Abstract: Disclosed embodiments may include a method for intelligent bookout discrepancy detection. The system may receive bookout data from an automotive dealer, which may include dealer-provided vehicle information that includes a dealer price and dealer-listed features. The system may identify a vehicle identification number from the dealer-provided vehicle information. The system may retrieve third-party vehicle information including at least one of a third-party price and third-party listed features from one or more third party sources using the vehicle identification number. The system may aggregate the third-party vehicle information from the one or more third party sources. The system may determine that a bookout discrepancy exists by comparing the dealer price to the third-party price and comparing dealer-listed features to third-party listed features. The system may flag a contract associated with the bookout data for further review.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing irrelevant alerts and/or network bandwidth usage from Internet of Things (IoT) cameras through the intelligent filtering out of video data. An example embodiment operates by receiving video data from an IoT camera. The embodiment further generates an ignore mask based on the video data, wherein the ignore mask specifies one or more regions in a video stream from the IoT camera that cannot trigger an alert. The embodiment then transmits the ignore mask to the IoT camera. The embodiment then receives the video stream from the IoT camera in response to transmitting the ignore mask to the IoT camera. The embodiment then determines that the video stream is associated with the alert. The embodiment may then transmit the alert to a user device in response to the determining.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for sharing video streams in a camera system network. The video streams may be modified and/or masked to obscure private video stream features from being shared. An example embodiment operates by linking a first camera system to a first user device. The first camera system detects a second camera system in its field of view, wherein the second camera system is linked to a second user device. Upon exchanging requests to share video streams, the first and second user devices may view the video streams from both camera systems. The video streams may be modified to mask and/or obscure private property and/or known persons to preserve privacy despite sharing video streams. For example, the first camera system may modify and stream a masked video stream to the second user device.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for loading one or more machine learning models into a camera system to detect features of a video stream. An example embodiment operates by downloading a machine learning model from an application exchange service. The machine learning model may be pre-trained prior to loading and/or be trained to identify particular features. The camera system may install and/or retrain loaded machine learning models using captured images and/or user inputs. The camera system may also detect an unknown feature and obtain a classification label from an external system. Upon detecting a feature and/or an unknown feature, the camera system may transmit a camera detection notification to a user device and/or allow the user device to view the video stream.

Abstract: Validating operations are described that determines a first difference between a first data set and a second data set, determines an action associated with the first difference, applies the action to at least one of the first data set and the second data set, determines a second difference between the first data set and the second dataset after applying the action and presents the information including the action applied to at least one of the first data set and the second data set after determining that the second difference satisfies predetermined criteria.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: In some embodiments, systems and methods are provided to recognize products in a physical facility through a portable device that comprises a decision control circuit configured to: process each frame of the subset of frames by multiple modeling techniques each relative to a corresponding image attribute and obtain a corresponding product identification probability; determine corresponding aggregated identification probabilities of the first product based on the product identification probabilities; collectively evaluate the aggregated identification probabilities and identify when a predefined relationship with a collective threshold probability exists; and cause an image of the first product to be displayed in response to identifying that one or more of the aggregated identification probabilities having the predefined relationship with the collective threshold probability.

Abstract: In one or more embodiments, method, system and computer program product for providing secure access to device data and/or device operations by an application are disclosed. The method for providing secure access to one or more devices by an application includes receiving application information for the application; receiving device information for the one or more devices to which the application is requesting access; receiving rules for allowing the application to access the one or more devices, wherein the access to the one or more devices includes device data, one or more device operations or a combination thereof; and allowing the application to access the device based on the rules.

Abstract: A low-profile track system includes low-profile joiner components or devices that releasably couple and electrically connect track segments together. In one example, the joiner device includes a protruding portion configured to fit within corresponding cavity of a receptacle at the end of a track segment. To maintain a low profile, the joiner device can include joiner contacts that exert an outward lateral force that engages with corresponding receptacle contacts disposed in the cavity of the receptacle. The joiner device further includes a secondary or complimentary fastener or connector associated with the protruding portion that releasably engages a corresponding fastener disposed in the cavity of the receptacle, where the complimentary fastener and exerts a force in a direction orthogonal to the outward lateral force.

Abstract: Methods and systems are described herein for transferring control of vehicle information. A transfer system may be used to facilitate the transfer. The system may receive a first request for transferring an association of a vehicle from a first user to a second user. The transfer system may generate a first blockchain operation for transferring control of the cryptographic token from a first to second cryptography-based storage application associated with the first user and second user, respectively, wherein the first blockchain operation comprises the second address associated with the second cryptography-based storage application. The system may transmit the first blockchain operation for transferring control of the cryptographic token to the second cryptography-based storage application. In response to receiving an indication of successful blockchain operation from a blockchain node, the system may transmit a command to update first user identification data stored at a link.

Abstract: In some implementations, a system may receive, from an augmented reality (AR) device, a set of images captured during an AR session. The system may detect a plurality of features included in the set of images. The system may identify an item, from a list of items, that corresponds to the object based on at least one feature of the plurality of features. The list of items may identify one or more characteristics that are expected to correspond to the plurality of features. The system may identify one or more noncorresponding characteristics of the item based on determining that the one or more characteristics do not correspond to the plurality of features. The system may determine a noncorresponding value associated with the one or more noncorresponding characteristics and may provide an indication of the noncorresponding value associated with the one or more noncorresponding characteristics.

Abstract: In some implementations, a recommendation system may receive information related to a browser context associated with the client device. The recommendation system may generate a vehicle feature vector that includes an array of elements to represent a plurality of vehicle attributes. The recommendation system may apply a similarity model to the vehicle feature vector to determine a vehicle recommendation dataset that includes a plurality of vehicles that are each associated with a respective set of vehicle attributes. The recommendation system may filter the vehicle recommendation dataset based on a subset of the information related to the browser context associated with the client device that indicates a profile of a user associated with the client device. The recommendation system may provide information related to the vehicle recommendation dataset to the client device for display in an interface of the client device.

Abstract: Systems, methods, apparatuses, and computer program products for unified data repository selection and parameters that can be associated with such selection are provided. For example, a method can include identifying, by a network element, a capability of a repository to store data applicable to all user equipment devices in a network. The method can further include selecting, by the network element, the repository from a plurality of repositories based on the identified capability.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: In one example embodiment, a computer-implemented method and system for immobilization of machines capable of moving are disclosed. The method for immobilization of machines capable of moving includes detecting an event by a tracking device; providing one or more rules to evaluate the detected event; evaluating the detected event using the one or more rules; and triggering an action type based on the outcome of the evaluation. The system for immobilization of machines capable of moving includes a machine capable of moving, a tracking device including at least one processor and logic, a starter relay and a fuel pump relay, wherein the tracking device detects an event; evaluates the detected event using one or more rules provided to evaluate the detected event; and triggers an action type based on the outcome of the evaluation.

Abstract: In some embodiments, systems and methods are provided to recognize retail products, comprising: a model training system configured to: identify a customer; access an associated customer profile; access and apply a set of filtering rules to a product database based on customer data; generate a listing of products specific to the customer; access and apply a model training set of rules to train a machine learning model based on the listing of products and corresponding image data for each of the products in the listing of products; and communicate the trained model to the portable user device associated with first customer.

Abstract: Some embodiments provide retail inventory identification systems, comprising: a mobile product mapping system comprising: a transport system, a tower housing, and a cluster processing system comprising: a plurality of product identifying (PI) systems each in different orientations and directed along different fields of view, and a mobile control circuit; wherein the PI systems each comprise an image capture system, and a location determination system, wherein each of the plurality of PI systems is configured to: capture images of products supported on product support systems; and locally process the images comprising applying a plurality of different machine learning modeling techniques, to infer identification of multiple products and generate product identifying information in association with location information; and the mobile control circuit is configured to generate product identifying mapping of products as the product mapping system moves; and communicate the generated product identifying mapping to

Abstract: In one aspect, the present disclosure relates to a system for improved transactional mainframes, the system comprising a processor, a database, an application programming interface (API) module, and a transaction import module. The API module can be configured to: receive transaction data comprising a transaction type, a user identifier, one or more transaction card identifiers, and one or more fields associated with the transaction card identifiers; validate the transaction data using one or more of the stored validation rules associated with the one or more transaction card identifiers; generate one or more transaction cards; and store the transaction data and transaction cards to the database. The transaction import module can be configured to submit the transaction cards to the mainframe, view the status of submitted records, and enable retrying of transaction cards that failed to process.

Abstract: In some embodiments, systems and methods are provided to recognize products in a physical facility through a portable device that comprises a decision control circuit configured to: process each frame of the subset of frames by multiple modeling techniques each relative to a corresponding image attribute and obtain a corresponding product identification probability; determine corresponding aggregated identification probabilities of the first product based on the product identification probabilities; collectively evaluate the aggregated identification probabilities and identify when a predefined relationship with a collective threshold probability exists; and cause an image of the first product to be displayed in response to identifying that one or more of the aggregated identification probabilities having the predefined relationship with the collective threshold probability.