A system and methods for suggesting beneficial actions may include generating a first predictive model to estimate a user profile, collecting user data pertinent to a user's circumstance or action at a point in time, collecting a database associating user profiles with sequences of user circumstances and user actions over time for one or more users, and generating a second predictive model to suggest an action or circumstance or a sequence of actions and circumstances to be communicated to a user based upon the estimated user profile and user data and the database of sequences of user circumstances and actions. The system delivers the suggested action or circumstance to a device carried by or worn by a user in the form of advice or a story, using text, speech, images, or video.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for suggesting beneficial actions, comprising: memory; one or more processors communicatively coupled to the memory; and one or more programs residing on the memory and executable by the one or more processors, the one or more programs configured to: receive user data from a user device; generate a first predictive model to estimate a user's self-actualization or happiness as a function of user data; generate a second predictive model to predict a sequence of future actions or circumstances to increase a user's self-actualization or happiness in accordance with the first predictive model; and deliver the sequence of future actions or circumstances to the user device. 2. The system as in claim 1, further comprising collecting the user data from a plurality of user devices. 3. The system as in claim 1, wherein the one or more programs are further configured to receive user data from one or more data providers. 4. The system as in claim 1, wherein the first predictive model combines evidence of a first user's self-actualization or happiness with evidence of a second user's self-actualization or happiness based on the user data, wherein the first user is positively related to user data associated with the second user. 5. The system of claim 4, wherein the determination that the first user is positively related to user data associated with the second user is by a collaborative filtering algorithm executed by the one or more processors. 6. The system as in claim 1, wherein the predicted sequence of future actions or circumstances is represented as a sequence of predicted future user data. 7. The system as in claim 1, wherein the second predictive model is rule based. 8. The system as in claim 1, wherein the second predictive model is generated using a reinforcement learning algorithm executed by the one or more processors. 9. The system as in claim 8, wherein the reinforcement learning algorithm comprises Q learning or deep reinforcement learning. 10. The system as in claim 1, wherein the one or more programs are further configured to execute the second predictive model to predict a sequence of future actions or circumstances to increase or maximize a user's self-actualization or happiness in accordance with the first predictive model. 11. The system as in claim 10, wherein the one or more programs are further configured to substitute, within the predicted sequence of future actions or circumstances, references to a first location with references to a second location, in accordance with a set of interchangeability criteria and substitution criteria. 12. The system as in claim 10, wherein the one or more programs are further configured to substitute, within the predicted sequence of future actions or circumstances, references to a first individual with references to a second individual, in accordance with a set of interchangeability criteria and substitution criteria. 13. The system as in claim 10, wherein the one or more programs are further configured to translate the predicted sequence of future actions or circumstances from machine representation to a human consumable form. 14. The system as in claim 13, wherein the human consumable form comprises text, speech, images, or video. 15. The system as in claim 14, wherein the human consumable form is at least partly assembled from text, speech, images, or video extracted from the user data. 16. The system as in claim 14, wherein the translation to human consumable form is at least partly based on a set of rules. 17. The system as in claim 13, wherein the one or more programs are further configured to communicate the human consumable form to a user. 18. A method for suggesting beneficial actions, comprising the steps of: receiving user data from a user device; generating, using a processor, a first predictive model to estimate a user's self-actualization or happiness as a function of user data, based on evidence of a user's self-actualization or happiness within the user data; generating, using a processor, a second predictive model to predict a sequence of future actions or circumstances to increase or maximize a user's self-actualization or happiness in accordance with the first predictive model; executing, using a processor, the second predictive model to predict a sequence of future actions or circumstances to increase or maximize a user's self-actualization or happiness in accordance with the first predictive model; translating the predicted sequence of future actions or circumstances from machine representation to a human consumable form; and communicating the human consumable form to a user. 19. The method of claim 18, further comprising: receiving user data from one or more data providers. 20. The method of claim 18, wherein the first predictive model combines evidence of a first user's self-actualization or happiness with evidence of a second user's self-actualization or happiness when user data associated with the first user is positively related to user data associated with the second user. 21. The method of claim 20, further comprising executing a collaborative filtering algorithm to determine the first user is positively related to user data associated with the second user. 22. The method of claim 18, wherein the predicted sequence of future actions or circumstances is represented as a sequence of predicted future user data. 23. The method of claim 18, wherein the second predictive model is rule based. 24. The method of claim 18, wherein the second predictive model is generated using a reinforcement learning algorithm, such as Q learning or deep reinforcement learning. 25. The method of claim 18, further comprising: substituting, within the predicted sequence of future actions or circumstances, references to a first location with references to a second location, in accordance with a set of interchangeability criteria and substitution criteria. 26. The method of claim 18, further comprising: substituting, within the predicted sequence of future actions or circumstances, references to a first individual with references to a second individual, in accordance with a set of interchangeability criteria and substitution criteria. 27. The method of claim 18, wherein the human consumable form comprises text, speech, images, or video. 28. The method of claim 27, wherein the human consumable form is at least partly assembled from text, speech, images, or video extracted from the user data. 29. The method of claim 27, wherein the translation to human consumable form is at least partly based on a set of rules. 30. A computer readable storage medium storing program instructions to suggest beneficial actions, the instructions comprising functionality to: receive user data from a user device; generate a first predictive model to estimate a user's self-actualization or happiness as a function of user data, based on evidence of a user's self-actualization or happiness within the user data; and generate a second predictive model to predict a sequence of future actions or circumstances to increase or maximize a user's self-actualization or happiness in accordance with the first predictive model. 31. The computer readable storage medium of claim 30, the instructions further comprising functionality to receive user data from one or more data providers. 32. The computer readable storage medium of claim 30, wherein the first predictive model combines evidence of a first user's self-actualization or happiness with evidence of a second user's self-actualization or happiness when user data associated with the first user is positively related to user data associated with the second user. 33. The computer readable storage medium of claim 32, further comprising executing a collaborative filtering algorithm to positively relate the first user to the second user. 34. The computer readable storage medium of claim 30, wherein the predicted sequence of future actions or circumstances is represented as a sequence of predicted future user data. 35. The computer readable storage medium of claim 30, wherein the second predictive model is rule based. 36. The computer readable storage medium of claim 30, wherein the second predictive model is generated using a reinforcement learning algorithm, such as Q learning or deep reinforcement learning, executed by the one or more processors. 37. The computer readable storage medium of claim 30, the instructions further comprising functionality to execute the second predictive model to predict a sequence of future actions or circumstances to increase or maximize a user's self-actualization or happiness in accordance with the first predictive model. 38. The computer readable storage medium of claim 37, the instructions further comprising functionality to substitute, within the predicted sequence of future actions or circumstances, references to a first location with references to a second location, in accordance with a set of interchangeability criteria and substitution criteria. 39. The computer readable storage medium of claim 37, the instructions further comprising functionality to substitute, within the predicted sequence of future actions or circumstances, references to a first individual with references to a second individual, in accordance with a set of interchangeability criteria and substitution criteria. 40. The computer readable storage medium of claim 37, the instructions further comprising functionality to translate the predicted sequence of future actions or circumstances from machine representation to a human consumable form. 41. The computer readable storage medium of claim 40, wherein the human consumable form comprises text, speech, images, or video. 42. The computer readable storage medium of claim 41, wherein the human consumable form is at least partly assembled from text, speech, images, or video extracted from the user data. 43. The computer readable storage medium of claim 41, wherein the translation to human consumable form is at least partly based on a set of rules. 44. The computer readable storage medium of claim 40, the instructions further comprising functionality to communicate the human consumable form to a user.