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 method of managing an inventory of household products through virtual storage, such as in a virtual refrigerator, freezer, pantry, using electronic devices such as a smart phone. The virtual storage includes at least one of a description of each product in the virtual storage; the date each product was purchased; the expiration date of each product; and the freshness level of each product, including whether each product is still fresh, needs to be used within a certain time period, or needs to be thrown out. Related features include generation of shopping lists, coupons and recipes for a consumer based on the products in virtual storage or depleted therefrom.

Abstract: Semantic entity manipulation technique embodiments are presented that generate a probabilistic program capable of manipulating character strings representing semantic entities based on input-output examples. The program can then be used to produce a desired output consistent with the input-output examples from inputs of a type included in the examples. The probabilistic program is generated based on the output of parsing, transform and formatting modules. The parsing module employs a probabilistic approach to parsing the input-output examples. The transform module identifies a weighted set of transforms that are capable of producing the output item from the input items of an input-output example to a likelihood specified by their assigned weight. The formatting module generates formatting instructions that transform selected output parts into a form specified by the output items in the input-output examples.