Generative response engine using chain-of-thought reasoning

Topics: LLM Readability, LLMO / GEO, OpenAI / ChatGPT

This OpenAI patent describes a generative response system developed by OpenAI that incorporates a chain-of-thought (CoT) reasoning model. When the system receives a prompt, instead of immediately generating a response, it first conducts an internal multi-step reasoning process — exploring different strategies, evaluating approaches, and even backtracking when necessary. The reasoning tokens generated during this internal process are combined with the input to produce a final response, but critically, these reasoning tokens are never shown to the user and are discarded from the conversation history. However, a human-readable summary of the reasoning steps can be generated by a separate, simpler language model and presented to the user for transparency and verification purposes. This architecture aims to significantly improve performance on complex tasks like mathematical proofs, coding, and scientific reasoning while keeping the user experience clean and understandable.

General Information

• Patent ID: US20260073295A1
• Assignee: OpenAI Opco LLC
• Countries: United States (with a PCT/international application filed as well)
• Last Publishing Date: March 12, 2026
• Inventors: Peter Vidani, Valerie Qi
• Status: Pending (Docketed New Case – Ready for Examination as of May 1, 2025)
• Expiration Date: Not yet determined (the patent has not been granted; it is a published application, so an expiration date would typically be 20 years from the filing date of April 4, 2025, if granted)
• Priority Date: September 11, 2024 (based on provisional application No. 63/693,683)
• Classification: G06N20/00 — Machine Learning

Background

Generative response engines such as large language models have transformed natural language understanding and generation, excelling at tasks like text generation, translation, summarization, and code generation. These models, particularly autoregressive ones, predict each token based on preceding tokens, which makes them effective for single-step reasoning and one-shot inference due to their training on massive and diverse datasets. However, a key limitation of autoregressive models is their struggle with multi-step reasoning tasks. Because they generate tokens sequentially without retaining or structuring intermediate reasoning steps, they perform poorly on problems like mathematical proofs that require scrutinizing logical steps, comparing multiple reasoning strategies, or backtracking from dead ends. This patent addresses that gap by introducing a chain-of-thought reasoning model that combines reinforcement learning with structured, step-by-step reasoning to improve performance on complex, multi-step problems.

Methodology: Step-by-Step Process

Step 1: Prompt Reception and Contextualization

The system receives a prompt from a requester (human or API). The prompt is combined with the existing conversation thread (prior turns) and any stored memory/personalization data to form a complete contextual input. As described in the patent, the prompt includes not just the current message but the entire conversation history, system prompts, and memory facts.

Step 2: Tokenization

The prompt and its context are tokenized to generate input tokens (called “first tokens”). These include prompt tokens, conversation thread tokens, and any supplemental context such as memory or system instructions.

Step 3: Chain-of-Thought Reasoning (Generating “Reasoning Tokens”)

A first ML model — the CoT model — processes the input tokens to generate reasoning tokens (called “second tokens”). This is the core innovation:

• The CoT inference engine conducts an internal conversation, exploring one or more reasoning frameworks for responding to the prompt.
• This process uses a combination of reinforcement learning and chain-of-thought reasoning to break complex problems into smaller, manageable components.
• The internal conversation can include trying different approaches, evaluating effectiveness, and backtracking when an approach is ineffective.
• An effort parameter (low/medium/high) can control how many reasoning tokens are generated, trading off speed/cost against reasoning depth.

Step 4: Response Generation

Once the reasoning process is sufficiently mature, the system processes the combination of input tokens and reasoning tokens to generate output tokens (called “third tokens”) — the actual response. The response can begin streaming before reasoning is fully complete, or after it finishes, depending on system configuration.

Step 5: Summary Generation (Parallel Process)

As chunks of reasoning tokens corresponding to discrete reasoning steps become available, they are passed to a summary engine — a separate, simpler autoregressive language model that lacks CoT functionality. This engine generates step-by-step summaries of the reasoning process. Summaries for early steps can be generated while the CoT model is still reasoning about later steps.

Step 6: Output Delivery and Token Management

• The response is provided to the requester.
• The reasoning tokens are NOT provided to the requester and are omitted from the conversation history — they are effectively discarded after the turn.
• The summary (if generated) is presented to the user but is also excluded from the conversation thread.
• The conversation thread for the next turn consists only of: prior input tokens + output tokens + new prompt.

Step 7: Context Window Management

Across multi-turn conversations, input and output tokens accumulate. When total tokens exceed the context window (e.g., 128k tokens), overflow tokens are truncated. Because reasoning tokens are invisible but consume processing during their turn, the actual token usage may be larger than what the user perceives.

Step 8: Safety and Moderation

Prompts pass through a prompt safety system before reaching the generative engine, and outputs can optionally pass through an output safety system for content moderation compliance.

Scoring Criteria / Ranking Factors

The patent explicitly mentions criteria used by the output validation engine to evaluate and select responses:

1. Relevance to the prompt — how well the response addresses the user’s query
2. Coherence — logical consistency and flow of the response
3. Fluency — quality of language expression
4. Informativeness — depth and usefulness of information provided
5. Adherence to specific guidelines or rules — compliance with application-specific constraints
6. Content moderation instructions/training — alignment with moderation policies

The system selects the response that “scores highest on the set criteria, balancing factors like relevance, informativeness, and coherence.”

Additionally, the effort parameter (low/medium/high) acts as a meta-scoring factor that governs the tradeoff between reasoning depth and speed/cost.

SEO and Generative Engine Optimization (GEO) Implications

Reasoning Tokens Are Invisible but Shape Everything

The patent reveals that the reasoning process — the most important part of how responses are formulated — is entirely hidden. The CoT model explores multiple approaches, backtracks, and evaluates before committing to an answer. Your content must survive this multi-framework evaluation process.

Actionable implication: Structure content so that it holds up under adversarial scrutiny. If your content makes a claim, support it with evidence, logical steps, and explicit reasoning within the same chunk. When the CoT model internally evaluates whether to use your content as source material, content that mirrors rigorous reasoning (clear premises → evidence → conclusions) will align with what the model considers a high-quality reasoning chain.

Example: Instead of writing “Product X is the best CRM for small businesses,” write: “Product X is designed for small businesses because it offers [specific feature], which addresses [specific pain point]. In independent benchmarks, it scored [metric] for [criterion], compared to [alternative] at [metric].” This mirrors the step-by-step logical evaluation the CoT model performs internally.

Multi-Step Reasoning Demands Explicit Information Hierarchies

The CoT model breaks problems into smaller components and processes them sequentially. Content that is organized into clear, logically connected steps directly maps onto how the reasoning engine works.

Actionable implication: Structure content in explicit multi-step formats where each section logically builds on the previous one. Use clear headings that signal the progression of reasoning (e.g., “Why this matters → How it works → What to consider → Recommended approach”).

Example: For a page about “How to choose a project management tool,” structure it as:

• Step 1: Define your team size and workflow type
• Step 2: Identify must-have features based on workflow
• Step 3: Compare tools against those criteria
• Step 4: Evaluate pricing at your scale

Each step should be self-contained enough to be a useful “chunk” while clearly connecting to the next step. This mirrors the CoT model’s chain-of-thought approach where “each step is logically connected to the next.”

The “Backtracking” Mechanism Has Content Selection Implications

The patent explicitly describes that the CoT model can try different approaches, evaluate them, and backtrack when one is ineffective. This means the model may initially consider your content as a source, then abandon it if it encounters contradictions or insufficient depth upon deeper reasoning.

Actionable implication: Eliminate content that could cause a reasoning model to “dead-end.” Ensure that every claim is internally consistent and that content doesn’t contradict itself across sections. Avoid vague or unsubstantiated superlatives that a reasoning model would evaluate and discard.

Example: If your page says “We offer the fastest delivery” in one section but provides no specific data, and another section mentions “delivery within 5-7 business days,” the reasoning model may backtrack away from your content entirely. Instead, be specific and consistent: “Standard delivery: 5-7 business days. Express delivery: 1-2 business days, available in [regions].”

The Summary Layer Creates a New Visibility Opportunity

The patent describes a summary engine that distills reasoning steps into titles and descriptions shown to users. This summary layer represents a separate content extraction point — a simpler model (without CoT) processes reasoning chunks into digestible summaries.

Actionable implication: Optimize for “summarizability.” Include clear, concise topic sentences at the beginning of each content section. These function as natural summary anchors that a summarization model can extract. If your brand’s information is used in the reasoning process, having clearly summarizable content increases the likelihood that it appears recognizably in any user-facing summary.

Example: Start each major content section with a single sentence that captures the key takeaway: “Brand X’s encryption protocol meets SOC 2 Type II compliance standards.” Follow with supporting details. This sentence structure makes it easy for a summary engine to extract and present your key claim.

Context Window Truncation Makes Position and Density Critical

The patent explicitly addresses that when tokens exceed the context window (128k tokens), content is truncated. In multi-turn conversations, earlier context gets pushed toward the truncation boundary.

Actionable implication for chunk relevance: Make your most important content information-dense and front-loaded. When an LLM retrieves your content as context, the most valuable claims and brand attributions should appear early in the content. Avoid lengthy preambles or “throat-clearing” content that consumes tokens without delivering value.

Example: On a product comparison page, don’t start with 300 words of category history. Lead with the comparison matrix and key differentiators immediately, then provide supporting context below. This ensures that when retrieved content is tokenized, your core value propositions survive potential truncation.

Conversation Memory Creates Persistent Brand Context Opportunities

The patent describes a memory system where facts about the user are stored and retrieved across sessions. The generative engine itself identifies which facts are worth storing.

Actionable implication for brand context optimization: Content that states clear, memorable brand facts in a structured way has a higher chance of being identified as “worth remembering” by the system. Brand identity blocks should contain discrete, factual statements that a model can recognize as persistent facts rather than marketing fluff.

Example: Instead of “We’re a leading innovator in sustainable fashion,” use: “Brand X manufactures clothing using 100% recycled ocean plastic, certified by Global Recycled Standard (GRS), with production facilities in Portugal and Vietnam.” These are discrete, verifiable facts that a memory system could flag for persistence.

The Effort Parameter Means Content Must Work at All Reasoning Depths

The patent reveals that users (or API callers) can set effort to low, medium, or high. At “low” effort, the model does minimal reasoning and favors speed. At “high” effort, it reasons extensively.

Actionable implication: Your content must be valuable at both shallow and deep processing levels. Include both quick-reference information (for low-effort queries) and deep, detailed analysis (for high-effort reasoning). This dual-layer structure ensures your content is relevant regardless of how much reasoning the model applies.

Example: Structure a product page with a clear, scannable summary at the top (serves low-effort queries: “Brand X is a [category] tool that does [core function] for [audience]”) followed by detailed technical specifications, comparisons, and methodology explanations below (serves high-effort queries that require multi-step evaluation).

Decision Boundaries Determine How Content Is Routed and Used

The patent describes decision boundaries that determine how the generative engine handles a prompt — whether to generate text, search the internet, call tools, or take agentic actions. The model determines whether it needs to “conduct a search of the Internet and use the results of that search in responding to the prompt.”

Actionable implication: Optimize for being the content that gets retrieved when the model decides an internet search is needed. This reinforces traditional SEO fundamentals but through the lens of CoT reasoning: the model first reasons about whether it needs external information, then retrieves it. Content that clearly addresses information gaps the model would identify during reasoning is more likely to be retrieved and used.

Example: Create content that answers specific, complex questions that a reasoning model would identify as requiring current or specialized information — not generic overviews that the model already knows, but specific data points, benchmarks, comparisons, or recent developments that would trigger the “I need to search for this” decision boundary.