Trigger repeat order notifications

• US 9,239,987 B1
• Filed: 06/01/2015
• Issued: 01/19/2016
• Est. Priority Date: 06/01/2015
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Tsao, Vivien
• Primary Examiner(s): Hill, Stanley K.
• Assistant Examiner(s): Chubb, Mikayla
• Application Number:
  US14/727,659
• Time in Patent Office: 232 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 30/0251 Targeted advertisements

Abstract

Methods, systems, and apparatus for receiving a particular set of user data; obtaining a predictive model that estimates a likelihood of a user to order a food item, wherein the predictive model is generated using observation data that includes historic user data and user data from other user devices; providing the particular set of user data to the predictive model; obtaining an indication of a likelihood of the user to order a food item; based on the indication of a likelihood of the user to order a food item, determining whether to output a notification on the user device inviting the user to order a food item; and in response to determining to output a notification on the user device inviting the user to order a food item, selectively outputting a notification on the user device inviting the user to order a food item.

Claims

1. A computer-implemented method comprising:

receiving a particular set of user data, the particular set of user data including user data obtained from a user device;

obtaining a predictive model that comprises (i) a first sub-predictive model that estimates a likelihood that a user has already ordered, but has not yet consumed, food, and (ii) a second sub-predictive model that received, as an input, the likelihood that the user has already ordered, but has not yet consumed, food, and that estimates a likelihood of the user to order a food item, wherein the predictive model is generated using observation data that includes historic user data and user data from other user devices;

providing the particular set of user data to the predictive model;

obtaining, from the predictive model, an indication of the likelihood of the user to order the food item;

based on the indication of the likelihood of the user to order the food item obtained from the predictive model, determining whether to output a notification on the user device inviting the user to order the food item; and

in response to determining whether to output the notification on the user device inviting the user to order the food item, selectively outputting the notification on the user device inviting the user to order the food item.

2. The method of claim 1, wherein the first sub-predictive model is generated using observation data that includes a user'"'"'s purchase history, browser history, search query history, calendar history and location history.

3. The method of claim 1, wherein the predictive model further comprises a third sub-predictive model that estimates an inclination of the user to cook.

4. The method of claim 1, wherein the predictive model further comprises a third sub-predictive model that estimates an inclination of the user to dine out.

5. The method of claim 1, wherein the predictive model further comprises a third sub-predictive model that estimates a likelihood that the user has already ordered food within a recent time interval.

6. The method of claim 1, wherein the predictive model further comprises a third sub-predictive model that estimates a likelihood that the user has performed an activity other than eating or visiting a restaurant that is associated with ordering food.

7. The method of claim 1, wherein the predictive model further comprises a third sub-predictive model that estimates a likelihood that the user has performed an activity that is associated with not wanting to order food.

8. The method of claim 1, wherein the second sub-predictive model comprises a final predictive model component and the second sub-predictive model receives one or more outputs from the first sub-predictive model.

9. The method of claim 8, wherein the second sub-predictive model estimates a likelihood of the user to order a food item, wherein the predictive model is generated using observation data that includes historic user data.

10. The method of claim 1, wherein the particular set of user data includes location information, time and date information, calendar entries information, weather information and mode of transport information.

11. The method of claim 1, wherein the predictive model comprises a decision tree.

12. The method of claim 1, wherein the indication of how likely a user of the user device wants to order the food item includes a confidence score.

13. The method of claim 12, wherein determining whether to output a notification on the user device inviting the user to order the food item comprises:

generating a confidence score threshold;

determining whether the confidence score exceeds or meets the confidence score threshold.

14. The method of claim 1, wherein selectively outputting a notification on the user device inviting the user to order the food item comprises:

generating one or more food item recommendations;

selecting a food item recommendation from the one or more food item recommendations;

generating a notification inviting the user to order the food item;

providing the generated notification inviting the user to order the food item for display on the user device.

15. The method of claim 1, further comprising:

receiving an acceptance of the invitation to order the food item;

processing the order for the food item.

16. A system comprising:

one or more computers; and

a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising;

receiving a particular set of user data, the particular set of user data including user data obtained from a user device;

obtaining a predictive model that comprises (i) a first sub-predictive model that estimates a likelihood that a user has already ordered, but has not yet consumed, food, and (ii) a second sub-predictive model that received, as an input, the likelihood that the user has already ordered, but has not yet consumed, food, and that estimates a likelihood of the user to order a food item, wherein the predictive model is generated using observation data that includes historic user data and user data from other user devices;

providing the particular set of user data to the predictive model;

obtaining, from the predictive model, an indication of the likelihood of the user to order the food item;

based on the indication of the likelihood of the user to order the food item obtained from the predictive model, determining whether to output a notification on the user device inviting the user to order the food item; and

in response to determining whether to output the notification on the user device inviting the user to order the food item, selectively outputting the notification on the user device inviting the user to order the food item.

17. The system of claim 16, wherein the first sub-predictive model is generated using observation data that includes a user'"'"'s purchase history, browser history, search query history, calendar history and location history.

18. The system of claim 16, wherein the predictive model further comprises a third sub-predictive model that estimates an inclination of the user to cook.

19. The system of claim 16, wherein the predictive model further comprises a third sub-predictive model that estimates a likelihood that the user has performed an activity other than eating or visiting a restaurant that is associated with ordering food.

20. The system of claim 16, wherein the predictive model further comprises a third sub-predictive model that estimates a likelihood that the user has performed an activity that is associated with not wanting to ordering food.

21. A computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations comprising:

receiving a particular set of user data, the particular set of user data including user data obtained from a user device;

obtaining a predictive model that comprises (i) a first sub-predictive model that estimates a likelihood that a user has already ordered, but has not yet consumed, food, and (ii) a second sub-predictive model that received, as an input, the likelihood that the user has already ordered, but has not yet consumed, food, and that estimates a likelihood of the user to order a food item, wherein the predictive model is generated using observation data that includes historic user data and user data from other user devices;

providing the particular set of user data to the predictive model;

obtaining, from the predictive model, an indication of the likelihood of the user to order the food item;

based on the indication of the likelihood of the user to order the food item obtained from the predictive model, determining whether to output a notification on the user device inviting the user to order the food item; and

in response to determining whether to output the notification on the user device inviting the user to order the food item, selectively outputting the notification on the user device inviting the user to order the food item.