Microsoft Technology Licensing, LLC

Ranjith Narayanan

Vijay Mital

Robin Abraham

Liang Du

Rui Liang

Srivatsa K Srinivasan

Vijay Kishen Hampapur Parthasarathy

Moustafa Noureddine

Performing collaborative search engine searching. The method includes receiving user input at a user interface for performing a plurality searches on a first search engine. The method further includes receiving user input at the user interface applying one or more augmentation AI models to searches in the plurality of searches. The method further includes creating a shareable, executable package executable by one or more search engines based on the plurality of searches and the applied AI models that when executed by the search engines causes the search engines to apply the AI models to searches performed at the search engines.

Search engine functionality using shared AI models

Expanding search engine functionality using AI models. A method includes, as part of a search session, receiving user input at a search engine. One or more searches on a set of data using the user input. Search results are provided from the one or more searches to a user. Based on a history of the search session, suggestions are provided in a user interface of AI models that could be applied to expand potential search results for the search session. User input is received at the user interface selecting one or more of the suggested AI model. The one or more selected AI models are applied to expand the set of data. Search results to the user based on searching the expanded set of data.

Expanding search engine capabilities using AI model recommendations

The improved exercise of artificial intelligence by providing a systematic way for a computing system to interface with output from AI models. To do this, the computing system obtains results of an input data set being applied to an AI model. The results are then refined based upon characteristic(s) of the AI model and perhaps the input data set. Based upon characteristic(s) of the AI model and perhaps the input data set, interface element(s) are identified that can be used to interface with the refined results. The interface element(s) are then communicated to an interface element that interfaces with the refined results. The interface element(s) may include, for instance, operator(s) or term(s) that may be used to query against the refined results and/or an identification of visualization(s) that may be used to present to a user results of queries against the refined results.

Interfacing with results of artificial intelligent models

The improved exercise of artificial intelligence. Raw output data is obtained by applying an input data set to an artificial intelligence (AI). Such raw output data is sometimes difficult to interpret. The principles defined herein provide a systematic way to refine the output for a wide variety of AI models. An AI model collection characterization structure is utilized for purpose of refining AI model output so as to be more useful. The characterization structure represents, for each of multiple and perhaps numerous AI models, a refinement of output data that resulted from application of an AI model to input data. Upon obtaining output data from the AI model, the appropriate refinement may then be applied. The refined data may then be semantically indexed to provide a semantic index. The characterization structure may also provide tailored information to allow for intuitive querying against the semantic index.

Exercising artificial intelligence by refining model output

The improved exercise of artificial intelligence by systematically refining and semantically indexing the output from AI models, so that the semantic index is highly relevant. To do this, the computing system obtains results of an input data set being applied to an AI model. The computing system then determines a refinement to apply to the obtained results. This determination may be based on one or more characteristics of the AI model and/or input data set. The determination may also be based on hints associated with that AI model, and/or learned behavior regarding how that AI model is typically used. The obtained results are then refined using the determined refinement. It is then this more relevant refined results that are semantically indexed to generate the semantic index. Thus, the semantic index represents, the more useful output from an AI model, which is semantically exposed so as to provide meaning.

Indexing refined output of artificial intelligence models

A uniform approach for web service discovery is provided through common auto-discovery logic for cloud-based or enterprise-based deployments for a specific web service. Appropriate credentials may be applied employing a uniform method using an appropriate authentication scheme for on-premise, fully hosted, or partially hosted auto-discovered web services. A common interface may be implemented even if the protocols for discovery differ from service to service or between cloud-based deployment and enterprise-based deployment for the same service. Web service locations auto-discovered through different protocols may be cached for subsequent use.

Unified web service discovery

Techniques to distribute messages using communication pipelines are described. An apparatus may comprise a message receiving component operative to receive a message at a first server for forwarding to a virtual receiver, a routing component operative to identify a second server maintaining the virtual receiver and determine a communication pipeline between the first server and the second server, and a message sending component operative to send the message from the first server to the second server over the communication pipeline. Other embodiments are described and claimed.

Techniques to distribute messages using communication pipelines

A pluggable token provider model for message level authentication across multiple web services is provided. Web service and token provider implementations within a client application are separated from an actual component that operates the business logic to formulate and understand a web request. The web service components may request web services to be executed and supply the body for the web service message while a common framework maintains the web services metadata, which includes definitions associated with respective tokens. The framework may further maintain token provider implementations that actually fetch authentication tokens and perform the web requests.

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Bellevue, WA, United States of America

Ranjith Narayanan