US-12619916-B1 - System and method for user-guided context reset in conversational artificial intelligence systems

US12619916B1US 12619916 B1US12619916 B1US 12619916B1US-12619916-B1

Abstract

Methods and computational devices for the implementation thereof that enable real-time Context Resets in conversational artificial intelligence (AI) systems are provided. The methods and computational devices improve the functioning of conversational AI systems by reducing non-productive computation, preventing propagation of irrelevant Active Context, and optimizing memory use during AI sessions. These operations restore conversational coherence by reducing Misalignment or Confusion between User Inputs and System Outputs. Accordingly, the invention provides a measurable improvement to computer functionality through adaptive, User-guided context management.

Inventors

Scot K Vorse

Assignees

Scot K Vorse

Dates

Publication Date: 20260505
Application Date: 20251103

Claims (17)

1 . A processor-implemented conversational AI system that, during a single AI session, generates a dialogue comprising user inputs and system outputs, and implements an improvement for a conversational AI engine comprising: a processor-implemented session context reset trigger configured to detect misalignment or confusion based on quantitative, processor-computed metrics derived from successive user inputs and system outputs; wherein detection of misalignment or confusion by the session context reset trigger is performed independently of any intent determination, and wherein the trigger is based solely on processor-computed metrics quantifying observable conversational divergence rather than any semantic reconstruction or prediction of the user's intended meaning; wherein, upon determination by a threshold detection module that a session context reset threshold associated with said trigger has been exceeded, a context reset module executes a session context reset to modify an active context; wherein the context reset module modifies the active context by at least one of removing, masking, suppressing, de-weighting, or isolating context portions, while preserving a stored personalization and integration state of the AI session; and wherein post-reset system outputs are generated, by a processor executing the conversational AI engine, with reference to the modified active context and the preserved personalization and integration state until termination of the AI session, thereby reducing propagation of irrelevant active context, lowering redundant computation cycles, optimizing memory utilization, and improving overall computational efficiency of the processor-implemented conversational AI system.
2 . The system according to claim 1 , wherein an indicator of session quality is processor-computed and may include one or more of: (i) divergence of semantic meaning across successive conversational turns, (ii) variability in sentiment across conversational turns, (iii) frequency of repeated content elements, or (iv) density of clarification prompts.
3 . The system according to claim 1 , wherein the session context reset trigger operates independently of any explicit or declarative statement of user objectives, enabling detection and correction of misalignment or confusion even when a user's stated objectives are incomplete, evolving, or inaccurate.
4 . The system according to claim 1 , wherein a semantic drift score is computed using cosine similarity between vector embeddings of consecutive conversational turns.
5 . The system according to claim 1 , wherein a sentiment volatility index is computed as the variance of polarity scores within a sliding window of n conversational turns.
6 . The system according to claim 1 , wherein repetition frequency is computed by detecting n-gram overlaps or embedding similarity above a predefined threshold between conversational turns.
7 . The system according to claim 1 , wherein clarification prompt density is determined as a proportion of conversational turns within a fixed-length sequence that are classified as clarification requests.
8 . The system according to claim 1 , wherein the context reset module integrates with an application programming interface (api) enabling external applications to trigger or manage context resets.
9 . The system according to claim 1 , wherein the session context reset trigger is computed as a composite score combining at least two of semantic drift score, sentiment volatility index, repetition frequency, and clarification prompt density.
10 . The system according to claim 1 , wherein a partial reset occurs if a single metric exceeds a lower threshold, an intermediate reset occurs if a composite score exceeds an intermediate threshold, and a full reset occurs if multiple metrics concurrently exceed higher thresholds.
11 . The system according to claim 1 , wherein executing a reset comprises partitioning and archiving affected portions of the active context for optional recall after the reset.
12 . The system according to claim 1 , wherein reset thresholds are dynamically adjusted based on session length, user profile, or prior reset frequency.
13 . The system according to claim 1 , wherein responses generated after a reset are based exclusively on the modified active context in combination with preserved personalization and integration state, excluding archived context.
14 . A computer-implemented method for reducing misalignment or confusion in a conversational AI system that generates multiple conversational turns of user inputs followed by system outputs during a single AI session, the method comprising: (a) detecting, by a session context reset trigger, misalignment or confusion based on one or more processor-computed dialogue metrics derived from user inputs and system outputs; wherein detection of misalignment or confusion by the session context reset trigger is performed independently of any intent determination, and wherein the trigger is based solely on processor-computed metrics quantifying observable conversational divergence rather than any semantic reconstruction or prediction of the user's intended meaning; (b) determining that a session context reset threshold has been reached or exceeded; (c) executing a session context reset that modifies an active context, while preserving a stored personalization and integration state; wherein executing the session context reset comprises isolating a portion of the active context and modifying it by one or more of removal, suppression, masking, de-weighting, or partitioning; and (d) continuing the AI session after the reset, with post-reset system outputs generated from the active context in combination with the preserved personalization and integration state until a session termination event; wherein the method reduces nonproductive computation, limits propagation of irrelevant active context, and optimizes memory use, thereby improving computational efficiency and restoring conversational coherence between user inputs and system outputs.
15 . The method according to claim 14 , wherein detecting misalignment or confusion by the session context reset trigger is performed independently of any intent determination, and wherein the detection is based solely on processor-computed metrics that quantify observable conversational divergence rather than any semantic reconstruction or prediction of the user's intended meaning.
16 . The method according to claim 14 , wherein the session context reset trigger is based on one or more of semantic drift score, sentiment volatility index, repetition frequency, or clarification prompt density.
17 . A conversational AI system for reducing misalignment or confusion during a single AI session in which user inputs generate corresponding system outputs, the system comprising: (a) a processor-implemented context analysis module configured to process successive user inputs and system outputs of each conversational turn; (b) a processor-implemented metric computation module configured to quantify a session context reset trigger, the trigger being based on processor-computed measures of conversational alignment, consistent with one or more defined metrics comprising semantic drift score, sentiment volatility index, repetition frequency, or clarification prompt density; wherein detection of misalignment or confusion by the session context reset trigger is performed independently of any intent determination, and wherein the trigger is based solely on processor-computed metrics quantifying observable conversational divergence rather than any semantic reconstruction or prediction of the user's intended meaning; (c) a processor-implemented threshold detection module configured to determine whether a session context reset threshold for any defined metric has been exceeded, or whether a user-initiated reset instruction has been received; and (d) a processor-implemented context reset module configured, upon such determination, to execute a session context reset while preserving a stored personalization and integration state; wherein post-reset system outputs are generated solely with reference to an active context and the preserved personalization and integration state until termination of the AI session, thereby reducing redundant processing and improving computational efficiency; wherein executing the session context reset comprises isolating a portion of the active context and modifying it by one or more of removal, suppression, masking, de-weighting, or partitioning.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63/869,020, filed on Aug. 22, 2025. BACKGROUND OF THE INVENTION This disclosure concerns Conversational AI Systems that maintain and adjust Active Conversational Context during Multi-Turn Interactions. The present specification relates to Conversational AI Systems, associated methods, and computational devices configured to operate such systems. The specification addresses the need for computational devices and methods that improve the functioning of Conversational AI Systems by reducing non-productive computation, preventing propagation of irrelevant Active Conversational Context, and optimizing memory use during Conversational AI Sessions. These improvements collectively enhance computational efficiency and reduce energy consumption while restoring conversational coherence by minimizing Misalignment or Confusion between User Inputs and System Outputs during an AI Session. As used in the specification, the following terms have the meanings set out below: Active Context or Active Conversational Context—The current set of information and state data (e.g., dialogue history, User intent, task state) that the Conversational AI System uses to interpret input and generate appropriate responses during an ongoing conversation. This includes the subset of stored interaction history—such as User Inputs, System Outputs, and System Prompts—currently accessible to the Conversational AI System within its Computation Graph for generating subsequent outputs. The Active Conversational Context represents the operational memory scope used to interpret new User Inputs and produce contextually coherent System Outputs. AI System or Artificial Intelligence System—a computational system employing Artificial Intelligence (AI) technology, including Conversational AI Systems Application Programming Interface (API)—A defined set of protocols and tools that allow software applications to communicate with each other or with external systems, enabling integration and data exchange between components or platforms. Archive and Recall Module—A component that manages long-term storage of segmented interactions and supports optional recall of suppressed or reset information. Unlike temporary Suppression in Partial Resets, it maintains archived data for later retrieval, either automatically (based on settings) or manually (upon User request). Artificial Intelligence (AI)—A computer system capable of performing tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and making predictions. Attention—A mechanism within certain AI models that assigns varying levels of importance, or Weight, to different parts of the input data when generating an output. When combined with Masking, this is referred to as Attention Masking. Clarification Prompt Density—The proportion of a conversation composed of prompts by the User or system asking for clarification. A high density may indicate degraded understanding or communication failure. Clarification Prompt Density may be determined as the proportion of conversational turns within a fixed-length sequence that are classified as clarification requests. Computation Graph—The structured internal representation through which the Conversational AI System manages contextual dependencies and token relationships during generation, including the subset of nodes corresponding to Active Conversational Context. The Computation Graph enables quantitative assessment of semantic continuity and supports efficient memory management during Session Context Reset operations. Context Blocks—Logical groupings of conversation elements (e.g., question-answer pairs, tasks, User goals) that are treated as modular units for managing, referencing, or resetting parts of the dialogue without affecting the whole. Context Management—The overarching framework or set of processes that handle how context is created, maintained, updated, pruned, or reset during interactions to ensure coherence and relevance in AI responses. Context Analysis Module—A system component that processes successive User Inputs and System Outputs to identify conversational features, such as intent, Sentiment, or Semantic Drift changes, for use in reset decisions. Context Reset—The modification or reinitialization of the Active Context so that prior conversational elements no longer influence System Outputs, while preserving the Personalization and Integration State. Context Reset Module—A system component that executes a Session Context Reset by modifying the Active Context (e.g., through removal, Suppression, or De-Weighting) while preserving the Personalization and Integration State. Conversational AI Engine—The core processing component that interprets User Inputs, manages Active Context, and generates System Outputs in a Conversational AI System. Conversational Artificial Intelligence Systems or Conversa