Abstract: Artificial intelligence (AI) in the form of machine learning (ML) models may be used to aggregate and digest video game play factors to produce a report to game developers identifying appealing and unappealing parts of a video game.

Abstract: This disclosure describes techniques for providing communications between an access point and one or more stations. Such techniques may involve determining, by the access point, transmission data to be provided to a station in an uplink transmission, determining a transmission time to perform the uplink transmission over a wireless channel, determining a point in time at which to reserve the wireless channel, the point in time being an amount of time before the transmission time, generating a series of beacons, individual beacons of the series of beacons comprising the uplink transmission of the wireless channel, and initiating transmission of the series of beacons at the point in time.

Abstract: Techniques are provided for enabling sections of an audience viewing a massive display to interact with the display via one or more sensors associated with each spectator location, such as a seat. Machine learning may be used to fuse sensor signals into group-based control signals to interact with video being presented on the display.

Abstract: As understood herein, not all objects in a computer game are of equal priority. Given this recognition, present principles apply a range of spatial and temporal resolutions to various objects in the same game depending on the priority of the objects in the game.

Abstract: Technologies directed to providing event detection using Antennas as Sensors (A2S) and microphone signals are described. One method of operating a device includes receiving audio data corresponding to audio captured by at least one microphone of the device, and impedance data from an Antenna as Sensor (A2S) system of the device, the impedance data is digital data representing impedance changes of an antenna captured by the A2S system. The method determines, using the audio data and the impedance data and a machine learning (ML) model, a user input event representing a physical interaction event with the device. The method performs an action in response to the user input event.

Abstract: An image of a computer game player wearing a headset that occludes part of the face is input to a trained machine learning (ML) model, which outputs in response a full-face image that is not occluded for use in, e.g., social network settings related to the game.

Abstract: Application state data from a main memory may be compressed and the compressed data may be written to a first location in a mass storage. Updated application state data is generated, and the updated application state data is compressed from the main memory. The updated application state data is then written to a second location in the mass storage. Processing may then be paused on the application state data and updated application state data. The compressed application state data and compressed updated application state data stored in the mass storage is scanned and information corresponding to compressed application state data and updated compressed application state data stored in the mass storage is displayed using information from the scanned compressed application state data and compressed updated application state data.

Abstract: Application state data from a main memory may be compressed and the compressed data may be written to a first location in a mass storage. Updated application state data is generated, and the updated application state data is compressed from the main memory. The updated application state data is then written to a second location in the mass storage. Processing may then be paused on the application state data and updated application state data. The compressed application state data and compressed updated application state data stored in the mass storage is scanned and information corresponding to compressed application state data and updated compressed application state data stored in the mass storage is displayed using information from the scanned compressed application state data and compressed updated application state data.

Abstract: Technologies directed to antennas as sensors for touch and hover events are described. A wireless device can include a processing device with classification logic and a detection circuit located in a radio frequency (RF) path between a radio and an SPMT antenna. The wireless device can sample the analog voltage signal at a plurality of frequencies over a period of time to obtain digital data. The classification logic uses the digital data to classify one or more touch points caused by a presence of an object in proximity to the SPMT antenna over the period of time as a touch event or a gesture event. The processing device can perform an action in response to the touch event or gesture event.

Abstract: An image of a computer game player wearing a headset that occludes part of the face is input to a trained machine learning (ML) model, which outputs in response a full-face image that is not occluded for use in, e.g., social network settings related to the game.

Abstract: A computer-implemented method for performing natural language-based data integration includes causing execution of a data integration application on a remote device via a network and causing surfacing of a GUI corresponding to the data integration application on a display of the remote device. The method includes receiving, via the GUI, a natural language input representing a data integration task, generating, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input, and selecting, via the LLM, one or more APIs for performing each activity within the set of ordered activities. The method also includes generating a data pipeline based on the set of ordered activities and the API(s) for performing each activity, as well as back-translating the data pipeline to a desired data format for execution by the data integration application.

Abstract: A computer-implemented method for performing natural language-based data integration includes causing execution of a data integration application on a remote device via a network and causing surfacing of a GUI corresponding to the data integration application on a display of the remote device. The method includes receiving, via the GUI, a natural language input representing a data integration task, generating, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input, and selecting, via the LLM, one or more APIs for performing each activity within the set of ordered activities. The method also includes generating a data pipeline based on the set of ordered activities and the API(s) for performing each activity, as well as back-translating the data pipeline to a desired data format for execution by the data integration application.

Abstract: As understood herein, not all objects in a computer game are of equal priority. Given this recognition, present principles apply a range of spatial and temporal resolutions to various objects in the same game depending on the priority of the objects in the game.

Abstract: Techniques are described for translating computer game chat to a different language for a partner gamer. If the chat is determined to be related to a game being played as opposed to social chat, a glossary may be used tailored to that game to first determine if a spoken term is in the glossary. If it is, a lookup of an equivalent term from the glossary can be used instead of passing the term through a translation engine, increasing accuracy and saving translation time.

Abstract: Techniques are described for determining if chat between two computer gamers requiring translation is related to the game or is simple social chat. Depending on the type of chat, a machine learning (ML) model is selected to predict completion of the sentence being input by the speaker to reduce latency in translating and transmitting the chat.

Abstract: Techniques are described for over-training a ML model on multiple gameplay videos of individual scenes of a computer game to better configure the model to reconstruct or enhance portions of the computer game at a receiver as the computer game is received over a streamlining network. Reconstruction of individual missing slices of a frame is contemplated such that a frame missing a slice need not be entirely discarded.

Abstract: Techniques are described for reducing latency in networked gaming by reducing I-frame sizes (which also results in automatically increasing P-frame sizes) to reduce the overall amount of video being transmitted. The reduced size of the I-frames is compensated for by increasing the size of other frames using a low pass filter (LPF) such as a Gaussian filter which reduces sharpness that the decoder can try to recover, or by use of lower resolution. The I-frame can be reduced by rotating it or flipping/mirroring it to produce the smaller coded frame, sending a flag to signal the orientation.

Abstract: A computer-implemented method for performing natural language-based data integration includes causing execution of a data integration application on a remote device via a network and causing surfacing of a GUI corresponding to the data integration application on a display of the remote device. The method includes receiving, via the GUI, a natural language input representing a data integration task, generating, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input, and selecting, via the LLM, one or more APIs for performing each activity within the set of ordered activities. The method also includes generating a data pipeline based on the set of ordered activities and the API(s) for performing each activity, as well as back-translating the data pipeline to a desired data format for execution by the data integration application.

Abstract: A method, system, and computer readable medium for improved decoding CABAC encoded media are described. A decoded binary string is output using two or more previously decoded elements at an end state for a decoding loop and on a first processing thread. A syntax of the decoded binary string is parsed on a second processing thread and a decoded symbol from the parsed syntax is generated on the second processing thread.