Abstract: Data formatting rules to convert data from one form to another form are automatically determined based on a user's edits. A machine learning heuristic is applied to a user's edits to determine a data formatting rule that may be applied to data. For example, a user may make edits that add/remove characters from data, concatenate data, extract data, rename data, and the like. The machine learning heuristic may be automatically triggered in response to an event (e.g. after a predetermined number of edits are made to a same type of data) or manually triggered (e.g. selecting a user interface option). The data formatting rule may be applied to other data and the results of the formatting reviewable by the user. Based on further edits/reviews, the data formatting rule may be updated. The data formatting rules may be stored for later use.

Abstract: A program creation system is described which generates sets of subprograms for respective input-output examples. The program creation system then groups the sets into partitions by performing an intersection operation. According to one aspect, the program creation system generates subprograms so as to exclude tokens that are not represented by the input strings of the input-output examples. According to another aspect, the program creation system first generates the subprograms without attempting to generate loop-type expressions. If this operation produces unsatisfactory results, the program creation system repeats its processing, this time including loop-type expressions. According to another aspect, the program creation system performs the grouping operation using an expedited graph-intersection operation.

Abstract: The subject disclosure is directed towards automatically labeling location-related information such as corresponding to GPS data or the like with a semantic label. A classifier trained with machine learning is provided with feature data corresponding to the location-related information and other features, such as user demographics data of a person associated with location-related information. The semantic label is received from the classifier, and associated with the location-related information. Other features may be used, such as other egocentric features corresponding to a person's particular visit, features from a sequence of visits, and/or features from other user information. The semantic label may be used to trigger an action, label a location on a map or the like, and so on.

Abstract: The subject disclosure is directed towards automatically labeling location-related information such as corresponding to GPS data or the like with a semantic label. A classifier trained with machine learning is provided with feature data corresponding to the location-related information and other features, such as user demographics data of a person associated with location-related information. The semantic label is received from the classifier, and associated with the location-related information. Other features may be used, such as other egocentric features corresponding to a person's particular visit, features from a sequence of visits, and/or features from other user information. The semantic label may be used to trigger an action, label a location on a map or the like, and so on.

Abstract: Inductive synthesis and combination framework technique embodiments are presented that generally perform string transformations involving lookup operations in one or more relational tables, either alone or in combination with other non-lookup operations. More particularly, a semantic string lookup transformation language is presented, which can be used to generate an inductive synthesis procedure that synthesizes a set of transformations involving lookup operations that are consistent with the given set of input-output examples. In addition, a combination framework for combining the lookup transformation language and its synthesis procedure, with other transformation languages and their associated synthesis procedures, is presented. The resulting combined synthesis procedures enable the combination framework to synthesize transformations on a rich variety of data-types.

Abstract: A program creation system is described which generates sets of subprograms for respective input-output examples. The program creation system then groups the sets into partitions by performing an intersection operation. According to one aspect, the program creation system generates subprograms so as to exclude tokens that are not represented by the input strings of the input-output examples. According to another aspect, the program creation system first generates the subprograms without attempting to generate loop-type expressions. If this operation produces unsatisfactory results, the program creation system repeats its processing, this time including loop-type expressions. According to another aspect, the program creation system performs the grouping operation using an expedited graph-intersection operation.

Abstract: Inductive synthesis and combination framework technique embodiments are presented that generally perform string transformations involving lookup operations in one or more relational tables, either alone or in combination with other non-lookup operations. More particularly, a semantic string lookup transformation language is presented, which can be used to generate an inductive synthesis procedure that synthesizes a set of transformations involving lookup operations that are consistent with the given set of input-output examples. In addition, a combination framework for combining the lookup transformation language and its synthesis procedure, with other transformation languages and their associated synthesis procedures, is presented. The resulting combined synthesis procedures enable the combination framework to synthesize transformations on a rich variety of data-types.

Abstract: Data formatting rules to convert data from one form to another form are automatically determined based on a user's edits. A machine learning heuristic is applied to a user's edits to determine a data formatting rule that may be applied to data. For example, a user may make edits that add/remove characters from data, concatenate data, extract data, rename data, and the like. The machine learning heuristic may be automatically triggered in response to an event (e.g. after a predetermined number of edits are made to a same type of data) or manually triggered (e.g. selecting a user interface option). The data formatting rule may be applied to other data and the results of the formatting reviewable by the user. Based on further edits/reviews, the data formatting rule may be updated. The data formatting rules may be stored for later use.