Accenture Global Solutions Limited

Edy S Liongosari

Alex M Kass

Niclas Almquist

Shimon Modi

Steven Tiell

Chetan R Kundavaram

Kevin Michael Conroy

Mark Grechanik

Mary Elizabeth Hamilton

David William Vinson

Marc Carrel-Billiard

Qing Xie

Matthew Hellige

This document describes self-sanitizing systems that sanitize the cabins of vehicles. In one aspect, a method includes initiating a sanitizing cycle for sanitizing one or more surfaces of one or more components of a vehicle cabin. During the sanitizing cycle, one or more UV light sources are activated, and at least one of a shape, a position, or orientation of the surface is adjusted with respect to the one or more UV light sources. The sanitizing cycle is terminated.

Self-sanitizing system for vehicle cabins

Implementations for include providing one or more product designs using an intelligent design platform receiving a product indicator that indicates a product that is to be designed, transmitting a request to a contextual requirements system, the request including the product identifier and requesting one or more contextual requirements, determining a set of context models based on the product identifier, each context model in the set of context models being generated based on one or more scenes represented in a digital video, each scene depicting a contextual use of the product, providing a set of contextual requirements to the design generation system based on one or more context models, and inputting a set of aggregate requirements to a generative design tool that generates the one or more product designs based on the set of aggregate requirements, the set of aggregate requirements including at least one contextual requirement.

Intelligent design platform using industrialized experience in product designs

Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.

Component design based on sensor data

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting higher level events based on lower level events. In one aspect, a method includes receiving a stream model that defines elements of a data domain. A stream mapping that defines sensor identifiers for real-world sensors and associates each sensor identifier with a respective element of the data domain is received. A user-specified stream matching pattern is received. The stream matching pattern specifies respective sensor identifiers of sensor identifiers of the real-world sensors, and for each sensor identifier, a tuple of data elements of the stream model and, for each tuple of data elements, co-occurrence criteria including at least one time window. A stream of events is obtained from the real-world sensors. A determination is made that two or more events co-occur within the time window and whether the one or more co-occurrence criteria are satisfied.

Event abstractor

Techniques are described for aggregating data generated by multiple platforms of different types. A particular user (e.g., end-user) may interact with multiple individual (e.g., siloed) platforms of different types and/or that support different business purposes or industries. The individual platforms may generate data describing and/or resulting from these interactions. The data may be received, ingested, and processed by a super-platform. The super-platform may generate aggregate data by aggregating the data received from different individual platforms. Data aggregation may be performed on data that is generated by different individual platforms and that is associated with a particular user or multiple users. Aggregation may also be performed on data that is independent of any particular user, such as sensor data that describes an environment in proximity to the platform.

Platform data aggregation and semantic modeling

Techniques are described for component configuration based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing the use of component(s) of the system by individual(s). The sensor data is analyzed (e.g., in real time) to determine an updated configuration for component(s) (e.g., an adjustment to the seat back, lumbar support of a car seat, etc.). The updated configuration may be communicated to the individual as a recommended configuration. In some implementations, the updated configuration may be communicated directly to the component which, on receiving and processing the configuration update, sends signals to various actuators to move the subcomponents of the component into the updated configuration. In some implementations, the sensor data is used to train, through machine learning, a model that provides configuration update(s) for component(s) based on the input sensor data.

Component configuration based on sensor data

Techniques are described for receiving data generated by multiple platforms of different types, and determining recommendations for end-user(s) of the multiple platforms based on an analysis of the received data. An end-user may interact with multiple individual platforms of different types. The individual platforms may generate data describing, and/or resulting from, such interactions. The data may be received, ingested, stored, analyzed, and/or otherwise processed by a super-platform. The data may be aggregated and the data and/or aggregate data may be analyzed by a recommendation engine executing on the super-platform to determine one or more recommendations for a particular end-user based on an analysis of the data and/or aggregate data associated with that end-user. Such recommendation(s) may be provided to the end-user through an end-user interface and/or search engine provided by the super-platform, or through a third-party entity.

Growing community of inventors

San Jose, CA, United States of America

Edy S Liongosari