Abstract: A system and method for training an AI model. A recorded video is divided into video frames that are input and read by a processor that identifies objects in the video frames using the object’s latent characteristics. The processor further classifies an event based on the identified object, the latent characteristics, and surrounding factors at the time the object is identified. Video frames are annotated based on the identified object and classified event. A user’s responses to annotated frames are tracked and the latent characteristics are adjusted based on the user’s responses.

Abstract: Apparatus and associated methods relate to identifying objects of interest and detecting motion to automatically detect a security threat as a function of video frame delta information received from a video encoder. In an illustrative example, the video encoder may be an H.264 encoder onboard a video camera. A cloud server may receive the video frame delta information in a compressed video stream from the camera. Threats may be detected by the cloud server processing the video frame delta information in the compressed video stream, without decompression, to identify objects and detect motion. The cloud server may employ artificial intelligence techniques to enhance threat detection by the cloud server. Various examples may advantageously provide increased capacity of a computer tasked with detecting security breaches, due to the significant reduction in the amount of data to be processed, relative to threat detection based on processing uncompressed video streams.

Abstract: Techniques are described related to selecting content items, such as by enabling user analysis and control of product-related content items selected for display to users. The content items may include advertisements or other promotional materials, and the selecting of the content items may be performed as part of determining particular promotional materials to display or otherwise present to particular users in particular situations. User analysis and control of selected content items that are displayed on a target electronic site may be enabled by providing, as part of the target electronic site, additional selection-related functionality whose availability is restricted to one or more authorized users—for example, such additional restricted access information and user-selectable controls may be provided on a version of a Web page of an online retailer to enable the retailer to analyze and influence future content items selected for display on the online retailer's Web page(s).

Abstract: Apparatus and associated methods relate to identifying objects of interest and detecting motion to automatically detect a security threat as a function of video frame delta information received from a video encoder. In an illustrative example, the video encoder may be an H.264 encoder onboard a video camera. A cloud server may receive the video frame delta information in a compressed video stream from the camera. Threats may be detected by the cloud server processing the video frame delta information in the compressed video stream, without decompression, to identify objects and detect motion. The cloud server may employ artificial intelligence techniques to enhance threat detection by the cloud server. Various examples may advantageously provide increased capacity of a computer tasked with detecting security breaches, due to the significant reduction in the amount of data to be processed, relative to threat detection based on processing uncompressed video streams.

Abstract: Apparatus and associated methods relate to transmitting video frames selected by a camera based on detected motion to a network hub configured with an artificial intelligence adapted to predict a region of interest within the selected video frames, configuring the camera with the region of interest predicted by the hub, and managing the energy consumption of the camera based on automatically governing camera operational parameters adapted as a function of the region of interest. In an illustrative example, the camera may be a battery-powered camera. In some embodiments, the camera may be configured in a low power mode with wireless interfaces off. In various implementations, the camera may be configured to detect motion within a region of interest and ignore motion elsewhere. Various examples may advantageously provide improved camera power management based on governing the camera operational parameters as a function of detected motion and the configured region of interest.

Abstract: Apparatus and associated methods relate to a network hub receiving from a video source video frames selected as a function of a video characteristic, determining a degree of interest of the selected video frames predicted as a function of artificial intelligence configured in the network hub, and automatically governing the video source operating parameters based on the degree of interest. In an illustrative example, the video source may be a camera. The video characteristic may be, for example, motion. In various implementations, video frames received by the network hub may be selected based on motion detection, and the video source may be, for example, a camera configured to detect motion in video frames captured by the camera. Various examples may advantageously provide improved camera power management, for example, using the degree of interest predicted by the artificial intelligence configured in the network hub to govern operational parameters of the camera.

Abstract: Techniques are described for determining personalized category-based product information for a user. The described techniques may in some situations be used by or on behalf of an online retailer to determine a sorted order of at least some products within a product category for the online retailer to display to a customer user on a category-specific Web page of the online retailer, such as based on a browse request by the customer for that category or on another identification of the category by the customer. The determination of particular personalized category-based sorted products for such a customer may in some situations be performed based at least in part on prior activities of the customer, including prior interactions by the customer with the online retailer (e.g., actions by the customer to view and/or purchase particular products, product brands, product sub-categories, product sizes, etc.).

Abstract: Apparatus and associated methods relate to identifying objects of interest and detecting motion to automatically detect a security threat as a function of video frame delta information received from a video encoder. In an illustrative example, the video encoder may be an H.264 encoder onboard a video camera. A cloud server may receive the video frame delta information in a compressed video stream from the camera. Threats may be detected by the cloud server processing the video frame delta information in the compressed video stream, without decompression, to identify objects and detect motion. The cloud server may employ artificial intelligence techniques to enhance threat detection by the cloud server. Various examples may advantageously provide increased capacity of a computer tasked with detecting security breaches, due to the significant reduction in the amount of data to be processed, relative to threat detection based on processing uncompressed video streams.

Abstract: A security device for monitoring the radio frequency signals generated by mobile phones and similar mobile computing and communication devices. The security device employs an antennae array and computer process that are configured to detect and provide a “fingerprint” for a mobile device based on the unique identifiers contained with the radio and other wireless signals utilized by such mobile device. The “fingerprint” that is obtained can be used to keep track of mobile devices as those devices enter and leave the area of the security device. Moreover, the security device can provide an alert when any new, foreign, or otherwise unrecognized device is within range of the security device and share “fingerprints” and alerts with other security devices in its network.

Abstract: A security device for monitoring the radio frequency signals generated by mobile phones and similar mobile computing and communication devices. The security device employs an antennae array and computer process that are configured to detect and provide a “fingerprint” for a mobile device based on the unique identifiers contained with the radio and other wireless signals utilized by such mobile device. The “fingerprint” that is obtained can be used to keep track of mobile devices as those devices enter and leave the area of the security device. Moreover, the security device can provide an alert when any new, foreign, or otherwise unrecognized device is within range of the security device and share “fingerprints” and alerts with other security devices in its network.

Abstract: Apparatus and associated methods relate to training a neural network on a first host system, sending the neural network to a second host system, training the neural network by the second host system based on data private to the second host system, and employing the neural network to filter events sent to the first host system. In an illustrative example, the first host system may be a server having a central repository including trained neural networks and historical data, and the second host system may be a remote server having a data source private to the remote server. The private remote data source may be a camera. In some examples, events may be filtered as a function of a prediction of error in the neural network. Various examples may advantageously provide remote intelligent filtering. For example, remote data may remain private while adaptively filtering events to the central server.

Abstract: Apparatus and associated methods relate to identifying objects of interest and detecting motion to automatically detect a security threat as a function of video frame delta information received from a video encoder. In an illustrative example, the video encoder may be an H.264 encoder onboard a video camera. A cloud server may receive the video frame delta information in a compressed video stream from the camera. Threats may be detected by the cloud server processing the video frame delta information in the compressed video stream, without decompression, to identify objects and detect motion. The cloud server may employ artificial intelligence techniques to enhance threat detection by the cloud server. Various examples may advantageously provide increased capacity of a computer tasked with detecting security breaches, due to the significant reduction in the amount of data to be processed, relative to threat detection based on processing uncompressed video streams.

Abstract: A video security system and method for monitoring active environments that detects a security-relevant breach of a virtual perimeter and can track a virtual perimeter breaching object to detect risk-relevant behavior of persons and objects such as loitering and parking, and provides fast and accurate alerts. The system is able to achieve advance alerts by monitoring an extended virtual perimeter. The image processing module of the system employs a deep learning neural network (DNN) for fast image processing. The system can further increase speed by reducing the image data that is being processed to data extracted from one or more reduced data sources including virtual perimeter zones, a delta of a series of image frames, and a representative image frame of a series of frames.

Abstract: A security device for monitoring the radio frequency signals generated by mobile phones and similar mobile computing and communication devices. The security device employs an antennae array and computer process that are configured to detect and provide a “fingerprint” for a mobile device based on the unique identifiers contained with the radio and other wireless signals utilized by such mobile device. The “fingerprint” that is obtained can be used to keep track of mobile devices as those devices enter and leave the area of the security device. Moreover, the security device can provide an alert when any new, foreign, or otherwise unrecognized device is within range of the security device and share “fingerprints” and alerts with other security devices in its network.

Abstract: A security device for monitoring the radio frequency signals generated by mobile phones and similar mobile computing and communication devices. The security device employs an antennae array and computer process that are configured to detect and provide a “fingerprint” for a mobile device based on the unique identifiers contained with the radio and other wireless signals utilized by such mobile device. The “fingerprint” that is obtained can be used to keep track of mobile devices as those devices enter and leave the area of the security device. Moreover, the security device can provide an alert when any new, foreign, or otherwise unrecognized device is within range of the security device and share “fingerprints” and alerts with other security devices in its network.

Abstract: Techniques are described for dynamically generating recommendations for users, such as for products and other items. In at least some situations, the techniques include using multiple recommendation strategies, such as by aggregating recommendation results from multiple different recommendation strategies. Such recommendation strategies may have various forms, and may be based at least in part on data regarding prior interactions of numerous users with numerous items. In addition, information about current selections of a particular user may be gathered based at least in part on providing a GUI (“graphical user interface”) for display to the user that includes selectable information about numerous recommended items, and dynamically updating the displayed GUI with newly generated recommendations of items as the user makes selections of particular displayed recommended items (e.g.

Abstract: Techniques are described for dynamically generating recommendations for users, such as for products and other items. In at least some situations, the techniques include using multiple recommendation strategies, such as by aggregating recommendation results from multiple different recommendation strategies. Such recommendation strategies may have various forms, and may be based at least in part on data regarding prior interactions of numerous users with numerous items. In addition, information about current selections of a particular user may be gathered based at least in part on providing a GUI (“graphical user interface”) for display to the user that includes selectable information about numerous recommended items, and dynamically updating the displayed GUI with newly generated recommendations of items as the user makes selections of particular displayed recommended items (e.g.

Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.

Abstract: Techniques are described for dynamically generating recommendations for users, such as for products and other items. In at least some situations, the techniques include using multiple recommendation strategies, such as by aggregating recommendation results from multiple different recommendation strategies. Such recommendation strategies may have various forms, and may be based at least in part on data regarding prior interactions of numerous users with numerous items. In addition, information about current selections of a particular user may be gathered based at least in part on providing a GUI (“graphical user interface”) for display to the user that includes selectable information about numerous recommended items, and dynamically updating the displayed GUI with newly generated recommendations of items as the user makes selections of particular displayed recommended items (e.g.

Abstract: Techniques are described for dynamically generating recommendations for users, such as for products and other items. In at least some situations, the techniques include using multiple recommendation strategies, such as by aggregating recommendation results from multiple different recommendation strategies. Such recommendation strategies may have various forms, and may be based at least in part on data regarding prior interactions of numerous users with numerous items. In addition, information about current selections of a particular user may be gathered based at least in part on providing a GUI (“graphical user interface”) for display to the user that includes selectable information about numerous recommended items, and dynamically updating the displayed GUI with newly generated recommendations of items as the user makes selections of particular displayed recommended items (e.g.