Abstract: Techniques to leverage machine learning for search engine optimization (SEO). Some techniques are applicable to turnkey e-commerce solutions operating on the Internet as an online platform. These techniques use machine learning to gain insights into patterns/relationships between search terms and items for sale on the online platform. Depending on a relevancy or an accuracy of a search term to a particular item for sale, the online platform inserts the search term into the online platform such that a public search engine crawler increases a search engine result position for the online platform. Other embodiments are described and claimed.

Abstract: Aspects described herein may facilitate an automated trade-in of a vehicle with limited human interaction. A server may receive a request to begin a value determination of a vehicle associated with the user. The server may receive first data comprising: vehicle-specific identifying information, and multimedia content showing a first aspect of the vehicle. The user may be directed to place the vehicle within a predetermined staging area. The server may receive, from one or more image sensors associated with the staging area, second data comprising multimedia content showing a second aspect of the vehicle. The server may create a feature vector comprising the first data and the second data. The feature vector may be inputted into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle. Based on the machine learning algorithm, the server may determine a value of the vehicle.

Abstract: In certain embodiments, a vehicle may obtain a video stream via a camera system of the vehicle. The video stream may be processed to extract one or more feature vectors from the video stream. Based on the feature vectors extracted from the video stream, vehicle description information related to one or more parked vehicles in the parking area may be determined. Space information indicating locations of the parked vehicles relative to available parking spaces in the parking area may also be determined based on the feature vectors extracted from the video stream. A mapping of the parking area may then be generated based on the vehicle description information and the space information.

Abstract: Disclosed herein are system, method, and computer program product embodiments for requesting in-person assistance with a purchase during a mobile device digital interaction (e.g., augmented reality) with a physical object of interest. Display of a real-time view includes the physical object image as well as interfaces for interacting with the image of the physical object. The interfaces include requesting in-person purchase assistance based on a mobile device location proximate to the physical object of interest.

Abstract: The present disclosure relates to systems and methods for generating real-time quotes using machine learning algorithms. The system may include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: receiving an image of a vehicle from the client device, extracting one or more features from the image, based on the extracted features and using a machine learning algorithm, identifying one or more attributes of the vehicle, based on the identified attributes of the vehicle, determining a make and a model of the vehicle, obtaining comparison information based at least in part on the determined make and model, estimating a quote for the vehicle based on the comparison information; and transmitting the estimated quote for display on the client device.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for filtering vehicle information. For instance, the method may include receiving a vehicle search request from a user device, the vehicle search request including conditions with corresponding weights. The method may also include, in response to receiving the vehicle search request, forming a search query based on the conditions and the weights of the vehicle search request; obtaining vehicle information; filtering the vehicle information based on the search query to obtain a result set and a partial match set; and transmitting a search result message based on the result set and the partial match set to the user device.

Abstract: Computer-implemented techniques and systems for generating vehicle profiles may include scanning a plurality of vehicles, each vehicle located at a respective vehicle location, identifying respective vehicle locations for each of the plurality of vehicles, based on scanning the plurality of vehicles, associating a vehicle identification tag for each vehicle with its respective vehicle location, interpreting a signal to determine a user location, determining that the user location is within a threshold distance from a first vehicle location of a first vehicle from the plurality of vehicles, providing a first vehicle identification tag corresponding to the first vehicle based on determining that the user location is within the threshold distance, generating a first vehicle profile based on the first vehicle identification tag, and providing the first vehicle profile to a user device.

Abstract: A computer system receives a request to locate a vehicle within a facility. The computer system parses the request to identify one or more characteristics of the vehicle. The computer system identifies a sensor corresponding to the vehicle based on the one or more characteristics of the vehicle. The computer system identifies a most recent location of the vehicle based on the last registered position of the vehicle within the facility. The computer system transmits a signal to a parking spot sensor at the most recent location of the vehicle in the facility. The computer system receives the unique identification. The computer system determines that the unique identification of the vehicle sensor co-located with the parking spot sensor matches the sensor of the requested vehicle. The computer system reports to the user a current location of the vehicle within the facility based on a location of the parking spot sensor.

Abstract: An electronic device may include logic to provide a trust credential for linking to a permissioned network over a local network, comprising a plurality of user devices; retrieve a document in an initial status for editing, corresponding to a first state of a distributed ledger, maintained by the permissioned network; generate a first change in the document, wherein the document is in a second status; send, to the permissioned network via the local network, the first change for storage in the distributed ledger, in a second state; retrieve the document in a third status, including a second change, performed subsequently to the first change, the second change being generated by a user device, external to the electronic device, and coupled to the local network; and link to the distributed ledger to retrieve a third state of the distributed ledger, the third state comprising the first change and the second change.

Abstract: Embodiments are directed to systems, devices, methods, and techniques to determine words or word combinations of search engine queries and selected items corresponding to the words or word combinations. Embodiments also include applying a machine learning model to the word or word combinations and the selected items to determine a mapping between a word or word combination and a particular selected item, and updating attribute data in a web document with the word or word combination, wherein the web document is associated with the particular item such that a search including the word or word combination returns a result including the web document.

Abstract: A vehicle receives sensor data that includes image data of frames that depict one or more individuals outside of the vehicle, and identifies, by analyzing the sensor data using one or more attribute recognition techniques, a set of attributes of an individual of the one or more individuals. The vehicle determines a set of scores indicating a set of likelihoods of a set of vehicle configurations being a preferred vehicle configuration for the individual, based on a data model performing a machine-learning-driven analysis of attribute data identifying the set of attributes, and/or location data identifying a location of the individual relative to the vehicle. The vehicle selects a particular vehicle configuration based on a score that indicates a likelihood of the particular vehicle configuration being the preferred vehicle configuration and provides an instruction to cause a vehicle component to implement the particular vehicle configuration by updating a configurable value.

Abstract: Systems and methods for preventing machine learning models from negatively affecting mobile devices are provided. For example, a mobile device including a camera, memory devices, and one or more processors is provided. In some embodiments, the processors may be configured to provide images captured by the camera to a machine learning model at a first rate. The processors may also be configured to determine whether one or more of the images includes an object. If one or more of the images includes the object, the processors may be further configured to adjust the first rate of providing the images to the machine learning model to a second rate, and in some embodiments, determine whether to adjust the second rate of providing the images to the machine learning model to a third rate based on output received from the machine learning model.

Abstract: A signal interpretation system includes a communication device, a memory storing instructions, and one or more processors configured to execute the instructions to perform operations including: receiving scanned information including a shape of at least one article scanned with a scanning device to identify the at least one article and identifying a geographic location of the scanning device, when a scan of the at least one article was performed, based on a location signal received from the scanning device. The operations also include evaluating a signal indicative of interest in the identified at least one article and transmitting, via the communication device, a notification based on the evaluated signal, the notification being indicative of the identified at least one article and the identified geographic location, the notification being configured to cause an update in at least one graphical element presented by the scanning device or another device.

Abstract: A computer system receives a request to locate a vehicle within a facility. The computer system parses the request to identify one or more characteristics of the vehicle. The computer system identifies a sensor corresponding to the vehicle based on the one or more characteristics of the vehicle. The computer system identifies a most recent location of the vehicle based on the last registered position of the vehicle within the facility. The computer system transmits a signal to a parking spot sensor at the most recent location of the vehicle in the facility. The computer system receives the unique identification. The computer system determines that the unique identification of the vehicle sensor co-located with the parking spot sensor matches the sensor of the requested vehicle. The computer system reports to the user a current location of the vehicle within the facility based on a location of the parking spot sensor.

Abstract: Computer-implemented techniques and systems for generating vehicle profiles may include scanning a plurality of vehicles, each vehicle located at a respective vehicle location, identifying respective vehicle locations for each of the plurality of vehicles, based on scanning the plurality of vehicles, associating a vehicle identification tag for each vehicle with its respective vehicle location, interpreting a signal to determine a user location, determining that the user location is within a threshold distance from a first vehicle location of a first vehicle from the plurality of vehicles, providing a first vehicle identification tag corresponding to the first vehicle based on determining that the user location is within the threshold distance, generating a first vehicle profile based on the first vehicle identification tag, and providing the first vehicle profile to a user device.

Abstract: A device having extended reality capabilities may capture a video feed including one or more video frames depicting an object that is visible in a field of view of the device. The device may provide the one or more video frames to a machine learning model that identifies the object and one or more parts of the object that are depicted in the one or more video frames. The device may obtain positional tracking information that represents a position and an orientation associated with the object relative to a coordinate space that corresponds to the field of view of the device. The device may obtain a workflow including a sequence of content items for visually inspecting the object using the extended reality capabilities of the device. The device may render digital content associated with the workflow using the extended reality capabilities of the device.

Abstract: One or more embodiments are generally directed to techniques to provide specific vehicle recommendations. Various techniques, methods, systems, and apparatuses include utilizing user web-traffic and/or one or more tags generated by application of a machine learning model to a data source, where the data source may include language with respect to one or more automobiles or vehicles, to provide a recommendation for a particular automobile.

Abstract: The present disclosure relates to interactive projection systems. In one implementation, an interactive projection system for a vehicle window may include a film having a plurality of sensors embedded therein, the plurality of sensors configured to detect local pressure disturbance on the film; a projector configured to project a user interface onto the film; and at least one processor. The at least one processor may be configured to perform operations, including receiving a detection of a local pressure disturbance from at least one of the plurality of sensors, based on a location of the detected local pressure disturbance, determining a change in the user interface, and transmitting a command to the projector to modify a projected user interface according to the determined change.

Abstract: A computer-implemented method for using a machine-learning trained predictive engine to predict failures includes receiving electronic prior transaction data corresponding to a plurality of prior successful transactions and a plurality of prior unsuccessful transactions, and training a machine learning predictive engine based on the plurality of prior successful transactions and the plurality of prior unsuccessful transactions. Electronic transaction data may be received, the electronic transaction data being associated with a user, an item, and candidate transaction terms, the electronic transaction data being associated with a candidate transaction. The machine learning predictive engine may determine a likelihood of success of the candidate transaction based on the electronic transaction data, and display the likelihood of success of the candidate transaction.

Abstract: Systems and methods for providing a disposal recommendation for an owned vehicle are provided. The method includes generating a profile for the owned vehicle, the profile including age, mileage, and location of the owned vehicle, receiving monitoring data of the owned vehicle, and receiving sales data for non-owned vehicles corresponding to the profile. The method may further include generating predicted depreciation data of the owned vehicle, based on the sales data and the monitoring data, and providing a recommended disposal time for the owned vehicle, based on the predicted depreciation data.