Systems and methods for creating and/or using an artificial intelligence memory system that models human memory are provided. The AI memory system creates and/or uses a user centric memory graph. The user centric memory graph implicitly links memory elements of a user utilizing relationships created in space, time, and cognitive dimensions similar to how the human brain stores and recalls different memory elements. The user centric memory graph is used by searching and/or constraining the user centric memory graph based on a determined user context and/or a user query.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for building and using an artificial intelligence memory system that models human memory for an application, the system comprising: at least one processor; and a memory for storing and encoding computer executable instructions that, when executed by the at least one processor is operative to: collect user signals that include memory elements from a user; enrich the memory elements with world knowledge to form enriched elements; create a user centric memory graph by linking the enriched elements based on relationships created in space, time, and cognitive dimensions; store the user centric memory graph; determine a user context based on the enriched elements; identify relevant elements on the user centric memory graph based on the user context to form a contextually constrained memory graph; rank the relevant elements on the contextually constrained memory graph based on the user context to form ranked elements; determine a response based the contextually constrained memory graph and the ranked elements; compare the response to a relevancy threshold; determine that the response meets the relevancy threshold based on the comparison; and provide the response to the user upon making the determination that the response meets the relevancy threshold. 2. The system of claim 1, wherein create the user centric memory graph by linking the enriched elements based on the relationships created in the space, the time, and the cognitive dimensions of the user is performed by utilizing machine learning and statistical modeling techniques. 3. The system of claim 2, is further operative to: analyze the user signals; determine a user pattern based on the analysis of the user signals, add the user pattern to the user centric memory graph. 4. The system of claim 3, wherein the machine learning and statistical modeling techniques are updated based on the user pattern. 5. The system of claim 2, is further operative to: receive user feedback to the provided response; and update the machine learning and statistical modeling techniques based on the user feedback, wherein the provided response is an unrequested memory prompt. 6. The system of claim 1, wherein the user context is a current user location, current time, current user digital behavior, and current user physical behavior. 7. The system of claim 1, wherein enrich the memory elements with the world knowledge to form the enriched elements comprises: converting digital artifacts in the user signals into the memory elements utilizing the world knowledge. 8. The system of claim 1, wherein the user signals include a user query; wherein rank the relevant elements on the contextually constrained memory graph is further based on the user query to form the ranked elements. 9. The system of claim 1, wherein the application is a digital assistant application. 10. The system of claim 1, wherein the user signals are GPS coordinates, photos, browser history, emails, text messages, social data, one notes, todos, calendar items, professional data, events, and user application data. 11. The system of claim 1, is further operative to: send the contextually constrained memory graph to a third party; receive third party enriched memory data in new user signals from the third party based on the contextually constrained memory graph; update the user centric memory graph based on the third party enriched memory data. 12. A system for building an artificial intelligence memory system that models human memory for an application, the system comprising: at least one processor; and a memory for storing and encoding computer executable instructions that, when executed by the at least one processor is operative to: collect user signals that include a first set of memory elements and digital artifacts; convert the digital artifacts into a second set of memory elements utilizing world knowledge; enrich the first set of memory elements and the second set of memory elements with the world knowledge to form enriched elements; create a user centric memory graph by linking the enriched elements based on relationships created in space, time, and cognitive dimensions utilizing machine learning and statistical modeling techniques; and store the user centric memory graph, wherein the application is configured to utilize the user centric memory graph to provide unrequested memory prompts and to provide responses to received user queries. 13. The system of claim 12, is further operative to: analyze the user signals; determine a user pattern based on the analysis of the user signals; update the machine learning and statistical modeling techniques based on the user pattern. 14. The system of claim 12, is further operative to: receive user feedback in the user signals; update the machine learning and statistical modeling techniques based on the user feedback. 15. A method for using an artificial intelligence memory system that models human memory, the method comprising: collecting user signals that include digital artifacts; converting the digital artifacts into memory elements utilizing world knowledge; enriching the memory elements with the world knowledge to form enriched elements; updating a user centric memory graph by linking the enriched elements based on relationships created in space, time, and cognitive dimensions utilizing machine learning and statistical modeling techniques to form an updated user centric memory graph; storing the updated user centric memory graph; determining a first user context based on the enriched elements; identifying relevant elements on the updated user centric memory graph based on the first user context; forming a contextually constrained memory graph based on the relevant elements; ranking the relevant elements on the contextually constrained memory graph based on the first user context to form ranked elements utilizing a ranking algorithm; determining an unrequested memory prompt based on the contextually constrained memory graph and the ranked elements; comparing the unrequested memory prompt to a relevancy threshold; determining that the unrequested memory prompt meets the relevancy threshold based on the comparing; and in response to the determining that the unrequested memory prompt meets the relevancy threshold, providing the unrequested memory prompt to a client computing device. 16. The method of claim 15, further comprising: sending the unrequested memory prompt to a third party; receiving an enriched third party memory elements from the third party based on the unrequested memory prompt; in further response to the determining that the unrequested memory prompt meets the relevancy threshold, sending instructions to the client computing device to provide the enriched third party memory elements to the user. 17. The method of claim 15, further comprising: analyzing the enriched elements; determining a user pattern based on the analyzing of the enriched elements; updating the machine learning and statistical modeling techniques based on the user pattern; and updating the updated user centric memory graph based on the user pattern. 18. The method of claim 15, further comprising: receiving new user signals including a user query; enriching the user query utilizing the world knowledge to form an enriched user query elements; updating the updated user centric memory graph by linking the enriched user query elements based on the relationships created in the space, the time, and the cognitive dimensions utilizing the machine learning and statistical modeling techniques to form a new updated user centric memory graph; storing the new updated user centric memory graph; determining a second user context based on the enriched user query elements; identifying new relevant elements on the updated user centric memory graph based on the enriched user query elements to form a query constrained memory graph; ranking the new relevant elements on the query constrained memory graph based on the enriched user query elements to form new ranked elements utilizing the ranking algorithm; determining a requested response based the query constrained memory graph and the new ranked elements; determining that the requested response meets the relevancy threshold; and providing the requested response to the user upon determining that the requested response meets the relevancy threshold. 19. The method of claim 18, wherein identifying the new relevant elements on the updated user centric memory graph is further based on the second user context; and wherein ranking the new relevant elements on the query constrained memory graph is further based on the second user context. 20. The method of claim 15, wherein the contextually constrained memory graph links the relevant elements based on the relationships created in the space, the time, and the cognitive dimensions.