[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-650324-105":59,"doc-detail-650324-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","patentvision-a-multimodal-method-for-drafting-patent-applications","PatentVision - A multimodal method for drafting patent applications","","Patent drafting is complex due to the need for detailed technical descriptions, legal compliance, and visual elements. Although Large Vision-Language Models (LVLMs) can support diverse tasks, automating patent writing with multimodal inputs remains underexplored. This paper proposes PatentVision, a multimodal framework combining patent claims and drawings to generate complete specifications. Experiments show it improves fidelity over text-only approaches by better capturing design and functional relationships, reducing manual effort while enhancing consistency and paving the way for broader LVLM use in specialized IP workflows.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/patentvision-a-multimodal-method-for-drafting-patent-applications/650324/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/patentvision-a-multimodal-method-for-drafting-patent-applications/650324.png","ImageObject",300,407,{"name":92,"@type":93},"Nguyễn Văn Học","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-24",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is PatentVision and what inputs does it use?","Question",{"text":112,"@type":113},"PatentVision is a multimodal framework for drafting patent applications. It integrates patent images and drawings with textual patent claims and instruction/prompts to generate complete patent specifications.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does PatentVision improve accuracy compared with text-only methods?",{"text":117,"@type":113},"By incorporating visual data alongside text, PatentVision better represents intricate design features and functional connections. Experiments indicate outputs have higher fidelity and alignment with human-written drafting standards than text-only approaches.",{"name":119,"@type":110,"acceptedAnswer":120},"What role do fine-tuned LVLMs play in the framework?",{"text":121,"@type":113},"The framework builds on advanced LVLMs and uses domain-specific training tailored to patent writing. Fine-tuning helps the model learn formal patent-specification style and generate coherent descriptions consistent with legal requirements.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},650324,1792921695,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},1374402739827,"https://ap-avatar.wpscdn.com/avatar/14000c97e7351f1a627?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787885694763230660","PatentVision: A multimodal method for drafting patent applications  \nRuo Yang  \nSamsung Semiconductor, Inc. San Jose, CA  \n[r.yang@partner.samsung.com](r.yang@partner.samsung.com)  \nSai Krishna Reddy Mudhiganti  \nSamsung Semiconductor, Inc. San Jose, CA[s.mudhiganti@samsung.com](s.mudhiganti@samsung.com)  \nManali Sharma  \nSamsung Semiconductor, Inc. San Jose, CA [manali.s@samsung.com](manali.s@samsung.com)  \nAbstract  \nPatent drafting is complex due to its need for detailed technical descriptions, legal compliance, and visual elements. Although Large VisionLanguage Models (LVLMs) show promise across various tasks, their application in automating patent writing remains underexplored. In this paper, we present PatentVision, a multimodal framework that integrates textual and visual inputs—such as patent claims and drawings—to generate complete patent specifications. Built on advanced LVLMs, PatentVision enhances accuracy by combining fine-tuned vision-language models with domain-specific training tailored to patents. Experiments reveal it surpasses text-only methods, producing outputs with greater fidelity and alignment with human-written standards. Its incorporation of visual data allows it to better represent intricate design features and functional connections, leading to richer and more precise results. This study underscores the value of multimodal techniques in patent automation, providing a scalable tool to reduce manual workloads and improve consistency. PatentVision not only advances patent drafting but also lays groundwork for broader use of LVLMs in specialized areas, potentially transforming intellectual property management and innovation processes.  \n1 Introduction  \nDrafting a comprehensive patent specification involves transforming intricate technical concepts, embodied in both written claims and accompanying illustrations, into precise and coherent legal documentation. Traditional methods predominantly focus on textual analysis, leveraging natural language processing techniques to interpret and generate patent specifications. However, these approaches often overlook the critical role of visual elements—patent drawings—which serve as indispensable carriers of design intent and functional details. As a result, existing systems struggle to  \nfully capture the nuanced interplay between textual and visual components, leading to limitations in accurately reflecting inventors’ intentions and meeting professional drafting standards. In recent years, advances in Large Vision-Language Models (LVLMs) have demonstrated significant potential in bridging the gap between linguistic and visual domains. By integrating multimodal data streams, LVLMs enable a deeper comprehension of contextually rich scenarios, offering new avenues for enhancing automated processes across diverse applications. This study investigates the application of state-of-the-art LVLMs, including models such as Gemma (Team et al., 2025), LLAVA (Liu et al., 2024), and LLaMA (Grattafiori et al., 2024), to address the challenges inherent in patent specification drafting. Specifically, we examine how these models can effectively combine patent claims and corresponding drawings to produce high-quality patent specifications. Through rigorous experimentation, our findings reveal that incorporating visual inputs significantly elevates the accuracy and coherence of generated texts, closely mirroring established human drafting practices.  \nThe proposed framework employs a dual-input architecture, where textual inputs consist of patent claims and descriptive annotations, while visual inputs encompass detailed patent diagrams. By fusing these modalities, the system achieves a holistic interpretation of the invention, enabling it to generate specifications that are not only technically accurate but also aligned with legal requirements. These insights underscore the transformative potential of multimodal approaches in automating patent drafting, paving the way for more effi","cbCaijLrU1nBInkK","https://ap.wps.com/l/cbCaijLrU1nBInkK","pdf",705352,"English","# Abstract\n# 1 Introduction\n# 2 Related Work\n# 1. User Inputs\n## Figure 1: PatentVision is a framework that generates high-quality patent specifications","[{\"question\":\"What is PatentVision and what inputs does it use?\",\"answer\":\"PatentVision is a multimodal framework for drafting patent applications. It integrates patent images and drawings with textual patent claims and instruction/prompts to generate complete patent specifications.\"},{\"question\":\"How does PatentVision improve accuracy compared with text-only methods?\",\"answer\":\"By incorporating visual data alongside text, PatentVision better represents intricate design features and functional connections. Experiments indicate outputs have higher fidelity and alignment with human-written drafting standards than text-only approaches.\"},{\"question\":\"What role do fine-tuned LVLMs play in the framework?\",\"answer\":\"The framework builds on advanced LVLMs and uses domain-specific training tailored to patent writing. Fine-tuning helps the model learn formal patent-specification style and generate coherent descriptions consistent with legal requirements.\"}]","PatentVision - A multimodal method for drafting patent applications | PDF",1792824898,23]