Facet Labs, LLC

Other

Wilfred P So

Stuart Ogawa

Koichi Nishimura

Lindsay Alexander Sparks

Justin A Eichel

Linda Vu

Peter Szabo

Jane W Chen

Typical graphical user interfaces and predefined data fields limit the interaction between a person and a computing system. An oral communication device and a data enablement platform are provided for ingesting oral conversational data from people, and using machine learning to provide intelligence. At the front end, an oral conversational bot, or chatbot, interacts with a user. On the backend, the data enablement platform has a computing architecture that ingests data from various external data sources as well as data from internal applications and databases. These data and algorithms are applied to surface new data, identify trends, provide recommendations, infer new understanding, predict actions and events, and automatically act on this computed information. The chatbot then provides audio data that reflects the information computed by the data enablement platform. The system and the devices, for example, are adaptable to various industries.

Oral communication device and computing system for processing data and outputting user feedback, and related methods

Assistive technologies are herein provided to assist leaders in engaging one or more group participants using a combination of private data specific to a participant and public data specific to a participant. The system includes: a group bot that has public group data and private group data, a first bot for a first participant that has private data and public data associated with the first participant, and a leader bot for a leader. The leader bot is data interactive with the group bot and the first bot, and can cause the first bot to appropriately serve private data on a permissioned private device of the first participant and to serve public data on a permissioned group output device.

Interactive group session computing systems and related methods

The amount and variety of data being generated is becoming too extreme for many computing systems to process, and is even more difficult for information systems to provide relevant data to users. A distributed computing system is provided that includes server machines that form a data enablement platform. The platform includes: a plurality of data collectors that stream data over a message bus to a streaming analytics and machine learning engine; a data lake and a massive indexing repository for respectively storing and indexing data; a behavioral analytics and machine learning module; and multiple application programming interfaces (APIs) to interact with the data lake and the massive indexing repository, and to interact with multiple applications. The multiple applications are command cards, and each command card includes a directive module, a memory module, search bots, and behavior bots that operate at least within the data enablement platform.

Computing architecture for multiple search bots and behavior bots and related devices and methods

A system and related methods for making distributed, autonomous decisions on the management of network traffic and data are provided. The disclosed system and methods increasingly become smarter and faster over time based on the accumulated data across the network, and any data science or decision science associated with the system can be updated or modified via remote, autonomous updates. The system can comprise computing devices and systems across multiple layers of the network, wherein each layer of the network can comprise a plurality of nodes. Each node of the network can be enhanced or embedded with the Smart Distributed system, and the Smart Distributed system can provide each node of the network the ability to apply data or decision science to any data in real or near real-time.

Smart distributed systems for managing network data

A smart and scalable dementia assistant device is provided that converses with a patient in voices familiar with the patient. It utilizes content learned from the patient and content provided by family, friends, caregivers, and doctors, and autonomously adjusts conversations based on the changing state of the patient's dementia state. The device autonomously controls IoT devices (e.g. doors, elevators, tvs, medical dispensers) to help and assist the dementia patient using oral and IoT sensors.

Interactive personal assistive devices and systems with artificial intelligence, and related methods

Oral communication device and computing systems for processing data and outputting oral feedback, and related methods

A system and method are provided for controlling a plurality of vehicles to affect positioning of a common payload. The system comprises of multiple vehicles having positioners to change the location of the common payload, where the group of vehicles form a swarm that is controlled by a driver or pilot station. Each vehicle is autonomously stabilized and guided through a swarm electronics unit, which further includes sensor, communication, and processing hardware. At the driver or pilot station, a system or a person remotely enters payload destinations, which is processed and communicated to each vehicle. The method for controlling a multi-vehicle system includes inputting the desired location of the payload and determining a series of intermediary payload waypoints. Next, these payload waypoints are used by the swarm waypoint controller to generate individual waypoints for each vehicle. A controller for each vehicle moves the vehicle to these individual waypoints.

System and method for multiple vehicles moving a common payload

A system and method are provided for controlling a plurality of aircraft to lift a common payload. The system comprises of multiple aircraft tethered to a common payload, where the group of aircraft form a swarm that is controlled by a pilot station. Each aircraft is autonomously stabilized and guided through a swarm avionics unit, which further includes sensor, communication, and processing hardware. At the pilot station, a pilot remotely enters payload destinations, which is processed and communicated to each aircraft. The method for controlling a multi-aircraft lifting system includes of inputting the desired location of the payload, and determining a series of intermediary payload waypoints. Next, these payload waypoints are used by the swarm waypoint controller to generate individual waypoints for each aircraft. A flight controller for each aircraft moves the aircraft to these individual waypoints.

System and method for multiple aircraft lifting a common payload

A system and method are provided for controlling a plurality of aircraft to lift a common payload. The system comprises of multiple aircraft tethered to a common payload, where the group of said aircraft form a swarm that is controlled by a pilot station. Each said aircraft is autonomously stabilized and guided through a swarm avionics unit, which further comprises of sensor, communication, and processing hardware. At the said pilot station, a pilot remotely enters payload destinations, which is processed and communicated to each said aircraft. The method for controlling a multi-aircraft lifting system comprises of first inputting the desired location of the payload, and then determining a series of intermediary payload waypoints. Next, these payload waypoints are used by the swarm waypoint controller to generate individual waypoints for each aircraft; a flight controller for each aircraft moves the aircraft to these individual waypoints.

Growing community of inventors

Mississauga, Canada

Wilfred P So