{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9852648","patent":{"patent_number":"US-9852648","title":"Extraction of knowledge points and relations from learning materials","assignee":null,"inventors":[],"filing_date":"2015-07-10T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["G09B","G06F","G06F","G06F","G06F","G09B","G06F"],"num_claims":20,"abstract":"A method of automated domain knowledge structure generation includes crawling learning materials. The method may include extracting structural information from the learning materials. The method may include extracting knowledge points from the learning materials. The method may include inferring dependency relationships between the knowledge points. The method may include aligning one or more of the knowledge points with one or more of the learning materials. The method may also include generating a domain knowledge structure. The domain knowledge structure may include the extracted knowledge points organized at least partially according to the inferred hierarchy and dependency relationships. The extracted knowledge points may include the aligned learning materials."},"analysis":{"summary":"The patent titled \"Extraction of Knowledge Points and Relations from Learning Materials\" (US-9852648) introduces a pivotal method for automating the generation of comprehensive domain knowledge structures from diverse educational content. At its core, this innovation addresses the significant challenge of information overload and the difficulty in discerning interconnected concepts within vast, unstructured learning materials.\n\nThis technology employs a sophisticated multi-step process. It begins by intelligently crawling various learning resources, such as digital documents, web pages, and potentially transcribed multimedia. Following this, the system extracts critical structural information, identifying the inherent organization and hierarchy within the content. A key component involves the precise extraction of individual knowledge points – the fundamental facts, concepts, and principles central to the subject matter. Crucially, the patent details an advanced mechanism for inferring dependency relationships between these knowledge points, enabling the system to understand how concepts build upon each other and are semantically linked.\n\nFurthermore, the invention ensures that each extracted knowledge point is meticulously aligned with its original source material. This provides invaluable context and traceability, allowing users to quickly reference the source content for deeper understanding. The culmination of these processes is the generation of a dynamic domain knowledge structure, which organizes all extracted knowledge points according to their inferred hierarchy and dependencies, complete with direct links to the relevant learning materials.\n\nThe business value and applications of this patent are substantial. It offers a scalable solution for creating personalized learning paths, powering intelligent tutoring systems, and enhancing content curation in educational technology (EdTech). For corporate training and knowledge management, it promises to streamline onboarding, facilitate skill development, and transform internal documentation into accessible, interconnected knowledge bases. The market opportunity spans the entire EdTech sector, corporate learning platforms, and any industry reliant on efficient knowledge acquisition and transfer. This invention represents a significant leap towards more intelligent and adaptive learning environments, offering a clear competitive advantage in the digital learning space.","layman_explanation":"## Unlocking the Value of Knowledge: A Business Professional's Guide to the 'Extraction of Knowledge Points and Relations from Learning Materials' Patent\n\nIn today's fast-paced business world, knowledge is power. Yet, most organizations grapple with an overwhelming volume of information – training manuals, market research, internal documentation, competitive analyses – often stored in disparate, unstructured formats. This 'information overload' isn't just a nuisance; it's a significant barrier to efficiency, innovation, and effective decision-making. The patent titled \"Extraction of Knowledge Points and Relations from Learning Materials\" offers a strategic solution, promising to transform how businesses acquire, organize, and leverage their collective intelligence.\n\n### 1. What Problem Does This Solve?\n\nConsider the challenges faced by a new employee trying to get up to speed, or a product development team researching a new market. They're typically given access to a mountain of documents, presentations, and videos. The critical business problem here is the lack of a structured, navigable pathway through this data. Information is siloed, key concepts are buried, and the relationships between different pieces of knowledge are unclear. This leads to:\n\n*   **Inefficient Learning & Onboarding:** Employees spend excessive time searching for information rather than applying it.\n*   **Missed Opportunities:** Critical insights remain undiscovered because connections between data points aren't made.\n*   **Inconsistent Knowledge:** Different teams or individuals may interpret the same information differently, leading to errors or duplicated efforts.\n*   **Scalability Issues:** As an organization grows, managing and updating this unstructured knowledge becomes increasingly unsustainable.\n\nExisting solutions, such as simple keyword searches or manual content tagging, are rudimentary. They lack the semantic understanding needed to truly grasp how concepts relate, leaving significant gaps in knowledge accessibility and utility.\n\n### 2. How Does It Work?\n\nThis patent introduces a sophisticated, automated approach to create a 'knowledge map' of any subject matter. Think of it like a highly intelligent digital assistant that can read, understand, and organize information with unprecedented clarity:\n\n*   **Digital Content Ingestion:** First, the system 'crawls' through all your learning materials – be it internal wikis, competitor reports, industry articles, or training videos. It can handle diverse formats, acting as a universal information collector.\n*   **Structural Understanding:** It then intelligently analyzes the structure of these materials. For instance, it recognizes chapter titles, section headings, bullet points, and other organizational cues, much like a human would skim a document to understand its layout.\n*   **Core Concept Extraction:** The system identifies and extracts the 'knowledge points' – the fundamental concepts, facts, and principles central to the content. It distinguishes between main ideas and supporting details, pinpointing what's truly important.\n*   **Relationship Mapping:** This is the game-changer. Beyond just identifying concepts, the system infers how these concepts relate to each other. For example, it might identify that 'Market Segmentation' is a *prerequisite* for 'Target Marketing', or that 'Agile Methodology' *includes* 'Scrum'. It builds a web of interconnected knowledge, showing dependencies and hierarchies.\n*   **Source Linking:** Crucially, every extracted knowledge point is linked back to its original source material. If you see a concept on the knowledge map, you can click it and instantly be taken to the exact paragraph or slide where it was discussed. This ensures context and verifiability.\n\nThe output is a dynamic, interactive domain knowledge structure – a visual and logical representation of all the information, organized by how concepts are related, making complex subjects easy to navigate and understand.\n\n### 3. Why Does This Matter?\n\nThe business implications of this innovation are profound:\n\n*   **Enhanced Employee Productivity & Onboarding:** Drastically reduces the time new hires spend getting up to speed, providing them with an instant, personalized knowledge map of their role and company processes. Existing employees can quickly find precise information, boosting efficiency.\n*   **Strategic Decision-Making:** By revealing hidden connections and dependencies within vast datasets, this technology empowers leaders with deeper insights, leading to more informed and agile strategic decisions.\n*   **Competitive Advantage:** Companies leveraging this system can outpace competitors in knowledge acquisition, training, and innovation. It's a foundational technology for building truly 'smart' organizations.\n*   **New Product & Service Opportunities:** The ability to automatically structure knowledge opens doors for developing new AI-powered tools, personalized learning platforms, or advanced knowledge management solutions that can be offered to clients.\n*   **Significant ROI:** The efficiency gains from automated content analysis, reduced training times, and improved decision-making can translate into substantial cost savings and increased revenue.\n\n### 4. What's Next?\n\nThis patent lays the groundwork for a future where knowledge management is less about storing documents and more about understanding and leveraging interconnected insights. We can expect to see rapid adoption in corporate learning platforms, AI-driven content analytics, and specialized knowledge domains. As AI continues to evolve, this technology will likely integrate with natural language generation to create dynamic learning content, and with predictive analytics to anticipate knowledge gaps. For forward-thinking businesses, investing in or adopting solutions based on this patent is not just an operational improvement; it's a strategic imperative for staying competitive in an increasingly knowledge-driven economy.","technical_analysis":"The patent titled \"Extraction of Knowledge Points and Relations from Learning Materials\" (US-9852648) describes a sophisticated method for automated domain knowledge structure generation, representing a significant advancement in semantic information extraction and knowledge representation. This invention leverages a pipeline approach, integrating several computational linguistics and machine learning techniques to transform unstructured learning content into a structured, navigable knowledge graph.\n\n**Technical Architecture and Workflow:**\nThe system's architecture can be conceptualized as a series of interconnected modules:\n1.  **Learning Material Crawler:** This initial module is responsible for ingesting diverse digital learning materials. This includes web pages (HTML), digital documents (PDF, DOCX, EPUB), and potentially transcribable audio/video content. The crawler must handle various data formats, potentially employing web scraping techniques, document parsing libraries, and API integrations for content acquisition.\n2.  **Structural Information Extractor:** Once materials are acquired, this module analyzes their inherent structure. For text documents, this involves identifying headings, subheadings, paragraphs, lists, and tables. For web content, it would parse the DOM structure. Advanced techniques like layout analysis (for PDFs) or visual content analysis (for presentations/videos) could be employed to infer logical sections and hierarchies, converting raw data into a more parseable intermediate representation.\n3.  **Knowledge Point Extractor:** This is a core NLP-intensive module. It identifies and extracts atomic knowledge units or concepts from the processed learning materials. This could involve:\n    *   **Named Entity Recognition (NER):** Identifying specific terms, proper nouns, and technical jargon as potential knowledge points.\n    *   **Term Extraction:** Using statistical or linguistic methods to identify multi-word terms and key phrases.\n    *   **Semantic Role Labeling (SRL) / Open Information Extraction (OpenIE):** Deconstructing sentences to identify predicates and their arguments, allowing for the extraction of factual assertions as knowledge points.\n    *   **Concept Tagging:** Leveraging pre-existing ontologies or taxonomies to tag relevant concepts.\n4.  **Dependency Relationship Inference Engine:** This module is critical for transforming isolated knowledge points into a connected structure. It infers various types of relationships (e.g., hierarchical, causal, prerequisite, definitional, part-of) between the extracted knowledge points. Techniques might include:\n    *   **Lexico-Syntactic Pattern Matching:** Identifying specific linguistic patterns (e\"X is a type of Y,\" \"X leads to Y\") to infer relationships.\n    *   **Embeddings and Semantic Similarity:** Using word and sentence embeddings (e.g., Word2Vec, BERT, GPT-variants) to calculate semantic proximity and infer relationships between concepts that frequently co-occur or are contextually similar.\n    *   **Graph Neural Networks (GNNs):** Training GNNs on existing knowledge graphs to predict new relationships based on the structural context of nodes.\n    *   **Coreference Resolution:** Ensuring consistent identification of entities across the document to build accurate relationships.\n    *   **Supervised Machine Learning:** Training classifiers on annotated data to identify relationship types.\n5.  **Knowledge Point Alignment Module:** This component links each extracted knowledge point back to its original source location within the learning material. This could involve storing precise pointers (e.g., document ID, page number, paragraph index, character offset) alongside the knowledge point metadata. This ensures traceability and allows for contextual retrieval.\n6.  **Domain Knowledge Structure Generator:** The final module assembles all extracted knowledge points and their inferred relationships into a coherent, queryable domain knowledge structure. This structure is typically a knowledge graph, which can be stored in a graph database (e.g., Neo4j, Apache Jena, Amazon Neptune) or represented using semantic web technologies like RDF/OWL. The organization is dictated by the inferred hierarchy and dependency relationships, providing a logical and intuitive representation of the domain.\n\n**Implementation Details and Performance Characteristics:**\nScalability is a critical consideration. The crawling and extraction phases would likely utilize distributed processing frameworks (e.g., Apache Spark) to handle large volumes of data. The NLP components might involve a cascade of models, from tokenization and POS tagging to more complex dependency parsing and semantic analysis. Performance would be measured by precision and recall of knowledge point extraction and relationship inference, as well as the efficiency of processing diverse document types.\n\n**Code-Level Implications:**\nDevelopers implementing this system would require expertise in Python/Java for NLP libraries (e.g., spaCy, NLTK, Hugging Face Transformers), graph database APIs, and potentially cloud-native services for scalable data processing and storage. Model training for NER, relationship extraction, and semantic similarity would be an ongoing process, requiring robust MLOps practices. The system would need to be highly configurable to adapt to different domains and learning material formats.\n\nIn essence, this patent provides a foundational framework for autonomous knowledge curation, moving beyond simple keyword indexing to construct a deep, semantic understanding of educational content. The ability to automatically build and maintain such rich knowledge structures has profound implications for AI-driven learning platforms and intelligent content management systems, promising more adaptive, personalized, and efficient knowledge acquisition.","business_analysis":"The patent titled \"Extraction of Knowledge Points and Relations from Learning Materials\" (US-9852648) presents a significant business opportunity by addressing the pervasive challenge of unstructured knowledge within educational and corporate learning environments. This innovation offers a scalable, automated solution to transform raw learning materials into highly organized, actionable domain knowledge structures, thereby unlocking substantial market value.\n\n**Market Opportunity Size:**\nThe global EdTech market is projected to reach hundreds of billions of dollars in the coming years, driven by digital transformation in education, demand for personalized learning, and continuous upskilling needs. Within this, the sub-segments of content creation, learning management systems (LMS), and AI-powered learning tools are particularly relevant. The corporate training market also represents a multi-billion-dollar industry, with a constant need to manage and deliver vast amounts of internal knowledge efficiently. This patent directly targets these markets, offering a foundational technology that can enhance existing platforms and enable entirely new business models.\n\n**Competitive Advantages:**\nThis invention provides several key competitive advantages:\n1.  **Automation and Scalability:** Unlike manual content curation or less sophisticated keyword-based systems, this technology automates the complex process of knowledge extraction and relationship inference. This allows for processing vast quantities of data quickly and consistently, a critical factor for large educational institutions or multinational corporations.\n2.  **Semantic Depth:** The ability to infer dependency relationships between knowledge points moves beyond superficial data extraction. It creates a rich, semantic understanding of the content, which is crucial for truly intelligent applications like adaptive learning paths or sophisticated Q&A systems.\n3.  **Contextual Alignment:** Linking knowledge points back to their source materials ensures transparency and verifiability, building trust and enhancing the learning experience by providing immediate context.\n4.  **Foundation for AI-driven Products:** This structured knowledge output serves as an ideal input for AI models, enabling the development of more accurate recommendation engines, personalized tutors, and automated assessment tools, thereby accelerating product development for EdTech companies.\n\n**Revenue Potential and Business Models:**\nThe revenue potential for this technology is substantial, primarily through several business models:\n1.  **SaaS/PaaS Licensing:** Offering the knowledge extraction and structuring as a service (SaaS) or platform as a service (PaaS) to educational institutions, online learning platforms, and corporate training departments. Pricing could be based on data volume processed, number of users, or features.\n2.  **API Integration:** Providing an API for developers to integrate this capability into their existing LMS, content management systems, or custom learning applications.\n3.  **Enterprise Solutions:** Custom implementations and consulting for large organizations with unique data governance or domain-specific requirements.\n4.  **Content Monetization:** The ability to rapidly structure and organize vast amounts of content could enable new forms of content monetization, such as creating dynamic, adaptive learning modules from existing libraries.\n\n**Strategic Positioning:**\nCompanies leveraging this patent can strategically position themselves as leaders in intelligent content management and AI-powered personalized learning. It allows for differentiation in a crowded EdTech market by offering a superior method for content understanding and delivery. Early adopters could gain a significant first-mover advantage, establishing robust knowledge infrastructure that competitors would struggle to replicate with manual or less advanced automated methods.\n\n**ROI Projections:**\nFor educational institutions, the ROI would come from improved student outcomes, reduced instructor workload in content preparation, and enhanced student retention through personalized learning. For corporations, it translates to faster employee onboarding, more effective upskilling, reduced training costs, and better overall knowledge retention, directly impacting productivity and innovation. The efficiency gains from automating content analysis and structuring alone could yield significant cost savings, while the improved learning outcomes contribute to long-term human capital development. This patent provides a robust framework for delivering tangible value across diverse sectors.","faqs":[{"answer":"The patent titled \"Extraction of Knowledge Points and Relations from Learning Materials\" (US-9852648) describes an innovative method for automatically generating a structured domain knowledge base from various educational resources. Essentially, it's an AI-powered system designed to read through books, articles, web pages, and other learning materials, then identify the most important concepts, which are called 'knowledge points'.\n\nBeyond just listing these concepts, the system goes a crucial step further: it figures out how these knowledge points are connected to each other. For example, it can determine if one concept is a prerequisite for another, if it's a sub-topic, or if it has a causal relationship.\n\nThe ultimate goal of this technology is to transform chaotic, unstructured learning data into an organized, navigable map of knowledge, making complex subjects easier to understand and learn from. This structured output is often visualized as a knowledge graph, where nodes are knowledge points and edges represent their relationships.","question":"What is Extraction of Knowledge Points and Relations from Learning Materials?"},{"answer":"The Extraction of Knowledge Points and Relations from Learning Materials patent outlines a multi-step, automated process. First, the system 'crawls' or ingests a wide variety of learning materials, which can include digital documents like PDFs, web pages, and even transcribed multimedia content.\n\nNext, it extracts structural information from these materials, identifying elements like headings, sections, and bullet points to understand the content's inherent organization. Following this, the system extracts 'knowledge points' – the core concepts, facts, and principles relevant to the subject matter, often using advanced Natural Language Processing (NLP) techniques.\n\nCrucially, the technology then infers dependency relationships between these extracted knowledge points, building a logical understanding of how different concepts connect and build upon one another. Finally, it aligns these knowledge points back to their original source materials, providing context and traceability, and then generates a comprehensive domain knowledge structure, typically a knowledge graph, organized by these inferred relationships.","question":"How does Extraction of Knowledge Points and Relations from Learning Materials work?"},{"answer":"The primary problem that the Extraction of Knowledge Points and Relations from Learning Materials patent solves is the challenge of information overload and the difficulty in extracting meaningful, structured knowledge from vast amounts of unstructured learning materials. In today's digital age, learners and professionals are inundated with content, but it's often fragmented, unorganized, and lacks clear connections between concepts.\n\nThis leads to inefficient learning, difficulty in identifying core ideas, and challenges in understanding the hierarchical or dependent relationships between different pieces of information. Traditional methods of content analysis are often manual, time-consuming, and fail to capture the deep semantic connections needed for effective knowledge acquisition and management. This invention automates this complex process, transforming chaotic data into coherent, actionable knowledge structures.","question":"What problem does Extraction of Knowledge Points and Relations from Learning Materials solve?"},{"answer":"While the patent document (US-9852648) for \"Extraction of Knowledge Points and Relations from Learning Materials\" lists a publication date of December 26, 2017, the specific inventors and assignee are not provided in the supplied patent data. Patents are typically filed by individuals or organizations who have developed a novel and non-obvious invention. The assignee is the entity to whom the patent rights are transferred or originally granted. Without this information, we cannot attribute the invention to specific individuals or a company.\n\nHowever, the concept itself is a testament to the ongoing innovation in artificial intelligence, natural language processing, and educational technology, reflecting a collaborative effort across these fields to enhance learning and knowledge management.","question":"Who invented Extraction of Knowledge Points and Relations from Learning Materials?"},{"answer":"The Extraction of Knowledge Points and Relations from Learning Materials patent offers several key benefits that can significantly impact education and knowledge management.\n\nFirstly, it enables **automated knowledge organization**, transforming vast, unstructured content into clear, navigable knowledge graphs without extensive manual effort. This saves considerable time and resources. Secondly, it provides **deeper semantic understanding** by inferring complex dependency relationships between concepts, moving beyond simple keyword matching to create a more intelligent and useful representation of knowledge.\n\nThirdly, it supports **personalized learning paths** by offering a structured foundation for adaptive learning systems. Learners can receive tailored content recommendations based on their existing knowledge and specific learning goals. Fourthly, it enhances **content traceability and context**, as every extracted knowledge point is linked back to its original source material, allowing for easy verification and deeper contextual understanding. Finally, this innovation acts as a **foundational technology for AI-driven applications**, powering intelligent tutoring systems, smart search engines, and advanced content curation tools.","question":"What are the key benefits of Extraction of Knowledge Points and Relations from Learning Materials?"},{"answer":"The Extraction of Knowledge Points and Relations from Learning Materials patent significantly differentiates itself from prior art in several critical ways. Older methods often focused on simple keyword extraction, named entity recognition (NER), or rule-based systems that were limited in scope and scalability.\n\nThis patent goes beyond these by introducing **automated dependency relationship inference**, meaning it doesn't just identify concepts but understands how they logically connect (e.g., prerequisites, hierarchies, causality). This semantic depth is a key differentiator. Furthermore, it integrates **structural information extraction** with knowledge point extraction, leveraging the document's layout and organization for more accurate results. Many prior art systems treated text as a flat sequence, losing valuable contextual cues. Finally, the emphasis on **granular alignment** of knowledge points with their precise source locations in the learning materials provides a level of traceability and context often missing in previous knowledge extraction systems, enhancing both reliability and user experience. These combined innovations create a more comprehensive, intelligent, and scalable solution for knowledge structuring.","question":"How is Extraction of Knowledge Points and Relations from Learning Materials different from prior art?"},{"answer":"The Extraction of Knowledge Points and Relations from Learning Materials patent is poised to impact a wide range of industries, primarily those heavily reliant on knowledge transfer, learning, and information management.\n\n**Education Technology (EdTech)** is a prime candidate, as the invention can power next-generation adaptive learning platforms, intelligent tutoring systems, and automated curriculum development. **Corporate Training and Development** will also see significant benefits, enabling more efficient employee onboarding, continuous upskilling, and the transformation of internal documentation into navigable knowledge bases. **Knowledge Management** in general, across sectors like legal, healthcare, and finance, can leverage this technology to structure vast amounts of industry-specific information, improving research, compliance, and decision-making. Any organization dealing with large volumes of unstructured data that needs to be understood and acted upon can benefit from this innovation, from content publishers to research institutions.","question":"What industries will Extraction of Knowledge Points and Relations from Learning Materials impact?"},{"answer":"The patent titled \"Extraction of Knowledge Points and Relations from Learning Materials\" (US-9852648) has a **Filing Date of July 10, 2015**. This is the date when the patent application was officially submitted to the patent office.\n\nThe **Publication Date (or Grant Date)** for this patent is **December 26, 2017**. This is the date when the patent was officially granted and published, making its details publicly available. These dates mark key milestones in the lifecycle of this intellectual property, indicating the period of its development and its eventual recognition as a novel invention.","question":"When was Extraction of Knowledge Points and Relations from Learning Materials filed/granted?"},{"answer":"The commercial applications of the Extraction of Knowledge Points and Relations from Learning Materials patent are extensive and diverse. In the **EdTech sector**, it can be integrated into Learning Management Systems (LMS) to offer personalized learning paths, intelligent content recommendations, and automated assessment generation. It can power AI-driven tutors that understand a student's knowledge gaps and provide contextually relevant explanations.\n\nFor **corporate environments**, this technology is invaluable for creating smart corporate training platforms, streamlining employee onboarding by automatically structuring company knowledge, and building dynamic knowledge bases for internal use. Content publishers can use it to enhance their digital offerings by automatically generating interactive knowledge maps for textbooks and articles, creating a more engaging user experience. Furthermore, it has applications in **research and development**, where it can help structure scientific literature, identify emerging trends, and accelerate knowledge discovery by connecting disparate research findings. Its ability to turn raw data into structured intelligence makes it a foundational technology for many AI-driven products and services.","question":"What are the commercial applications of Extraction of Knowledge Points and Relations from Learning Materials?"},{"answer":"Looking ahead, the Extraction of Knowledge Points and Relations from Learning Materials patent lays the groundwork for several exciting future developments. We can anticipate enhanced capabilities in **multimodal knowledge extraction**, where the system not only processes text but also extracts knowledge points and relationships from images, videos, and audio content, creating a truly holistic understanding of learning materials.\n\nFurther advancements will likely include more sophisticated **reasoning and inference engines**, allowing the knowledge graph to perform complex logical deductions and answer more nuanced questions. Integration with **Generative AI** is also a strong future direction, enabling the system to not only structure existing knowledge but also to generate new, coherent learning content, summaries, or explanations based on the inferred knowledge graph. Finally, we can expect the development of **self-healing and continuously learning knowledge graphs**, which can automatically update, refine relationships, and resolve inconsistencies as new information becomes available, ensuring the knowledge structure remains accurate and current over time. These developments will further solidify its role as a cornerstone for intelligent, adaptive learning environments.","question":"What are the future developments expected for Extraction of Knowledge Points and Relations from Learning Materials?"}],"topics":["knowledge extraction","learning materials","knowledge graph","dependency inference","automated learning","burgeoning","volume","digital"],"tech_cluster":null},"seo":{"title":"Knowledge Points & Relations Extraction - Patent US-9852648","description":"Discover the 'Extraction of Knowledge Points and Relations from Learning Materials' patent. Automated knowledge graph generation, dependency inference, and structured learning for EdTech.","keywords":["knowledge extraction","learning materials","knowledge graph","dependency inference","automated learning","EdTech patent","AI in education","US-9852648","semantic analysis","content structuring","patent US-9852648"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9852648","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9852648","citation_suggestion":"Patentable. \"Extraction of knowledge points and relations from learning materials\" (US-9852648). https://patentable.app/patents/US-9852648","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9852648","json":"https://patentable.app/api/llm-context/US-9852648","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T09:00:48.340Z"}