Abstract: 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.

Abstract: Systems and methods are provided for fall prediction. Such system may comprise one or more sensors configured to obtain at least gait information of a person, and one or more processors coupled to the one or more sensors. The one or more processors may be configured to receive the gait information of the person, obtain gait dynamic features based at least in part on the obtained gait information, and apply the obtained gait dynamic features to a learning model to predict at least a fall movement of the person.

Abstract: A massage robot uses machine vision to locate treatment spots for massage. In one approach, a massage robot includes one or more robotic arms, an image sensor and a control system. The image sensor captures images of the user. The control system includes an image processing module and a motion controller. The image processing module processes the images to locate a treatment spot on the user. The motion controller controls the robotic arm to perform a massage procedure on the identified treatment spot.

Abstract: 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.

Abstract: A method includes: generating a first nutritional plan for a first user to be implemented in a first time period; monitoring events relevant to real-world implementation of the first nutritional plan by the first user; detecting, via a first kitchen appliance, a first user action associated with the first kitchen appliance; determining a first deviation between the first nutritional plan and a projected nutritional outcome derived based on the monitored events that occurred during a first portion of the first time period up to detection of the first user action; in accordance with the first deviation, revising the first nutritional plan to obtain a second nutritional plan to be implemented in a remainder portion of the first time period; generating a first suggestion regarding a second user action associated with the first kitchen appliance based on the second nutritional plan; and outputting the first suggestion via the first kitchen appliance.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 recommendatio

Abstract: A massage robot uses machine vision to locate treatment spots for massage. In one approach, a massage robot includes one or more robotic arms, an image sensor and a control system. The image sensor captures images of the user. The control system includes an image processing module and a motion controller. The image processing module processes the images to locate a treatment spot on the user. The motion controller controls the robotic arm to perform a massage procedure on the identified treatment spot.

Abstract: 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.

Abstract: A method of improved quality inspection includes (i) receiving a first command to capture a first image of a surface of a product, (ii) positioning, by actuating a plurality of rotatable joints, a camera at a first position that is substantially adjacent to the surface of the product, and capturing the first image of the surface of the product. The method further includes, after capturing the first image: (i) processing the first image to identify a defect in the first image and a relative location of the defect in the first image, and (ii) determining a second position of the camera in accordance with the first position of the camera and the relative location of the defect in the first image, and (iii) repositioning, by actuating the plurality of rotatable joints, the camera from the first position to the second position. The method further includes capturing the second image.

Abstract: 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

Abstract: A method of improved quality inspection includes (i) receiving a first command to capture a first image of a surface of a product, (ii) positioning, by actuating a plurality of rotatable joints, a camera at a first position that is substantially adjacent to the surface of the product, and capturing the first image of the surface of the product. The method further includes, after capturing the first image: (i) processing the first image to identify a defect in the first image and a relative location of the defect in the first image, and (ii) determining a second position of the camera in accordance with the first position of the camera and the relative location of the defect in the first image, and (iii) repositioning, by actuating the plurality of rotatable joints, the camera from the first position to the second position. The method further includes capturing the second image.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of identifying product defects on a production line includes receiving data from a plurality of edge devices monitoring a product on a production line for product defects. The data includes unique perspectives of the product captured by the edge devices. The method further includes generating an overall view of the product by merging each unique perspective of the product captured by respective edge devices of the plurality, and comparing the overall view with characteristic(s) of the product. Based on the comparing, the method further includes determining whether a degree of difference between the overall view and the characteristic(s) satisfies one or more criteria, and upon determining that the degree of difference between the overall view and the characteristic(s) satisfies at least one criterion of the one or more criteria, recording and reporting a defect associated with the product according to the difference between the view and the characteristic(s).

Abstract: Systems and methods are provided for fall prediction. Such system may comprise one or more sensors configured to obtain at least gait information of a person, and one or more processors coupled to the one or more sensors. The one or more processors may be configured to receive the gait information of the person, obtain gait dynamic features based at least in part on the obtained gait information, and apply the obtained gait dynamic features to a learning model to predict at least a fall movement of the person.