Facet Labs, LLC

Nasdaq, Inc.

Stuart Ogawa

Wilfred P So

Lindsay Alexander Sparks

Koichi Nishimura

Jane W Chen

Systems and methods are provided for data security. A server system provides data security using one or more processor devices, one or more communication interfaces, and one or more memory devices including computer-executable instructions. Those instructions cause the one or more processor devices to: monitor one or more requests or activities of a computing device; compare the monitored one or more requests or activities with a database of predetermined characteristics to determine whether the monitored one or more requests or activities indicates that the computing device downloaded or attempted to download more than a threshold number of data files or objects; and determine that the one or more requests or activities is suspicious when the comparing determines that the one or more requests or activities indicates that the computing device downloaded or attempted to download more than the threshold number of data files or objects, which causes a response to hinder the monitored one or more requests or activities.

Systems, methods, and computer-readable media for data security

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

Systems and methods are provided for data security. A server system provides data security using one or more processor devices, one or more communication interfaces, and one or more memory devices including computer-executable instructions. Those instructions case the one or more processor devices to: monitor one or more requests or activities of a computing device and compare the monitored one or more requests or activities with a database of predetermined characteristics to determine whether the monitored one or more requests or activities indicates that the computing device (i) accessed or attempted to access sequentially more than A data files or objects in less than a predetermined period of time, where A is a positive integer greater than two, and (ii) downloaded X data files or objects, where X is a positive integer greater than two. The monitored one or more requests or activities is determined suspicious when the comparing determines that (i) and (ii) are true, which causes a response to hinder the monitored one or more requests or activities.

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

In a server system providing data security, a processor monitors requests or activities of a computing device and compares the monitored requests or activities with a database of predetermined characteristics to determine whether the monitored requests or activities indicates that the computing device (i) accessed or attempted to access sequentially more than a predetermined number of data files or objects in less than a predetermined period of time, and (ii) downloaded a predetermined number of data files or objects. The monitored requests or activities is determined suspicious when the comparing determines that (i) and (ii) are true, which causes a response to hinder the monitored requests or activities.

Systems and methods are provided for active continuous data security. An active receiver module, an active marker module, an active transmitter module and an active profiler module work together to monitor data requests, detect suspicious activity and characteristics, and responds to hinder the suspicious activity. A method includes: obtaining a request for data; obtaining a characteristic associated with the request for data; comparing the characteristic with a database of known patterns and characteristics to determine if the request is suspicious; storing the request and the characteristic in the database for future comparison; and initiating a response to hinder the request for the data when the request is determined to be suspicious. Markers embedded in data are used to track the data, including data that is exposed to a security risk. Pattern detection is used to uncover suspicious activity and the systems are able self-learn as more data is provided.

Growing community of inventors

Los Gatos, CA, United States of America

Stuart Ogawa