Granted Patent B2

US 12,461,762 · App. 18/669,308 · Granted Nov 4, 2025

Inventor: Alston Ghafourifar (Los Altos Hills, CA)

Assignee: Entefy Inc.

G06F9/451G06F16/9535G10L15/22H04L43/045

Abstract

Claims (64)

1 . A method comprising:

receiving a user query associated with a user account of a user;

accessing a context graph linking user life events of the user with predicted activities of the user based on one or more relationships between the user life events and the predicted activities, wherein the predicted activities are inferred by an artificial intelligence (AI) from the user life events, wherein the context graph includes a plurality of nodes for the user life events and the predicted activities and a plurality of edges connecting the plurality of nodes based on the one or more relationships, and wherein each of the plurality of edges has a corresponding weight attributed to a corresponding one of the one or more relationships;

determining a current context for the user query based on one or more categories of properties of the user query;

determining one or more of the plurality of nodes associated with the current context;

processing the user query based on the current context and the one or more of the plurality of nodes, wherein the processing includes connecting the one or more of the plurality of nodes in the context graph; and

outputting a result of the user query based on the processing.

2 . The method of claim 1 , wherein each of the plurality of edges connects a pair of the plurality of nodes based on a corresponding one of the one or more relationships, and wherein the pair of the plurality of nodes is selected when traversing the context graph for the user query based on the corresponding weight.

3 . The method of claim 1 , wherein the processing the user query comprises:

traversing the context graph using a neural network model based on the current context and one or more previous contexts associated with the current context.

4 . The method of claim 3 , further comprising:

assigning a content tier to the current context based on the result, wherein the context tier designates a granularity level of the result that corresponds to one or more of the predicted activities for the user; and

storing the content tier for the current context so that the result is accessible for the current context during one or more future user queries independent of further traversing the context graph.

5 . The method of claim 1 , further comprising

providing a graph traversal of the context graph with the result, wherein the graph traversal identifies one or more of the plurality of edges utilized to connect a subset of the plurality of nodes for the processing the user query.

6 . The method of claim 1 , wherein, prior to determining the context graph, the method further comprises:

receiving device data from a user device associated with the user, wherein the device data is associated with the user life events; and

determining the user life events from the device data.

7 . The method of claim 1 , wherein the determining the current context comprises:

determining at least one micro context based on the user query; and

associating the at least one micro context with a macro context,

and wherein the one or more of the plurality of nodes are determined based on the at least one micro context and the macro context.

8 . A non-transitory computer readable medium comprising instructions stored thereon to cause one or more processing units to perform operations comprising:

receiving a user query associated with a user account of a user;

accessing a context graph linking user life events of the user with predicted activities of the user based on one or more relationships between the user life events and the predicted activities, wherein the predicted activities are inferred by an artificial intelligence (AI) from the user life events, wherein the context graph includes a plurality of nodes for the user life events and the predicted activities and a plurality of edges connecting the plurality of nodes based on the one or more relationships, and wherein each of the plurality of edges has a corresponding weight attributed to a corresponding one of the one or more relationships;

determining a current context for the user query based on a category associated with the user query;

determining one or more of the plurality of nodes associated with the current context;

processing the user query based on the current context and the one or more of the plurality of nodes, wherein the processing includes connecting the one or more of the plurality of nodes in the context graph; and

outputting a result of the user query based on the processing.

9 . The non-transitory computer readable medium of claim 8 , wherein each of the plurality of edges connects a pair of the plurality of nodes based on a corresponding one of the one or more relationships, and wherein the pair of the plurality of nodes is selected when traversing the context graph for the user query based on the corresponding weight.

10 . The non-transitory computer readable medium of claim 8 , wherein the processing the user query comprises:

traversing the context graph using a neural network model based on the current context and one or more previous contexts associated with the current context.

11 . The non-transitory computer readable medium of claim 10 , wherein the operations further comprise:

assigning a content tier to the current context based on the result, wherein the context tier designates a granularity level of the result that corresponds to one or more of the predicted activities for the user; and

storing the content tier for the current context so that the result is accessible for the current context during one or more future user queries independent of further traversing the context graph.

12 . The non-transitory computer readable medium of claim 8 , wherein the operations further comprise:

providing a graph traversal of the context graph with the result, wherein the graph traversal identifies one or more of the plurality of edges utilized to connect a subset of the plurality of nodes for the processing the user query.

13 . The non-transitory computer readable medium of claim 8 , wherein, prior to determining the context graph, the operations further comprise:

receiving device data from a user device associated with the user, wherein the device data is associated with the user life events; and

determining the user life events from the device data.

14 . The non-transitory computer readable medium of claim 8 , wherein the determining the current context comprises:

determining at least one micro context based on the user query; and

associating the at least one micro context with a macro context,

and wherein the one or more of the plurality of nodes are determined based on the at least one micro context and the macro context.

15 . A system comprising:

a non-transitory memory; and

one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:

receiving a user query associated with a user account of a user;

accessing a context graph linking user life events of the user with predicted activities of the user based on one or more relationships between the user life events and the predicted activities, wherein the predicted activities are inferred by an artificial intelligence (AI) from the user life events, wherein the context graph includes a plurality of nodes for the user life events and the predicted activities and a plurality of edges connecting the plurality of nodes based on the one or more relationships, and wherein each of the plurality of edges has a corresponding weight attributed to a corresponding one of the one or more relationships;

determining a current context for the user query based on one or more categories of the user life events associated with the user query;

determining one or more of the plurality of nodes associated with the current context;

processing the user query based on the current context and the one or more of the plurality of nodes, wherein the processing includes connecting the one or more of the plurality of nodes in the context graph; and

outputting a result of the user query based on the processing.

16 . The system of claim 15 , wherein each of the plurality of edges connect a pair of the plurality of nodes based on a corresponding one of the one or more relationships, and wherein the pair of the plurality of nodes is selected when traversing the context graph for the user query based on the corresponding weighted.

17 . The system of claim 15 , wherein the processing the user query comprises:

traversing the context graph using a neural network model based on the current context and one or more previous contexts associated with the current context.

18 . The system of claim 17 , wherein the operations further comprise:

assigning a content tier to the current context based on the result, wherein the context tier designates a granularity level of the result that corresponds to one or more of the predicted activities for the user; and

storing the content tier for the current context so that the result is accessible for the current context during one or more future user queries independent of further traversing the context graph.

19 . The system of claim 15 , wherein the operations further comprise:

providing a graph traversal of the context graph with the result, wherein the graph traversal identifies one or more of the plurality of edges utilized to connect a subset of the plurality of nodes for the processing the user query.

20 . The system of claim 15 , wherein, prior to determining the context graph, the operations further comprise:

receiving device data from a user device associated with the user, wherein the device data is associated with the user life events; and

determining the user life events from the device data.

Assignments (1)

Continuity (4)

References Cited (100)