processor systems, microprocessor-based or programmable consumer electronics, set-top boxes, network PCs, mini-computers, mainframe computers, and the like. Indeed, the terms “computer,” “host,” and “host computer,” and “mobile device” and “handset” are generally used interchangeably herein, and refer to any of the above devices and systems, as well as any data processor.
Aspects of the system can be embodied in a special purpose computing device or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions explained in detail herein. Aspects of the system may also be practiced in distributed computing environments where tasks or modules are performed by remote processing devices, which are linked through a communications network, such as a Local Area Network (LAN), Wide Area Network (WAN), or the Internet. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
Aspects of the system may be stored or distributed on computer-readable media (e.g., physical and/or tangible non-transitory computer-readable storage media), including magnetically or optically readable computer discs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, or other data storage media. Indeed, computer implemented instructions, data structures, screen displays, and other data under aspects of the system may be distributed over the Internet or over other networks (including wireless networks), on a propagated signal on a propagation medium (e.g., an electromagnetic wave(s), a sound wave, etc.) over a period of time, or they may be provided on any analog or digital network (packet switched, circuit switched, or other scheme). Those skilled in the relevant art will recognize that portions of the system reside on a server computer, while corresponding portions reside on a client computer such as a mobile or portable device, and thus, while certain hardware platforms are described herein, aspects of the system are equally applicable to nodes on a network. In an alternative embodiment, the mobile device or portable device may represent the server portion, while the server may represent the client portion.
As described herein, the beverage profile determination server 130, therefore, may perform various processes, methods, or operations when creating and/or making beverages (e.g., customized pod-based smoothies) for users. FIG. 2 is a flow diagram illustrating a method 200 for making a beverage for a user that is based on mental or physical characteristics of a user. Aspects of the method 200 may be performed by the beverage profile determination server 130 and, accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 200 may be performed on any suitable hardware.
In operation 210, the server 130 accessed information associated with a user's physical or mental characteristics. For example, the server 130, via one or more associated systems, may access user activity information, user sleep activity information, mental acuity information, and so on, measured or provided by the user device 110, the wearable device 115, and/or the beverage machine 120 (e.g., input by the user to the GUI 122 of the machine 122).
In operation 220, the server 130 matches the accessed information to a beverage profile associated with a beverage, such as a smoothie. For example, the server 130 may compare the user information to information associated with different ingredients, additives, and so on, and generate or identify one or more beverage profiles (e.g., mixtures of ingredients at certain quantities) that match or are otherwise associated with the user information. The server 130 may then provide the one or more beverage profiles to the beverage machine 120.
In operation 230, the machine 120 makes a beverage having the beverage profile that matched the user information. For example, using instructions (e.g., beverage profiles) received from the server 130, the beverage making device 124 of the beverage machine 120 may select one or more beverage pods whose contents include ingredients that represent the beverage profile, and make the beverage using the contents of the pods. In some cases, the machine 120 may order the beverage pods, and make the beverages once the pods are received and provided to the machine 120.
Therefore, in some embodiments, the beverage profile determination server 130 performs various processes for identifying, determining, recommending, and/or suggesting beverages, such as smoothies, to users based on various aspects associated with the users. The following sections describe, in greater detail, the different systems supported by the server 130.

Examples of Customizing Beverages Based on User Activities

As described herein, in some embodiments, the systems and methods determine and/or generate customized beverage profiles and associated consumption programs based on user activities, such as current or predicted workout routines, and other exercises or activities. For example, the systems and methods may provide an automated beverage machine (e.g., smoothie maker or other beverage machine 120, which makes smoothies from ingredients contained in smoothie pods) configured to receive information associated with a user's activities, such as workout routines, exercises, and so on.
In some embodiments, the beverage machine 120 or other devices 110, 115 may collect information associated with a user's workout routine and/or physical activity, transmit the collected information to the server 130 over the network 125, and calculate, via the user activity system 135, an estimated loss in calories, vitamins, carbohydrates, and so on, due to the activity/workout that was performed or scheduled to be performed.
The user activity system 135 receives the values, such as the estimated deficiencies, matches the identified deficiencies with beverage available and desirable to the user (e.g., for weight loss users, beverages that maintain a net loss of calories), and sends a list of beverages that include beverage profiles associated with satisfying or meeting the user's deficiencies due to the completed, running, or planned activities. The machine 120 and/or user device 110 may display the list of beverages via an associated GUI, and make (or, order) a beverage selected by the user.
FIG. 3 is a block diagram illustrating a suitable computing environment 300 for providing customized beverages to users based on activities of the users. As described herein, the user activity system 135, located at or within the beverage profile determination server 140, communicates over the network 125 with the beverage machine 120, the user device 110, and/or one or more wearable or peripheral devices 115 associated with the user.
For example, the wearable devices 115 may include smart watches, activity monitors, heart rate monitors, peripheral devices, and so on. The measured activity levels and/or parameters may include steps taken by a user, a user's heart rate, distance walked or ran by the user, calories burned (or estimated to be burned), temperature of the user, physical characteristics during the activity, and so on. Also, in some cases, the device 115, or another device, may be a connected workout machine (e.g., treadmill, elliptical, stationary bike, and so on) that communicates workout data for the user to the user device 110, beverage machine 120, and/or server 130.
In addition, health data may be provided by or to the user device 110, such as a smartphone, where a user is tracking his/her food intake, and determines suitable intake levels of calories, carbohydrates, fats, proteins, or other nutrients. The user may log caloric/food intake over the course of the day (type of food, quantity size, time of day, and so on), and the user, via the user device 110, may upload or sent the logged data to the beverage machine 120 or user activity system 135, which performs utilizes the various information to determine recommendations associated with one or more beverages for consumption by the user.
In some embodiments, the beverage machine 120 includes various operating software programs, located in the machine's memory, which may gather incoming data and transmit the data to various remote or networked systems, such as the user activity system 135. As described herein, the external devices (e.g., a mobile phone 110, wearable device 115, smart workout machine, or other user device), captures and sends data associated with the user's workout or activity level to the machine 120. The beverage machine 120 may receive the data via a wireless connection (e.g., via network 125) through the use of a plug-in device (e.g., USB stick, SD card, and so on), via direct communication channels (e.g., Bluetooth), and so on.
The beverage machine 120 receives the data via the communication component or port 126. When a user interacts with the machine 120, via a user GUI provided by the user interface component 122, the information is loaded, and if a suitable option presented by the user GUI screen is selected by the user, the data is transferred to the user activity system 135, which generates beverage scores or other metrics. For example, the system 135, via a determination module 320, compares the beverage scores to beverages stored in a beverage database 330, and then, via a recommendation module 310, recommends beverages that match the beverage scores by sending information to the beverage machine 120. The beverage machine 120, via the GUI, displays user-selectable options for the user that represent the recommended beverages.
Therefore, the user activity system 135 performs various processes or methods when preparing beverages for a user based on the user's activities. FIG. 4A is a flow diagram illustrating a method 400 for preparing a beverage for a user based on activity information associated with the user. Aspects of the method 400 may be performed by the beverage machine 120 and, accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 400 may be performed on any suitable hardware.
In operation 405, the machine 120 receives a user request for a beverage, such as a smoothie or other drink. For example, the machine 120 may receive a user selection of one or more options displayed by a GUI of the machine 120 and/or the user device 110.
In operation 410, the machine 120 determines whether the request includes a request or indication of user activity information. If the request does not include user activity information (or, an indication to utilize user activity information), the method 400 proceeds to operation 415, and the machine displays a complete list of beverages available to the user, such as beverages available to be made at that time by the machine 120.
If the request does include a request or indication of user activity information, the method 400 proceeds to operation 420, and the machine 120 determines whether user activity information has been received or provided by the user. If no user activity information has been received, the method 400 proceeds to operation 412, and the machine 120, via the GUI, prompts the user to provide activity information (e.g., manually or via an associate device). If the user activity information has been received, the method 400 proceeds to operation 425, and the machine 120 transmits the received or accessed user activity information to the recommendation module or system 310 of the user activity system 135.
In operation 430, the machine 120 receives one or more beverage recommendations from the recommendation module or system 310, such as indications of beverages having beverage profile information that matched the user activity information. In operation 435, the machine 120 displays a list of the recommended beverages to the user, via the machine 120 GUI.
In operation 440, the machine 120 determines that the user has selected one or more displayed beverages, either from a presented list of all available beverages (via operation 415) and/or from a presented list of recommended beverages (via operation 440), and, in operation 445, prepares or makes the selected beverage using the beverage making device 124 and one or more beverage pods that include ingredients matching the selected beverage.
As described herein, in some embodiments, the user activity system 135 may perform various processes to compare and match user activity information to one or more beverage profiles that represent beverages to be made by the machine 120 and consumed by the user. FIG. 4B is a flow diagram illustrating a method 450 for determining a beverage recommendation that is based on user activity information. Aspects of the method 450 may be performed by the determination module 320 and, accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 450 may be performed on any suitable hardware.
In operation 460, the module 320 receives activity information from the beverage machine 120. For example, the user activity system 135 may receive information from the machine 120 that is associated with a user's completed, current, or future workout routine and/or physical activity, as described herein.
In operation 465, the module 320 determines a beverage profile score for the received activity information. For example, the module 320 may determine the score by taking the total calories expended in the workout (e.g., 450 calories) and dividing it by 10 (or some other normalization factor), which provides a score of 45. Of course, the module 320 may utilize other activity information and/or other scoring algorithms or rules (e.g., scores based on total calories, average heart rate, miles logged, steps walked, and so on) when scoring the activity information.
In operation 470, the module 320 compares the beverage profile score, or beverage score, to the scores associated with beverages profiles stored in the beverage database 330. In some cases, the score may be based certain user goals, where there are selections of beverages useful in the user achieving their goals. Thus, in some embodiments, the module 320 matches the score, along with the user's goals (e.g., loaded as part of the workout information), with values associated with beverage profiles stored by the beverage database 330.
In some cases, the module 320 receives user goal information (e.g., target weight information, fitness level information, and so on) from third party health/fitness programs, such as online program 160. For example, the module 320 may receive the goal information (e.g. via the network 125 and/or via devices provided to the machine 120) from a third party health practitioner (e.g., trainer, nurse, doctor, and so on) who can provide medically approved goals for the user.
In operation 475, the module 320 identifies one or more beverages having beverage profiles that match the determined beverage profile scores. Table 1 represents a data structure stored by the database 330 that includes entries that relate a user goal (e.g., “lose weight”) to beverage scores and beverage profiles.

TABLE 1

Goal	Score Range	Beverage Profile

Gain weight	0-25	Beverages #1 and #4
	26-50	Beverages #6 and #2
	50+	Beverages #8 and #12
Maintain weight	0-20	Beverage #3
	21-44	Beverages #5, #10, #11
	45+	Beverages #15, #19, #20
Lose weight	0-22	Beverages #7, #22
	23-35	Beverage #16
	35+	Beverages #13, #17, #18

For example, the module 320, having received a beverage profile score of 25 and a user goal of maintain weight, searches Table 1 for matching beverage profiles, and identifies Beverages #5, #10, and #11 as matching the score.
In operation 480, the module transmits information that identifies the recommended (e.g., matching) beverage profiles to the beverage machine 120, which presents the information to the user. FIG. 5 is a display diagram illustrating a user interface 500 that displays recommended beverages to a user based on activities of the user.
The user interface 500 presents various states of interaction with a user. A user first requests to make a beverage, and selects a manual option 510 to manually choose a beverage from a list or menu, or a user activity option 505 to use their workout information/data, such as data gathered by the user device 110 or device 115. In some cases, the user interface may include elements than enable a user to directly input their daily health data (e.g., activity, desired goals, and so on), and the interface 500 may include elements that facilitate the user to input both activity data and specified goals. For example, the user interface 500 may receive the information via a questionnaire type interaction with the user, where the user answers questions regarding goals and activities posed by the machine 120, and/or the user may voluntarily input information that represents regards the user's goals, activities, and other information.
As described herein, when the user selects the manual option 520, the machine 120 presents a list of some or all available beverages. However, when the user selects the user activity option 505, the machine 120 presents a list 520 of recommended beverages, based upon user activity information. The user may select one or more of the recommended beverages 530, and the machine 120, in response to the selections, makes (or, orders), a beverage, such as a smoothie for the user. As described herein, the machine may make a smoothie using a smoothie pod of ingredients that match the beverage profile associated with the selected beverage.
Thus, in some embodiments, the systems and methods collect information associated with a user's workout routine or physical activity via an automated beverage machine, where the automated beverage machine makes beverages from beverage pods provided to the automated beverage machine, determines a workout score based on the collected information, matches the workout score to one or more beverage profiles associated with beverages to be consumed by the user, and makes, at the automated beverage machine, a beverage having ingredients based on the one or more beverage profiles.
Therefore, in some embodiments, the systems and methods enable a user to provide workout and/or health goal information to an automated beverage machine (e.g., beverage machine 120), which identifies and makes a pod-based beverage (e.g., smoothie from a smoothie pod) based on the provided information.

Examples of Customizing Beverages Based on User Sleep Activities

The systems and methods described herein, in some embodiments, determine and/or generate customized beverage profiles, and make associated beverages, for users based on characteristics of the user's sleep activities, patterns, habits, and/or cycles. The systems and methods utilize sleep and activity data measured by a user's wearable device (e.g., device 115) to determine when and how well the user sleeps. The systems and methods combine the sleep data with usage data (e.g., beverage consumption data), and determine recommendations for the user regarding the types (e.g., ingredient profiles) and timing (e.g., when to consume) of smoothies he/she should consume to increase his/her quality of sleep, among other things.
For example, the systems and methods may attempt to improve or modify a user's sleep (or, quality of sleep), by determining a user's current or historical quality of sleep from sleep and activity data obtained from a user's wearable device or other monitoring device, combining or comparing the determined data with usage data associated with the user's consumption of various smoothies, and determining recommendations for the user about the types and timing of smoothies and other beverages to consume to improve the quality of sleep. The system and methods may then tracks changes in the user's quality of sleep based on the recommendations to provide more accurate recommendations for the specific user and/or a population of other similar users.
FIG. 6 is a block diagram illustrating a suitable computing environment 600 for providing customized beverages to users based on sleep activities of the users. As described herein, a wearable device 110 (e.g., a Withings, FitBit device, or other device configured to monitor a user's sleep activities) may capture data associated with a user's sleep activities, as well as other non-sleep data (e.g., temperature, heart rate, and so on). The systems and methods may combine the user's sleep data with the user's smoothie consumption history, determine recommendations to be made to the user about the types and timing of smoothies that can increase the quality of their sleep.
The computing environment 600 includes the beverage profile determination server 130, which includes the user sleep system 140. The user sleep system 140 includes various components or modules, such as a recommendation module 610 configured to determine one or more beverages having beverage profiles that match user sleep activity information. Further, the user sleep system 140 includes a smoothie database 620, which stores information that includes the ingredients of all available beverage pods (e.g., beverage pods at the machine and/or to be ordered and provided to the machine), such as smoothie pods, for the beverage machine 120, and a consumption database 630, which stores user usage or beverage consumption data as well as user sleep activity data.
As an example, when a user's wearable device 115 comes within wireless communications range of the beverage machine 120, the device 115 transfers the user's sleep and activity data, which is then relayed by the beverage machine 120 to the consumption database 630 of the user sleep system 140.
When the user goes to get a smoothie after a certain time (e.g., after 5 pm or so, as the user may not want a relaxation smoothie when they are getting up to go to work), the recommendation module 610 may compare the user's sleep on any given day to their average night's sleep. For example, the user's sleep activity may be measured with respect to both quantity and quality to determine a current or prior level of sleep (e.g., poor, ok, average, good, short, long, and so on), via the wearable device 115. Using the information, the system 140 determines a recommendation for a beverage to be provided to the users, and sends the recommendation over the network 125 to the beverage machine 120, which makes the beverage for the user.
Therefore, the sleep activity system 140, via the recommendation module 610, performs various processes, operations, or methods when determining beverages to recommend to users based on their sleep activity information. FIG. 7 is a flow diagram illustrating a method 700 for determining a beverage recommendation for a user based on sleep information associated with the user. Aspects of the method 700 may be performed by the user sleep system 140 and, accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 700 may be performed on any suitable hardware.
In operation 710, the system 140 provides an initial stock of beverage pods to the beverage machine 120. For example, a user associated with the machine 120 may receive an initial order of various different beverage pods for popular or initially targeted beverages, and provide them to the beverage machine 120. In some cases, various analysis systems may utilize data within the consumption database 630 to determine supplements that are effective in enhancing sleep quality, sleep quantity, and so on. For example, these systems may determine initial orders of smoothie pods based on determinations of a large user population, modifying the initial orders based on the demographics of users, the specific user, and their wearable devices.
In operation 720, various devices monitor the user's sleep activities. For example, the wearable device 115 may capture data associated with a user's sleep cycle activity, such as time periods of deep sleep, time periods of light sleep, time periods of REM sleep, time periods of wakefulness, a total sleep time, user movement data, non-sleep data, and so on.
In operation 730, the system 140 receives or otherwise accesses the sleep activity information from the beverage machine 120 and/or directly from the wearable device 115 and/or the user device 110. For example, the system 140 may receive the sleep activity information from a most recent night's sleep, as well as any usage or beverage consumption data for the user, and store, in operation 735, the information in the consumption database 630.
In operation 740, the system 140 determines whether the sleep activity information indicates a low or sub-optimal quality of sleep for the user. For example, the system 140 may determine a time period of deep sleep or REM sleep is below a minimum time period associated with good sleep, and/or may determine the overall sleep quality for the previous night's sleep is below an average sleep quality for the user, among other determinations.
When the sleep quality does not indicate a low quality of sleep, the method 700 proceeds back to operation 720, and the system 140 continues to monitor the user's sleep activity, else the method proceeds to operation 750. In operation 750, the system 140 determines and causes the machine 120 to display one or more beverage recommendations for the user. For example, the system 140 may utilize information stored in the consumption database 630 to determine one or more beverages to recommend to the user.
The following table (Table 2) illustrates the various data structures stored in the database 630, via which the system 140 identified beverages to recommend to a user.

TABLE 2

Date	Sleep Quality	Ingredients	Volume	Time

Jan. 1, 2016	Poor	Melatonin, ABC	8	oz.	7:15
Jan. 2, 2016	OK	Chamomile, ABC	8	oz.	6:15
Jan. 3, 2016	OK	ABC	8	oz.	7:15
Jan. 4, 2016	OK	DEF	8	oz.	8:15
Jan. 5, 2016	Good	Melatonin, DEF	12	oz.	7:15

Thus, the system 140 may utilize the information stored by the table to identify and/or modify beverage profiles based on the user's sleep activities or quality. For example the table indicates that the user experience a good sleep quality (e.g., sufficient amounts of REM sleep) after consuming a smoothie with ingredients DEF.
In operation 760, the machine 120 updates a stock of beverage pods based on the recommended beverage and/or based on a user selection of the recommended beverage. Thus, in some cases, the machine 120 tracks and/or monitors consumption of beverages by the user to maintain sufficient stock of beverage pods for making recommended beverages.
For example, an initial stock of sleep supplement smoothies or other beverages are provided to the machine 120, and a wait period counter is set to initialize to a 4 day wait period. Next, the activity/sleep monitor (e.g., device 115) communicates when within wireless range of the beverage machine 120, which triggers a download from the wearable device 115 to the beverage machine 120 of user sleep data.
As described herein, the user sleep system 140 determines a quality of the user's sleep (e.g., is poor for >2 days or some other threshold), and when the quality is low, the system 140, within the 4 day wait period (initialization period) and if causes the machine 120 to present a suggestion to take a sleep supplement or relaxing beverage. The machine 120 then starts a period for 4 days and subtracts one sleep supplement smoothie from the stock, updating the number of pods in stock within the machine 120. In some cases, when the stock of pods is less than 2 (or below a defined threshold number), the machine 120 may suggest other beverages, and initiates an order/reorder routine to replenish the stock of beverage pods.
As described herein, the beverage machine 120 presents the recommended smoothies and other beverages to the user via a GUI of the machine 120. FIG. 8 is a display diagram illustrating a user interface 800 that displays recommended beverages to a user based on sleep information of the user.
The interface 800 displays various interface elements or buttons, including user-selectable display elements 820 representing smoothies 830 recommended by the system 140. In some cases, the interface 800 provides a user-selectable option to request a smoothie 805, log or record consumption of a smoothie 810, and or order a smoothie or smoothie pods 840. In some cases, the user interface may mark recommended beverages as being “in stock” when the inventory database indicates the user has that pod available, or “order,’ which will automatically send an order to a server to order additional smoothie pods. The interface 800 may also include other display and input elements, such as elements informing the user of their monitored sleep activities, elements that receive user input regarding the consumption of smoothies, and so on.
The beverage machine 120, therefore, may include an input component that receives a request from a user to make a beverage (via the interface 800, a communication component that receives information from a wearable device associated with the user that identifies sleep activity characteristics of the user (via wearable device 115), and a beverage making component that makes a beverage having a beverage profile that is associated with the sleep activity characteristics of the user (e.g., determined via the user sleep system 140 located at the server 130 and/or within the machine 120).
Thus, in some embodiments, the systems and methods may receive input from a wearable device of a user that identifies sleep activity characteristics of the user, compare the sleep activity characteristics of the user with usage data associated with the user's previous consumption of smoothies and received from a smoothie machine that prepared the smoothies, and determine a smoothie to recommend to the user that is based on the comparison.

Examples of Customizing Beverages Based on User Mental Acuity

The systems and methods described herein, in some embodiments, determine and/or generate customized beverage profiles for users based on the users' measured mental acuity. The systems and methods utilize or provide games or tests that measure mental acuity, in some cases delivered before and after the user consumes a smoothie, to identify those ingredients that yield increases in mental acuity for the specific user.
For example, the systems and methods may measure a user's mental acuity each time the user obtains a beverage, and presents tests, games, and so on, via the user's mobile device or via a user interface of the beverage machine 120, which provides cognitive assessment systems with data points associated with the user's mental acuity. The systems and methods identify a state of the user's mental acuity in certain contexts, and generates or suggests a smoothie program (types and/or timing) of smoothies (and associated supplements) predicted to be of benefit to the user.
FIG. 9 is a block diagram illustrating a suitable computing environment 900 for providing customized beverages to users based on mental acuity information for the users. As described herein, the user acuity system 145 includes various components, modules, or systems for determining a current mental acuity or sharpness of a user, and determining smoothies and other beverages (or, supplements to be added to base smoothies) to recommended to the user for consumption.
For example, the system 145 includes a recommendation module 910, which receives information associated with the user's mental acuity, such as a score obtained while playing an online or virtual game, and identifies beverages having certain beverage profiles, and/or supplements, to recommended to the user. The system stores various user information (e.g., previous scores, beverage consumption data, and so on) in a consumption database 930, and stores various games virtual games or tests in a game database 920.
In some cases, when the user mobile device 110 triggers, based upon the user profile (not shown), a game the user will interact with, the user acuity system 145 may retrieve and present or display a game from the game database 920 to the user. Once the user plays the game, the recommendation module 910 receives or accesses the results or outcomes (e.g., scores) of the game play to determine whether a smoothie with stimulants or supplements should be recommended or suggested to the user.
Once the user has taken the stimulants or supplements, the user may play another game. For example, the system 910 may receive an indication from the user or from the beverage machine 120 that the user has consumed a recommended smoothie (e.g., the beverage machine 120 prepared the smoothie for the user). The system 145 determines whether the recommended smoothie (with stimulants or supplements) improved results based on a comparison of the scores of the games played before and after consumption of the smoothie. The system may store the results of the comparison in order to provide more accurate or targeted recommendations to the user or other users regarding the consumption of certain ingredients, stimulants, or supplements.
Thus, the system 145 may identify optimal or helpful smoothie stimulants or supplements based on user performances during presented games and other activities. The system 145, via various channels, such as via advertising services, nutritional and wellness programs, social media, and so on, may then promote or recommend the identified ingredients to others. Further, in some embodiments, the system 145 facilitates ordering and reordering of the smoothie stimulants or supplements targeted to the user.
Therefore, the user acuity system 145 may perform various processes, operations, or methods when determining smoothie recommendations for users based on their performance in playing certain games, tests, or other mental activities. FIG. 10 is a flow diagram illustrating a method 1000 for determining a beverage recommendation based on user performance on one or more acuity tests before and after consuming a customized beverage. Aspects of the method 1000 may be performed by the user acuity system 145 and, accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 1000 may be performed on any suitable hardware.
In operation 1010, the system 145, via one or more user interfaces, causes display of a first mental acuity test or virtual game. For example, the system 145 may send a virtual game stored in the game database 920 to the mobile device 110 or beverage machine 120 for display to a user via associated user interfaces. In some cases, the system 145 may utilize one or more games provided by online or third party game providers, and cause displays or interaction between the online game and the user via the associated user interfaces.
In operation 1020, the system 145 determines whether a score associated with the user playing the game or test indicates a beverage recommendation. For example, the system may compare the score with an expected score, a minimum score, a maximum score, and so on, and determine whether the current mental acuity of the user is below, above, or within a threshold associated with recommending beverages to the user to enhance or improved the user's current or temporal acuity.
When the system 145 determines the user's score does not indicate a recommendation (e.g., the score is a maximum or high score), the method 1000 proceeds to operation 1025, and the system polls the user and other users to take additional or later tests or play games. When the system 145 determines the user's score does indicate a recommendation (e.g., the score is low or within a certain low performance threshold), the method 1000 proceeds to operation 1030, and the system 145 randomly selects a beverage profile for the user.
After receiving an indication that the user consumed a smoothie or other beverage, the system 145, at operation 1040, displays a second, or additional mental acuity test or game to be played by the user. Once the user plays the game, the system 145, in operation 1050, determines whether the user's score indicates a changed mental acuity (e.g., the score is above a threshold, or the difference between scores exceeds a threshold).
When the score exceeds the threshold, the method 1000 proceeds to operation 1060, and the system 145 updates the consumption database 930 with the results of the comparison. Therefore, the system 145 obtains a data point for the user that indicates the smoothie and/or its contents consumed by the user improved or modified the user's mental acuity. Table 3 depicts a data structure that represents that data stored in the consumption database 930.

TABLE 3

Prefered DB

		Keep
Pre Drink	Smoothie	Recommendation
Score	Stimulant	Delta >20

40-55	Caffene
	Maca
	. . .
56-65	Ginseng	Yes
	Cayene
	. . .
66-75	B12
	Cocunut Oil
	. . .
76-84	Green Tea
	Guarana
	. . .
84-100	None

Score DB

	Pre			Post		Keep
	drink	Recom-		drink		Recomen-
	test	mend	Smoothie	test		dation
Date	score	>=85	Stimulant	score	Delta	>=20

Jan. 1, 2016	85	No	N/A	N/A	N/A	N/A
Jan. 2, 2016	75	Yes	B12	70	5	no
Jan. 3, 2016	55	Yes	Caffene	85	30	Yes
Jan. 4, 2016	55	Yes	Caffene	95	40	Yes
Jan. 5, 2016	85	No	N/A	75	N/A	N/A
Jan. 6, 2016	65	Yes	Ginseng	65	0	No

As shown in Table 3, the data structures log the results of a pre-test game, and if the score is greater than 85, the system 145 does not suggest smoothie stimulants or supplements. When the scores range between 40-84, the system 145 recommends various smoothie stimulants or supplements, and if second, or subsequent game results show improvement (e.g., with a score difference of 20), the system 145 logs the improvement data for future recommendations.
As described herein, the user acuity system 145 may present various games or other displayed information when attempting to ascertain a current or temporal mental acuity, sharpness, or alertness for a user.
FIG. 11A is a display diagram illustrating a user interface 1100 that present a mental acuity test 1110 for a user. As depicted, the example game or test 1110 prompts the user to play a logic puzzle and repeat or complete a pattern. The user may play the game 1110 via the interface 1100, or select an option 1115 to skip the presented game 1110 and play a different game.
Of course, the system may display a variety of different games to test a user's mental acuity, include puzzle games, first person games, journey games, tests, quizzes, and so on. In some cases, the games database 920, or a third party provider of the games, may advertise certain smoothies within the games, and/or develop specific games for certain smoothie types. In addition, other scored games or activities may be utilized when recommending smoothies. For example, the system 145 may receive a student's test scores, and recommend smoothies or supplements based on the test scores or based on their online gaming results.
Once the game is played, the system 145, via the user interface, presents the user with recommended beverages for consumption. FIG. 11B is a display diagram 1120 illustrating a user interface that displays recommended beverages to a user based on test performance for a user. The GUI 1120 may present recommended smoothies, amounts of stimulants, frequencies of smoothie or supplements or doses, and so on, based on results of the user playing the game or games. The interface 1120 may include user-selectable options to take another acuity test 1130, order one or more recommended smoothies 1135, as well as present recommended beverages 1140 with options 1145 to make the beverages using the beverage machine 120.
For example, a smoothie machine may include an input component that presents, via a user interface of the smoothie machine, a virtual game to be played by a user, a recommendation component that recommends one or more smoothies to make for the user based upon a result associated with the user playing the virtual game, and a beverage making component that makes the one or more smoothies (via smoothie pods).
Thus, in some embodiments, the systems and methods may present a game to a user via a mobile device associated with the user, receive a score associated with the user playing the presented game, and identify a smoothie to recommend to the user that is based on the received score.
Further, the systems and methods may present a second game to the user via the mobile device, receive a score associated with the user playing the presented second game, determine the score associated with the user playing the presented second game is greater than the score associated with the user playing the presented game, and recommend the smoothie to other users.

Examples of Customizing Beverages to User Wellness Programs

The systems and methods described herein, in some embodiments, determine and/or generate customized beverage profiles for users of wellness and other online health programs, via integrated communications between services and supporting servers.
For example, the systems and methods may provide a Software as a service (SaaS) application programming interface (API) to various online health, weight loss and/or wellness programs, facilitating exchanges of information between a smoothie recommendation program (e.g., a system that orders smoothie pods and/or recommends or makes smoothies for users) and the various online programs. The online programs may be various partner services, such as diet partners, exercise partners, ingredient or nutrition partners, blender device partners, medical partners, doctor network partners (e.g., partner systems develop having their own algorithms) to integrate their software programs (services) and data through various APIs to the smoothie recommendation program.
Thus, the systems and methods provide users, members, and/or subscribers of online partnership programs to obtain smoothie pods (for various smoothies and other beverages) and/or made smoothies and other beverages from the cloud-based service (e.g., “Smoothie as a Service”). The service facilitates the exchange of data between the smoothie recommendation program and the online partner programs (e.g., wellness programs, exercise and health programs, nutrition programs, diet and weight loss programs, and so on). In some cases, the systems and methods may provide a subscription system to tie in with Weight Watchers or NutriSystem or other partners, allowing a user to get a smoothie pod or beverage integrated with or based on the partnership's programs or services.
For example, a person on a long term weight loss program (e.g., a program that restricts the person to a certain number of calories per day) will receive smoothies that have low calorie, high protein ingredient or nutrition profiles (and possibly energy and metabolisms boosters), whereas a person looking to get in shape will receive smoothies that have high protein enhancement ingredient or nutrition profiles, as instructed by the health or diet programs to which they subscribe (e.g., a smoothie program, provided by a smoothie making machine, may receive instructions via a SaaS API provided to the online programs).
Thus, the systems and methods integrate the automated provision of smoothies (e.g., based on customized smoothie pods) and a weight loss or other user health partner. In some cases, the products (e.g., pods or made beverages) may be white labeled through the partners so that product kits are shipped with both the partners' weight loss program branding and the smoothie pods' branding, with ordering and provision of pods being performed by the smoothie program or recommendation server.
FIG. 12 is a block diagram illustrating a suitable computing environment 1200 for providing customized beverages to users of online wellness programs. Similar to the other computing environments described herein, the beverage profile server 130 communicates over the network 125 with various devices or systems, such as the beverage machine 120 and the online health system 160. Additionally, the server 130 may provide access to various recommendation systems (as described herein) via the beverage network system, which provides APIs to the online health system and other systems requesting information (e.g., beverage recommendation information) from the beverage profile server 130.
For example, a customer network 1210 of sites, such as consumers associated with computing devices, retail entities, and/or restaurants and other service provider entities (e.g., cafes, gyms, snack bars, and so on), may access the systems of the server 130 via APIs or other SaaS services provided by the beverage network system 150. As another example, a networked blender 1220, refrigerator, or other smoothie making entity or device may communicate with the server 130 over the network 125 via published APIs. Further, as described herein, the online health system 160 may be part of or associated with partner entities, such as online diet or health programs.
Further, the system 150 may include ordering services via an ordering database, weight analysis services via a weight database, exercise services via an exercise database, and other health services (e.g., special medical analyses), and may issue reports, alerts and other dashboard indicators or displays, depending on the needs of the users or partner systems.
FIG. 13 illustrates various components of a beverage network system 1300 that interacts online wellness programs. The system 1300, which may be part of the beverage network system 150 or beverage profile server 130, includes various components or modules configured to provide recommended beverages to users of online systems.
For example, the system 1300 includes an ordering service 1305 and associated ordering database 1307 configured to order stock of beverage pods for users and other entities, a weight analysis service 1310 and associated diet database 1312 that stores information associated with analyzing weight loss goals for users, an exercise service 1315 and associated exercise database that includes and stores information associated with analyzing user exercise and workout activities, and other health service 1320 modules.
The system 1300 also includes a base recommendation system 1330, which includes a diet analysis system 1332 and action system 1334 configured to perform various operations described herein and directed to receiving user information and determining beverage profiles to recommend to the users based on the user information. The base system 1330 may provide information to a partners system 1335, which includes partner information, algorithm partner information 1337, and is configured to modify recommendation information to transmit to various online systems or partners using their formats, structures, and/or relevant APIs or syntaxes.
A health database 1340 stores information received by the system 1300 and/or generated by the system 1300. The health database 1300 includes a nutrition database 1342 that stores nutrition information for ingredients, profiles, available beverages, and so on, a medicine database 1344 that stores information for various medical goals or issues provided by users, and a recipe database 1346 that stores recipes for creating beverage profiles, such as profiles for smoothies to be made for users.
The system 1300 may also include components that generate reports 1350, send alerts 1352, provide various informational dashboards 1354, or otherwise provide information to users and online systems that is associated with their consumption, progress, health, and so on. The system 1300 may include billing software that handles billing and payments for use of the system 1300 by the online systems. The billing software 1356 and/or the partners system 1335 may store data in a partners database 1360, which may also transfer data to the various health databases 1340 via APIs 1365 provided by the system 1300.
As described herein, the system 1300 performs various processes, operations, or methods when determining and/or providing recommendations for beverages, such as smoothies, to online systems 160. FIG. 14 is a flow diagram illustrating a method 1400 for determining a beverage recommendation for a user of an online wellness program. Aspects of the method 1400 may be performed by the system 1300 or various connected devices and, accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 1400 may be performed on any suitable hardware.
In operation 1405, a request for a smoothie, or other beverage, is received at a connected device, machine (beverage machine 120), or system (restaurant device). In operation 1410, the system 1300 receives the request, along with wellness goal information and/or daily smoothie consumption data for the user.
In operation 1420, the system 1300 determines a nutritional profile for the user. For example, the system 1300 may employ one or more recommendation systems described herein to determine a nutritional profile that meets the received request and associated data for the user.
In operation 1430, the system 1300 transmits the determined nutritional profile to the requesting device, machine or system. The receiving device, machine, or system, in operation 1440, matches the received nutritional profile to beverage profiles of available beverages (e.g., beverages associated with pods contained by the device).
In operation 1450, the device, such as a smoothie making entity, displays one or more available smoothies that match the nutritional profile for user selection, and in response to a selection, makes, in operation 1460, the selected smoothie (e.g., using one or more associated pods). In operation 1470, the system 1330 receives an indication that the smoothie was made for the user, and updates various databases with the nutritional profile for the smoothie and the consumption of the smoothie by the user.
FIG. 15 is a flow diagram illustrating a method 1500 for determining a nutritional profile for a user. Aspects of the method 1500 may be performed by the system 1300 or various connected devices and, accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 1500 may be performed on any suitable hardware.
In operation 1510, the system 1300 accesses a user activity database of the beverage network server 130, and, in operation 1520, identifies nutritional needs for the user based on the information. For example, the database may store data received from one or more wearable devices 115 and/or via one or more online systems 160 associated with wellness programs that include the user as a member.
In operation 1530, the system 1300 determines a nutritional profile for the user based on identified nutritional needs. As described herein, the system 1300 may access health goal information or user health information to identify the nutritional needs for the user. In operation 1540, the system 1300 stores the nutritional profile to one or more health databases 1340 of the system.
FIG. 16 is a flow diagram illustrating a method 1600 for determining a customized beverage for a user based on the user's nutritional profile. Aspects of the method 1600 may be performed by the system 1300 or various connected devices and, accordingly, is described herein merely by way of reference thereto. It will be appreciated that the method 1600 may be performed on any suitable hardware.
In operation 1610, a smoothie making entity (e.g., a connected device or beverage machine 120) receives a nutritional profile from the system 1300 (e.g., a “smoothie service”). In operation 1620, the entity accesses beverage profile information for smoothies available at the entity.
In operation 1630, the entity determines an optimal smoothie based on matching the profiles of the available smoothies to the received nutritional profile, and, in operation 1640, retrieves a recipe for the smoothie. Using the recipe, the entity, in operation 1650, displays information representative of the available smoothie or smoothies via an interface of the entity. Upon receiving a selection of a displayed smoothie, the entity makes the smoothie (or, orders the related smoothie pods) for the user.
For example, FIG. 17 is a display diagram illustrating a user interface 1700 that presents recommended beverages to a user and facilitates making and ordering of beverages on behalf of the user. The user interface 1700 presents options to select a partner 1710 and access data 1720 associated with the user. The user interface also presents options to request a smoothie, and displays recommended smoothies 1740. Further displayed options include partner site navigation options 1750, options to order associated smoothie pods 1760, and other user-selectable elements associated with ordering or making recommended smoothies for the user.
Further, the interface 1700 includes user-selectable elements associated with the system 1300 generating reports 1770, sending or setting alerts 1722, presenting dashboards 1774, or performing other services 1776. Thus, the interface 1700 may facilitate the access of reports, alerts, dashboards (e.g., trends, and so on) and other health service information.
In addition, the system 1300 may provide other aspects or features, including:
Allow partner systems to disable aspects of the recommendations, such as after determining a user is over their calorie allotment for the day, preventing the user from further hurting their diet;
Receiving data from various wearable tracking devices, including: communicating previous exercise data (e.g., 3 mile run burning 380 calories) to the system, which may modify smoothie recommendations, and inform the user how the calories burned by the exercise translates to consumption, educating the user about the results of exercise (e.g., how much work it takes to burn off enough calories), or communicating calories of a recommended beverage to the fitness tracker to give the user a goal calorie amount to burn, which informs the user about how many calories their smoothie was, and that amount is set as a goal for the user during their workout session;
Facilitating connections to POS devices, so a user's food order may be automatically uploaded to a partner system, enabling immediate ordering of the user's meal choice, allowing the partner to make smoothie suggestions based on up to date information, allowing automatic logging of user choices, and so on;
Directly logging exercise data (e.g., at the gym) for a user to directly log their workout data, as well as have smart gym equipment (e.g., treadmill) send data to the system, which can then be uploaded to the partner sites. The system may also prompt the user to enter their previous meals for the day if the user hasn't done so, ensuring the partner receives updates of exercise/diet data before making a smoothie suggestion; and so on.
Thus, in some embodiments, the systems and methods receive, via an application programming interface (API), a request for a smoothie from a smoothie making entity associated with a user, determine a nutritional profile for the user that is based on a wellness goal associated with the user and daily consumption data of the user, and send the nutritional profile to the smoothie making entity, which makes a pod-based smoothie having the nutritional profile. As described herein, the smoothie making entity may be a restaurant that communicates with the API using an online ordering system, a networked smoothie making machine or device, an online diet program, and so on.
Therefore, in some embodiments, the systems and methods provide a smoothie SaaS for various smoothie making (or, smoothie pod ordering) endpoints, such as restaurants, networked machines, and so on. The endpoints receive requests from users, access the various processes provided by the SaaS, and provide users with smoothies based on recommendations or instructions received from the smoothie SaaS.

CONCLUSION

Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to.” As used herein, the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling or connection between the elements can be physical, logical, or a combination thereof. Additionally, the words “herein,” “above,” “below,” and words of similar import, when used in this application, refer to this application as a whole and not to any particular portions of this application. Where the context permits, words in the above Detailed Description using the singular or plural number may also include the plural or singular number respectively. The word “or,” in reference to a list of two or more items, covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list.
The above Detailed Description of examples of the technology is not intended to be exhaustive or to limit the technology to the precise form disclosed above. While specific examples for the technology are described above for illustrative purposes, various equivalent modifications are possible within the scope of the technology, as those skilled in the relevant art will recognize. For example, while processes or blocks are presented in a given order, alternative implementations may perform routines having steps, or employ systems having blocks, in a different order, and some processes or blocks may be deleted, moved, added, subdivided, combined, and/or modified to provide alternative or subcombinations. Each of these processes or blocks may be implemented in a variety of different ways. Also, while processes or blocks are at times shown as being performed in series, these processes or blocks may instead be performed or implemented in parallel, or may be performed at different times. Further any specific numbers noted herein are only examples: alternative implementations may employ differing values or ranges.
The teachings of the technology provided herein can be applied to other systems, not necessarily the system described above. The elements and acts of the various examples described above can be combined to provide further implementations of the technology. Some alternative implementations of the technology may include not only additional elements to those implementations noted above, but also may include fewer elements.
These and other changes can be made to the technology in light of the above Detailed Description. While the above description describes certain examples of the technology, and describes the best mode contemplated, no matter how detailed the above appears in text, the technology can be practiced in many ways. Details of the system may vary considerably in its specific implementation, while still being encompassed by the technology disclosed herein. As noted above, particular terminology used when describing certain features or aspects of the technology should not be taken to imply that the terminology is being redefined herein to be restricted to any specific characteristics, features, or aspects of the technology with which that terminology is associated. In general, the terms used in the following claims should not be construed to limit the technology to the specific examples disclosed in the specification, unless the above Detailed Description section explicitly defines such terms. Accordingly, the actual scope of the technology encompasses not only the disclosed examples, but also all equivalent ways of practicing or implementing the technology under the claims.
To reduce the number of claims, certain aspects of the technology are presented below in certain claim forms, but the applicant contemplates the various aspects of the technology in any number of claim forms. For example, while only one aspect of the technology is recited as a computer-readable medium claim, other aspects may likewise be embodied as a computer-readable medium claim, or in other forms, such as being embodied in a means-plus-function claim. Any claims intended to be treated under 35 U.S.C. § 112(f) will begin with the words “means for”, but use of the term “for” in any other context is not intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly, the applicant reserves the right to pursue additional claims after filing this application to pursue such additional claim forms, in either this application or in a continuing application.

Claims

1. A method, comprising

collecting information associated with a user's workout routine or physical activity;

determining a workout score based on the collected information;

matching the workout score to one or more beverage profiles associated with beverages to be consumed by the user; and

making, at an automated beverage machine, a beverage having ingredients based on the one or more beverage profiles,

wherein the automated beverage machine makes beverages from beverage pods provided to the automated beverage machine.

2. The method of claim 1, further comprising:

receiving information identifying a health goal for the user,

matching the health goal to the one or more beverage profiles; and

making a beverage having ingredients based on a beverage profile that matches the workout score and the information identifying the health goal for the user.

3. The method of claim 1, wherein matching the workout score to one or more beverage profiles associated with beverages to be consumed by the user includes identifying information stored in a database of the automated beverage machine that represents the one or more beverage profiles associated with the workout score.

4. The method of claim 1, wherein snaking a beverage having ingredients based on the one or more beverage profiles includes making a smoothie having the beverage profile.

5. The method of claim 1, wherein determining a workout score based on the collected information includes determining a workout score based on a number f calories burned by the user during a completed workout routine or physical activity.

6. The method of claim 1, wherein determining a workout score based on the collected information includes determining a workout score based on a number of calories expected to be burned during an upcoming workout routine or physical activity.

7. The method of claim 1, wherein determining a workout score based on the collected information includes determining a workout score based on one or more activity metrics measured associated with the workout routine or physical activity.

8. The method of claim 1, wherein collecting information associated with a user's workout routine or physical activity includes collecting the information from input provided by the user to the automated beverage machine.

9. The method of claim 1, wherein collecting information associated with a user's workout routine or physical activity includes collecting the information from a mobile device associated with the user.

10. The method of claim 1, wherein collecting information associated with a user's workout routine or physical activity includes collecting the information from wearable device that monitors activities performed by the user.

11. A system, comprising:

at least one processor;

at least one data storage device coupled to the at least one processor and storing instructions for implementing a method, the method comprising: collecting information associated with a user's workout routine or physical activity;

determining a workout score based on the collected information; matching the workout score to one or more beverage profiles associated with beverages to be consumed by the user; and

making, at an automated beverage machine, a beverage having ingredients based on the one or more beverage profiles.

12. The system of claim 11, wherein the automated beverage machine snakes beverages from beverage pods provided to the automated beverage machine.

13. The system of claim 11, wherein the implemented method further comprises:

receiving information identifying a health goal for the user;

matching the health goal to the one or more beverage profiles; and

14. The system of claim 11, wherein matching the workout score to one or more beverage profiles associated with beverages to be consumed by the user includes identifying information stored in a database of the automated beverage machine that represents the one or more beverage profiles associated with the workout score.

15. The system of claim 11, wherein making a beverage having ingredients based on the one or more beverage profiles includes making a smoothie having the beverage profile.

16. The system of claim 11, wherein determining a workout score based on the collected information includes determining a workout score based on a number of calories burned by the user during a completed workout routine or physical activity.

17. The system of claim 11, wherein determining a workout score based on the collected information includes determining a workout score based on a number of calories expected to be burned during an upcoming workout routine or physical activity.

18. The system of claim 11, wherein determining a workout score based on the collected information includes determining a workout score based on one or more activity metrics measured associated with the workout routine or physical activity.

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61. A method performed by a server, h method comprising:

receiving, via an application programming interface (API) provided by the server, a request for a smoothie from a smoothie making entity associated with a user,

wherein the request includes wellness goal information and daily consumption data for the user;

determining a nutritional profile for the user that is based on the wellness goal information associated with the user and the daily consumption data of the user; and

sending the nutritional profile to the smoothie making entity, which makes a pod-based smoothie having the determined nutritional profile.

62. The method of claim 61, wherein the smoothie making entity is part of a restaurant that communicates with the API using an online ordering system of the restaurant.

63. The method of claim 61, wherein the smoothie making entity is part of an online diet or health program that includes the user as a member of one or more programs or services provided by the online diet or health program.

64. The method of claim 61, wherein the smoothie making entity is a networked smoothie making machine associated with the user.

65. The method of claim 61, wherein the smoothie making entity is part of a refrigerator associated with the user.

66. The method of claim 61, wherein the wellness goal information and daily consumption data for the user is captured and tracked by an online diet or health program that includes the smoothie making entity.

67. The method of claim 61, wherein determining a nutritional profile for the user includes determining a beverage profile for the smoothie that includes one or more ingredients that are associated with the wellness goal information for the user.

68. The method of claim 61, further comprising:

receiving, via the API provided by the server, activity data associated with activities performed by the user within a given time period from one or more devices that track the activities performed by the user; wherein determining a nutritional profile for the user includes determining one or more ingredients, supplements, or additives to add to the smoothie based on the activity data for the user.

69. The method of claim 61, further comprising:

receiving, via the API provided by the server, user sleep data associated with previous sleep activities of the user within a given time period from one or more devices that track sleep activities performed by the user;

wherein determining a nutritional profile for the user includes determining one or more ingredients, supplements, or additives to add to the smoothie based on the user sleep data for the user.

70. The method of claim 61, further comprising:

receiving, via the API provided by the server, mental acuity data associated with a determined current mental acuity for the user;

wherein determining a nutritional profile for the user includes determining one or more ingredients, supplements, or additives to add to the smoothie based on the mental acuity data for the user.

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80. A beverage recommendation system that communicates with a beverage machine associated with an online wellness program, the system comprising:

at least one processor; and

at least one data storage device coupled to the at least one processor and storing instructions for implementing a method, the method comprising: receiving, via an application programming interface (API) provided by the server, a request for a beverage from the beverage machine associated with the online wellness program wherein the request includes wellness goal information and daily consumption data for the user;

sending the nutritional profile to the beverage machine associated with the online wellness program, which makes a pod-based beverage having the determined nutritional profile.