Midea Group Co., Ltd.

Samsung Electronics Co., Ltd.

Research Frontiers Inc.

Landing Ai

Midea America Corp.

Other

Cisco Technology, Inc.

Visionx Inc.

Dongyan Wang

Richard James Humpleman

Haisong Gu

Steven M Slovak

Yiwen Rong

Andrew Yan-Tak Ng

Robert L Saxe

Seth Van Voorhees

Kai Yang

Gopi Prashanth Gopal

Xin Chen

Tao Liu

Suresh Mani

Hua Zhou

Yu Qing Zhou

Pingyang He

Sanjeev Satheesh

Karthikeyan Thiruppathisamy Nathillvar

Bo Tan

Rustam Hashimov

Dillon Anthony Laird

Beom Sik Kim

Alejandro Betancourt

Timothy Viatcheslavovich Rosenflanz

Greg Frederick Diamos

Tian Wu

Chen Zhang

Junyang Zhou

Chi Zhang

Xiao-Ping Chen

Xiaochun Li

Haibin Huang

Linnan Zhu

Yujie Zhu

Mingyi Yang

Xiaofeng Xu

Edwin Lichteveld

Gregory M Sottile

Mingze Xu

Kuangyuan Sun

Shijun Ma

William J Hull

Mani Suresh

Systems and methods are disclosed herein for detecting impurities of harvested plants in a receptacle of a harvester. In an embodiment, a harvester controller receives, from a camera facing the contents of the receptacle, an image of the contents. The harvester controller applies the image as input to a machine learning model. The harvester controller receives, as output from the machine learning model, an identification of an impurity of the harvested plants. The harvester controller transmits a control signal based on the impurity.

AI-optimized harvester configured to maximize yield and minimize impurities

A process and a system for creating a visual guide for developing training data for a classification of image, where the training data includes images tagged with labels for the classification of the images. A processor may prompt a user to define a framework for the classification. For an initial set of images within the training data, qualified human classifiers are prompted to locate the images within the framework and to tag the images with labels. The processor determines whether the tagged images have consistent labels, and, if so, the processor adds images to the training data. The processor may add the images by providing a visual guide, the visual guide including tagged images arranged according to their locations within the framework their labels, and prompting human classifiers to tag the additional images with labels for the classification, according to the visual guide.

User-generated visual guide for the classification of images

Systems and methods are disclosed herein for optimizing harvester yield. In an embodiment, a controller receives a pre-harvest image from a front-facing camera of a harvester. The controller inputs the pre-harvest image into a model, and receives as output from the model a predicted harvest yield. The controller receives, from an interior camera of the harvester, a post-harvest image including the plants as harvested. The controller inputs the post-harvest image into a second model and receives, as output, an actual harvest yield of the plants as-harvested. The controller determines that the predicted harvest yield does not match the actual harvest yield, and outputs a control signal.

A rice cooker assembly uses machine learning models to identify and classify content in grain mixtures thereby to provide better automation of the cooking process. As one example, a rice cooker has a chamber storing grains. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model determines whether the contents of the chamber includes one type or multiple types of grain or whether the contents of the chamber includes any inedible objects. The machine learning model further classifies the one or more types of grains and inedible objects if any. The cooking process may be controlled accordingly. The machine learning model may be resident in the rice cooker or it may be accessed via a network.

Method of smart rice cookers capable of mixed grain cooking and abnormal conditions detection

The disclosed computer-implemented method for artificial-intelligence-based automated surface inspection can include receiving customer data, a request for a targeted model, and compensation for the requested targeted model. The compensation can include an agreement to contribute the customer data and/or targeted model to be available for other third-party entities. The method can also include retrieving the pre-trained model from a pre-trained model pool. The pre-trained model can be related to objects in a second industry. The method can include generating the targeted model from the pre-trained model and the customer data. The targeted model can be related to mapping sensor data to surface anomalies. The method can also include providing the targeted model to the third-party entity. The method can further include updating a distributed blockchain structure to include the at least one of the customer data and the targeted model.

Systems and methods for artificial-intelligence-based automated surface inspection

A laminated light valve film comprising: (a) a film having first and second opposed outer surfaces; (b) a first layer of a polymeric interlayer material upon at least a portion of each opposed outer surface; (c) a first pair of substrates, one of which is adhered to the interlayer material upon the first outer opposed surface of the light valve film and the second is adhered to the interlayer material upon the second outer opposed surface of the light valve film, these substrates being formed from plastic or glass; (d) a second layer of polymeric interlayer material applied to at least a portion of an outer surface of each one of the first pair of substrates; and (e) a second pair of substrates, one being adhered to the interlayer upon the outer surface of one of the first pair of substrates and a second one adhered to the interlayer material on the outer surface of a second one of the first pair of substrates, the second pair of substrates being formed from plastic or glass, with the proviso that when the first pair of substrates is formed of plastic, the second pair of substrates is formed of glass, and vice-versa.

Light valve films laminated between thin glass and plastic substrates

A method includes: deploying a frontend system (FIES); registering a current deployment location of the FIES; detecting a close-proximity interaction between a user and the FIES that specifies a first category; generating a product recommendation that includes a first product of the first category; detecting a respective prior purchase record of the user for a second product in a second category associated with the first category; if the prior purchase record indicates a purchase or delivery location outside of a geographic region of the registered current deployment location, automatically augmenting the product recommendation to include a third product from the second category selected based on characteristics of the second product; and otherwise, automatically refining the first product recommendation to further define characteristics of the first product based on the characteristics of the second product; and providing the product recommendation to the first user after the automatic augmenting or refining.

Method and system for providing personalized on-location information exchange

A method and system of controlling a digital assistant with dynamically switchable endpoint devices, comprising: dynamically selecting a respective input endpoint device and a respective controlled device for each of a plurality of voice-based requests from a user to the computing system, including: at a first point in time, acquiring respective instances of a first voice input from a first set of two or more input endpoint devices; obtaining a representative copy of the first voice input based on the respective instances of the first voice input that have been acquired from the first set of two or more input endpoint devices; determining a first actionable intent based on the representative copy of the first voice input; and dispatching a first encoded instruction to a first controlled endpoint device selected from the plurality of controlled endpoint devices in accordance with the first actionable intent.

Voice-based user interface with dynamically switchable endpoints

A method includes: deploying a frontend system (FIES); activating input streams from on-site cameras located at a current deployment location of the FIES; in accordance with the respective input stream of a first camera, registering an inspection event of a user in association with a first sample product on display; detecting a close-proximity interaction between the user and the FIES; automatically generating a first product recommendation, including: if the inspection event meets enhanced inspection criteria, which are met when a second inspection event of the first user exists in previously stored inspection events associated with the respective first sample product, automatically adding a product-specific description of the first sample product in the first product recommendation; and otherwise, forgoing including the product-specific description of the first sample product in the first product recommendation; and providing, through the frontend information exchange system, the first product recommendation to the first user.

A face recognition system of a residential environment identifies an individual present in the residential environment. The residential environment include a plurality of home devices and is associated with a group of different persons. The face recognition system identifies which person in the group is the individual and generate an operating instruction for a home device based on identity of the individual. For example, the face recognition system captures an image set of the individual's head and face and applies the image set to a machine learning model that is trained to distinguish between the different persons based on images of their heads and faces. The face recognition system can retrieve a personal profile of the identified individual, which includes settings of the home device for the identified individual. The face recognition system generates the operating instruction based on the personal profile.

Face recognition in a residential environment

A method of quality inspection is performed by a robotic arm that includes a plurality of segments, a camera at an end of the robotic arm, and a plurality of joints connecting two segments of the plurality of segments. The method includes (i) inspecting, via the camera, a surface of a product with the camera positioned at a first position, (ii) based on the inspecting, identifying: (a) an area of interest on the surface of the product, and (b) a relative location of the area of interest on the surface, (iii) positioning, based on the relative location of the area of interest on the surface, the camera at a second position, and (iv) inspecting, via the camera, the area of interest on the surface of the product with the camera positioned at the second position. Inspecting the area of interest includes inspecting a subset of the surface of the product.

Methods, devices, and systems for improved quality inspection of products

A quality management platform for home devices is implemented on a computer system and comprises a knowledge repository and a query engine. The knowledge repository includes a knowledge graph of nodes connected by edges. The nodes represent concepts relating to home devices and their operation and faults, and the edges represent relations between the concepts. The query engine receives requests relating to home devices, queries the knowledge graph based on the requests, and returns responses based on the query results.

Computer-based platform for quality management of home devices, including knowledge extraction

Computer-based platform for quality management of home devices

A voice command module is used to execute voice commands in a residential environment that contains a plurality of home devices. The voice command module includes a speech recognition module and command logic. The speech recognition module receives utterances from users and converts the utterances to commands from a vocabulary of predetermined commands, which includes a customization command to define a new wake-up utterance as corresponding to a wake-up command. The command logic executes the commands. When the customization command is received, the command logic changes the voice command module so that it now executes the wake-up command upon detection of the new wake-up utterance.

Customizable wake-up voice commands

Growing community of inventors

San Jose, CA, United States of America

Dongyan Wang