Time-stamped activity data, indicative of detected user activity, is received. A generative model explicitly models the rates of certain actions during certain activities and infers values based on observed data corresponding to those activities. A control system generates control signals, based on the inferred values, to control one or more different controlled systems or subsystems.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing system, comprising: a hierarchical generative model that receives a call for an inferred value of a variable and accesses inference logic that consumes user activity data including email activity data indicative of time-stamped, detected user activity in an electronic mail (email) system, and generates the inferred value indicative of a working characteristic of the user; and a control system that receives the inferred value and generates a control signal to control a controlled system based on the inferred value. 2. The computing system of claim 1 wherein the hierarchical generative model comprises: hierarchical declarative variable connection logic configured with variables connected to one another through causal connections based on the email activity data. 3. The computing system of claim 2 wherein the hierarchical generative model comprises: inference logic configured to receive the call for the inferred value, including an input variable, and to apply the input variable to the hierarchical declarative variable connection logic to identify the inferred value. 4. The computing system of claim 3 wherein the inference logic comprises: probability generator logic configured to generate a probability distribution for values corresponding to the variable. 5. The computing system of claim 4 wherein the hierarchical generative model comprises a hierarchical Bayesian model. 6. The computing system of claim 3 wherein the hierarchical declarative variable connection logic is configured with a value for a working hour variable, indicative of whether a given hour is a working hour for the user, being dependent on a value for a usual working hour variable, indicative of whether the given hour is usually a working hour for the user, and a value for an exceptional day variable, indicative of whether a given day is a working day for the user. 7. The computing system of claim 6 wherein the hierarchical declarative variable connection logic is configured with the value for the usual working hour variable being dependent on a value for an hour of day variable, indicative of a particular hour of a day corresponding to the given hour, and a value for a day of week variable, indicative of a particular day of week corresponding to the given day. 8. The computing system of claim 7 wherein the hierarchical declarative variable connection logic is configured with a value for a meeting variable being dependent on the value for the working hour variable and having an expected email activity rate indicative of a rate at which the user performs activities in the email system when the user is attending a meeting. 9. The computing system of claim 7 wherein the hierarchical declarative variable connection logic is configured with a value for a working variable being dependent on the value for the working hour variable and having an expected email activity rate indicative of a rate at which the user performs activities in the email system when the user is working. 10. The computing system of claim 7 wherein the hierarchical declarative variable connection logic is configured with a value for a distracted variable being dependent on the value for the working hour variable and having an expected email activity rate indicative of a rate at which the user performs activities in the email system when the user is distracted. 11. The computing system of claim 7 wherein the inference logic comprises: data merging logic configured to combine the email activity data with other activity data from another source to generate the inferred value. 12. The computing system of claim 1 and further comprising: data cleaning logic configured to receive time-stamped detected activity data indicative of user activity in a plurality of different systems and store the time-stamped detected activity data in a data store, for access by the hierarchical generative model. 13. The computing system of claim 12 wherein the data cleaning logic is configured to access the hierarchical generative model to identify a less reliable time period during which the time-stamped detected activity data is less reliable in representing a user activity pattern than at other time periods and to remove any time-stamped detected activity data, having a time stamp corresponding to the less reliable time period, from access by the hierarchical generative model. 14. A computer implemented method, comprising: receiving a call for an inferred value of a variable, at a hierarchical generative model; generating the inferred value, indicative of a working characteristic of a user, by accessing inference logic in the hierarchical generative model that consumes user activity data including email activity data indicative of time-stamped, detected user activity in an electronic mail (email) system; and generating a control signal to control a controlled system based on the inferred value. 15. The computer implemented method of claim 14 and further comprising: receiving time-stamped detected activity data indicative of user activity in a plurality of different systems; storing the time-stamped detected activity data in a data store, for access by the hierarchical generative model. 16. The computer implemented method of claim 15 and further comprising; accessing the hierarchical generative model to identify a less reliable time period during which the time-stamped detected activity data is less reliable in representing a user activity pattern than at other time periods; and removing any time-stamped detected activity data, having a time stamp corresponding to the less reliable time period, from access by the hierarchical generative model. 17. The computer implemented method of claim 14 wherein receiving the call comprises receiving the call for the inferred value, including an input variable, and wherein accessing the inference logic in the hierarchical generative model comprises applying the input variable to hierarchical declarative variable connection logic, that hierarchically connects variables to one another with causal connections, to identify the inferred value. 18. The computer implemented method of claim 17 wherein generating the inferred value comprises: generating a probability corresponding to the inferred value. 19. A computing system, comprising: a hierarchical generative model that receives a call for an inferred value and accesses inference logic that consumes user activity data including email activity data indicative of time-stamped, detected user activity in an electronic mail (email) system, and generates the inferred value indicative of a working characteristic of the user; data cleaning logic configured to receive time-stamped detected activity data indicative of user activity in a plurality of different systems and store the time-stamped detected activity data in a data store, for access by the hierarchical generative model and to access the hierarchical generative model to identify a less reliable time period during which the time-stamped detected activity data is less reliable in representing a user activity pattern than at other time periods and to remove any time-stamped detected activity data, having a time stamp corresponding to the less reliable time period, from access by the hierarchical generative model; and a control system that receives the inferred value and generates a control signal to control a controlled system based on the inferred value. 20. The computing system of claim 19 wherein the hierarchical generative model comprises a hierarchical Bayesian model.