Abstract: Machine-learned prediction of a blame frame of a crash stack. Specifically, a crash stack associated with a crash is parsed into a sequence of frames. The blame frame of the crash stack is estimated by, for each of a plurality of the sequence of frames, identifying a plurality of features of the corresponding frame, feeding the plurality of features to a neural network, and using the output of the neural network to make a prediction on whether the corresponding frame is a blame frame of the crash. If this is done during training time, the predicted blame frame can be compared against the actual blame frame, resulting in an adjustment of the neural network. Through appropriate featurization of the frames, and by use of the neural network, the prediction can be made cross-application and considering the context of the frame within the crash stack.

Abstract: Machine-learned prediction of a blame frame of a crash stack. Specifically, a crash stack associated with a crash is parsed into a sequence of frames. The blame frame of the crash stack is estimated by, for each of a plurality of the sequence of frames, identifying a plurality of features of the corresponding frame, feeding the plurality of features to a neural network, and using the output of the neural network to make a prediction on whether the corresponding frame is a blame frame of the crash. If this is done during training time, the predicted blame frame can be compared against the actual blame frame, resulting in an adjustment of the neural network. Through appropriate featurization of the frames, and by use of the neural network, the prediction can be made cross-application and considering the context of the frame within the crash stack.

Abstract: Systems and methods for extracting and sharing application-related user data are disclosed. A method may include extracting in-app data for at least one of the plurality of apps running on a computing device, the in-app data including content consumed by a user while the at least one app is running, and/or at least one user action taken in connection with the content. Using an entity template associated with the app, a plurality of text strings within the in-app data are classified into at least one of a plurality of data types specified by the template. At least one user data item (UDI) may be generated by combining at least a portion of the classified plurality of text strings, the at least one UDI being accessible by a second app, an operating system running on the, a service of the operating system, and/or a service running on at least another device.

Abstract: Various technologies described herein pertain to prefetching content units. A prefetch request is transmitted to a server from a client device. The prefetch request includes data indicative of probabilities of slots for content units being available during an upcoming time period. The probabilities can be based on likely interaction with application(s) executed by the client device during the upcoming time period. Prefetched content units assigned to the client device for the upcoming time period can be received from the server responsive to the prefetch request. One or more of the prefetched content units can be served for display on a display screen of the client device during execution the application(s). Further, statuses of the prefetched content units can be monitored, and information that specifies a subset of the prefetched content units that are unlikely to be displayed on the display screen prior to corresponding deadlines for expiration can be transmitted.

Abstract: System and methods are provided for optimal error detection in programmatic environments through the utilization of at least one user-defined condition. Illustratively, the conditions can include one or more triggers initiating the collection of log data for methods associated with the provided at least one condition. Operatively, the disclosed systems and methods observe the run-time of the programmatic environment and initiate the collection of log data based on the occurrence of a condition trigger. A rank score can also be calculated to rank the methods associated with the defined condition to isolate those methods that have higher probability of causing the defined condition. Dynamic instrumentation of the methods associated with the user defined conditions during run time are used to calculate the rank score, which is used for ranking the methods.

Abstract: Various technologies described herein pertain to prefetching content units. A prefetch request is transmitted to a server from a client device. The prefetch request includes data indicative of probabilities of slots for content units being available during an upcoming time period. The probabilities can be based on likely interaction with application(s) executed by the client device during the upcoming time period. Prefetched content units assigned to the client device for the upcoming time period can be received from the server responsive to the prefetch request. One or more of the prefetched content units can be served for display on a display screen of the client device during execution the application(s). Further, statuses of the prefetched content units can be monitored, and information that specifies a subset of the prefetched content units that are unlikely to be displayed on the display screen prior to corresponding deadlines for expiration can be transmitted.

Abstract: Systems and methods for extracting and sharing application-related user data are disclosed. A method may include extracting in-app data for at least one of the plurality of apps running on a computing device, the in-app data including content consumed by a user while the at least one app is running, and/or at least one user action taken in connection with the content. Using an entity template associated with the app, a plurality of text strings within the in-app data are classified into at least one of a plurality of data types specified by the template. At least one user data item (UDI) may be generated by combining at least a portion of the classified plurality of text strings, the at least one UDI being accessible by a second app, an operating system running on the, a service of the operating system, and/or a service running on at least another device.

Abstract: Various technologies described herein pertain to allocating content units to client devices. Content units can be requested from an exchange by a server. The content units can be received by the server from the exchange, where the content units each has a corresponding deadline for expiration. Further, an indication of a penalty can be transmitted to the exchange if one of the content units is not displayed by one or more client devices upon expiration of the corresponding deadline.

Abstract: Systems and methods for extracting and sharing application-related user data are disclosed. A method may include extracting in-app data for at least one of the plurality of apps running on a computing device, the in-app data including content consumed by a user while the at least one app is running, and/or at least one user action taken in connection with the content. Using an entity template associated with the app, a plurality of text strings within the in-app data are classified into at least one of a plurality of data types specified by the template. At least one user data item (UDI) may be generated by combining at least a portion of the classified plurality of text strings, the at least one UDI being accessible by a second app, an operating system running on the, a service of the operating system, and/or a service running on at least another device.

Abstract: Various technologies described herein pertain to prefetching content units. A prefetch request is transmitted to a server from a client device. The prefetch request includes data indicative of probabilities of slots for content units being available during an upcoming time period. The probabilities can be based on likely interaction with application(s) executed by the client device during the upcoming time period. Prefetched content units assigned to the client device for the upcoming time period can be received from the server responsive to the prefetch request. One or more of the prefetched content units can be served for display on a display screen of the client device during execution the application(s). Further, statuses of the prefetched content units can be monitored, and information that specifies a subset of the prefetched content units that are unlikely to be displayed on the display screen prior to corresponding deadlines for expiration can be transmitted.

Abstract: Techniques are described for validating stateful app links. Validation can be performed when stateful app links are created, activated, shared, or at other times. Validation can be performed to determine whether a stateful app link has a dependency on a resource external to the mobile application. Validation can also be performed to detect other issues, such as security issues, privacy issues, or other issues.

Abstract: System and methods are provided for optimal error detection in programmatic environments through the utilization of at least one user-defined condition. Illustratively, the conditions can include one or more triggers initiating the collection of log data for methods associated with the provided at least one condition. Operatively, the disclosed systems and methods observe the run-time of the programmatic environment and initiate the collection of log data based on the occurrence of a condition trigger. A rank score can also be calculated to rank the methods associated with the defined condition to isolate those methods that have higher probability of causing the defined condition. Dynamic instrumentation of the methods associated with the user defined conditions during run time are used to calculate the rank score, which is used for ranking the methods.

Abstract: The deep application crawling technique described herein crawls one or more applications, commonly referred to as “apps”, in order to extract information inside of them. This can involve crawling and extracting static data that are embedded within apps or resource files that are associated with the apps. The technique can also crawl and extract dynamic data that apps download from the Internet or display to the user on demand, in order to extract data. This extracted static and/or data can then be used by another application or an engine to perform various functions. For example, the technique can use the extracted data to provide search results in response to a user query entered into a search engine. Alternately, the extracted static and/or dynamic data can be used by an advertisement engine to select application-specific advertisements. Or the data can be used by a recommendation engine to make recommendations for goods/services.

Abstract: Operations of a variety of components of a storage system stack are redefined to make the system more efficient when the underlying media has a “multi-log” type interface such as the case with NAND flash SSD memory or shingled magnetic recording media. The responsibilities of components of the storage system stack are modified such that each responsibility is performed at the most efficient component (level of abstraction) of the storage stack.

Abstract: Examples of the disclosure enable a plurality of applications to be managed. Some examples include a mobile device that receives a template associated with an application, captures one or more user interactions with the application, and determines one or more user interests by comparing the one or more user interactions with a plurality of entities included in the template. Examples of the disclosure enable the mobile device to analyze the user interactions to provide valuable information about user interest, preference, and/or intent and provide tailored services to a user.

Abstract: Identifying products in a physical store shopping environment. The method includes, using a first detection method, identifying that a given product likely belongs to a given set of products. The method further includes, using one or more other detection methods, determining that the product is likely a specific product from the given set of products.

Abstract: Operations of a variety of components of a storage system stack are redefined to make the system more efficient when the underlying media has a “multi-log” type interface such as the case with NAND flash SSD memory or shingled magnetic recording media. The responsibilities of components of the storage system stack are modified such that each responsibility is performed at the most efficient component (level of abstraction) of the storage stack.

Abstract: Identifying products in a physical store shopping environment. The method includes, using a first detection method, identifying that a given product likely belongs to a given set of products. The method further includes, using one or more other detection methods, determining that the product is likely a specific product from the given set of products.

Abstract: Providing product recommendations in a physical retail store. A method includes detecting that the user arrives at the physical retail store. The method further includes, in response, receiving information from a recommendation server for a particular user. The method further includes storing locally, the information from the recommendation server. The method further includes, detecting a plurality of user interactions for the user with products in the retail store as part of the shopping experience and prior to a check-out phase of the shopping experience. The method further includes based on the locally stored information and the user interaction, providing product recommendations.

Abstract: Techniques are described for dynamically generating stateful app links. For example, page launcher events can be intercepted during user interaction with a mobile application. During the interaction, a request can be received to dynamically generate a stateful app link to a current page of the mobile application. In response to the request, a stateful app link to the current page can be created. The stateful app link can then be output. When the stateful app link is activated later, it returns to the current page of the mobile application. In some implementations, user interface events are also captured and used when creating the stateful app link.