Abstract: The system automatically generates a structured knowledge base from group chat data by leveraging AI models. It uses automated labeling and annotation to create training data, trains classification and extraction models to detect user intents and entities, and verifies extracted knowledge against subsequent chat content and external data sources. The system dynamically responds to user queries using the verified knowledge base, enabling accurate, context-aware answers with reduced computational requirements.

Abstract: The described system provides a novel, efficient, and scalable approach to using LLMs for domain-specific applications. By combining a pre-trained LLM model with a semantic search engine and a range of specialized modules, the system overcomes the limitations of traditional LLM-based systems. It offers accurate, context-aware responses to domain-specific queries without the need for costly retraining, making it an ideal solution for dynamic and complex business environments.

Abstract: A system is disclosed for generating context-aware, domain-specific responses using a pre-trained language model in combination with a semantic search engine and specialized processing modules. The system includes a classification model to identify user intent, an extraction model to determine parameters, and a plurality of parameter functions that generate outputs such as sentiment classifications, semantically similar content, and dynamically constructed prompts. By integrating retrieved structured and unstructured content into tailored prompts, the system provides accurate, domain-specific query responses without requiring retraining of the underlying language model. The architecture supports efficient and scalable workflow management in dynamic environments while reducing computational overhead compared to conventional approaches.

Abstract: A system is disclosed that integrates a context-aware, domain-specific artificial intelligence architecture into a location-based or interest-based peer-to-peer communication platform. The system improves operation of such platforms by enabling streamlined content discovery, enhanced personalization, efficient user and group administration, and dynamic content and user moderation with minimal computational requirements. Technical improvements include leveraging a pre-trained language model in conjunction with a procedural function framework, semantic search, and content retrieval modules to generate context-aware responses without resource-intensive retraining. The architecture supports classification, parameter extraction, and sentiment analysis to provide accurate and scalable query handling while reducing latency and computational load.

Abstract: Systems and methods are disclosed for cross-device clipboard synchronization. A first client device detects a copy event, encrypts the copied content, and transmits it to a conversational application server. The server classifies the copied content, stores it as an entry in an account-scoped memory referred to as Angel Memory, and signals availability to a second client device. The second device retrieves and decrypts the entry for insertion into an active input field. Angel Memory persists multiple entries with metadata such as timestamp, originating device, and content type, enabling context-aware retrieval across devices. In some embodiments, code snippets are preserved with formatting fidelity, one-time passcodes are enforced with expiration and single-use policies, and secured entries require authentication before release. Policy modules may apply governance decisions such as block, mask, retain, or expire.

Abstract: Systems and methods are disclosed for gaze-adaptive assistance in a conversational user interface. During a chat session, an eye-tracking engine provides gaze samples that are mapped to rendered utterance regions of the interface. From the mapped samples, the system detects attention events—such as fixations and regressions—and maintains per-utterance reread and revisit counts within a sliding time window. When thresholds are satisfied, subject to confidence and false-positive gating, the system emits a contextual assistance prompt targeted to the implicated utterance. The prompt is presented inline as a chip or as an expanded card with actions including clarification, rephrase, example, step-by-step guidance, or additional detail, and subsequent assistant output is adapted based on user input.

Abstract: Systems and methods are provided for automatically generating prioritized, color-coded tasks based on user input received through a conversation interface of a task management application. User input provided to an automated software assistant is analyzed along with contextual data from multiple sources, including existing tasks, calendar events, emails, and messages. The system determines task attributes such as title, status, deadline, geographic location, urgency, and frequency, and assigns a color tag based on color assignment parameters including task category, priority, and location. Machine learning algorithms analyze historical user-assigned colors in relation to task attributes to predict color assignments for new tasks and establish intelligent color relationships, thereby dynamically updating a prioritized, color-coded task list displayed in a graphical user interface.

Abstract: Systems and methods are provided for delivering context-aware assistance through an overlay interface. An overlay rendered on a user device captures content of a host application underlying the overlay via a scan-snap process. Captured content is filtered by privacy controls, transmitted securely to a server, analyzed to determine contextual meaning, and used to generate a response displayed in the overlay. In certain embodiments, capture includes document object model (DOM) snapshots, pixel-buffer screenshots, or fusion of DOM and bitmap data, optionally preceded by lightweight on-device optical character recognition. Mobile embodiments invoke the overlay through gesture inputs, while desktop embodiments employ a clipping interface with live preview of the selected capture region. Enterprise-oriented embodiments enforce policy restrictions, mask sensitive fields, and maintain audit logs, while multi-device embodiments synchronize responses across mobile and desktop sessions.

Abstract: A computer-implemented system and method provide context-preserving private messaging and inline editing within a conversational user interface. A client device renders a chat interface including a message timeline, participant indicators, and a morphable input box. A gesture recognizer detects a long-press and drag of the input box toward a target. When the target corresponds to a participant, the input box transitions into a private composer and a private sub-thread is presented co-located with the timeline. Messages in the private sub-thread are encrypted using per-thread cryptographic keys issued and rotated by a key management service. A persistent notification bubble remains anchored in the timeline to identify the sub-thread and signal unread activity. When the input box is dropped onto a message authored by the user, the system enters an inline editing mode with auditability through a revision history data store.

Abstract: A system and method provide context-preserving pinning and AI-assisted retrieval in a conversational interface. In response to a pin command on a message bubble, the system stores a pinned record including the bubble and a context window of surrounding messages with interaction metadata. A persistent sidebar surfaces latest pinned entry(ies) for low-latency recall. An AI engine updates a user profile from pinned records and provides personalized recommendations and proactive pin suggestions. The approach improves organization, efficiency, and comprehension while enabling privacy-preserving analytics and multi-device synchronization.

Abstract: Systems and methods are provided for facilitating digital asset transfer via natural language commands using a conversation or chat interface associated with a digital wallet application. The digital wallet owner may exchange commands with an assistant user. The assistant user may comprise an automated software agent. The digital wallet owner may transfer digital assets to a recipient's digital wallet identified by command to the assistant user. The transfer command may comprise the recipient's name preceded with a “@” notation.

Abstract: A system and method for managing guest interactions in vacation rental properties using an AI-driven conversational interface. Each property is associated with a customized account storing owner-defined rules, preferences, and media. Guests interact with the system through a chat interface to receive personalized responses generated based on emotional tone, contextual intent, and historical data. A guest interaction engine processes input and routes queries through a property rules processor, knowledge base, and response generation module. A personalization profile manager adapts future interactions based on guest behavior and sentiment. An owner console enables hosts to configure welcome messages, select AI voice characteristics, manage property photos, and monitor current guests. The system supports multi-property accounts, escalation pathways, and integration with external services for real-time updates, navigation, and verification.

Abstract: A system and method for transferring digital assets using natural language commands via a conversation interface. A user logs into a first digital wallet using login credentials, such as a username and password. Upon successful authentication, the system stores the wallet's digital asset balance and receives a natural language transfer command from the user through a conversation interface. The system parses the command to extract a recipient identifier and asset amount, identifies a second digital wallet associated with the recipient, and records a transfer of digital assets. An assistant user may provide contextual responses and prompt the user to confirm the transfer.

Abstract: Disclosed are systems and methods for enabling collaborative AI-human voice interactions in an outbound call environment. An AI voice engine synthesizes real-time speech responses, optionally using a voice model representing a specific human agent. A sentiment analysis engine monitors user speech to classify emotional state, and an emotion modulation engine dynamically adjusts tone, pitch, and prosody of AI output based on inferred sentiment or operator commands. An operator console allows live human oversight, including editing AI-generated text, selecting emotional tone presets, or overriding responses entirely. The system supports real-time disclosure of AI identity and records call metadata for compliance. Training modules log operator interventions, sentiment patterns, and interaction outcomes to improve future AI behavior, enabling a scalable voice platform that balances automation with human empathy.

Abstract: Systems and methods are provided for maintaining secure mirrored interactions across SMS and web-based conversational interfaces. Upon session initiation, chat updates are mirrored to the SMS channel in real time. Sensitive information within mirrored messages is automatically detected using natural language processing and masked prior to SMS delivery. Full chat histories, form inputs, and session metadata are persistently stored in encrypted storage, allowing users to pause and later resume the session by reinitiating an SMS conversation. The system ensures continuity, privacy, and real-time synchronization while minimizing exposure of sensitive user data across messaging channels.

Abstract: According to various embodiments, a system and method for verifying a user's identity using both document-based and biometric data is disclosed. The system may prompt a user to upload an image of a government-issued identification document and extract user information from the image using optical character recognition (OCR). The system may also extract the embedded face image from the ID and prompt the user to take a real-time selfie while performing one or more randomized actions or poses. A facial recognition engine may compare the extracted ID image to the live selfie to determine a similarity score. Based on this match, along with optional document authenticity checks, the system may confirm the user's identity in real-time for use cases such as account access, onboarding, and regulatory KYC compliance.

Abstract: Systems and methods are provided for synchronizing chat-based interactions between SMS and secure web-based interfaces. A conversational AI assistant receives user messages via SMS, extracts intent using natural language processing, and generates a secure hyperlink to initiate a mirrored session in a browser-based chat interface. The system masks sensitive information when mirroring chat content to SMS, ensuring that personally identifiable data remains secure. Users can pause and resume workflows by re-engaging the SMS channel, with the system retrieving and restoring prior session state. Machine learning models are used to optimize data classification, session continuation, and workflow efficiency across channels.

Abstract: Systems and methods are disclosed for real-time multilingual translation and contextual clarification within a private group chat interface. A computing system receives user-generated messages and translates them into each recipient's preferred language using a neural translation engine. When a term lacks a direct translation or is likely to cause confusion, a contextual clarification module generates a personalized explanation based on user profiles, language proficiency, and prior interactions. Clarifications are delivered privately to the intended recipient, preserving the conversational flow. An adaptive learning engine monitors user behavior across chat sessions, identifies patterns of confusion, and continuously refines the translation and clarification logic over time. The system may further include external API integration for idiomatic or cultural references and a user interface with interactive controls for viewing original message content and translation indicators.

Abstract: A system and method for assigning user-specific and context-based icons in a conversational interface. The method includes detecting structured objects in message prompts using a context analysis engine, selecting an icon via a machine learning model or stored user mapping, and rendering the icon across multiple interface regions, including adjacent to the prompt, within the user input field, and—if applicable—next to the user's response. Icon assignment may be personalized per user and updated based on user behavior or operator feedback. If the user response does not satisfy the expected object type, the icon is omitted, signaling a break in conversational continuity. The system improves message clarity, user experience consistency, and domain-aware scalability across structured data collection workflows.

Abstract: Systems and methods are disclosed for enabling gesture-based controls within a conversational interface. The system interprets swipe gestures and dual-touch interactions to streamline chat-based workflows. A swipe-right gesture across a chat input field triggers predictive message autofill based on conversational history and user-specific context. A swipe-left gesture recalls the user's last-sent message and places the input field into an editable state. Additionally, emotional reactions may be applied to specific chat messages through dual-hand gestures, such as upward swipes or semicircular motions, which map to reactions including thumbs up, love, thumbs down, hate, happy, and sad. In domain-specific implementations, users may swipe across AI-recommended content—such as real estate listings—to indicate preferences, allowing the assistant to adapt future suggestions accordingly. These features enable low-friction, expressive interaction within mobile and web-based messaging environments.