Abstract: A technique of using collaborative family medical history (CFMH) to estimate disease risk includes establishing CFMH information of a user and a plurality of relatives of the user, the CFMH information including genetic information of at least some of the family members, genetic information of the user, or both. It further includes analyzing the CFMH information, including the genetic information. It further includes determining a potential risk condition of the user and a potential risk condition of at least a family member based on the CFMH information. It also includes outputting the potential risk condition of the user and the potential risk condition of the at least one family member.

Abstract: Aspects of the present disclosure provide techniques for displaying reduced data sets based on pre-classification of a larger data set. Embodiments include receiving a plurality of activity records describing a plurality of activities associated with the user. Embodiments further include grouping the plurality of activities into one or more pre-classified data sets based on the plurality of activity records. Embodiments further include providing the user with a summary of a pre-classified data set of the one or more pre-classified data sets via a user interface. Embodiments further include providing the user, via the user interface, with a user interface element that allows the user to provide input related to the pre-classified data set based on the summary. Embodiments further include receiving input from the user via the user interface, the input relating to the pre-classified data set.

Abstract: Aspects of the present disclosure provide techniques for displaying reduced data sets based on pre-classification of a larger data set. Embodiments include receiving a plurality of activity records describing a plurality of activities associated with the user. Embodiments further include grouping the plurality of activities into one or more pre-classified data sets based on the plurality of activity records. Embodiments further include providing the user with a summary of a pre-classified data set of the one or more pre-classified data sets via a user interface. Embodiments further include providing the user, via the user interface, with a user interface element that allows the user to provide input related to the pre-classified data set based on the summary. Embodiments further include receiving input from the user via the user interface, the input relating to the pre-classified data set.

Abstract: Aspects of the present disclosure provide techniques for displaying reduced data sets based on pre-classification of a larger data set. Embodiments include receiving a plurality of activity records describing a plurality of activities associated with the user. Embodiments further include grouping the plurality of activities into one or more pre-classified data sets based on the plurality of activity records. Embodiments further include providing the user with a summary of a pre-classified data set of the one or more pre-classified data sets via a user interface. Embodiments further include providing the user, via the user interface, with a user interface element that allows the user to categorize all activities in the pre-classified data set together based on the summary. Embodiments further include receiving input from the user via the user interface, the input assigning a category to all activities in the pre-classified data set together based on the summary.

Abstract: A technique of using collaborative family medical history (CFMH) to estimate disease risk includes establishing CFMH information of a user and a plurality of relatives of the user, the CFMH information including genetic information of at least some of the family members, genetic information of the user, or both. It further includes analyzing the CFMH information, including the genetic information. It further includes determining a potential risk condition of the user and a potential risk condition of at least a family member based on the CFMH information. It also includes outputting the potential risk condition of the user and the potential risk condition of the at least one family member.

Abstract: Aspects of the present disclosure provide techniques for displaying reduced data sets based on pre-classification of a larger data set. Embodiments include receiving a plurality of activity records describing a plurality of activities associated with the user. Embodiments further include grouping the plurality of activities into one or more pre-classified data sets based on the plurality of activity records. Embodiments further include providing the user with a summary of a pre-classified data set of the one or more pre-classified data sets via a user interface. Embodiments further include providing the user, via the user interface, with a user interface element that allows the user to categorize all activities in the pre-classified data set together based on the summary. Embodiments further include receiving input from the user via the user interface, the input assigning a category to all activities in the pre-classified data set together based on the summary.

Abstract: A technique of using collaborative family medical history (CFMH) to estimate disease risk includes establishing CFMH information of a user and a plurality of relatives of the user, the CFMH information including genetic information of at least some of the family members, genetic information of the user, or both. It further includes analyzing the CFMH information, including the genetic information. It further includes determining a potential risk condition of the user and a potential risk condition of at least a family member based on the CFMH information. It also includes outputting the potential risk condition of the user and the potential risk condition of the at least one family member.

Abstract: In general, the invention relates to a method of tracking purchases for a consumer using a unique identifier associated with the consumer. The method includes receiving first itemized data and the unique identifier from a first merchant system of a first merchant, where the first itemized data includes each item purchased in a first transaction associated with the consumer. The method further includes receiving second itemized data and the unique identifier from a second merchant system of a second merchant, where the second itemized data includes each item purchased in a second transaction associated with the consumer. The method further includes storing the first itemized data and the second itemized data based on the unique identifier to obtain aggregated itemized data, where the aggregated itemized data is categorized at an item level.