The present disclosure provides a technique that enables customization of a vehicle. In an information processing apparatus, a controller presents vehicle units extracted from among a plurality of types of under units each of which is provided with a driving mechanism configured to cause wheels to rotate and a plurality of types of upper units to be loaded on any of the under units to a user, the vehicle units being combinable as a vehicle. Furthermore, the controller executes: accepting selection by a user from among the presented vehicle units; and calculating a fee in the case of providing the selected vehicle units.

A method and system for recommending articles including: receiving a customer request from the customer during the session; generating case data for a case, by an article recommender app; configuring a training set based on the subject and description data of the customer request; identifying, by an artificial intelligence (AI) app, a first pool of articles from a knowledge database; identifying by at least one query, a second pool of articles from a case article database to into a merged pool of articles; assigning, by the AI app, an implicit label to one of the first pool and the second pool of the articles; applying a model derived by the AI app based on customer behavior and a set of features related to the case to classify each article of the merged pool of articles based at least in part on the predicted relevance of the article.

Methods and systems for analyzing and evaluating item recommendations presented on a web site are disclosed. In one aspect, a user interface is generated for display on a website. Item recommendations personalized for an individual user are displayed within in item recommendation region of the website. Impression, clickstream, and sales data are received from user activity on the website. The impression, clickstream, and sales data are displayed on a dashboard of an administrator user interface. An administrator user can select visualizations to display on the dashboard using selectors for impressions, clicks, sales, users, and time. Two or more sets of data can be overlaid to illustrate relationships between user interactions with recommended items and actual sales of those items. The dashboard is dynamically updated in response to new data received from the website in real-time.

A visual tracking system for tracking and identifying persons within a monitored location, comprising a plurality of cameras and a visual processing unit, each camera produces a sequence of video frames depicting one or more of the persons, the visual processing unit is adapted to maintain a coherent track identity for each person across the plurality of cameras using a combination of motion data and visual featurization data, and further determine demographic data and sentiment data using the visual featurization data, the visual tracking system further having a recommendation module adapted to identify a customer need for each person using the sentiment data of the person in addition to context data, and generate an action recommendation for addressing the customer need, the visual tracking system is operably connected to a customer-oriented device configured to perform a customer-oriented action in accordance with the action recommendation.

A cloud-based system for use by retail store employees or customers at any location to facilitate the sale of automotive tires to consumers is provided. The system accesses multiple independent tire inventory systems from different distributors/manufacturers and provides a personalized set of recommendation tire options and accompanying TPMS service packs.

A digital attribution system is described to generate predictions of word attributions from subject data, e.g., titles, subject lines of emails, and so on. To do so, an attribution score is first generated by the digital attribution system that describe an amount to which respective words in the subject data cause performance of a corresponding outcome. The attribution scores are then used by the digital attribution system to generate representations for display in a user interface for respective words in the subject data and may also be used to generate attribution recommendations of changes to be made to the subject data.

Embodiments herein relate to fuel monitoring systems and related methods. In an embodiment, a fuel monitoring system for a vehicle is included having a fuel filter sensor device configured to generate data reflecting a filter restriction value of a fuel filter, a geolocation circuit configured to generate or receive geolocation data, and a system control circuit configured to evaluate the sensor data to determine changes in the filter restriction value. The control circuit can receive fuel level data, cross-reference geolocation data and fuel level data to identify refueling locations utilized, and correlate refueling locations with subsequent changes in filter restriction to identify an effect of specific refueling locations on fuel filter loading. In some embodiments, a refueling guidance system for a vehicle is included that can provide route and/or refueling site recommendations based on fuel filter loading rate data. Other embodiments are also included herein.

Systems and methods may be used to predict an applicable a hearing assistance device shell or model. For example, a method may include obtaining patient information, determining, using a machine learning trained model, a correlation between an input vector and each of a plurality of feature vectors corresponding to a plurality of hearing assistance device models, and ranking the plurality of hearing assistance device models based on respective correlations to the input vector. Information corresponding to a highest ranked hearing assistance device model may be output.

A product recommendation method includes an indexing step implementing a Bayesian network capable of creating direct matches between each respective product of a product catalog and an ideal user whose characteristics are the most likely to be suitable for the respective products. The indexing step includes inputting a first descriptor vector of a product into the Bayesian network to obtain the user-characteristics most likely to be suitable for the product. The product recommendation method further includes a refining process which identifies determinants associated with nodes and conditional probabilities of the Bayesian network to minimize errors at the nodes to which the arcs of the Bayesian network point.

The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops. The improvement is achieved by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, and then creating shop-specific versions of the recommendation model for each of the shops in the subset. Each shop-specific model is created by optimizing the baseline model to predict user-item interactions in a first training dataset for the applicable shop. Each of the shop-specific models is then tested using a second training dataset for the shop. A loss is calculated for each shop-specific model based on the model's predicted user-item interactions and the actual user-item interactions in the second training dataset for the shop. A global loss is calculated based on each of the shop-specific losses, and the baseline model is updated to minimize the global loss.