Intel Corporation

Oded Vainas

Ronen Aharon Soffer

Omri Mendels

Oren Pereg

Noam Sagi

Avi Samoucha

Gili Ilan

Boris Kodner

Tali Zvi

Gil Sharon

Rafi Cohen

Ariel Benou

Dganit Lebedev

Various systems and methods for processing activity data with a knowledge engine to generate actionable insights for a human subject are described. These actionable insights may include identifying a most likely action given a particular state of the human subject, identifying a most likely state in which the human subject performs a particular activity, or identifying anomalies in human activity patterns. In an example, an electronic processing system operates the knowledge engine with operations that: identify patterns of activity using clustering of events, identify meaningful patterns of activity from the patterns of activity based on co-occurrence of characteristics for respective events, rank the identified meaningful patterns of activity based on confidence and support of respective patterns to occur for a human subject, and generate a personalization action (such as an action for a software application) based on the ranked, identified meaningful patterns of activity.

Pattern recognition and prediction using a knowledge engine

System and techniques for search dimensionality expansion are described herein. A history of intelligent agent activity may be received. A search result generated by an external entity may be obtained that includes a set of geographic points of interest (POI). A geographic segment may be retrieved from a geographic segment library when the geographic segment contains a member of the set of POI. Here, the geographic segment defines a geographic area and a dimension set. The search result may be modified to create a modified search result that includes a member of the dimension set. The modified search result may then be transmitted to a user device.

Search dimensionality expansion

Various techniques for performing contextual event scheduling with an event scheduling service are disclosed herein. In an example, data is processed at an event scheduling service, based on the use of a trained machine learning model that is specific to a user. This trained machine learning model is operated by the event scheduling service determine a proposed time and proposed scheduling parameters based on the contextual information, to identify a proposed event time and event scheduling parameters based on the model, the data indicating a user state, or external data. Further examples to evaluate user activity and identify schedule characteristics based on data inputs from a user's mobile computing device, wearable sensors, and external weather, traffic, or event data sources, are also disclosed.

Contextual model-based event scheduling

Methods, apparatus, and system to parse an unstructured, natural language input of a user, infer a semantic meaning of an intent of the user, and determine a present or future user state, including a location and a time, in which the intent can be fulfilled.

Natural language intent and location determination method and apparatus

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Petah Tiqwa, Israel

Oded Vainas