Predictive food logging

• US 9,817,559 B2
• Filed: 07/11/2014
• Issued: 11/14/2017
• Est. Priority Date: 07/11/2014
• Status: Active Grant

• Current Assignee: WorkSmart Labs, Inc.
• Original Assignee: Noom, Inc.
• Inventors: Simon, Mark, Evancha, Betina, Shafranovich, Gennadiy, Kim, Yong Woo, Gunnarsson, Ketill, Connell, James, Suh, Young In, Avgerinos, Christos, Yin, Bo, Petakov, Artem, Nesmith, Ken, Jeong, Jesse Sae-ju
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US14/329,594
• Publication Number:
  US 20160012342A1
• Time in Patent Office: 1,222 Days
• Field of Search: 706 52
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/04 Billing or invoicing
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

A method of predicting food items consumed by a user of a food-logging application is disclosed. Loggings of consumptions of food items are received. A predictive model is generated based on the received loggings. The predictive model generates a prediction of one or more additional food items that a target user will consume or is likely to have consumed (e.g., at a particular time). The prediction is generated based on an application of the predictive model to one or more data items (e.g., data items streaming into the system in real time from the target user or other users that are relevant to food consumptions by the target user). The prediction of the consumption of the one or more additional food items by the user may then be communicated for presentation to the target user in a user interface.

Claims

1. A system comprising:

one or more modules implemented by one or more computer processors, the one or more modules configured to;

receive a plurality of loggings pertaining to a plurality of consumptions of a plurality of food items by a plurality of users, the plurality of users including a target user;

generate a predictive model based on an analysis of the plurality of loggings;

receive a data item that is relevant to a consumption of an additional food item by the target user, the relevancy of the data item based on an association of a location of the user with a food venue and an association of the food venue with a type of food;

generate a prediction pertaining to the consumption of the additional food item by the target user based on an application of the predictive model to the data item; and

communicate the prediction for presentation on a device of the target user.

2. The system of claim 1, wherein the one or more modules are further configured to generate the predictive model based on a plurality of relationships between the plurality of consumptions of the plurality of food items and a plurality of data items corresponding to the plurality of consumptions.

3. The system of claim 1, wherein the prediction includes a probability of a likelihood of the consumption of the additional food item at a particular time by the target user.

4. The system of claim 1, wherein the prediction includes a likelihood of the consumption of the additional food item in a particular quantity by the target user.

5. The system of claim 1, wherein the one or more modules are further configured to perform the analysis of the plurality of loggings based on a one or more data items pertaining to the loggings, the one or more data items including one or more of a location data item, a timing data item, a personal data item, or a commercial data item.

6. The system of claim 1, wherein the presentation on the device of the target user includes presenting the prediction pertaining to the consumption of the additional food item in a user interface for selection by the target user as a consumed food item.

7. The system of claim 1, wherein the one or more modules are further configured to receive a notification of a selection by the target user of the additional food item as a consumed food item and update the predictive model based on the notification.

8. A method comprising:

receiving a plurality of loggings pertaining to a plurality of consumptions of a plurality of food items by a plurality of users, the plurality of users including a target user;

generating a predictive model based on an analysis of the plurality of loggings;

receiving a data item that is relevant to a consumption of an additional food item by the target user, the relevancy of the data item based on an association of a location of the user with a food venue and an association of the food venue with a type of food;

generating, using a hardware unit of a machine, a prediction pertaining to the consumption of the additional food item by the target user based on an application of the predictive model to the data item; and

communicating the prediction for presentation on a device of the target user.

9. The method of claim 8, further comprising generating the predictive model based on a plurality of relationships between the plurality of consumptions of the plurality of food items and a plurality of data items corresponding to the plurality of consumptions.

10. The method of claim 8, wherein the prediction includes a probability of a likelihood of the consumption of the additional food item at a particular time by the target user.

11. The method of claim 8, wherein the prediction includes a likelihood of the consumption of the additional food item in a particular quantity by the target user.

12. The method of claim 8, further comprising performing the analysis of the plurality of loggings based on a one or more data items pertaining to the loggings, the one or more data items including one or more of a location data item, a timing data item, a personal data item, or a commercial data item.

13. The method of claim 8, wherein the presentation on the device of the target user includes presenting the prediction pertaining to the consumption of the additional food item in a user interface for selection by the target user as a consumed food item.

14. The method of claim 8, wherein the one or more modules are further configured to receive a notification of a selection by the target user of the additional food item as a consumed food item and updating the predictive model based on the notification.

15. A non-transitory machine readable storage medium storing a set of instructions that, when executed by at least one processor of a machine, cause the machine to perform operations comprising:

receiving a plurality of loggings pertaining to a plurality of consumptions of a plurality of food items by a plurality of users, the plurality of users including a target user;

generating a predictive model based on an analysis of the plurality of loggings;

receiving a data item that is relevant to a consumption of an additional food item by the target user, the relevancy of the data item based on an association of a location of the user with a food venue and an association of the food venue with a type of food;

generating, using a hardware unit of a machine, a prediction pertaining to the consumption of the additional food item by the target user based on an application of the predictive model to the data item; and

communicate the prediction for presentation on a device of the target user.

16. The non-transitory machine readable storage medium of claim 15, further comprising generating the predictive model based on a plurality of relationships between the plurality of consumptions of the plurality of food items and a plurality of data items corresponding to the plurality of consumptions.

17. The non-transitory machine readable storage medium of claim 15, wherein the prediction includes a probability of a likelihood of the consumption of the additional food item at a particular time by the target user.

18. The non-transitory machine readable storage medium of claim 15, wherein the prediction includes a likelihood of the consumption of the additional food item in a particular quantity by the target user.

19. The non-transitory machine readable storage medium of claim 15, further comprising performing the analysis of the plurality of loggings based on a one or more data items pertaining to the loggings, the one or more data items including one or more of a location data item, a timing data item, a personal data item, or a commercial data item.

20. The non-transitory machine readable storage medium of claim 15, wherein the presentation on the device of the target user includes presenting the prediction pertaining to the consumption of the additional food item in a user interface for selection by the target user as a consumed food item.