Microsoft Technology Licensing, LLC

Bernard L Johnston

Brian C Beckman

Gur Kimchi

Henricus Johannes Maria Meijer

Eyal Ofek

Shai Herzog

Carolyn P Johnston

Kenn Cartier

Gil Shklarski

Texts may be classified by mapping the texts to concept space, and by dividing the concept space based on substantive classes. A concept space containing a diverse set of concepts is defined. One example of a concept space is the set of on-line encyclopedia articles, each of which is an example of a concept. A text is scored for relevance against each concept, and a vector is created containing each of the scores. The vector represents the text's position in concept space. For any given substantive class of texts, the concept space may be divided into regions containing texts that are members/non-members of the class. The dividing boundary may be determined by training a classifier on a set of labeled examples of texts that fall inside and outside the class.

Text classification using concept kernel

Language-integrated query (LINQ) operators can be extended to a set of vectors associated with a digital signal processing (DSP) environment. A language-integrated query (LINQ) operator can be created to execute a change of basis for a set of vectors. LINQ operators can be further be utilized with specifically generated parameters to perform a change of basis for the set of vectors. Additionally, the standard LINQ operators can be extended to enable querying with LINQ against the set of vectors.

Language integrated query over vector spaces

Positional information is provided while minimizing the possibility that personally identifiable information can be derived therefrom. Positional information is received in the form of trails that can be aggregated. Individual cells of a grid reflect a quantity of aggregated trails through those cells, an average intensity and direction of movement through those cells, or a more detailed distribution thereof. Alternatively, individual trails are aggregated to an aggregated trail in the form of a line. Further obfuscation of personally identifiable information occurs by resampling aggregated positional information, by introducing false positional information, or by falsely modifying existing positional information, in a manner that does not impact the overall aggregations, and by pruning, or deleting, positional information, especially around sensitive locations, such as a user's home, place of business, or other location that users typically would seek to keep private. Provision of positional information is delayed until a sufficient amount is received.

Depersonalizing location traces

Growing community of inventors

Redmond, WA, United States of America

Bernard L Johnston