Microsoft Technology Licensing, LLC

Limor Lahiani

Daniel Sitton

Roy Varshavsky

Brian C Beckman

Eyal Ofek

Haim Somech

Dikla Dotan-Cohen

Benny Schlesinger

Ido Priness

Eran Yariv

Hen Fitoussi

Kfir Karmon

Hadas Bitran

Ido Omer

Ron Jack Karidi

Oded Elyada

Elad Gerson

Gal Lavee

Oded Vainas

Noga Amit

In implementations, a divergence from a pattern of detected instances of an event is identified, where the event is of a routine of a user. The identified divergence corresponds to an out of routine event. Contextual information is generated corresponding to the identified out of routine event. Furthermore, one or more recommended actions for presenting content to the user are selected based on the contextual information. An indication of the identified out of routine event, the contextual information, and the one or more recommended actions are provided to a service, which may be an application on a user device. The service can present content to the user based on the identified out of routine event, the generated contextual information corresponding to the identified out of routine event, and the selected one or more recommended actions. The service may follow or disregard any of the selected one or more recommended actions.

Service content tailored to out of routine events

Messages to a focal user are organized by relevance of the message originators. A visual representation of the messages includes a focal user representation (textual or graphic) and multiple contact representations (textual or graphic). The contact representations are displayed at respective relevance distances from the focal user representation. Text regions present the contents of messages from the source contacts, e.g., using graphic novel-style word balloons. The contact representations can be positioned on screen in maps, radar format, or other configurations. Users can filter contacts according to relevance, and can filter messages by pertinence.

Relevance oriented graphical representation of discussion messages

Architecture that motivates and utilizes users as the means for capturing geographical data of a desired location. The architecture incentivizes users (e.g., large numbers of mobile-phone and mobile-computer users) to provide the data in the form of geolocation information trails and images captured by user devices. Thus, users take multiple pictures, for example, and can validate existing coverage of specific points of interest based on variable needs of the requestor. One motivational technique is by using augmented reality (AR) games, which include shooting targets associated with the point of interest (e.g., a street). Thus, the game can be designed for the data accumulation, which includes visual data. Additionally, the architecture can determine the areas or points of interest for validation and/or additional coverage by comparing live video data to an image database to decide of the need for update.

Geographic data acquisition by user motivation

Information is automatically located which is relevant to source content that a user is viewing on a user interface without requiring the user to perform an additional search or navigate links of the source content. The source content can be, e.g., a web page or a document from a word processing or email application. The relevant information can include images, videos, web pages, maps or other location-based information, people-based information and special services which aggregate different types of information. Related content is located by analyzing textual content, user behavior and connectivity relative to the source. The related content is scored for similarity to the source. Content which is sufficiently similar but not too similar is selected. Similar related content is grouped to select representative results. The selected content is filtering in multiple stages based on attribute priorities to avoid unnecessary processing of content which is filtered out an early stage.

Online relevance engine

Growing community of inventors

Tel Aviv, Israel

Limor Lahiani