[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126872-en":3,"doc-seo-126872-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126872,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Customer Feedback Segmentation, Summarization, and Natural Language Querying Using Machine Learning - Defensive Publications Series","Customer feedback often contains unstructured narratives that are difficult to convert into statistically meaningful, actionable insights. This disclosure presents machine learning methods that automatically summarize customer feedback and produce insights through natural language queries over those summaries. The approach scales with business size and product volume, enabling identification of top-of-mind product issues from large feedback streams. It supports semantic embedding, clustering, and LLM-driven summarization, transforming raw reviews into usable intelligence. A user interface can let non-technical personnel ask questions in natural language.","Technical Disclosure Commons  \nDefensive Publications Series  \nJanuary 2024  \nCustomer Feedback Segmentation, Summarization, and Natural Language Querying Using Machine Learning  \nYuanzhi Xu  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nXu, Yuanzhi, \"Customer Feedback Segmentation, Summarization, and Natural Language Querying Using Machine Learning\", Technical Disclosure Commons,(January 29, 2024)  \n[https://www.tdcommons.org/dpubs_series/6641](https://www.tdcommons.org/dpubs_series/6641)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nCustomer Feedback Segmentation, Summarization, and Natural Language Querying Using  \nMachine Learning  \nABSTRACT  \nIt is important for businesses to have an insightful understanding of their customers'needs. It is often difficult to extract statistically meaningful and actionable insights from customer feedback. This disclosure describes the use of machine learning techniques to automatically summarize customer feedback and to generate insights from such summaries using natural language queries. The techniques scale with business size and product volume such that top of mind product issues can be identified from unstructured feedback. By enabling scalable examination, categorization, and natural language querying of customer feedback, the described techniques can help a business discover customer pain points. Raw customer feedback is transformed into actionable intelligence. A user interface can be provided that enables users to provide natural language queries, thus enabling personnel with little or no technical expertise to obtain insights from customer feedback.  \nKEYWORDS  \n● Customer feedback  \n● Text summarization  \n● K-means clustering  \n● Prompt engineering  \n● Large language model (LLM)  \n● Generative artificial intelligence (Gen AI)  \n● Natural language query  \n● Customer review  \nBACKGROUND  \nIt is important for businesses to have an insightful understanding of their customers'needs. Although customers do leave feedback at business websites, via customer feedback emails, or other mechanisms, it is often difficult to extract statistically meaningful and actionable  \nPublished by Technical Disclosure Commons, 2024 2  \ninsights from such feedback. Furthermore, examining and categorizing feedback can become increasingly time and resource intensive as a business scales and the volume of feedback rises.  \nWhile some tools (e.g., [2]) excel in data manipulation and processing, in-memory nature of such tools limits their usability for large datasets. Also, such tools do not inherently support multithreading or distributed computing, which are essential for scalable data analysis. The lack of direct support for concurrency and distribution requires data scientists to seek alternative solutions or additional tools when working with data at scale, adding complexity to the data processing pipeline. Additionally, conversion between data formats is necessary to use deep learning libraries which introduces an additional layer of complexity and can lead to increased memory overhead, thus impacting scalability of data processing for larger and more complex deep learning tasks.  \nDESCRIPTION  \nThis disclosure describes the use of machine learning techniques to automatically summarize customer feedback and to generate insights from such summaries using natural language queries. The techniques scale with business size and product volume such that top of mind product issues can be identified from unstructured feedback.  \nFig. 1: Summarizing customer feedback  \nFig. 1 illustrates summarizing customer feedback. The text of customer feedback (102) is transformed to","cbCaivx8zzo8X4VW","https://ap.wps.com/l/cbCaivx8zzo8X4VW","pdf",282634,1,"English","en",105,"# Abstract\n# Keywords\n# Background\n# Description\n## Figure 1: Summarizing customer feedback\n## Example of summarization\n## Figure 2: Summarizing and clustering customer feedback","[{\"question\":\"How does the method turn raw customer feedback into usable insights?\",\"answer\":\"It transforms feedback text into numerical embeddings, clusters semantically similar embeddings, and uses an LLM to summarize each cluster. These summaries then serve as the basis for extracting insights via natural language queries.\"},{\"question\":\"Why is segmentation and summarization helpful for customer pain point discovery?\",\"answer\":\"Segmentation and clustering reveal common themes and patterns across complaints. Summaries of clusters help surface frequently mentioned issues, making pain points easier to identify at scale.\"},{\"question\":\"How can users without technical expertise obtain insights?\",\"answer\":\"The disclosure supports a natural language querying interface, allowing users to ask questions in plain language and retrieve insights derived from the summarized customer feedback.\"}]","Customer Feedback Segmentation, Summarization, and Natural Language Querying Using Machine Learning - Defensive Publications Series | PDF",1785935328,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"customer-feedback-segmentation-summarization-and-natural-language-querying-using-machine-learning-defensive-publications-series","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/customer-feedback-segmentation-summarization-and-natural-language-querying-using-machine-learning-defensive-publications-series/126872/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the method turn raw customer feedback into usable insights?","Question",{"text":75,"@type":76},"It transforms feedback text into numerical embeddings, clusters semantically similar embeddings, and uses an LLM to summarize each cluster. These summaries then serve as the basis for extracting insights via natural language queries.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is segmentation and summarization helpful for customer pain point discovery?",{"text":80,"@type":76},"Segmentation and clustering reveal common themes and patterns across complaints. Summaries of clusters help surface frequently mentioned issues, making pain points easier to identify at scale.",{"name":82,"@type":73,"acceptedAnswer":83},"How can users without technical expertise obtain insights?",{"text":84,"@type":76},"The disclosure supports a natural language querying interface, allowing users to ask questions in plain language and retrieve insights derived from the summarized customer feedback.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":28,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":28,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]