Abstract: A laundry folding appliance includes one or more folding panels mechanically coupled to a cabinet and at least partially defining a folding surface for receiving articles of clothing. An actuating assembly selectively pivots the folding panels to fold the articles of clothing and a camera assembly is positioned in view of the folding surface. A controller obtains one or more images using the camera assembly, identifies the laundry articles positioned on the folding surface by analyzing the one or more images using a laundry identification model, determines a folding protocol for folding the laundry article, and operates the actuating assembly to fold the laundry article in accordance with the folding protocol.

Abstract: Disclosed embodiments may provide a framework to measure and leverage the observable attributes that most directly affect the data stability of a customer. In addition, embodiments track the dynamics of the observable components that sustain the data stability of a customer. Embodiments may be used to estimate the stability of a variety of conditions for various contexts, such as the stability of a computing system over time.

Abstract: Disclosed embodiments may provide a framework to measure and leverage the observable attributes that most directly affect the data stability of a customer. In addition, embodiments track the dynamics of the observable components that sustain the data stability of a customer. Embodiments may be used to estimate the stability of a variety of conditions for various contexts, such as the stability of a computing system over time.

Abstract: An automatic control system for a cooking appliance monitors and adjusts a cooking operation on the cooking appliance. The automatic control system includes at least one control knob assembly, an image capturing device, and a controller operably coupled to the at least one control knob assembly and the image capturing device. The controller is configured to perform a series of operations, including receiving a desired temperature of a food item; capturing a first image of the food item; analyzing, by one or more computing devices using a machine learning image recognition model, the first image to determine one or more features of the food item; generating an input state of the food item based on the first image analysis; and determining an output action via a reinforcement learning system.

Abstract: A dryer appliance includes a microphone for monitoring sound generated during operation of the dryer appliance and a controller is operably coupled to the microphone. The controller is configured for obtaining a sound signal generated during operation of the dryer appliance and converting the sound signal into a spectrogram that represents a sound frequency and a sound amplitude over time. An artificial intelligence image recognition process is used to analyze the spectrogram to identify one or more sound signatures that are associated with particular operating conditions, and operation of the dryer appliance is adjusted based at least in part on the identification of the sound signature.

Abstract: A method of operating a home appliance includes generating raw data on the appliance, transmitting the raw data to a remote computing device, receiving a data analysis from the remote computing device, and implementing a responsive action based on the data analysis

Abstract: A computer implemented method for amassing and interpreting data from a home appliance includes obtaining a plurality of data points that respectively correspond to user data descriptive of usage of a plurality of home appliances, each of the plurality of home appliances being associated with a different user among a plurality of users, processing, by one or more computing devices using a neural network, the plurality of data points to generate a plurality of embeddings associated with the plurality of data points, categorizing the plurality of embeddings to generate clusters of the plurality of users, and determining a predicted event for a new home appliance, the predicted event being based on a categorization of the new home appliance among the clusters of the plurality of users.

Abstract: A laundry folding appliance includes one or more folding panels mechanically coupled to a cabinet and at least partially defining a folding surface for receiving articles of clothing. An actuating assembly selectively pivots the folding panels to fold the articles of clothing and a camera assembly is positioned in view of the folding surface. A controller obtains one or more images using the camera assembly, identifies the laundry articles positioned on the folding surface by analyzing the one or more images using a laundry identification model, determines a folding protocol for folding the laundry article, and operates the actuating assembly to fold the laundry article in accordance with the folding protocol.

Abstract: A laundry folding appliance includes one or more folding panels mechanically coupled to a cabinet and at least partially defining a folding surface for receiving articles of clothing. An actuating assembly selectively pivots the folding panels to fold the articles of clothing and a camera assembly is positioned in view of the folding surface. A controller obtains one or more images using the camera assembly, identifies the laundry articles positioned on the folding surface by analyzing the one or more images using a laundry identification model, adds the laundry articles to a closet inventory list, and subsequently provides a clothing recommendation selected from the closet inventory list based at least in part on a recommendation model.

Abstract: A dryer appliance includes a microphone for monitoring sound generated during operation of the dryer appliance and a controller is operably coupled to the microphone. The controller is configured for obtaining a sound signal generated during operation of the dryer appliance and converting the sound signal into a spectrogram that represents a sound frequency and a sound amplitude over time. An artificial intelligence image recognition process is used to analyze the spectrogram to identify one or more sound signatures that are associated with particular operating conditions, and operation of the dryer appliance is adjusted based at least in part on the identification of the sound signature.

Abstract: A washing machine appliance includes a microphone for monitoring sound generated during operation of the washing machine appliance and a controller is operably coupled to the microphone. The controller is configured for obtaining a sound signal generated during operation of the washing machine appliance and converting the sound signal into a spectrogram that represents a sound frequency and a sound amplitude over time. An artificial intelligence image recognition process is used to analyze the spectrogram to identify one or more sound signatures that are associated with particular operating conditions, and operation of the washing machine appliance is adjusted based at least in part on the identification of the sound signature.

Abstract: A washing machine appliance includes a wash tub defining a wash chamber, a wash basket rotatably mounted within the wash tub, and a camera mounted within the cabinet in view of the wash chamber. A controller is operably coupled to the camera for obtaining images of the load of clothes in the wash chamber and analyzing the images using a neural network image recognition process (such as a Mask R-CNN process) to determine a load characteristic of the load of clothes. The load characteristic can then be used to adjust at least one operating parameter of the washing machine appliance based on the load characteristic, e.g., for improved or optimized wash performance.

Abstract: Disclosed embodiments may provide a framework to measure and leverage the observable attributes that most directly affect the data stability of a customer. In addition, embodiments track the dynamics of the observable components that sustain the data stability of a customer. Embodiments may be used to estimate the stability of a variety of conditions for various contexts, such as the stability of a computing system over time.

Abstract: A method for managing dialogue interaction includes generating a hierarchical tree representation of a set of hierarchical data entities, and identifying a leaf node in the hierarchical tree representation. A system for managing dialogue interaction includes a hierarchical data format having a plurality of hierarchical data entities, and a dialogue interaction manager that manages a dialogue with a user based upon the hierarchical data format.

Abstract: A learning support method and system for a classroom includes a monitoring device configured to capture real-time participant events and stimuli in a learning environment. The participant events include classroom and collaboration-oriented input. A database is configured to store and organize a-priori skills of a typical student and a-priori knowledge a participating student. A set of cognitive model profiles are stored in system memory representing typical student behaviors and participant student behaviors with access to the captured participant events, the stimuli and the a-priori knowledge and skills. An interaction manager is configured to be responsive to the participant events and stimuli to perform interactive tasks during a class session. The interactive tasks may include posing a question, supplementing a lecture, tracking progress and rating teacher performance.

Abstract: A method for multiple value confirmation and correction in spoken dialog systems. A user is allowed to correct errors in values captured by the spoken dialog system, such that the interaction necessary for error correction between the system and the user is reduced. When the spoken dialog system collects a set of values from a user, the system provides a spoken confirmation of the set of values to the user. The spoken confirmation comprises the set of values and possibly pause associated with each value. Upon hearing an incorrect value, the user may react and barge-in the spoken confirmation and provide a corrected value. Responsive to detecting the user interruption during the pause or after the system speaking of a value, the system halts the spoken confirmation and collects the corrected value. The system then provides a new spoken confirmation to the user, wherein the new spoken confirmation includes the corrected value.

Abstract: A learning support method and system for a classroom includes a monitoring device configured to capture real-time participant events and stimuli in a learning environment. The participant events include classroom and collaboration-oriented input. A database is configured to store and organize a-priori skills of a typical student and a-priori knowledge a participating student. A set of cognitive model profiles are stored in system memory representing typical student behaviors and participant student behaviors with access to the captured participant events, the stimuli and the a-priori knowledge and skills. An interaction manager is configured to be responsive to the participant events and stimuli to perform interactive tasks during a class session. The interactive tasks may include posing a question, supplementing a lecture, tracking progress and rating teacher performance.

Abstract: A one-step correction mechanism for voice interaction is provided. Correction of a previous state is enabled simultaneously with recognition in a current or subsequent state. An application is decomposed into a set of tasks. Each task is associated with the collection of one piece of information. Each task may be in a different state. At any point during the interaction, while a task/state pair is active, the dialog manager may enable multiple other task/state pairs to be active in latent fashion. The application developer may then use those facilities or resources to the active task/state and the latent task/state pairs depending on contextual condition of the interaction state of the application.

Abstract: A learning support method and system for a classroom includes a monitoring device configured to capture real-time participant events and stimuli in a learning environment. The participant events include classroom and collaboration-oriented input. A database is configured to store and organize a-priori skills of a typical student and a-priori knowledge a participating student. A set of cognitive model profiles are stored in system memory representing typical student behaviors and participant student behaviors with access to the captured participant events, the stimuli and the a-priori knowledge and skills. An interaction manager is configured to be responsive to the participant events and stimuli to perform interactive tasks during a class session. The interactive tasks may include posing a question, supplementing a lecture, tracking progress and rating teacher performance.

Abstract: A system and method to support embedded complete controller specification through explicit controller overlays are provided. The system and method partitions the controller layer of the MVC into a plurality of controller overlays. For example, a macro controller overlay, micro controller overlay, and global controller overlay are established for specifying macro controller objects, micro controller objects and global controller objects. Editors are provided for each of the plurality of controller overlays. The combination of the micro control overlay, macro control overlay, and global control overlay results in a complete controller specification for an application.