# How an AI System Creates and Monetizes Search Patterns for User Interaction > This patent describes an AI system that uses a neural network to understand information, create optimized search patterns from human knowledge, and then uses those patterns to interact with users and make money. - **Patent:** US 12737353 - **Original title:** Site rank codex search patterns - **Granted:** 2026 - **Status:** Active - **Times cited:** 0 - **Field:** ai_ml, software, telecommunications, ecommerce ## What it does Based on the abstract, a "Codex system" of computers, linked into a neural network, continuously scans and gathers information from its environment, understanding and interacting with it. An "optimizer software" then executes instructions based on rules of grammar and semantics to search an "encyclopedia of human knowledge," transforming input into a "search pattern." The system then monetizes and commercializes each transformed input and its optimal output. Finally, an artificial intelligence interaction software, called a "virtual maestro," uses this search pattern and optimal output to engage in scripted communication with the end user. For example, a user might ask a complex question, and the Codex system would process it, find the best answer from its knowledge base, and then have the virtual maestro deliver a tailored, monetized response. ## What it does NOT cover - Does not cover systems that do not specifically use a neural network for continuous information gathering and environmental interaction. - Does not cover AI systems where an optimizer software does not apply rules of grammar and semantics to an "encyclopedia of human knowledge" to create search patterns. - Does not cover systems that do not explicitly monetize and commercialize each transformed input and its corresponding optimal output. - Does not cover user interaction that is not specifically "scripted communication" delivered by an "artificial intelligence interaction software" referred to as a "virtual maestro." - Does not cover search or interaction systems that do not specifically interact with an "encyclopedia of human knowledge." ## The clever bit The clever bit is the integration of continuous environmental scanning with an optimizer that applies grammar and semantics to human knowledge, specifically for generating "search patterns" that are then monetized. This system also introduces a "virtual maestro" for scripted user interaction, creating a complete, self-contained, and monetizable AI information delivery loop. ## Real-world examples 1. Advanced AI search engines with built-in monetization 2. Automated customer service bots with transaction-based charges 3. Personalized AI tutors that charge per interaction 4. AI-driven content recommendation systems with direct revenue streams ## Why it matters If implemented, this type of system could significantly change how information is accessed and monetized online. By creating optimized search patterns and using a 'virtual maestro' for scripted communication, it could offer a highly structured and potentially profitable way to deliver information to users. The focus on monetizing each interaction suggests a new business model for AI-driven information services. ## Frequently asked questions ### What does How an AI System Creates and Monetizes Search Patterns for User Interaction cover? This patent describes an AI system that uses a neural network to understand information, create optimized search patterns from human knowledge, and then uses those patterns to interact with users and make money. ### When does this patent expire? This patent is expected to expire on September 15, 2046, when the invention enters the public domain. ### What problem does this patent solve? If implemented, this type of system could significantly change how information is accessed and monetized online. By creating optimized search patterns and using a 'virtual maestro' for scripted communication, it could offer a highly structured and potentially profitable way to deliver information to users. The focus on monetizing each interaction suggests a new business model for AI-driven information services. ### What does this patent NOT cover? Does not cover systems that do not specifically use a neural network for continuous information gathering and environmental interaction. **Full plain-English explainer:** https://patentbrief.org/patent/us/12737353/site-rank-codex-search-patterns **Original patent:** https://patents.google.com/patent/US12737353 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How AI Understands Answers Across Multiple Documents](https://patentbrief.org/patent/us/20240338414/inter-document-attention-mechanism) — Microsoft's 2024 patent on using AI to find answers by understanding how information in one document relates to information in another. - [How a Digital Assistant Launches Apps Using Your Voice](https://patentbrief.org/patent/us/9548050/continuity-handoff) — This patent describes how a digital assistant like Siri uses your spoken words and understanding of your conversation to figure out what you want and launch the right app. - [How Human Searchers Unlock Restricted Web Content for Users](https://patentbrief.org/patent/us/8577894/bing-knowledge-graph-satori) — A system that uses human searchers to access password-protected or subscription-based websites to retrieve information for users who otherwise cannot see that content. - [How Computers Group Files Based on Meaning and Context](https://patentbrief.org/patent/us/20150206070/generative-adversarial-networks-gans) — A system that organizes digital files and data into related groups by analyzing their meaning, context, and how they relate to one another. - [How AI Learns to Control Game Characters Based on Their Surroundings](https://patentbrief.org/patent/us/10607134/artificially-intelligent-systems-devices-and-methods-for-learning-andor-using-an-avatars-circumstances-for-autonomous-avatar-operation) — A system that allows digital characters to automatically perform actions by matching their current environment to previously learned experiences stored in a database.