Abstract: Representative embodiments disclose mechanisms to assign search results associated with a user query to locations based in part on features associated with the user's Internet of Things (IoT) devices. This results in a search page that presents results in an order and prominence that improves user understanding and information accessibility. Features from IoT devices are used to train a machine learning model that receives as an input information from an IoT entity index, the query, and results from the search engine along with their static rankings. The resultant IoT positioning scores are used to assign an initial location on the search results page. A second machine learning model trained using IoT features is then used to assign a final location on the page. The system causes display of the page to the user.

Abstract: Representative embodiments disclose mechanisms to assign search results associated with a user query to locations based in part on features associated with the user's Internet of Things (IoT) devices. This results in a search page that presents results in an order and prominence that improves user understanding and information accessibility. Features from IoT devices are used to train a machine learning model that receives as an input information from an IoT entity index, the query, and results from the search engine along with their static rankings. The resultant IoT positioning scores are used to assign an initial location on the search results page. A second machine learning model trained using IoT features is then used to assign a final location on the page. The system causes display of the page to the user.

Abstract: A system for generating sensor data based query results can include a processor to detect sensor data from a plurality of sensors and store the sensor data as aggregated sensor data. The sensor data can be aggregated based on a time corresponding to the sensor data, wherein the sensor data is stored with a notification based push operation in response to a change in value of the sensor data exceeding a predetermined threshold. The processor can also convert the aggregated sensor data to an indexable data format (IDF) and provide a low latency query result to a query based on the aggregated sensor data in the IDF.

Abstract: Techniques for providing a recommendation engine for personal digital assistant functionality on hardware devices. In an aspect, sensor data and other signals for a user are collected and processed to extract user patterns. A user profile is further constructed using machine learning techniques. The insights obtained from the user patterns and user profile are combined by digital service routines to generate customized recommendations for users. In a further aspect, user feedback may be utilized to improve the accuracy and relevance of the recommendations.

Abstract: Representative embodiments disclose mechanisms to assign search results associated with a user query to locations based in part on features associated with the user's Internet of Things (IoT) devices. This results in a search page that presents results in an order and prominence that improves user understanding and information accessibility. Features from IoT devices are used to train a machine learning model that receives as an input information from an IoT entity index, the query, and results from the search engine along with their static rankings. The resultant IoT positioning scores are used to assign an initial location on the search results page. A second machine learning model trained using IoT features is then used to assign a final location on the page. The system causes display of the page to the user.

Abstract: A system for executing tasks can include a processor to detect a plurality of user signals for a user, the plurality of user signals comprising a search history of the user. The processor can also identify activity information from the plurality of user signals, the activity information comprising an action executed by the user. The processor can generate an activity cluster based on the activity information, wherein generating the activity cluster comprises applying a clustering technique and an ordering technique to the activity information. Furthermore, the processor can execute a search query for the user based on the activity cluster comprising a plurality of activities that are clustered and ordered.

Abstract: Techniques for generating enhanced query formulations by leveraging data from network-coupled devices, e.g., sensors coupled to the Internet of Things (IoT). In an aspect, one or more query-related sensor candidates are retrieved from a sensor entity data store. The most relevant sensor candidates are determined, and the sensor's identities and data are leveraged to alter and enhance the quality and accuracy of query formulations submitted to an online search engine. The techniques may be applied to improve the query understanding capabilities of search engines, as well as natural language processing capabilities of personal digital assistants.

Abstract: Techniques for providing a recommendation engine for personal digital assistant functionality on hardware devices. In an aspect, sensor data and other signals for a user are collected and processed to extract user patterns. A user profile is further constructed using machine learning techniques. The insights obtained from the user patterns and user profile are combined by digital service routines to generate customized recommendations for users. In a further aspect, user feedback may be utilized to improve the accuracy and relevance of the recommendations.

Abstract: The discussion relates to providing more relevant search results to a user based upon information relating to IoT devices associated with the user. One example can receive search results for a search query associated with the user. The example can obtain entities from IoT devices associated with the user and rank the search results utilizing the entities from the IoT devices.

Abstract: A system for generating sensor data based query results can include a processor to detect sensor data from a plurality of sensors and store the sensor data as aggregated sensor data. The sensor data can be aggregated based on a time corresponding to the sensor data, wherein the sensor data is stored with a notification based push operation in response to a change in value of the sensor data exceeding a predetermined threshold. The processor can also convert the aggregated sensor data to an indexable data format (IDF) and provide a low latency query result to a query based on the aggregated sensor data in the IDF.