Systems and computerized methods for determining patterns in user activity such that user contextual information can be provided based on the patterns. Historic data associated with a first user is received and entity information is determined based on the historic data. Current location data associated with the first user is received and prediction information is generated based on a combination of the entity information and the current location data for a current time period. Contextual insight information is determined based on a combination of the prediction information with at least one of the current location data, real time data, and contextual data, the contextual insight information including a recommendation of at least one of an activity, a purchase, and an accessory associated with the current location of the first user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computerized method of determining patterns in user activity thereby providing the user contextual information based on the patterns, the computerized method comprising: receiving, by a computing device, historic data associated with a first user, the historic data including at least one of: historic measured time-stamped location data associated with the first user, and historic inferred time-stamped location data associated with the first data user; determining, by the computing device, entity information based on the historic data, the entity information including information about user activity occurring within historic recurring time periods, the information about the user activity including at least one of: a type of user activity, a time associated with the user activity, and a likelihood of the user activity occurring with the historic recurring time periods; receiving, by the computing device, current location data associated with the first user, the current location data being received after the historic data, the current location data including: current measured time-stamped location data associated with the first user, and current inferred time-stamped location data associated with the first data user; generating, by the computing device, prediction information based on a combination of the entity information and the current location data for a current time period, the prediction information including at least one of: an assignment of a higher confidence level associated with a type of user activity occurring within at least one of the current time period and a future time period that also occurred in at least one of the historic recurring time periods, and an indication of a new type of user activity in at least one of the current time period and a future time period; and determining, by the computing device, contextual insight information based on a combination of the prediction information with at least one of the current location data, real time data, and contextual data, the contextual insight information including a recommendation of at least one of an activity, a purchase, and an accessory associated with at least one of the current location of the first user and a future location of the first user, and wherein: the real time data includes a condition associated with at least one of the current location of the first user and the future location of the first user, and the contextual data is associated with an interaction between the first user and a first user device. 2. The computerized method of claim 1, wherein determining entity information further comprises: determining, by the computing device, a similarity score for a first user activity occurring within the recurring historic time periods based on at least one of the historic data associated with the first user and historic data associated with a second user, the similarity score associated with an amount of similarity between the first user activity during a first and second historic time period of the recurring historic time periods; and aggregating, by the computing device, at least the first user activity with a second user activity based on a threshold number of times the first user activity and second user activity occur within the same recurring historic time periods. 3. The computerized method of claim 1, further comprising transforming, by the computing device, the received historic data into at least one of: time series data including at least one of time spent at home, time spent at work, a distance from home over time, a distance from work over time; categorical data including at least one of a visit to a restaurant, a visit to a park, a weather condition; numerical data including at least one of a number of different places visited and an outside temperature; and Boolean data. 4. The computerized method of claim 1, wherein the historic measured time-stamped location data and the current measured time-stamped location data are associated with data collected by a location tracking device, and the historic inferred time-stamped location data and the current inferred time-stamped location data include at least one of: a user transaction; a manual check-in by the first user; a distance from a timed location point to a location associated with at least one of the historic inferred time-stamped location data and the current inferred time-stamped location data; and weather information associated with at least one of the historic inferred time-stamped location data and the current inferred time-stamped location data. 5. The computerized method of claim 1, wherein: the condition associated with at least one of the current location of the first user and the future location of the first user includes at least one of weather, a traffic pattern, a train schedule, and a restaurant proximity and availability, presence of a second user, time since a prior visit to the current location, and news events; and the interaction between the first user and a first user device includes a number of push notifications accessed by the first user on the first user device. 6. A non-transitory computer readable medium having executable instructions operable to cause an apparatus to: receive historic data associated with a first user, the historic data including at least one of: historic measured time-stamped location data associated with the first user, and historic inferred time-stamped location data associated with the first data user; determine entity information based on the historic data, the entity information including information about user activity occurring within historic recurring time periods, the information about the user activity including at least one of: a type of user activity, a time associated with the user activity, and a likelihood of the user activity occurring with the historic recurring time periods; receive current location data associated with the first user, the current location data being received after the historic data, the current location data including: current measured time-stamped location data associated with the first user, and current inferred time-stamped location data associated with the first data user; generate prediction information based on a combination of the entity information and the current location data for a current time period, the prediction information including at least one of: an assignment of a higher confidence level associated with a type of user activity occurring within at least one of the current time period and a future time period that also occurred in at least one of the historic recurring time periods, and an indication of a new type of user activity in at least one of the current time period and a future time period; and determine contextual insight information based on a combination of the prediction information with at least one of the current location data, real time data, and contextual data, the contextual insight information including a recommendation of at least one of an activity, a purchase, and an accessory associated with the current location of the first user, and wherein: the real time data includes a condition associated with a current location of the first user, and the contextual data is associated with an interaction between the first user and a first user device. 7. The non-transitory computer readable medium of claim 6, wherein to determine entity information, the apparatus is further caused to: determine a similarity score for a first user activity occurring within the recurring historic time periods based on at least one of the historic data associated with the first user and historic data associated with a second user, the similarity score associated with an amount of similarity between the first user activity during a first and second historic time period of the recurring historic time periods; and aggregate at least the first user activity with a second user activity based on a threshold number of times the first user activity and second user activity occur within the same recurring historic time periods. 8. The non-transitory computer readable medium of claim 6, wherein the apparatus is further caused to transform the received historic data into at least one of: time series data including at least one of time spent at home, time spent at work, a distance from home over time, a distance from work over time; categorical data including at least one of a visit to a restaurant, a visit to a park, a weather condition; numerical data including at least one of a number of different places visited and an outside temperature; and Boolean data. 9. The non-transitory computer readable medium of claim 6, wherein the historic measured time-stamped location data and the current measured time-stamped location data are associated with data collected by a location tracking device, and the historic inferred time-stamped location data and the current inferred time-stamped location data include at least one of: a user transaction; a manual check-in by the first user; a distance from a timed location point to a location associated with at least one of the historic inferred time-stamped location data and the current inferred time-stamped location data; and weather information associated with at least one of the historic inferred time-stamped location data and the current inferred time-stamped location data. 10. The non-transitory computer readable medium of claim 6, wherein: the condition associated with at least one of the current location of the first user and the future location of the first user includes at least one of weather, a traffic pattern, a train schedule, and a restaurant proximity and availability, presence of a second user, time since a prior visit to the current location, and news events; and the interaction between the first user and a first user device includes a number of push notifications accessed by the first user on the first user device. 11. A computing device for determining patterns in user activity thereby providing the user contextual information based on the patterns, the computing device comprising: data storage; and a processor in communication with the data storage, and configured to run a module stored in memory that is configured to cause the processor to: receive historic data associated with a first user, the historic data including at least one of: historic measured time-stamped location data associated with the first user, and historic inferred time-stamped location data associated with the first data user; determine entity information based on the historic data, the entity information including information about user activity occurring within historic recurring time periods, the information about the user activity including at least one of: a type of user activity, a time associated with the user activity, and a likelihood of the user activity occurring with the historic recurring time periods; receive current location data associated with the first user, the current location data being received after the historic data, the current location data including: current measured time-stamped location data associated with the first user, and current inferred time-stamped location data associated with the first data user; generate prediction information based on a combination of the entity information and the current location data for a current time period, the prediction information including at least one of: an assignment of a higher confidence level associated with a type of user activity occurring within at least one of the current time period and a future time period that also occurred in at least one of the historic recurring time periods, and an indication of a new type of user activity in at least one of the current time period and a future time period; and determine contextual insight information based on a combination of the prediction information with at least one of the current location data, real time data, and contextual data, the contextual insight information including a recommendation of at least one of an activity, a purchase, and an accessory associated with the current location of the first user, and wherein: the real time data includes a condition associated with a current location of the first user, and the contextual data is associated with an interaction between the first user and a first user device. 12. The computing device of claim 11, wherein to determine entity information, the processor is further caused to: determine a similarity score for a first user activity occurring within the recurring historic time periods based on at least one of the historic data associated with the first user and historic data associated with a second user, the similarity score associated with an amount of similarity between the first user activity during a first and second historic time period of the recurring historic time periods; and aggregate at least the first user activity with a second user activity based on a threshold number of times the first user activity and second user activity occur within the same recurring historic time periods. 13. The computing device of claim 11, wherein the processor is further caused to transform the received historic data into at least one of: time series data including at least one of time spent at home, time spent at work, a distance from home over time, a distance from work over time; categorical data including at least one of a visit to a restaurant, a visit to a park, a weather condition; numerical data including at least one of a number of different places visited and an outside temperature; and Boolean data. 14. The computing device of claim 11, wherein the historic measured time-stamped location data and the current measured time-stamped location data are associated with data collected by a location tracking device, and the historic inferred time-stamped location data and the current inferred time-stamped location data include at least one of: a user transaction; a manual check-in by the first user; a distance from a timed location point to a location associated with at least one of the historic inferred time-stamped location data and the current inferred time-stamped location data; and weather information associated with at least one of the historic inferred time-stamped location data and the current inferred time-stamped location data. 15. The computing device of claim 11, wherein: the condition associated with at least one of the current location of the first user and the future location of the first user includes at least one of weather, a traffic pattern, a train schedule, and a restaurant proximity and availability, presence of a second user, time since a prior visit to the current location, and news events; and the interaction between the first user and a first user device includes a number of push notifications accessed by the first user on the first user device.