Abstract: A computer system automatically generates service documentation based on usage of a web service. The computer system captures network traffic including actual requests to a service endpoint of the web service and actual responses from the service endpoint of the web service. The captured network traffic can be analyzed using machine learning to determine one or more operations that are available at the service endpoint, input arguments that are accepted by the service endpoint, and output arguments that are provided by the service endpoint. The computer system can automatically generate service documentation for the web service based on metadata that identifies the operations, the input arguments, and the output arguments.

Abstract: Systems and methods for providing client-side integration of apps and services is provided. An integration framework execution upon a computing device provides integration of various apps, applications, services, sensors and the like. Upon receiving a request for a service, the integration framework accesses a registry of a plurality of services of a respective plurality of providers registered with the integration framework. Each of the services is registered with the integration framework is associated with a trust level of a hierarchy of trust levels. The integration framework iteratively searches the registry for a provider of the requested service according to the hierarchy of trust levels, beginning with the most trusted level of the trust levels to the less trusted trust level until a provider of the requested service is found or until all levels of the hierarchy are searched without finding a provider of the requested service.

Abstract: Systems and methods for providing client-side integration of apps and services is provided. An integration framework execution upon a computing device provides integration of various apps, applications, services, sensors and the like. Upon receiving a request for a service, the integration framework accesses a registry of a plurality of services of a respective plurality of providers registered with the integration framework. Each of the services is registered with the integration framework is associated with a trust level of a hierarchy of trust levels. The integration framework iteratively searches the registry for a provider of the requested service according to the hierarchy of trust levels, beginning with the most trusted level of the trust levels to the less trusted trust level until a provider of the requested service is found or until all levels of the hierarchy are searched without finding a provider of the requested service.

Abstract: A device set may interoperate to perform an action, such as providing a message to a user or storing photos taken by a user. Performing the action on all available devices may cause redundancy (e.g., presenting the same message to the user on each device), and depending on user selection (e.g., the user requesting to play music and specifically designating a device) may become onerous. A centralized, server-driven selection of the device to perform the action may be problematic if the server is inaccessible and/or inaccurate if not currently informed of the current state of each device. In accordance with the techniques presented herein, a requesting device broadcasts an action invitation to the device set. Each device provides a suitability report indicating its suitability to perform the action. The requesting device identifies one or more suitable devices, and sends an action request to identified devices to perform the action.

Abstract: Systems and methods for providing client-side integration of apps and services is provided. An integration framework execution upon a computing device provides integration of various apps, applications, services, sensors and the like. Upon receiving a request for a service, the integration framework accesses a registry of a plurality of services of a respective plurality of providers registered with the integration framework. Each of the services is registered with the integration framework is associated with a trust level of a hierarchy of trust levels. The integration framework iteratively searches the registry for a provider of the requested service according to the hierarchy of trust levels, beginning with the most trusted level of the trust levels to the less trusted trust level until a provider of the requested service is found or until all levels of the hierarchy are searched without finding a provider of the requested service.

Abstract: A computer system automatically generates service documentation based on usage of a web service. The computer system captures network traffic including actual requests to a service endpoint of the web service and actual responses from the service endpoint of the web service. The captured network traffic can be analyzed using machine learning to determine one or more operations that are available at the service endpoint, input arguments that are accepted by the service endpoint, and output arguments that are provided by the service endpoint. The computer system can automatically generate service documentation for the web service based on metadata that identifies the operations, the input arguments, and the output arguments.

Abstract: Architecture that enables users to define user interests, and when two or more people with matching interests (mutual) are identified in the same geographical area, the architecture actively notifies either one or both of the users (e.g., based on a given scenario). After being notified, either or both users can decide how to act (e.g., the user that wants to buy the car can call the user that wants to sell the car and then set a meeting at a suitable time). The architecture automatically creates dynamic geo-fences in association with users to perform different location-based matching scenarios. The locations of the users are continuously identified as the users move in a geographical area. When a matching interest is identified, the architecture actively notifies the users of items that might be of interest and which are currently occurring in the user vicinity.

Abstract: In many scenarios, a device may detect one or more audio conversations, and may be capable of evaluating such audio conversations, e.g., in order to present a text transcript to a user. However, the user's attention to such audio conversations may waver, and the user may miss the audio conversation and/or an opportunity to participate in the audio conversation. Presented herein are techniques for enabling devices to assist users in such scenarios by monitoring audio conversations to detect conversation cues that pertain to the user (e.g., the user's name, names of the user's friends, and/or topics of interest to the user). Upon detecting a conversation cue within an audio conversation that pertains to the user, the device notifies the user (e.g., alerting the user that the audio conversation may be of interest, and/or presenting a text transcript of the portion of the audio conversation containing the conversation cue).

Abstract: A device set may interoperate to perform an action, such as providing a message to a user or storing photos taken by a user. Performing the action on all available devices may cause redundancy (e.g., presenting the same message to the user on each device), and depending on user selection (e.g., the user requesting to play music and specifically designating a device) may become onerous. A centralized, server-driven selection of the device to perform the action may be problematic if the server is inaccessible and/or inaccurate if not currently informed of the current state of each device. In accordance with the techniques presented herein, a requesting device broadcasts an action invitation to the device set. Each device provides a suitability report indicating its suitability to perform the action. The requesting device identifies one or more suitable devices, and sends an action request to identified devices to perform the action.

Abstract: A “traveling” map-reduce operation with full context that can skip between data stores and devices. The “traveling” aspect means the map-reduce operation request can be communicated to specific agents to operate on local data of the agents. The traveling map-reduce operation protects privacy and avoids leakage of user private data. The traveling map-reduce operation can run over long periods of time and work on data stores which are not always connected (offline). The architecture employs a context free online controller and a set of on-premise (on device) agents that reside in the data store (device).

Abstract: Systems and methods for providing client-side integration of apps and services is provided. An integration framework execution upon a computing device provides integration of various apps, applications, services, sensors and the like. Upon receiving a request for a service, the integration framework accesses a registry of a plurality of services of a respective plurality of providers registered with the integration framework. Each of the services is registered with the integration framework is associated with a trust level of a hierarchy of trust levels. The integration framework iteratively searches the registry for a provider of the requested service according to the hierarchy of trust levels, beginning with the most trusted level of the trust levels to the less trusted trust level until a provider of the requested service is found or until all levels of the hierarchy are searched without finding a provider of the requested service.

Abstract: One or more techniques and/or systems are provided for locally filtering content on a device, which preserves privacy of a user (e.g., user specific data is not sent from the device to obtain content tailored to the user). A set of content candidates may be retrieved from a remote source (e.g., a restaurant app may retrieve menu items from a restaurant server). A user personalization profile may be used to locally filter the set of content candidates to generate a filtered set of content. For example, the user personalization profile may indicate that the user maintains a low-carb diet and that the user prefers expensive Asian restaurants (e.g., based upon a low-carb diet document saved on the user's device and/or device locational information indicating the user frequents expensive Asian restaurants). In this way, the restaurant app may display the filtered set of content (e.g., expensive Asian restaurants serving low-carb food).

Abstract: Architecture that enables users to define user interests, and when two or more people with matching interests (mutual) are identified in the same geographical area, the architecture actively notifies either one or both of the users (e.g., based on a given scenario). After being notified, either or both users can decide how to act (e.g., the user that wants to buy the car can call the user that wants to sell the car and then set a meeting at a suitable time). The architecture automatically creates dynamic geo-fences in association with users to perform different location-based matching scenarios. The locations of the users are continuously identified as the users move in a geographical area. When a matching interest is identified, the architecture actively notifies the users of items that might be of interest and which are currently occurring in the user vicinity.

Abstract: Architecture that detects emerging trends in realtime based on sampling from mobile devices. Realtime detection is obtained for events (e.g., entertainment, sporting, religious, etc.) and gatherings (e.g., groups of people), locations (e.g., geographical location of a gathering), for example. Time-based samples are obtained from each subscribing mobile device and then merged into a time-series of location data on which detection is performed. Trend rules are processed as part of the detection process to identify specific trends defined by the rules. Detected trends are announced to the subscribing consumers and are immediately made functionally available for consumption for any subscribing entity. A datastore stores the trend rules, which are updatable at any point in time and functional immediately after being updated and uploaded to the database. This applies equally to new rules uploaded to the database. Thus, detected trends are available for consumption in near realtime.

Abstract: Architecture that creates a geo-fence based on geo-tagged item (e.g., a photo. The geo-tagged item can be used to share virtual boundaries, such as geo-fences, between users via conventional methods (e.g., email) for sharing digital media. An extraction component that extracts geolocation information (e.g., latitude and longitude coordinates, altitude, bearing, distance, place names, etc.) of a geo-tagged item. The geolocation information can be related to a geographical location at which the item is geo-tagged. A boundary component then creates a virtual boundary (e.g., geo-fence) in association with the geographical location and based on the geolocation information. Thereafter, the virtual boundary is triggered when the user crosses (e.g., engages, intersects) the boundary and the attached action is triggered. The geo-tagged item can be shared with another user, which when is processed, creates a virtual boundary for that other user.

Abstract: A method of providing impressions of a mobile ad is provided in which a vendor may bid for impressions of the ad responsive to a distribution of a number of potential consumers of a given vendor service and/or product as a function of consumer location and probability of purchasing the given service or product.

Abstract: Architecture that manages a location guestbook by enabling a user to add a review based on actually having visited the location. The location of the user can be validated as part of ensuring that the user visited the location. If a user did not actually visit the location the user is not allowed to add a review in the guestbook. The architecture can also identify that a user has left the location (checked out), and hence, suggest to the user to add a review for the location. If a user has visited the location multiple times, the user review is given a higher weighting, and hence, can be considered more reliable.