Abstract: 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.

Abstract: Composing messages and receiving messages using text is time consuming and can lose meaning for recipients. Systems and methods are provided to generate a digital gift. A digital gift wrapper is used to precede the digital gift. A gift recipient interacts with the digital gift wrapper and the digital gift using a voice interface. The digital gift includes, for example, both audio and visual data. A physical gift is also generated and sent in a coordinated manner with the digital gift.

Abstract: 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.

Abstract: 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.

Abstract: Devices, systems and computing processes are provided that can ingest human creativity using human inputs (e.g. oral data, text, pictures, musical riffs, white board drawings, gestures, dances, haptic inputs, brain waves, biological inputs, etc.), process and organize these creative inputs using data science, perform real time, autonomous searching, apply data science to search results for supporting and contrasting results related to each topic, subtopic, etc., and provide results and recommendations to the human in real time. As a result, the human is not encumbered with the labor and time intensive research that might already be answered, solved, experimented, written, painted, etc. These efficient tools eliminate research time so that the person focuses on unique theories, thought works, and new artistic works (e.g. what humans do very well). Furthermore, contextually relevant “shadow results” are presented or outputted to the user, which spurs more creative ideas and thoughts.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems, devices and methods are provided that can make distributed and autonomous decision science based recommendations, decisions, and actions that increasingly become smarter and faster over time. The system includes intelligent computing devices, networks, electronic devices and other intelligent components or devices, including intelligent transceivers, receivers, and buses. Each of these intelligent devices can optionally have the ability to transmit and receive new data or decision science, software, data, and metadata to other intelligent devices and third party components and devices so that data or decision science, whether real-time, batch, or manual processing, can be updated and data or decision science driven queries, recommendations and autonomous actions can be broadcasted to other intelligent devices and third party systems in real-time.

Abstract: Biological human-computer interfaces are used to control devices, such a robotic limbs, using brain signals, nerve signals and muscle signals. These interfaces are sometimes called brain-computing interfaces. The learning process for a user to control devices using these interfaces is cumbersome. A computing platform is provided that includes a library of human model templates that are used to take biological inputs signals to generate outputs that control the device. Different templates are more appropriate for users of different attributes, such as demographic attributes. The computing platform collects data from multiple other users with these interfaces and devices in order to calibrate the human model templates. The calibrated human model templates are then published to the individual users, so that they can more accurately control their devices using their biological input signals.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A conversational limbic computing system is provided that converses with a patient and provides familiar media content based on the patient's emotions and psychological state. The media content includes audio data of familiar people talking 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 psychological or emotional state.

Abstract: The display of digital media content includes graphical user interfaces and predefined data fields that limit 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. The chatbot is specific to a customized digital magazine, which both evolve over time to a user for a given topic. 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 reads out the content to the user.

Abstract: 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.

Abstract: 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.

Abstract: 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.