Method for Transactional Neural Reasoning with Real-Time Feedback and Adaptive Logic A computer-implemented method for transactional neural reasoning using an AI system, the method comprising:
- (a) collecting interaction data from a user session and retrieving related historical context;
- (b) generating an intent vector from the collected data that encapsulates the user's probable intent;
- (c) predicting an outcome of the session via concurrently evaluating an ideal outcome path and known adversarial patterns against the session data;
- (d) incorporating ambient context information into the evaluation by adjusting decision parameters based on external conditions;
- (e) reasoning across multiple logic domains by applying rule-based checks, statistical inference, and historical pattern matching to the intent vector and context to determine a recommended action;
- (f) triggering a conditional event during said reasoning if a confidence or pattern criterion is met, to modify the flow of interaction;
- (g) executing an action responsive to the recommended action using an actionable logic engine;
- and
- (h) updating the AI system's models or rules in real time by feeding back the outcome of the action and the session into a continuous integration pipeline, such that future iterations of steps (b)-(g) are improved.
- Relevant Prior Art:
- U.S. Pat. No. 10,248,916B1—“Decision engines with reactive trigger modules”
- U.S. Pat. No. 10,846,732 B2—“Hybrid reasoning systems”
- U.S. Pat. No. 10,467,351B2—“Action tuning based on reinforcement signals”
- US20210212789A1—“Self-improving digital assistants”
- Comparative Analysis:
- U.S. Pat. No. 10,248,916B1 teaches using confidence scores to affect conversation flow in NLP systems, but does not combine adversarial logic, ambient context, or multi-logic arbitration in a single workflow. It also lacks real-time system learning via outcome feedback.
- U.S. Pat. No. 10,846,732B2 describes switching between reasoning models but does not include logic fusion, vector memory, or contextual deltas. The models operate in isolation and lack unified adaptation mechanisms.
- U.S. Pat. No. 10,467,351B2 and US20210212789A1 rely on reinforcement learning and post-session feedback aggregation, without any real-time adjustment to logic models, delta path evaluation, or simultaneous adversarial reasoning.
- Novelty Assessment:
- TNRAI's method is a closed-loop, adaptive reasoning framework that processes multimodal input, calculates session-specific deltas, evaluates logic concurrently across multiple domains, injects situational data, and immediately refines future reasoning through continuous model integration. This coordinated interaction of subsystems—across steps (a) through (h)—is not shown in any prior reference as a unified process. TNRAI transforms the reasoning session into an evolving event with learning, logic modulation, and ambient awareness all occurring in the same pipeline
- Novelty and Non-Obviousness Statement:
- Whereas the cited references disclose partial workflows involving intent classification, logic switching, or feedback aggregation, the present invention instead provides an integrated method comprising real-time session data capture, adaptive intent modeling, delta-based reasoning, ambient-aware logic fusion, conditional interruption, action execution, and immediate feedback-based rule optimization. This holistic pipeline enables continuous improvement within the same engagement context and is not taught, suggested, or rendered obvious by any single reference or combination thereof. Accordingly, the claimed method constitutes a novel and non-obvious advancement in transactional AI reasoning under 35 U.S.C. § § 102 and 103.