Diffusing a root identity of an entity among association and event covenants in a multi-dimensional computing security system involves generating a first generation of diffusion of identities of entities participating in mediated association and generating a second generation of diffusion of identities of the entities through recombinant mediated association of the entities and at least one other entity. The second generation of diffusion of identities facilitates securely constraining a computing system action associated with one of the entities.

Techniques for responding to a healthcare inquiry from a user are disclosed. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; instantiating a conversational engine based on the intent; eliciting, by the conversational engine, information from the user; and presenting one or more medical recommendations to the user based at least in part on the information.

There is provided a method for providing a super self-management service to provide step-by-step consulting and mentoring of a life value house (LVH) for self-management, the method executed on a super self-management service providing server, and the method comprising a cornerstone creation step for receiving, from a user terminal, cornerstone data including a mission and core value corresponding to a cornerstone of the LVH and storing the cornerstone data, a pillar creation step for receiving, from the user terminal, pillar data corresponding to health, personal connection, reading, language, and asset corresponding to a pillar of the LVH, an interior creation step for receiving, from the user terminal, differential knowledge, careful planning, bold action, excellent achievements, efficient promotion, and continuous management corresponding to an interior of the LVH, a roof creation step for receiving, from the user terminal, love data corresponding to a roof of the LVH, and a step for automatically generating consulting data by analyzing the data received in the cornerstone, pillar, interior, and roof creation steps and transmitting the consulting data to the user terminal.

A device can receive information associated with content that is to be provided to a first set of user devices. The device can receive information associated with a set of rules that identifies a set of conditions for providing the content to the first set of user devices. The device can receive network information associated with a second set of user devices. The device can determine that at least one condition, of the set of conditions identified in the set of rules, is satisfied based on the network information associated with the second set of user devices. The device can determine an action, associated with the content, to be performed based on determining that the at least one condition is satisfied, and can perform the action.

Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network.

The present invention is a method and system for automatically producing a learning plan. Upon receiving at least one type of data input, the system analyzes the data input and produces a learning plan based on the results of the analysis of the data input. This process may be used to either generate or update a learning plan, and may be repeated to update an existing learning plan.

A computer-implemented method includes receiving a plurality of expert datasets representing computer-generated beliefs; generating respective expert tensors for each expert dataset; fusing each of the respective expert tensors into a final result tensor, wherein the final result tensor represents the simultaneous fusing of the plurality of expert datasets; and storing or outputting the final result for use in an application.

A computer-implemented method, computer program product and computing system for identifying one or more portions of a website structure of a specific website; and associating the one or more portions of the website structure with one or more descriptors of the specific website to define a specific data description model corresponding to the specific website.

A computer-implementable method employs network signal metadata to train a cognitive learning and inference system to produce an inferred function, wherein the metadata comprises a syntactic structure of at least one network communication protocol. The inferred function is used to map metadata of a detected network signal to a cognitive profile of a transmitter of the detected network signal. The mapping effects intelligent discrimination of the transmitter from at least one other transmitter through corroborative or negating evidentiary observation of properties associated with the metadata of the detected network signal. Information content in the network signal can be determined from the inferred function or the cognitive profile without demodulating the network signal.

An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.