Abstract: A system generates a response to a change of state by an automated agent using structure-based constraints. The system receives input regarding an interaction with a client, external data, and current operations data. The received input is used to generate a content sketch. The content sketch can include a plan for how to generate the response. The program language can be constrained to a set of known atoms, such as for example particular specified function, values, and flow control. Relevant instructions for generating a response are generated from the content sketch and an instruction bank. The relevant instructions are then used to generate a response. The generated response may then be executed by the automated agent system.

Abstract: The system provides improved controls for an automated agent by generating a declarative specification for an automated agent. The declarative specification is generated based at least in part from previous conversation data associated with interactions between an automated agent and a customer. The policies may include general policies and specific policies. A general policy is one that is applied to all automated agents. After creating the declarative specification, an automated agent can interact with a customer in an interaction based on the specification. The automated agent responses are evaluated based on checklists associated with the policies to determine if a response was proper.

Abstract: A system that generates relevant and vetted training data using intelligent simulated users and evaluation of conversation data. A simulated user and an automated agent engage in a conversation to generate conversation and/or interaction data. The simulated user is guided by scenarios which are generated based on one or more controls to be followed by the automated agent. Using a simulated user driven by control-derived scenarios ensures the ensuing conversation data is relevant to the desired scope of operation for the automated agent. The conversation data is evaluated based on the controls to confirm the automated agent actions and responses followed the controls properly. Evaluating the conversation data based on the controls ensures that conversation data associated with properly followed controls is used as subsequent training data.

Abstract: A system providing a state-driven automated agent. The state machine can include multiple nested state machines to receive, process, and generate responses to a user inquiry received through a chat application. The state machine has a plurality of states, and navigates between states based on one or more machine learning (ML) models. In some instances, each state is associated with a machine learning model that can predict what state the state machine should transition to from its current state. The machine learning model(s) can be implemented using one or more large language models (LLM). The state-driven automated agent includes an auditing state that analyzes a predicted response to a user inquiry and identifies errors with the response. The errors may be identified and automatically self-corrected.

Abstract: A system for providing and managing an automated agent. The automated agent may interact with a customer and utilize rules and instructions to determine a response to the customer and actions to perform. The customer interactions by the automated agent are driven by a set and/or subset of instructions that can be analyzed before each automated agent response or action. The subset of instructions, customer conversational input, and other content can be processed by a machine learning model, which may be implemented as a large language model (LLM).

Abstract: A system for generating instructions based on part from human training materials, wherein the generated instructions are used to train automated agents. The training materials may include training manuals, knowledge-base documents, articles, and other content. The training materials are processed and converted to a format that is digestible by a machine learning model. The processed training materials can be submitted as a prompt, along with role information and prompt instruction information. The prompt instructions provided with the role and training materials may direct the machine learning model to find relevant generated instructions from the provided training materials. The machine learning model processes the prompt and outputs extracted instructions from the content. The instruction document may be stored in a vector database for later use.

Abstract: The invention relates to crystalline forms of Alpha-1062 gluconate. In one aspect, the invention relates to a crystalline solid form of Alpha-1062 gluconate (Form C), wherein said crystalline form has prominent peaks at 3.90, 9.74, 10.35 and 21.43 degrees 2-theta (±0.2) in a powder X-ray diffraction pattern. The invention further relates to methods for treating a brain disease associated with cognitive impairment and/or with a cholinergic deficit in a subject, including administering the crystalline forms of Alpha-1062 gluconate to a subject in need thereof.

Abstract: A computer-implemented method and system includes accessing neurophysiological and neurovascular data recorded during sleep. A device mounted on a subject's head during sleep can gather neurovascular data by measuring transcranial impedance. The device can be in the form of a single band, multiple bands, or a headcap that are worn on the subject's head. Electrodes located along an inner surface of the wearable device contact the subject's head and measure transcranial impedance. Transcranial impedance measurements are gathered from multiple locations on the subject's head and over multiple frequencies to obtain a more complete assessment of glymphatic flow.

Abstract: A reading comprehension system may include an authoring tool to help generate adaptable dialogs and a reading tool to conduct adaptable dialog sessions with students. The authoring tool may receive and process stories to generate labeled stories and information models. The information models may provide the conceptual structures that an effective reader should build while reading and understanding a story. The system may use dialog models for general dialogs and information models for story specific dialogs to guide adaptable dialog sessions with students. During the adaptable dialog sessions, the system may constantly assess and guide the student's progress in the understanding the current story and in general reading comprehension development. Using the labeled stories and dialog sessions as training data, the system may learn how to dialog effectively with the students, to gather an evolving understanding of the student's abilities, and to acquire knowledge about the world or the story.

Abstract: A computer-implemented method and system includes a head-mounted wearable device with two current electrodes that provide input alternating currents over a range of frequencies to a subject during at least one intervention. The head-mounted wearable device includes at least two sense electrodes that collect voltage difference measurements in response to the input alternating currents. A measurement validation module applies a Hilbert transform or a Kramer-Kronig test to the voltage difference measurements to generate validated time-series impedance spectra. A cranial fluid dynamics module fits the validated time-series impedance spectra to an electrical circuit model to determine a glymphatic flow response to the at least one intervention.

Abstract: A research assistant system may include a research tool and components and a user interface to discover and evidence answers to complex research questions. The research tools may include components to iteratively perform steps in a research process, including searching, analyzing, connecting, aggregating, synthesizing, and chaining together evidence from a diverse set of knowledge sources. The system may receive an input query and perform a semantic search for key concepts in a text corpus. A semantic parser may interpret the search results. The system may aggregate and synthesize information from interpreted results. The system may rank and score the aggregated results data and present data on the user interface. The user interface may include prompts to iteratively guide user input to explore evidentiary chains and connect research concepts to produce research results annotated by evidence passages.

Abstract: A dynamic reasoning system may include a symbolic reasoning engine that iteratively calls a dynamic rule generator to answer an input query. The symbolic reasoning engine may determine a primary goal and/or secondary goals to generate proofs for the answer. The symbolic reasoning engine may call a rules component to provide rules to prove a current input goal. The rules component may use a static rule knowledge base and/or the dynamic rule generator to retrieve and rank rules relevant to the current input goal. The dynamic rule generator may generate new rules that lead to the current input goal. The dynamic rule generator may include a statistical model that generates unstructured or structured probabilistic rules based on context related to the input query. The symbolic reasoning engine may return a list of rules with confidence for explaining the answer to the input goal.

Abstract: A research assistant system may include a research tool and components and a user interface to discover and evidence answers to complex research questions. The research tools may include components to iteratively perform steps in a research process, including searching, analyzing, connecting, aggregating, synthesizing, and chaining together evidence from a diverse set of knowledge sources. The system may receive an input query and perform a semantic search for key concepts in a text corpus. A semantic parser may interpret the search results. The system may aggregate and synthesize information from interpreted results. The system may rank and score the aggregated results data and present data on the user interface. The user interface may include prompts to iteratively guide user input to explore evidentiary chains and connect research concepts to produce research results annotated by evidence passages.

Abstract: A research assistant system may include a research tool and components and a user interface to discover and evidence answers to complex research questions. The research tools may include components to iteratively perform steps in a research process, including searching, analyzing, connecting, aggregating, synthesizing, and chaining together evidence from a diverse set of knowledge sources. The system may receive an input query and perform a semantic search for key concepts in a text corpus. A semantic parser may interpret the search results. The system may aggregate and synthesize information from interpreted results. The system may rank and score the aggregated results data and present data on the user interface. The user interface may include prompts to iteratively guide user input to explore evidentiary chains and connect research concepts to produce research results annotated by evidence passages.

Abstract: A natural language interfacing system may use a knowledge acquisition tool to obtain structured representations from user input text. The system may initiate interaction with a request for input and a partial statement with blank text slots labeled by field types. The system may receive input text to fill in a slot of the partial statement and perform semantic parsing on the input text to identify a trigger concept. The system may generate a list of templates defining different semantic frames for the trigger concept. A generated template may include additional generated slots and/or suggested slot-fillers to guide user input. In response to a template selection, the partial statement includes the trigger concept annotated with a semantic frame. This process is repeated by iteratively updating the list of templates until the statement is completed. The statement is mapped to a structured representation including semantic frames.

Abstract: The invention relates to crystalline forms of Alpha-1062 gluconate. In one aspect, the invention relates to a crystalline solid form of Alpha-1062 gluconate (Form A), wherein said crystalline form has prominent peaks at 3.61, 10.98, 14.41 and 18.44 degrees 2-theta (±0.2) in a powder X-ray diffraction pattern. The invention further relates to methods for manufacturing crystalline forms and compositions comprising said crystalline forms.

Abstract: A selector configured to provide tactile feedback to the user. The selector can be incorporated into the fingertip of a glove, or many other suitable locations. Different size selectors can be provided for different application. Arrays of multiple selectors can also be provided. An embodiment of the inventive device includes a central plunger lying behind a conductive covering. A tactile surface is provided on the rear of the plunger. A compressive element is positioned to urge the plunger toward the conductive covering. When a user presses the selector against a touchscreen, the plunger is pushed away from the touchscreen and the tactile surface on the plunger presses against the user. The pressing of the tactile surface provides feedback to the user.

Abstract: A research assistant system may include a research tool and components and a user interface to discover and evidence answers to complex research questions. The research tools may include components to iteratively perform steps in a research process, including searching, analyzing, connecting, aggregating, synthesizing, and chaining together evidence from a diverse set of knowledge sources. The system may receive an input query and perform a semantic search for key concepts in a text corpus. A semantic parser may interpret the search results. The system may aggregate and synthesize information from interpreted results. The system may rank and score the aggregated results data and present data on the user interface. The user interface may include prompts to iteratively guide user input to explore evidentiary chains and connect research concepts to produce research results annotated by evidence passages.

Abstract: An individualized machine learning model embedded on a mobile health application, coupled to autonomous workflow agents systems to operate as a small artificial intelligence program. The machine learning algorithm learns from small datasets—making it possible to compute real-time data even on a low-grade smartphone device. It predicts early worsening outcomes by extracting patterns (features) that correlate with the individual baseline changes in heart failure status. The workflow agents support dynamic machine learning model deployment to different devices and configurations for real-time prediction and intervention. The different device configurations include patients who own only a smartphone device, patients who own a smartphone and wearable/biosensors, and patients who own a smartphone and who have an implanted cardiac device.

Abstract: A graphical user interface allowing one or more users to rapidly perceive and respond to varying operational states. Glyphs depicting differing levels of detail are provided for a top-level manager versus a lower-level operator. As applied to the vehicle paradigm, each individual glyph presents information as to the vehicle's current assigned task, mode, state, status, and its progress in completing that task. Each display shows multiple glyphs representing multiple vehicles. A vehicle in need of attention rises in the depiction whereas a vehicle not needing attention remains in a low position.