Abstract: A system configured to perform content recognition using fingerprinting to recognize known media content. A device determines fingerprints based on decoded content data to be sent using a media interface component to an output component. Metadata related to the content/device/fingerprint may also be created. The fingerprints and metadata are sent by the device to a supporting system for orchestration and matching of the fingerprints to known media content.

Abstract: A system configured to perform content identification using fingerprinting to recognize known media content. The system may generate a reference database including reference fingerprints for each media content item to include in the content identification. In addition, the system may generate a hash table that associates individual frames of the reference fingerprints with identification information for corresponding media content items. When a device is playing media content, the system may perform content identification by generating query fingerprints representing the media content and comparing the query fingerprints to the reference database. For example, the system may match a query fingerprint to a reference fingerprint by identifying which of the reference fingerprints shares the most frames with the query fingerprint using the hash table. In addition, the system may use additional decision criteria to confirm a match, such as fine-grain matching or tracking successive fingerprints over time.

Abstract: Techniques for training a machine learning model to generate life-cycle classifications for product-store pairs are disclosed. A system generates training data sets for training a machine learning model by comparing sets of time-series data to a set of feature-based rules mapped to life-cycle labels. The system trains the machine learning model using the training data sets to classify time-series data associated with product-store pairs. The system applies the trained machine learning model to a particular set of time-series data for a particular product-store pair, such as sales data for a particular product at a particular store. The machine-learning model generates a life-cycle classification for the set of time-series data and the corresponding product-store pair. The system selects a forecasting model to forecast attributes of the product-store pair based on the life-cycle classification.

Abstract: A device is configured to detect multiple different wakewords. A device may operate a joint encoder that operates on audio data to determine encoded audio data. The device may operate multiple different decoders which process the encoded audio data to determine if a wakeword is detected. Each decoder may correspond to a different wakeword. The decoders may use fewer computing resources than the joint encoder, allowing for the device to more easily perform multiple wakeword processing. Enabling/disabling wakeword(s) may involve the reconfiguring of a wakeword detector to add/remove data for respective decoder(s).

Abstract: A system that generates a consumer decision tree receives retail item transactional sales data. The system aggregates the sales data to an item/store/time duration level and aggregates the sales data to an attribute-value/store/time duration level. The system determines sales shares for the time duration and determines similarities for attribute-value pairs based on correlations between attribute-value pairs. The system then determines a most significant attribute based on the determined similarities.

Abstract: A system that determines promotional pricing for a product and for an objective function receives a non-linear time-dependent optimization problem for the product, where the non-linear problem includes a demand model and a plurality of constraints, and the constraints include a price ladder that includes a plurality of time periods and a non-promotional price for the product at each time period. For each of the time periods, the system determines a change in the objective function when the price at that time period includes a promotional price and all other prices on the price ladder are set to the non-promotional price to generate coefficients. The system determines a maximum value of the coefficients at each time period, and generates an approximate Mixed Integer Programming (“MIP”) problem based on the coefficients. The system determines a Linear Programming (“LP”) relaxation of the MIP problem, and solves the LP relaxation.

Abstract: Techniques for enabling inter-subsystem resource sharing are provided. A subsystem executing an application (“app subsystem”) receives resources from another subsystem providing a resource (“resource subsystem”), such as a DLL. Then, when an application of the app subsystem is executed, the application may request a resource from the app subsystem, such as a DLL. The app subsystem, upon determining that the requested resource is associated with the resource subsystem, may communicate with the resource subsystem to request the resource. The resource subsystem may then load the resource. When the application utilizes the resource to perform an activity, such as by invoking a method of the DLL, the app subsystem may communicate with the resource subsystem to request the resource to perform the activity. The resource subsystem may provide an indication of a result to the resource subsystem upon completing the activity.

Abstract: Techniques for enabling inter-subsystem resource sharing are provided. The techniques include providing an RPC client on a first subsystem for acting as a proxy for a resource of a second subsystem. When the first subsystem receives a request from an application to access the resource, the RPC client may invoke a function of an RPC server associated with the second subsystem. The RPC server may load the resource on the second subsystem. The RPC client may then act as a proxy for the resource.