Motorola Mobility LLC

Zhengping Ji

Rachid M Alameh

Yi Wu

Amit Kumar Agrawal

John Joseph Gorsica

Jarrett K Simerson

Olivier David Rene Meirhaeghe

Thomas Y Merrell

Robert S Witte

Leo S Woiceshyn

James Patrick Ashley

Eric LeRoy Krenz

Michael E Russell

Karthik Mohan Kumar

Thomas E Gitzinger

Olivier David Meirhaeghe

Christian Flowers

Alvin J Von Ruff

James Wylder

Michael Thomas Corrigan

Techniques for subtask assignment for an artificial intelligence (AI) task are described, and may be implemented to leverage a local set of devices to distribute portions of an AI task between the devices. Generally, the described techniques enable AI task allocation based on a variety of factors, such as device capabilities, device availability, task complexity, and so forth.

Subtask assignment for an artificial intelligence task

A user can customize (also referred to as train) an AI module, which includes any of a variety of machine learning systems. The AI module can be used by the device on which the AI module is trained or can be communicated to other devices in the same environment (e.g., the same home). The AI module can also be communicated in a secure and private manner to a device in another environment (e.g., another user's home). To do so, the AI module is encrypted and added to a blockchain, and the blockchain is communicated via a peer-to-peer network to the device in the other environment. The recipient of the blockchain can then decrypt the AI module and use the AI module in that other environment, including further training the AI module for use in that other environment.

Securely communicating and customizing artificial intelligence modules

A change in the emotional state of a user of a device is detected, and in response to this change a reason for the change in user emotional state is determined. This determination is made based on both the current user emotional state and context data for the device or user. The device then adapts to the user based on the current emotional state and the reason for the change in user emotional state. This adaptation of the computing device refers to an alteration of the operation of the computing device with a goal of increasing the likelihood of the user being in a good emotional state (e.g., happy, relaxed) and reducing the likelihood of the user being in a bad emotional state (e.g., sad, angry).

Adapting a device to a user based on user emotional state

An electronic device has pair(s) of conductive plates that is coupled to a housing and is electrically isolated by a dielectric material. The pair(s) of conductive plates is positioned to change relative orientation in relation to mechanical force that deforms the housing. Capacitive driver(s) is electrically coupled to pair(s) of conductive plates. A controller is communicatively coupled to the capacitive driver(s). The controller identifies a first capacitance of the pair(s) of conductive plates associated with the housing being in a normal state. The controller detects, via the capacitor driver(s), a change in capacitance from the first capacitance to a second capacitance of the pair(s) of conductive plates. The controller compares the change in capacitance to a threshold. In response to the change exceeding the threshold, the controller generates a notification.

Electronic device having capacitance-based force sensing

Techniques for artificial intelligence (AI) modules for computation tasks are described, and may be implemented to enable multiple AI modules to be aggregated to form an execution structure (e.g., an execution chain) for performing a computation task. Generally, the described techniques aggregate AI modules based on their respective functions to perform an overall computation task.

Artificial intelligence modules for computation tasks

An electronic device includes a capacitive driver that is electrically coupled to a pair of conductive plates coupled to a housing and electrically isolated by a dielectric material. The conductive plates are positioned to change relative orientation in relation to deformation of the housing in a first direction. A controller identifies a first capacitance of the pair of conductive plates associated with the housing being in a normal alignment relative to the first direction. The controller presents an object on a user interface device in a first kinematic state. The controller detects, via the first capacitor driver, a change in capacitance from the first capacitance to a second capacitance of the pair of conductive plates. The controller associates the change to a first user input. The controller presents the object on the user interface device in a second kinematic state based on the first user input.

Electronic device having capacitance-based force sensing for user interface input

Systems and methods are provided for generating a personalizable user interface (UI) component layout on a curved screen on a mobile electronic communication device, the curved screen having a front screen display surface and a plurality of edge screen display surfaces. In an example, the described technique entails detecting that a device user has opened an application on the mobile electronic communication device, determining whether the application opened by the user supports placement of UI components on edge screen display surfaces, and determining a coverage area on the front screen display surface and the edge screen display surfaces areas when the application supports placement of UI components on edge screen display surfaces, and estimating a user hand size based on the determined coverage area, and placing one or more UI components on an edge screen display surface to be reached by the user based on the estimated hand size.

Personalizable UI component layout on curved glass

In aspects of generating action suggestions based on a change in user mood, a computing device implements an action generation module that generates action suggestions. The action generation module analyzes sensor data from sensors to determine a current user state associated with a user. The action generation module also analyzes environment data received from Internet-of-Things (IoT) devices to determine a current environment context associated with the user. The action generation module can detect a change in a user mood based on a comparison of the current user state with user history data that includes user state parameters and environment context parameters that indicate the user mood. The action generation module can analyze the user history data to determine the user state parameters and/or the environment context parameters that correlate to the change in the user mood, and generate an action suggestion that is intended to modify the current user state.

Generating action suggestions based on a change in user mood

Content visibility on a computing device is controlled based at least in part on the current location of the computing device. When the current location is a safe location for making user-selectable content visible on a display screen of the computing device and the computing device is unlocked, the computing device operates in a full content visibility mode. In the full content visibility mode all user-selectable content on the computing device is accessible to a user of the computing device. When the current location is not a safe location for making user-selectable content visible on the display screen, the computing device operates in a reduced content visibility mode. In the reduced content visibility mode content visibility on the display screen is reduced, such as by limiting which applications (e.g., application icons or widgets) are displayed or otherwise accessible to the user.

Controlling content visibility on a computing device based on computing device location

An electronic device includes one or more low-power context sensors, one or more high-power context sensors, and one or more processors operable with the one or more low-power context sensors and the one or more high-power context sensors. The one or more processors, working with context engines associated with the sensors, minimize usage of the high-power context sensors when determining a context of the electronic device, where that determined context has a confidence score above a predefined confidence level threshold.

Methods and electronic devices for determining context while minimizing high-power sensor usage

Systems and methods for providing smart internet of things (IOT) menus utilize a camera of an electronic communications device and entail capturing an image of an IOT device of interest using a camera of the electronic communications device and identifying the IOT device of interest. The IOT of interest is displayed and may be selected by the device user. The user of the electronic communications device is identified and their historical behavior with respect to the selected IOT device is analyzed. A customized, rather than full, IOT device control menu is then generated and presented to the user.

Smart internet of things menus with cameras

A system and method of adaptive interface switching in a mobile electronic communications device entails engaging in an audio interface task at the mobile electronic communications device. If during the audio interface task it is detected that at least one microphone or at least one speaker of the mobile electronic communications device is compromised, then the system activates a data link to a wearable communication device associated with a user of the mobile electronic communications device. At least one of a mic and speaker on the wearable communication device is then used to continue the audio interface task.

Adaptive interface switching

Systems and methods of managing notifications on a curved display surface of a mobile electronic communications device are provided, wherein the curved display surface includes a front screen area and lateral edge screen areas. A notification is obtained and a holding direction of the device is detected. If the holding direction does not allow display of the notification on the lateral edge screen areas, it is displayed on the front screen area. If the holding direction allows display of the notification on the lateral edge screen areas, it is displayed on an uncovered edge screen area facing the user if the user is in a known direction. Otherwise, the notification is displayed on all edge screen areas. If the one or more edge screens are partially covered, the notification is displayed on the edge screen area having the least coverage.

Managing notifications on a curved display surface

A system and method of adaptive video interface switching between a mobile electronic communications device and a wearable device entail determining during an ongoing video session via a first one of the devices that an audio or video input or output mechanism on the first device is compromised. It is determined that the audio and video input and output mechanisms on a second of the devices are uncompromised. The video session is then continued using at least one of the audio and video input and output mechanisms on the second device.

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Hinsdale, IL, United States of America

Zhengping Ji