Abstract: A system and method of vehicle parts to effect a repair on a vehicle is provided. The parts selected for a repair are determined based upon characteristics associated with a client, owner of the vehicle, of a service center. The parts are selected based upon the characteristics in comparison to metrics associated with the parts. The parts that are selected by the client for the repair can then be utilized to generate part performance metrics and also be associated with vehicles on which they were utilized. The selection and identification of parts can be geographically based to account for environmental conditions which can impact performance of a particular part.

Abstract: A method of providing a custom formulated product, including skincare products, is described. A user may enter user skincare data and a user skin profile may be generated from this data. The user skin profile may be mapped to skin matrix factors of a user skin profile matrix to derive a skin profile identifier. The skin profile identifier may be used to select ingredients to manufacture a custom formulated skin product, specific to the skin issues that the user identified. The custom formulated product can then be distributed directly to the user.

Abstract: An online concierge system uses a domain-adaptive suggestion module to score products that may be presented to a user as suggestions in response to a user's search query. The domain-adaptive suggestion module receives data that is relevant to scoring products as suggestions in response to a search query. The domain-adaptive suggestion module uses one or more domain-neutral representation models to generate a domain-neutral representation of the received data. The domain-neutral representation is a featurized representation of the received data that can be used by machine-learning models in the search domain or the suggestion domain. The domain-adaptive suggestion module then scores products by applying one or more machine-learning models to domain-neutral representations generated based on those products. By using domain-neutral representations, the domain-adaptive suggestion module can be trained based on training examples from a similar prediction task in a different domain.

Abstract: A computer-implemented system for managing online, event-driven asset network events is disclosed. The system presents role-specific electronic dashboards to different parties via a web application. Prior to a scheduled event, a user selection and associated value are captured and stored within an immutable executable file containing asset attributes and computer-readable instructions. The file is stored in both a system database and on the user device to prevent unauthorized modification. Upon detecting commencement of the scheduled event, the system automatically executes the file, submitting the instructed value across connected platforms and confirming completion of the network operation in accordance with predefined transfer rules. Integrated synchronization and external data interfaces provide real-time updates, secure recordkeeping, and coordinated process management for all stakeholders.

Abstract: Recommendations are generated for users by identifying items held by the users defined by a shallow representation and attributes; defining the items based on a deep representation derived from attributes; generating a deep holding matrix identifying the items held by the users based on deep representations; generating a shallow holding matrix identifying the items held by the users based on shallow representations; generating a similarity score matrix between the deep representations; decomposing the shallow holding matrix into a user latent representation and a product feature latent representation; concatenating the product feature latent representation and product information and pass to a first neural network; concatenating the user latent representation and user information and pass to a second neural network; performing a dot product matrix multiplication on the output of the first neural and the output of the second neural network to generate, for every user and every product, a probability.

Abstract: Disclosed herein are embodiments for processing an information screen to enable filtering of another information screen. Embodiments operate by identifying a UI element code in the information screen based on a parameter data structure. The embodiments request a set of filter responses from a database based on the parameter data structure. The embodiments generate a set of UI elements that are each configured to perform a first action performed by the UI element code. Each UI element is further configured to perform a second action comprising filtering the other information screen using one of the set of filter responses. The embodiments then present the information screen with the generated set of UI elements to enable filtering of the other information screen.

Abstract: A system includes a computing device including a processor communicatively coupled to a camera. The computing device is configured to, in response to receiving a request, capture an image via the camera and detect, within the image, a first plurality of locations of a first object. The computing device is further configured to segment the first plurality of locations of the first object by determining, for each location of the first plurality of locations, a likelihood the corresponding location includes a part of the first object. The computing device is configured to inpaint a second plurality of locations with an associated likelihood the corresponding location includes the part of the first object above a threshold value. The computing device is additionally configured to generate an augmented image by superimposing a selected second object over the image and display the augmented image on a user interface of the computing device.

Abstract: The disclosed technology includes a computer-implemented technique for predicting or estimating physical attributes of products depicted in images is disclosed. The method includes receiving one or more images—either photographs of real-world products or synthetic images generated from user-provided descriptions—processing the images using one or more machine learning (ML) models trained to detect and measure physical characteristics, and predicting or estimating measures such as volume, weight, and dimensions. The system can identify product categories, select appropriate ML models, and generate natural language descriptions of predicted attributes for user display. Training datasets may include both real and synthetic images with known attributes, and user feedback can be incorporated to improve model accuracy.

Abstract: The present disclosure relates to systems and methods for improved vehicle transaction platforms. An example method may include receiving, by a vehicle transaction platform, a search request for vehicles from a user device associated with a first user account. The example method may also include receiving identifying information associated with the user account, wherein at least a portion of the identifying information is received from a third party. The example method may also include identifying, based on the identifying information, a predetermined search pattern, wherein the predetermined search pattern was generated based on a prior search request associated with a second user account and identifying information associated with the second user account.

Abstract: The integration of preparation and production of the product, labeling, payment, and storage of individual recipes and name is facilitated under a principal brand and shipped directly to the customer. The selected product is made according to customer specific ingredient selections. The selected product may be a personalized or private-label branded cosmetic where the branded packaging or packaging label is similarly contemporaneously created. The automated manufacturing cell receives each relevant specific selected formulation ingredients are combined and filled into the selected the private labeled branded product packaging to create the finished product.

Abstract: The disclosed technology includes a computer-implemented technique for generating images of synthetic products that are manufacturable as physical products is disclosed. The system receives user input describing a conceptual product via a user interface, including text and/or image data. Based on the input, a manufacturer-specific or general generative model is selected and provided with a text-based prompt derived from the conceptual product description. The model generates one or more images of synthetic products, each conforming to manufacturing constraints. The images are presented to the user, each optionally associated with pricing and delivery information, and represent products that are manufacturable and acquirable via the interface. The system may incorporate manufacturability analysis, quality assurance, and model training using user feedback and product data.

Abstract: Methods and systems are provided for using generative AI to optimize product search queries. In embodiments described herein, product descriptions and product images for a plurality of products are obtained. A multi-modal style classification model classifies each product into a corresponding style of a plurality of styles based on the product's product description and product image. Relationships of each product to other products in the plurality of products are stored in a knowledge graph based on the corresponding style of each product and the corresponding product description of each product. An image is generated by a text-to-image diffusion model with a set of products of the plurality of products based on the relationships of each product of the plurality of products to other products in the plurality of products.

Abstract: A computer system and method for transacting business over the Internet relating to vehicle purchasing. A first computer data base is provided having information regarding vehicles for sale wherein each vehicle has associated vehicle data parameters and a second computer data base is provided having information regarding a user wherein each user has associated user data parameters. A vehicle purchase server is provided that includes application software including a routine permitting the selection of specific vehicle data parameters from the first data base contingent upon user data parameters contained in the second data base based upon inquiries from a user regarding a vehicle description. Vehicle matching results including one or more vehicles recommended for the user are presented via a GUI accessible by the user.

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing programs and methods for performing operations comprising: receiving a request from a client device of a first user to engage in a shared virtual reality shopping experience with a second user; generating, for display on respective client devices of the first and second users, the shared virtual reality shopping experience comprising a plurality of virtual reality items that represent real-world products; receiving, from the client device of the second user, data indicating a selection of a first virtual reality item of the plurality of virtual reality items made by the second user; and modifying a display attribute of the first virtual item in the display of the shared virtual reality shopping experience on the client device of the first user to indicate the selection of the first virtual reality item made by the second user.

Abstract: There is provided an energy transaction platform to facilitate a wholesale?distribution?DER owner marketplace for DER-based energy products, including contracting, delivery, and settlement, for example, based on blockchain technology. The platform provides components to enrol and verify DER owners having a DER device. Platform components define and communicate contracts for energy services for bidding by DER owners to facilitate e.g. EV charging, GHG reduction, or demand response goals. Contracts are cleared in response to bidding, for example, by DER owners. The platform triggers contract delivery, which delivery is monitored and confirmed. Settlement is made between contract parties according to confirmed participation. Participation rewards and/or credit tokens are transferrable to DER owners, which can be further transferred. DER owner credibility measures can encourage participation and assist with clearing contracts.

Abstract: Systems and methods for attribute recommendation are disclosed. Transaction data related a user is received and attribute recommendations for the user are generated based on the transaction data. The attribute recommendations are generated by a variational inference model configured using a transaction matrix and a loyalty matrix. A set of N recommendations is generated by ranking the generated attribute recommendations based on a combined transaction score and loyalty score and a user interface is generated including the set of N recommendations.

Abstract: In some examples, a system may be configured to, for at least a first user of the plurality of users, implement a first set of operations that generate, for each of a first set of item types, attribute value data. Additionally, the system may implement a second set of operations that generate, for each of a second set of item types identified in catalogue data, clique data. Moreover, the system may, for the at least first user, implement a third set of operations that generate preference dependency data. Further, the system may, for the at least first user, based on the preference dependency data, the clique data, the attribute value data, generate, for each item type of a set of item types, output data including an affinity value for each item type of the first set of item types.

Abstract: A method for categorizing a product, the method including receiving information for the product; inputting the information into a trained machine learning model for a taxonomy tree; receiving a plurality of arrays, each array representing a level in the taxonomy tree and consisting of probabilities for each category represented in the level that the product is categorized in that category; choosing, from a highest level tier array, a category having a highest probability, thereby designating a tier prediction; collecting, from a second level tier array, all children of the tier prediction; determining whether a highest probability from the children of the tier prediction exceeds a threshold, and if yes, choosing the category with the highest probably as a new tier prediction; and repeating the determining; when the threshold is not exceeded or if the tier prediction has no children, and selecting the tier prediction as a predicted category.

Abstract: There are provided systems and methods for a network of personalized devices determining data for shopping predictions. A personalized device may include processing components for determining purchase predictions for one or more items to purchase on behalf of a user. The purchase predictions may be based on personality traits and other information that may indicate interests of the user. The purchase predictions may also be based on purchase information for the item, including inventory of the item, price of the item, availability of the item to the user, or other factor. Once the purchase prediction is determined, the personalized device may generate a purchase request for the item from a merchant having the item at a physical merchant location accessible to the personalized device or a delivery device associated with the personalized device. The personalized device may retrieve and deliver the purchased item.

Abstract: An example operation includes one or more of: determining a degradation of a battery of each vehicle in a group of electric vehicles, wherein the degradation is at a rate greater than a threshold; determining a return when the degradation is lowered; and providing a portion of the return to each of the vehicles in the group, wherein the portion is commensurate with the lowering.