Ebay, Incorporated

Pilot Ai Labs, Inc.

Jonathan Su

Jatin Chhugani

Mihir Naware

Neelakantan Sundaresan

Jiri Medlen

Brian Pierce

Cheri Nola Leonard

Ankit Kumar

Elliot English

Techniques for mapping size information associated with a client to target brands, garments, sizes, shapes, and styles for which there is no standardized correlation. The size information associated with a client may be generated by modeling client garments, accessing computer aided drawing (CAD) files associated with client garments, or by analyzing a history of garment purchases associated with the client. Information for target garments may be generated in a similar fashion. A system may then create a standardized scale with a set of sizes for a target, and map a client base size to that standardized size scale. Similar matching and mapping may also be done with shape and style considerations. A recommendation based on the mapping may then be communicated to the client.

Garment size mapping

Techniques for generating a digital wardrobe are presented herein. A transceiver can be configured to receive a request having a garment identifier and a user identifier. Additionally, an access module can be configured to access a first garment model, access a body model of the user corresponding to the user identifier, and access a second garment model corresponding to the user identifier. Furthermore, a processor can be configured by a garment simulation module to position the body model inside the first garment model and the second garment model, and calculate simulated forces based on the positioning. Moreover, a rendering module can be configured to generate an image of the garment models draped on the body model based on the calculated simulated forces. Subsequently, a display module can be configured to cause presentation of the generated image on a display of a device.

Digital wardrobe

Techniques for generated and presenting images of items within user selected context images are presented herein. In an example embodiment, an access module can be configured to receive a first environment model and a first wearable item model. A simulation module coupled to the access module may process the environment model to identify placement volumes within the environment model and to place a clothed body model within the placement volume to generate a context model. A rendering module may then generate a context image from the context model. In various embodiments, the environment model used for the context, the wearable item positioned within the environment model, and rendering values used to generate context images may be changed in response to user inputs to generate new context images that are displayed to a user.

Method system and medium for generating virtual contexts from three dimensional models

Techniques for extraction of body parameters, dimensions and shape of a customer are presented herein. A model descriptive of a garment, a corresponding calibration factor and reference garment shapes can be assessed. A garment shape corresponding to the three-dimensional model can be selected from the reference garment shapes based on a comparison of the three-dimensional model with the reference garment shapes. A reference feature from the plurality of reference features may be associated with the model feature. A measurement of the reference feature may be calculated based on the association and the calibration factor. The computed measurement can be stored in a body profile associated with a user. An avatar can be generated for the user based on the body profile and be used to show or indicate fit of a garment, as well as make fit and size recommendations.

Extraction of body dimensions from planar garment photographs of fitting garments

Systems and methods for automatic generation of new listings are provided. In example embodiments, time data is received that corresponds to a destination geolocation. An indication of a purchase of an item associated with the destination geolocation is received. A return time from the destination geolocation is determined based on the time data. A new listing is automatically generated for selling the item at a time determined from the return time.

Regional item recommendations

In general, certain embodiments of the present disclosure provide methods and systems for object detection by a neural network comprising a convolution-nonlinearity step and a recurrent step. In a training mode, a dataset is passed into the neural network, and the neural network is trained to accurately output a box size and a center location of an object of interest. The box size corresponds to the smallest possible bounding box around the object of interest and the center location corresponds to the location of the center of the bounding box. In an inference mode, an image that is not part of the dataset is passed into the neural network. The neural network automatically identifies an object of interest and draws a box around the identified object of interest. The box drawn around the identified object of interest corresponds to the smallest possible bounding box around the object of interest.

System and method for improved general object detection using neural networks

Systems and methods for regional item recommendations are provided. In example embodiments, an indication of a destination geolocation from a user device of a user is received. Destination data corresponding to the destination geolocation is retrieved. A destination characteristic from the destination data is extracted. The destination characteristic indicates an affinity for apparel associated with the destination geolocation. A candidate apparel item is determined based on the extracted destination characteristic. An item listing corresponding to the candidate apparel item is identified. The item listing is presented on a user interface of the user device.

Growing community of inventors

San Jose, CA, United States of America

Jonathan Su