Microsoft Technology Licensing, LLC

Roy Varshavsky

Ron Jack Karidi

Eran Yariv

Daniel Sitton

Kfir Karmon

Kira Radinsky

Moshe Tennenholtz

Limor Lahiani

Benny Schlesinger

Hen Fitoussi

Yoram Yaacovi

Vladimir Holostov

Jack W Stokes

Eugene (John) Neystadt

Oded Elyada

Avigad Oron

Edward William Schaefer

Keren Master

Vadim Mesonzhnik

Barak Shein

Noga Amit

Yitzhak Tzahi Weisfeild

Systems, methods, and computer media for integrating requests for reserved allocations of advertisement impressions and requests for dynamic allocations of advertisement impressions are provided. A request is received from a first advertiser to purchase a reserved allocation of advertisement impressions. The request specifies a requested number of impressions that each have one or more requested attributes. One or more bids to dynamically purchase one or more impressions through a real-time bidding process are received from an external bidding agent. An internal bidding agent bids, on behalf of the first advertiser, to dynamically purchase one or more impressions through the real-time bidding process until the request from the first advertiser to purchase the reserved allocation of impressions is satisfied.

Integration of reserved and dynamic advertisement allocations

A ranked set of users may be calculated from an expertise categorization for each user and a person's trust in the users for specific categories. The ranked set of users may be used for presenting search results, recommendations, social marketing, or other uses. A person's reputation may be determined through various online activities. A person's trust in another person may be related to their proximity and activity in one or more social networks.

Social network based contextual ranking

Architecture that selects a classification engine based on the expertise of the engine to process a given entity (e.g., a file). Selection of an engine is based on a probability that the engine will detect an unknown entity classification using properties of the entity. One or more of the highest ranked engines are activated in order to achieve the desired performance. A statistical, performance-light module is employed to skip or select several performance-demanding processes. Methods and algorithms are utilized for learning based on matching the best classification engine(s) to detect the entity class based on the entity properties. A user selection option is provided for specifying a maximum number of ranked, classification engines to consider for each state of the machine. A user can also select the minimum probability of detection for a specific entity (e.g., unknown file). The best classifications are re-evaluated over time as the classification engines are updated.

Realtime multiple engine selection and combining

A proximity matching system may use broadcast wireless identifiers transmitted by users' devices to match users with other nearby users. The identifiers may be collected by a plurality of agents, then the identifiers may be matched with pre-defined profiles to generate physically proximate users by a remote service. The group of proximate users may be provided to various applications and consumed with summarized properties or individual properties, depending on the approved privacy settings as selected by the users. In some embodiments, the broadcast wireless identifiers may be personal area network identifiers, local area network identifiers, cellular network identifiers, or other broadcast identifier. In some embodiments, the agents may not establish a peer to peer or other connection with the broadcasting device. The agents may be fixed or mobile agents, and the proximity of users may be generated through links between nearby agents in a meshed fashion.

Wireless identifiers for proximity applications

A recommendation system may use a network of relationships between many different entities to find search results and establish a relevance value for the search results. The relevance value may be calculated by analyzing trust and similarity components of each relationship between the search user and the entity providing the search results. The entities may be, for example, persons associated within express or implied social networks, or corporations or other organizations with a historical or other reputation. The relationships may be created through many different contact mechanisms and may be unidirectional, asymmetric bidirectional, or symmetric bidirectional relationships. The relationships may be different based on topic or other factors.

Hybrid recommendation system

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

A data storage medium may have data stored on one physical portion of the medium and error correction and recovery data stored on a second physical portion of the medium. In one embodiment, a write once, read many medium may be written with data and the remaining capacity of the medium may be filled with error correction and recovery data. If a portion of the main data is corrupted, the error correction and recovery data may be used to recreate the corrupted data. The error correction and recovery data may be created to fill the unused capacity of the medium by prioritizing and selectively backing up the data when the data use more than half of the medium's capacity, or may create one or more redundant copies of the data if the data consume less than half of the medium's capacity, for example.

Redundant data in storage medium

User input actions from one or more user input devices can be received, and unfiltered user input information about the user input actions can be collected at a client computing environment. The unfiltered user input information can be filtered to produce filtered user input information. The filtered user input information can exclude personal information and include non-personal information. In addition, a user profile can be created using the filtered user input information.

Client-centered usage classification

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.

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Tel Aviv, Israel

Roy Varshavsky