Detecting compatibility mismatch by generative artificial intelligence is described. Compatibility data is obtained (e.g., by accessing a database). The compatibility data is associated with a compatibility between items (e.g., items and categories of vehicles or an item and another item) and includes a list of recommended compatibilities between the items and a user reported compatibility for at least one item. A machine learning model is generated for detecting a compatibility mismatch between a first item and a second item and/or between an item and a category of vehicle. At least a portion of the compatibility data is provided as input to generative artificial intelligence to generate the machine learning model. An update to the list of recommended compatibilities is determined based on the detected compatibility mismatch.

An online system retrieves a set of user data for a user and applies a machine-learning model to predict a measure of preference of the user associated with an item category based on the set of user data. For an item included in the item category, the online system generates a prompt requesting generation of an image of the item that is consistent with the predicted measure of preference of the user associated with the item category. The online system provides the prompt to a generative artificial intelligence model to obtain an output and extracts, from the output, the image of the item that is consistent with the predicted measure of preference of the user associated with the item category. The online system then updates a database of items by storing the extracted image of the item in a database record associated with the item.

The disclosure features a method which includes inputting or receiving information on one or more features of a plurality of residential properties and prices of the residential properties including a marketed price, a listing price, and a closing price, providing the information to a Machine Learning Algorithm to determine the relationship between the one or more features and the prices of the residential properties to create a Machine Learned Model, inputting or receiving information on one or more features of a new residential property into the Machine Learned Model, and predicting a base price of the new residential property from the Machine Learned Model based on the one or more features of the new residential property. The disclosure also features one or more non-transitory, computer-readable storage media storing instructions capable of performing the method and a computer or computer system capable of performing the method.

A computer-implemented method for guiding a user interacting with an object is provided. The method comprises receiving informational cues of a scene including the object, the informational cues pertaining to sensor-retrieved data; identifying the object based on the informational cues; extracting contextual information based on the informational cues; determining at least one interaction indicator configured to indicate an interaction performable on the identified object, the at least one interaction indicator being based at least in part on the contextual information; and guiding the user to perform the interaction on the object based on the at least one interaction indicator through control of a mixed reality (MR) device.

An online system retrieves a set of user data for a user and applies a machine-learning model to predict a measure of preference of the user associated with an item category based on the set of user data. For an item included in the item category, the online system generates a prompt requesting generation of an image of the item that is consistent with the predicted measure of preference of the user associated with the item category. The online system provides the prompt to a generative artificial intelligence model to obtain an output and extracts, from the output, the image of the item that is consistent with the predicted measure of preference of the user associated with the item category. The online system then updates a database of items by storing the extracted image of the item in a database record associated with the item.

Provided are a device and a method in which a virtual object image of a product determined according to a user who enters a shop can be presented to the user, and a real product item can also be provided. A product to be presented is determined according to a user who enters a shop, and a mixed reality image in which a virtual object image of the determined product to be presented can be observed together with a real object in the shop is generated and output to a glass worn by a user who has entered the shop. The virtual object image of the product to be presented is sequentially updated according to a motion of the glass, and a mixed reality image is generated in which the product appears to be arranged in a product arrangement space in the shop.

System and method for virtual office spaces, marketplaces, malls, that realistically mimics working in brick-and-mortar spaces. This innovation allows online shoppers or work from home employees to experience a shared virtual roof similar to a physical environment. In the example of the virtual office, the layout can have individual cabins, open working spaces, cafeteria, lobby, reception, etc. Similar to a physical office, people can freely move around, chat, attend meetings, and collaborate with colleagues/visitors, without disturbing others working in the virtual office. Employees have complete privacy while working in their seat or cabin. Similar to going to a physical office, the employees working from home join virtually, and can see who is present in the office at any given time from the office layout. This innovation removes the barrier of working from home, simulating the physical office environment of spontaneous meetings and collaborations with colleagues, vendors, and customers.

Product selection and display system and methods of facilitating product selection are disclosed. A product selection and display system includes a product display system (PDS) and a companion device. The companion device is configured to display product selection options via the companion device, receive companion device input designating one or more product selections, and generate and transmit one or more companion device product collection model control commands in response to the companion device input. The PDS is configured to display a PDS display device view of a product collection model, receive PDS input designating one or more PDS user product selections, and configure the product collection model from selected product models.

A 3D model utilization apparatus includes an acquisition means, a generation means, and a utilization means. The acquisition means acquires appearance information and genre information related to digital content corresponding to a product purchased by a user. The generation means generates a 3D model corresponding to the digital content based on the acquired appearance information and genre information. The utilization means uses the generated 3D model in a metaverse space.