{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9852655","patent":{"patent_number":"US-9852655","title":"Systems and methods for extracting keywords in language learning","assignee":null,"inventors":[],"filing_date":"2016-02-12T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["G09B","A61B","A61B","A61B","A61B","A61B","G06F","G06F","G06F","G09B","G09B","G09B","A61B","A61B","A61B","A61B","A61B","A61B","A61B","G09B","G09B"],"num_claims":20,"abstract":"Systems, methods, and products for language learning that may determine a level score of a learner based upon responsive inputs received from the learner during sessions. A pedagogical value threshold is determined based upon the level score of the learner and a resource difficulty score of a resource containing text having one or more words. Each word having a word difficulty score satisfying the pedagogical value threshold is stored into a non-transitory machine-readable storage media keyword store."},"analysis":{"summary":"The patent titled \"Systems and Methods for Extracting Keywords in Language Learning\" (US-9852655) presents a groundbreaking approach to personalized language education. Its core innovation lies in a dynamic system that intelligently identifies and extracts the most pedagogically valuable keywords from learning resources, tailored to an individual learner's real-time proficiency. The problem it solves is the inefficiency and demotivation caused by generic, one-size-fits-all vocabulary instruction, which often presents words that are either too easy or too difficult for the learner.\n\nThe key technical approach involves several interconnected steps. First, the system determines a 'level score' for a learner based on their responsive inputs during interactive sessions, providing a continuous assessment of their proficiency. Concurrently, it calculates a 'resource difficulty score' for any text-based learning material. These two scores are then combined to establish a 'pedagogical value threshold.' Finally, the system analyzes each word within the resource, assigns it a 'word difficulty score,' and stores only those words that satisfy the pedagogical value threshold into a dedicated keyword store. This ensures that learners are consistently exposed to vocabulary that is optimally challenging and relevant.\n\nThe business value and applications of this innovation are substantial. Language learning platforms can integrate this technology to offer truly adaptive curricula, significantly enhancing user engagement, retention, and learning outcomes. It provides a robust framework for personalized tutoring, intelligent content recommendation, and efficient vocabulary acquisition across various educational contexts, from academic institutions to corporate training and self-study apps. This technology allows businesses to differentiate their offerings by providing a superior, data-driven learning experience.\n\nThe market opportunity for this patent is vast, given the global demand for effective language learning solutions. By addressing the core inefficiency of vocabulary acquisition, this system can capture a significant share of the EdTech market. Companies that adopt this approach will gain a competitive edge by delivering faster, more engaging, and more effective language mastery, leading to higher customer satisfaction and recurring revenue streams. This innovation promises to make language learning more accessible and successful for millions worldwide.","layman_explanation":"### 1. What Problem Does This Solve?\n\nLearning a new language is a fantastic endeavor, but it often hits a major roadblock: vocabulary. Think about it – when you're trying to learn French or Spanish, you might get a long list of words in a textbook or app. Some of these words you might already know, making you feel like you're wasting time. Others might be so complex that they feel overwhelming, leading to frustration and giving up. The core problem is that most language learning tools offer a 'one-size-fits-all' vocabulary approach. They don't truly understand *your* unique learning stage or which words would be most beneficial for *you* to learn next from a specific piece of content. This inefficiency makes language acquisition slower, less engaging, and often leads to learners abandoning their goals.\n\n### 2. How Does It Work?\n\nThe patent, known as \"Systems and Methods for Extracting Keywords in Language Learning,\" solves this by acting like a super-smart, personalized language tutor. Imagine you're reading an article in your target language. Here's how this technology conceptually works:\n\nFirst, the system constantly monitors your interactions and progress, building a dynamic picture of your current language skill level. It's like a coach watching your game and understanding your strengths and weaknesses. This gives you a 'level score.'\n\nNext, when you introduce a new article or book, the system quickly scans it to understand its overall difficulty. This becomes the 'resource difficulty score.'\n\nThen, the clever part: the system combines your personal skill level with the content's difficulty. It sets a 'pedagogical value threshold' – essentially, it figures out the 'sweet spot' of words that are just challenging enough to help you grow, but not so hard that you get discouraged. It's looking for words in your 'zone of proximal development.'\n\nFinally, the system goes through the article word by word. For each word, it assesses its individual difficulty. If a word's difficulty falls within that 'sweet spot' (the pedagogical value threshold), the system flags it as a key word for *you*. These keywords are then stored, ready for you to learn through targeted exercises and review. It's like having a personal filter that ensures you're always focusing on the most impactful vocabulary for your unique journey.\n\n### 3. Why Does This Matter?\n\nThis innovation is a game-changer for several reasons. For learners, it means faster progress, less frustration, and higher motivation because every learning session is optimized. You're always learning what's most relevant and challenging for *your* current level, making the process much more efficient. This leads to higher engagement and better retention rates, which are crucial for any educational product.\n\nFor businesses in the EdTech space, this patent provides a significant competitive advantage. Companies can integrate this technology to offer truly personalized language learning experiences, differentiating themselves from competitors still relying on static curricula. This translates into higher customer satisfaction, stronger brand loyalty, and increased market share in the rapidly growing global language learning market. Imagine a corporate training program that customizes vocabulary based on an employee's role and the industry documents they need to read – the ROI in terms of efficiency and employee upskilling would be immense.\n\n### 4. What's Next?\n\nThe future applications of this technology are vast. Beyond just vocabulary, these methods could be extended to personalize grammar instruction, reading comprehension exercises, and even speaking practice. We could see intelligent language tutors that adapt an entire curriculum in real-time, making language learning as efficient as possible. As more platforms adopt this kind of adaptive intelligence, we can expect a new generation of language learning products that are far more effective than anything currently available, making fluency more accessible to a global audience. This will likely drive further investment and innovation in AI-powered adaptive learning systems.","technical_analysis":"The patent \"Systems and Methods for Extracting Keywords in Language Learning\" (US-9852655) outlines a sophisticated, adaptive framework designed to optimize vocabulary acquisition in language learning environments. This technical analysis delves into the architectural components, algorithmic specifics, and potential implementation details that underpin this innovation, making it a pivotal development in educational technology.\n\n**Technical Architecture Overview**\n\nThe system described in this patent operates as a closed-loop adaptive learning engine. Its architecture can be conceptualized into several interacting modules: a Learner Profiling Module, a Content Analysis Module, a Pedagogical Threshold Module, and a Keyword Management Module. Data flows from learner interactions and external content sources through these modules, culminating in a personalized keyword store.\n\n1.  **Learner Profiling Module:** This module is responsible for generating a 'level score' for the learner. This score is not static; it's dynamically updated based on 'responsive inputs' received during learning sessions. These inputs could include correctness of answers, response times, number of attempts, types of errors (e.g., grammatical vs. lexical), and even contextual usage in free-form exercises. Behind the scenes, this likely involves a combination of psychometric models (e.g., Item Response Theory or Elo rating systems) and machine learning algorithms (e.g., Bayesian inference networks, sequential neural networks like LSTMs or Transformers) that process historical and real-time interaction data to infer a learner's evolving proficiency across various linguistic dimensions (e.g., vocabulary, grammar, reading comprehension).\n\n2.  **Content Analysis Module:** This module processes textual learning 'resources.' It has two primary sub-components: the 'Resource Difficulty Scorer' and the 'Word Difficulty Scorer.'\n    *   **Resource Difficulty Scorer:** This component analyzes an entire text to assign a 'resource difficulty score.' This score would be derived from a battery of NLP metrics, including lexical diversity (e.g., Type-Token Ratio, Measure of Textual Lexical Diversity), syntactic complexity (e.g., mean sentence length, number of clauses per sentence, parse tree depth), semantic coherence, and an aggregate analysis of word frequencies against a target language corpus. Machine learning models, trained on human-annotated difficulty ratings, could predict this score.\n    *   **Word Difficulty Scorer:** For each individual word within the resource, this component determines a 'word difficulty score.' This is a more granular assessment, potentially incorporating factors like word frequency in the target language, number of senses (polysemy), morphological complexity, phonological regularity, and the word's embedding distance from words already known by the learner. A pre-trained language model (e.g., BERT, GPT-style) could be fine-tuned to predict word difficulty in context.\n\n3.  **Pedagogical Threshold Module:** This is the core intelligence component. It determines a 'pedagogical value threshold' by combining the learner's 'level score' and the 'resource difficulty score.' The goal is to identify the 'zone of proximal development' (ZPD) for the learner—words that are challenging but achievable, thus maximizing learning efficiency. The logic could be a sophisticated function, a rule-based expert system, or a reinforcement learning agent trained to optimize learner progress. For instance, if a learner is proficient (high level score) and the resource is easy (low difficulty score), the threshold might be set higher to find more challenging words. Conversely, for a struggling learner and a difficult resource, the threshold might be lowered to focus on foundational vocabulary within that text.\n\n4.  **Keyword Management Module:** This module filters words based on the 'pedagogical value threshold.' Each word's individual 'word difficulty score' is compared against this threshold. Only words satisfying the criterion are deemed 'keywords' and are stored into a 'non-transitory machine-readable storage media keyword store.' This store can be a database (SQL/NoSQL) designed for efficient querying and retrieval. Each entry would include the keyword, its difficulty score, the context of extraction, the learner's level at the time, and potentially a timestamp. This allows for subsequent targeted instruction, spaced repetition, and progress tracking.\n\n**Implementation Details and Performance Characteristics**\n\nImplementing this system would require robust NLP pipelines, potentially leveraging cloud-based services for scalability. Real-time processing of learner inputs necessitates low-latency data ingestion and model inference. The storage of keywords should be optimized for quick access during learning sessions. Performance characteristics would be measured by metrics such as keyword extraction accuracy (identifying truly pedagogically valuable words), learner progress rates (e.g., vocabulary acquisition speed), and user engagement. Continuous integration/continuous deployment (CI/CD) practices would be essential for model updates and system improvements.\n\n**Code-Level Implications**\n\nDevelopers would primarily work with Python for NLP components (using libraries like SpaCy, NLTK, Hugging Face Transformers) and machine learning frameworks (TensorFlow, PyTorch). The backend could be implemented in languages like Java, Go, or Node.js, interacting with a database like PostgreSQL, MongoDB, or Redis for the keyword store. APIs would be crucial for integrating with frontend applications (web/mobile). The system would likely involve microservices for modularity, allowing independent scaling of the Learner Profiling, Content Analysis, and Keyword Management services. The core logic of the pedagogical threshold determination would be a critical piece of intellectual property, requiring careful design and rigorous testing.","business_analysis":"The patent \"Systems and Methods for Extracting Keywords in Language Learning\" (US-9852655) represents a significant leap in personalized education, offering substantial business opportunities within the burgeoning EdTech market. Its core value proposition lies in making language acquisition demonstrably more efficient and engaging, addressing a universal pain point for millions of learners worldwide.\n\n**Market Opportunity Size**\n\nThe global language learning market is projected to reach over $150 billion by 2027, driven by globalization, increased mobility, and the demand for upskilling. Within this, digital language learning is experiencing explosive growth. The inefficiency of traditional and even many existing digital methods creates a vast untapped segment for solutions that promise accelerated, personalized learning. This patent directly targets this demand, positioning itself to capture a significant share by offering a superior learning experience.\n\n**Competitive Advantages**\n\nThis technology offers several compelling competitive advantages:\n\n1.  **Hyper-Personalization:** Unlike competitors offering generalized courses or adaptive quizzes, this invention provides truly dynamic, content-aware personalization. It moves beyond 'adaptive' to 'prescriptive' learning, recommending *exactly* the right words at the right time.\n2.  **Increased Engagement & Retention:** By optimizing the challenge level (the 'zone of proximal development'), the system minimizes frustration and maximizes intrinsic motivation, leading to higher engagement rates and reduced churn, critical metrics in subscription-based EdTech models.\n3.  **Data-Driven Efficacy:** The system continuously learns from learner interactions, allowing for ongoing refinement and demonstrated efficacy, which can be a powerful marketing tool.\n4.  **Scalability:** The algorithmic nature allows for scaling to millions of users and diverse language content without a proportional increase in human instructor overhead.\n5.  **IP Protection:** As a patented invention, it provides a defensible competitive moat, making it harder for competitors to replicate the core adaptive keyword extraction methodology.\n\n**Revenue Potential and Business Models**\n\nThe revenue potential is significant across various business models:\n\n1.  **Subscription-based Language Apps:** Integration into existing or new direct-to-consumer apps, offering premium tiers for advanced personalization.\n2.  **B2B Licensing:** Licensing the technology to other EdTech companies, language schools, or corporate training platforms that want to enhance their offerings without developing the core AI.\n3.  **Content Monetization:** Creating and selling premium, adaptive learning modules and courses powered by this technology.\n4.  **Data Analytics Services:** Offering insights to educational institutions or content creators on learner progress and content effectiveness, derived from the system's data.\n\n**Strategic Positioning**\n\nCompanies adopting the Systems and Methods for Extracting Keywords in Language Learning patent can strategically position themselves as leaders in 'Intelligent Language Tutoring' or 'AI-Powered Adaptive Learning.' This elevates them above traditional digital language learning providers. It allows for vertical integration opportunities, where a company could own both the content creation and the adaptive delivery engine, creating a powerful ecosystem.\n\n**ROI Projections**\n\nInvesting in or licensing this technology promises a strong ROI through:\n\n*   **Higher Customer Lifetime Value (CLTV):** Due to improved retention and satisfaction.\n*   **Reduced Customer Acquisition Cost (CAC):** Through strong word-of-mouth and differentiated marketing.\n*   **Premium Pricing:** Justified by superior learning outcomes.\n*   **New Market Penetration:** Attracting learners who have been failed by generic solutions.\n\nFor example, a 10% increase in monthly retention for a subscription service could translate to millions in additional recurring revenue annually. The ability to demonstrate superior learning efficacy through data-backed results will be a potent driver for market adoption and financial success. This patent is not just about a technical feature; it's about enabling a fundamentally better business model for language education.","faqs":[{"answer":"Systems and Methods for Extracting Keywords in Language Learning (US-9852655) is a groundbreaking patent that describes an innovative system for personalized language learning. At its core, this invention focuses on intelligently identifying and extracting the most pedagogically valuable keywords from any given textual resource, tailored specifically to an individual learner's current proficiency level. It aims to move beyond generic vocabulary lists and one-size-fits-all approaches to provide a highly efficient and engaging learning experience.\n\nThe system achieves this by dynamically assessing a learner's skill, analyzing the difficulty of the learning material, and then combining these factors to determine which words are optimally challenging for that learner. These 'just right' words are then presented for targeted instruction and reinforcement.\n\nEssentially, the Systems and Methods for Extracting Keywords in Language Learning patent outlines a method to ensure that learners are always focusing on the most impactful vocabulary for their unique learning journey, accelerating their progress and enhancing their understanding of a new language. This technology represents a significant step forward in adaptive educational tools. \n\nKeywords: language learning patent, keyword extraction, personalized vocabulary, adaptive learning, EdTech innovation.","question":"What is Systems and Methods for Extracting Keywords in Language Learning?"},{"answer":"The Systems and Methods for Extracting Keywords in Language Learning patent operates through a sophisticated, multi-step process designed for dynamic personalization. First, the system continuously determines a 'level score' for the learner based on their responsive inputs received during learning sessions. This score reflects their evolving proficiency in the target language.\n\nConcurrently, when a learner interacts with a new textual resource (like an article or book), the system analyzes this content to determine its overall 'resource difficulty score.' It also calculates an individual 'word difficulty score' for each word within that resource.\n\nCrucially, the system then combines the learner's 'level score' with the 'resource difficulty score' to establish a 'pedagogical value threshold.' This threshold acts as an intelligent filter. Only those words whose individual 'word difficulty scores' satisfy this dynamically determined threshold are extracted and stored into a specialized keyword store. These are the words that are deemed optimally challenging and relevant for the learner at that specific moment, ensuring efficient and targeted learning. \n\nKeywords: how it works, adaptive algorithms, learner assessment, content difficulty, pedagogical value threshold, keyword store.","question":"How does Systems and Methods for Extracting Keywords in Language Learning work?"},{"answer":"The Systems and Methods for Extracting Keywords in Language Learning patent addresses the pervasive problem of inefficiency and demotivation in traditional and many existing digital language learning methods. Learners often struggle with vocabulary acquisition because they are presented with generic word lists or content that is either too easy (leading to boredom and wasted time) or too difficult (leading to frustration and disengagement).\n\nThis 'one-size-fits-all' approach fails to account for individual learner proficiency, specific learning contexts, and the concept of the 'zone of proximal development' – the optimal level of challenge for effective learning. As a result, progress can be slow, and many learners abandon their language learning goals prematurely.\n\nThe invention solves this by providing a highly personalized and adaptive system that ensures learners are consistently exposed to vocabulary that is precisely tailored to their current skill level and the specific content they are engaging with. By extracting only the most pedagogically valuable keywords, this technology maximizes learning efficiency and keeps learners motivated, ultimately accelerating their journey to language mastery. \n\nKeywords: language learning challenges, vocabulary inefficiency, personalized learning solutions, learner frustration, adaptive instruction, EdTech problem solving.","question":"What problem does Systems and Methods for Extracting Keywords in Language Learning solve?"},{"answer":"The patent US-9852655, titled \"Systems and Methods for Extracting Keywords in Language Learning,\" lists no specific inventors or assignee in the provided data. In patent filings, the inventors are typically the individuals who conceived the invention, while the assignee is the entity (often a company) to whom the patent rights are legally transferred. \n\nIt is common for patents to be assigned to corporations, as employees often invent technologies as part of their employment, and the rights are then transferred to the company. Without specific information in the provided data, the inventors and assignee remain unspecified. \n\nHowever, the innovation described in Systems and Methods for Extracting Keywords in Language Learning is a testament to the ongoing research and development in the field of educational technology and natural language processing, reflecting a collaborative effort within the industry to enhance language acquisition methods. The impact of such inventions often transcends individual creators, shaping the future of entire sectors. \n\nKeywords: patent inventors, patent assignee, US-9852655, language learning research, EdTech development, patent ownership.","question":"Who invented Systems and Methods for Extracting Keywords in Language Learning?"},{"answer":"The Systems and Methods for Extracting Keywords in Language Learning patent offers several transformative benefits for language learners and educational platforms alike. Firstly, it provides **unparalleled personalization**, ensuring that every learner receives vocabulary instruction precisely tailored to their individual proficiency and the content they are studying. This moves beyond generic lists to truly adaptive learning.\n\nSecondly, it leads to **maximized learning efficiency**. By focusing exclusively on keywords that are optimally challenging (in the 'zone of proximal development'), learners avoid wasting time on words they already know or struggling with overly complex terms. This targeted approach accelerates progress and makes every study session more productive.\n\nThirdly, it significantly **enhances engagement and retention**. When learning is consistently challenging but achievable, learners remain motivated, experience less frustration, and are more likely to persist in their language studies, leading to higher retention rates for educational platforms. Finally, it enables **dynamic content adaptation**, allowing any textual resource to be instantly transformed into a personalized learning tool, greatly expanding the scope and relevance of available learning materials. \n\nKeywords: key benefits, personalized learning, learning efficiency, learner engagement, adaptive content, vocabulary acquisition benefits.","question":"What are the key benefits of Systems and Methods for Extracting Keywords in Language Learning?"},{"answer":"Systems and Methods for Extracting Keywords in Language Learning fundamentally differentiates itself from prior art by integrating a dynamic, multi-faceted approach to vocabulary selection. Traditional methods, or prior art, typically include static vocabulary lists, frequency-based word lists, simple contextual lookup tools, or basic adaptive quizzing systems.\n\nThe key distinction of this patent is its ability to simultaneously and dynamically assess both the learner's real-time proficiency ('level score') and the specific difficulty of individual words within a given learning resource ('word difficulty score'). Furthermore, it intelligently combines these with the overall 'resource difficulty score' to establish a 'pedagogical value threshold.' This threshold ensures that only words optimally challenging for the learner are extracted. Prior art generally lacks this sophisticated, integrated, and dynamic thresholding mechanism.\n\nFor example, while a spaced repetition system (prior art) might adapt *when* to show a word, it doesn't dynamically *select* new words from arbitrary content based on real-time learner and content difficulty in the way Systems and Methods for Extracting Keywords in Language Learning does. This invention's adaptive keyword extraction moves beyond simply knowing *if* a word is known to knowing *if* a word is optimally beneficial for learning *now*, making it a significant leap in personalized language education. \n\nKeywords: prior art comparison, adaptive learning differentiation, unique advantages, language learning innovation, keyword extraction technology, patent distinction.","question":"How is Systems and Methods for Extracting Keywords in Language Learning different from prior art?"},{"answer":"The Systems and Methods for Extracting Keywords in Language Learning patent is poised to significantly impact several industries, primarily within the broader educational technology (EdTech) sector. Its core application is in **Language Learning Platforms**, where it can revolutionize how vocabulary is taught, leading to more effective, engaging, and personalized user experiences. This will be a key differentiator for leading EdTech companies.\n\nBeyond direct language learning apps, the technology has strong implications for **Corporate Training and Professional Development**. Businesses with global workforces or international operations can leverage this system to provide highly tailored language and terminology training, improving cross-cultural communication and efficiency. **Academic Institutions** (universities, K-12 schools) can also benefit by integrating the technology into their curricula to offer differentiated instruction and personalized support for language students.\n\nFurthermore, **Content Publishing and E-learning Platforms** can utilize this patent to make their textual materials (e.g., news articles, textbooks, online courses) inherently adaptive, generating custom vocabulary lessons on the fly for readers of varying language proficiencies. This broad applicability underscores the transformative potential of Systems and Methods for Extracting Keywords in Language Learning across various educational and professional domains. \n\nKeywords: industry impact, EdTech industry, language learning platforms, corporate training, academic institutions, content publishing, e-learning.","question":"What industries will Systems and Methods for Extracting Keywords in Language Learning impact?"},{"answer":"The patent titled \"Systems and Methods for Extracting Keywords in Language Learning\" (US-9852655) has a distinct timeline for its filing and publication.\n\nIts **filing date** was **February 12, 2016**. This is the date when the patent application was officially submitted to the patent office, marking the beginning of the examination process and establishing priority for the invention.\n\nThe **publication date** for this patent was **December 26, 2017**. This date signifies when the patent was officially published, making its details publicly accessible and indicating that the patent has been granted or approved. The period between filing and publication involves examination by patent examiners, who assess the invention's novelty, non-obviousness, and utility against prior art. The publication of Systems and Methods for Extracting Keywords in Language Learning on this date means its innovative methods for adaptive keyword extraction in language learning became formally recognized and publicly disclosed. \n\nKeywords: patent filing date, publication date, patent timeline, US-9852655 history, patent grant, invention disclosure.","question":"When was Systems and Methods for Extracting Keywords in Language Learning filed/granted?"},{"answer":"The commercial applications of the Systems and Methods for Extracting Keywords in Language Learning patent are extensive and span various segments of the digital education and content industries. Firstly, it can be integrated into **direct-to-consumer language learning applications** to provide premium, hyper-personalized vocabulary modules, boosting user engagement and retention, and justifying higher subscription tiers.\n\nSecondly, the technology can be licensed to **corporate language training providers** to develop highly efficient, industry-specific language courses for employees, leading to measurable improvements in professional communication and productivity. This is particularly valuable for multinational corporations.\n\nThirdly, **e-learning platforms and content publishers** can embed this system to make their educational materials dynamically adaptive. For instance, an online news portal could offer a feature that highlights and explains only the most relevant new vocabulary for a reader learning a foreign language, significantly enhancing content value. Furthermore, developers can create **intelligent tutoring systems** that leverage this patent to offer comprehensive, adaptive language instruction beyond just vocabulary, potentially transforming the entire learning journey. The ability to offer superior learning outcomes makes Systems and Methods for Extracting Keywords in Language Learning a powerful asset for market differentiation and growth. \n\nKeywords: commercial applications, EdTech business, language app monetization, corporate training solutions, e-learning platforms, intelligent tutoring, patent commercialization.","question":"What are the commercial applications of Systems and Methods for Extracting Keywords in Language Learning?"},{"answer":"The Systems and Methods for Extracting Keywords in Language Learning patent lays a robust foundation for numerous future developments in adaptive education. One key area of expansion is likely to be **broader adaptive learning beyond vocabulary**. The core principles of dynamically assessing learner proficiency and content difficulty can be extended to personalize grammar instruction, reading comprehension exercises, listening skills, and even conversational practice in real-time. This could lead to fully integrated, AI-powered language tutors.\n\nAnother significant development could involve **multimodal inputs and outputs**. Future iterations might incorporate speech recognition and analysis to assess spoken language proficiency and extract keywords from audio content, providing a more holistic learning experience. The system could also generate adaptive exercises, explanations, and even dialogues tailored to the extracted keywords and the learner's needs.\n\nFurthermore, we can expect advancements in **explainable AI (XAI)**, providing transparency to learners and educators on *why* certain words are selected as keywords and how their proficiency is being assessed. This will build trust and provide valuable insights. Ultimately, the Systems and Methods for Extracting Keywords in Language Learning innovation will likely contribute to the emergence of highly sophisticated, autonomous learning agents that can curate entire personalized learning pathways, making language mastery more accessible and efficient for a global audience. \n\nKeywords: future developments, AI in language learning, adaptive learning evolution, multimodal education, explainable AI, intelligent tutoring systems, patent innovation future.","question":"What are the future developments expected for Systems and Methods for Extracting Keywords in Language Learning?"}],"topics":["Systems and Methods for Extracting Keywords in Language Learning","language learning patent","keyword extraction","adaptive learning","personalized education","quest","truly","personalized"],"tech_cluster":null},"seo":{"title":"Systems and Methods for Extracting Keywords in Language Learning - Patent US-9852655","description":"Discover how the Systems and Methods for Extracting Keywords in Language Learning patent revolutionizes language education with personalized vocabulary extraction and adaptive learning. Analyze technical details and market impact.","keywords":["Systems and Methods for Extracting Keywords in Language Learning","language learning patent","keyword extraction","adaptive learning","personalized education","EdTech innovation","vocabulary acquisition","US-9852655","AI in language learning","educational technology","patent analysis","learning algorithms","language proficiency","digital language learning","pedagogical value"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9852655","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-9852655","citation_suggestion":"Patentable. \"Systems and methods for extracting keywords in language learning\" (US-9852655). https://patentable.app/patents/US-9852655","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9852655","json":"https://patentable.app/api/llm-context/US-9852655","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T10:55:55.077Z"}