{"id":694,"date":"2026-07-06T18:29:47","date_gmt":"2026-07-06T09:29:47","guid":{"rendered":"https:\/\/conceptminer.ai\/?page_id=694"},"modified":"2026-07-06T18:35:23","modified_gmt":"2026-07-06T09:35:23","slug":"auto-research","status":"publish","type":"page","link":"https:\/\/conceptminer.ai\/?page_id=694&lang=en","title":{"rendered":"Auto Research"},"content":{"rendered":"\n
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ConceptMiner Auto Research<\/div>\n

Automatically collect and generate text chunks for conceptual structure modeling.<\/h1>\n

\n Auto Research is a research-support tool designed as the first step before building a conceptual structure model in Concept Map.\n It collects and generates text chunks from markets, competitors, news, academic papers, patents,\n sensory experiences, free-form ideas, interview records, and other sources.\n <\/p>\n

\n The collected text can be saved as CSV and used in Concept Map for embedding, clustering,\n and conceptual structure modeling with GNG+MST.\n Auto Research connects research, ideation, hypothesis testing, and positioning analysis into one workflow.\n <\/p>\n

\n Try Auto Research<\/a>\n See Research Sources<\/a>\n <\/div>\n

\n Prepare free-form text, competitor data, news, papers, patents, and interview records as CSV data suitable for concept mapping.\n <\/p>\n <\/div>\n\n

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SRC<\/div>\n
\n Define a research theme<\/strong>\n Markets, competitors, technologies, papers, patents, free text<\/span>\n <\/div>\n <\/div>\n
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AI<\/div>\n
\n Collect and generate with AI<\/strong>\n Create text chunks, summaries, codes, and descriptions<\/span>\n <\/div>\n <\/div>\n
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CSV<\/div>\n
\n Save and edit as CSV<\/strong>\n Remove noise, organize columns, prepare analysis data<\/span>\n <\/div>\n <\/div>\n
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MAP<\/div>\n
\n Connect to Concept Map<\/strong>\n Embedding, clustering, and conceptual structure modeling<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n
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Why Auto Research<\/div>\n

The quality of a concept map depends on the text chunks you start with.<\/h2>\n

\n To build a meaningful conceptual structure model in Concept Map, you first need text chunks to analyze.\n Manually collecting and organizing competitor products, user issues, news, papers, patents,\n sensory experiences, and interview records into CSV format takes significant time.\n <\/p>\n

\n Auto Research supports this preparation process.\n By entering a research theme and selecting the appropriate source type,\n you can create text-chunk tables that are ready to use in Concept Map.\n <\/p>\n <\/div>\n\n

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\n Prepare research data quickly<\/strong>\n Efficiently gather initial data for market research, competitor analysis, paper reviews, and patent exploration.<\/span>\n <\/div>\n
\n Organize data for concept mapping<\/strong>\n Save research results as CSV and use them as input data for Concept Map.<\/span>\n <\/div>\n
\n Connect ideation and analysis<\/strong>\n Move from idea generation and hypothesis testing to positioning analysis and cluster exploration.<\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n
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Research Flow<\/div>\n

From information gathering to conceptual structure modeling.<\/h2>\n

\n Auto Research is not just a search tool.\n It organizes collected information into text chunks, codes, summaries, and reference fields\n so that the data can be modeled later in Concept Map.\n <\/p>\n <\/div>\n\n

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\n Theme<\/b>\n Specify research themes, keywords, and target categories<\/small>\n <\/div>\n
\n Collect<\/b>\n Collect information from AI, news, papers, patents, and free-form prompts<\/small>\n <\/div>\n
\n Chunk<\/b>\n Create text chunks as the unit of analysis<\/small>\n <\/div>\n
\n Code<\/b>\n Add GTA-style coding when needed<\/small>\n <\/div>\n
\n Model<\/b>\n Build a conceptual structure model in Concept Map<\/small>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n
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Research Sources<\/div>\n

Create concept-map-ready data from many types of information sources.<\/h2>\n

\n Depending on the research theme, Auto Research can use free-form prompts, competitor products,\n news, academic papers, patents, interview records, and other text sources.\n <\/p>\n <\/div>\n\n

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\ud83d\udca1<\/div>\n

Free-form prompts<\/h3>\n

\n Generate text chunks from themes such as \u201cproblems with using social media,\u201d\n \u201cissues in later-life living,\u201d or \u201cnew service ideas in a specific field.\u201d\n This is useful for ideation, social research, and early-stage market exploration.\n <\/p>\n <\/div>\n\n

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\ud83d\udecd\ufe0f<\/div>\n

Competitor products and services<\/h3>\n

\n Collect real products and services related to a specified theme,\n then organize their descriptions as text chunks for positioning analysis in Concept Map.\n <\/p>\n <\/div>\n\n

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\ud83e\udded<\/div>\n

Competitor concept collection<\/h3>\n

\n Given a new service or business idea, collect descriptions of real products or services that may compete with it.\n This helps you understand where your hypothesis may sit within an existing market.\n <\/p>\n <\/div>\n\n

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\ud83c\udf77<\/div>\n

Sensory evaluation text<\/h3>\n

\n Collect or generate natural-language descriptions of taste, aroma, texture, usability, material feel,\n and other sensory experiences. Subtle nuances that are hard to express numerically can be analyzed as conceptual space.\n <\/p>\n <\/div>\n\n

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\ud83d\udcf0<\/div>\n

Online articles and news<\/h3>\n

\n Search news articles based on keywords and create text chunks about trends and industry developments.\n Use cases may include Google News, News API, Super API News, and similar sources.\n <\/p>\n <\/div>\n\n

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\ud83c\udf93<\/div>\n

Academic papers<\/h3>\n

\n Collect paper abstracts from sources such as OpenAlex, Semantic Scholar, arXiv, and IEEE Xplore,\n then use them to explore conceptual structures across research themes and technology fields.\n <\/p>\n <\/div>\n\n

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\ud83d\udcdc<\/div>\n

Patent information<\/h3>\n

\n Search and organize patent information from sources such as USPTO and Google Patents.\n This can support exploration of technology trends, competing technologies, and unmet opportunity areas.\n <\/p>\n <\/div>\n\n

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\ud83c\udff7\ufe0f<\/div>\n

Concept extraction<\/h3>\n

\n Create tables consisting of multiple types of text chunks and codes from document text.\n Use this to organize issues, concepts, problems, functions, and values contained in a document.\n <\/p>\n <\/div>\n\n

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\ud83e\uddfe<\/div>\n

Coding<\/h3>\n

\n Import interview transcripts, field notes, usability-test records, and other qualitative data,\n then apply AI-assisted GTA-style coding.\n <\/p>\n <\/div>\n\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Positioning<\/div>\n

Auto Research is not a tool for merely reading search results. It is a data-preparation tool for concept modeling.<\/h2>\n

\n Ordinary search and news collection are mainly about reading articles and pages.\n Auto Research is designed to transform gathered information into text chunks,\n save them as CSV, and use them for clustering and network modeling in Concept Map.\n <\/p>\n

\n Its value lies not only in collecting information, but in connecting that information to concept mapping,\n positioning analysis, hypothesis discovery, and model-based interpretation.\n <\/p>\n <\/div>\n <\/div>\n <\/section>\n\n\n

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CSV Management<\/div>\n

Save and edit collected data as CSV.<\/h2>\n

\n Text-chunk tables created by Auto Research can be saved as CSV.\n You can remove unnecessary rows, organize columns, and use the cleaned data later in Concept Map.\n <\/p>\n <\/div>\n\n

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\ud83d\udcbe<\/div>\n

CSV saving<\/h3>\n

\n Save collected and generated text chunks, codes, summaries, and reference fields as CSV for reuse.\n <\/p>\n <\/div>\n\n

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\u2702\ufe0f<\/div>\n

Remove unnecessary rows<\/h3>\n

\n Delete duplicates, noise, irrelevant entries, and inappropriate information\n to improve data quality before modeling.\n <\/p>\n <\/div>\n\n

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\ud83e\udde9<\/div>\n

Column organization<\/h3>\n

\n Organize text columns, code columns, category columns, reference columns,\n and other fields into a format that is easy to use in Concept Map.\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Connection to Concept Map<\/div>\n

Collect with Auto Research, then structure with Concept Map.<\/h2>\n

\n Auto Research is designed as the upstream process for Concept Map.\n Collected text chunks are converted into embedding vectors and then modeled with GNG+MST,\n allowing you to group semantically similar information and explore it as a network.\n <\/p>\n <\/div>\n\n

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\n Auto Research<\/b>\n Collect and generate text chunks according to the theme<\/small>\n <\/div>\n
\n CSV<\/b>\n Save and edit collected results as tabular data<\/small>\n <\/div>\n
\n Embedding<\/b>\n Convert text into semantic vectors<\/small>\n <\/div>\n
\n GNG+MST<\/b>\n Generate concept nodes and network structures<\/small>\n <\/div>\n
\n Concept Map<\/b>\n Explore clusters, nodes, profiles, and AI-assisted analysis<\/small>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n
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Use Cases<\/div>\n

For research, ideation, hypothesis testing, and positioning analysis.<\/h2>\n

\n Auto Research is designed not only to gather information, but to prepare research data\n for Concept Map modeling and downstream analysis.\n <\/p>\n <\/div>\n\n

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\ud83d\udecd\ufe0f<\/div>\n

Competitor positioning analysis<\/h3>\n

\n Collect descriptions of competitor products and services,\n then examine their positions and differentiation axes on a concept map.\n <\/p>\n <\/div>\n\n

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\ud83d\udca1<\/div>\n

New service and idea generation<\/h3>\n

\n Generate text chunks based on a chosen theme,\n then discover semantic groups and empty regions that can inspire new ideas.\n <\/p>\n <\/div>\n\n

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\ud83e\udded<\/div>\n

Market positioning of hypotheses<\/h3>\n

\n Compare your own concept or hypothesis with competitor concepts\n and check its position within an existing market.\n <\/p>\n <\/div>\n\n

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\ud83c\udf75<\/div>\n

Sensory and experiential value analysis<\/h3>\n

\n Gather natural-language expressions of taste, aroma, texture, usability, and other experiences,\n then analyze subtle perceptual differences as conceptual space.\n <\/p>\n <\/div>\n\n

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\ud83e\uddea<\/div>\n

Research and technology exploration<\/h3>\n

\n Collect paper abstracts and patent information to explore relationships among research themes,\n technology fields, and application areas.\n <\/p>\n <\/div>\n\n

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\ud83e\uddfe<\/div>\n

Qualitative research organization<\/h3>\n

\n Use AI-assisted coding on interviews, field notes, and usability-test records,\n then connect the results to conceptual structure modeling.\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Research Quality<\/div>\n

AI-collected information is a starting point for exploration. Human verification is still necessary.<\/h2>\n

\n Auto Research improves the efficiency of early-stage information gathering and data preparation.\n However, AI-generated or summarized text and external information may contain errors, outdated details, or bias.\n <\/p>\n

\n For practical use, important information should be checked against primary sources\n and reviewed by experts before being used for Concept Map modeling or hypothesis validation.\n <\/p>\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Positioning<\/div>\n

Auto Research is ConceptMiner\u2019s input-data generation layer.<\/h2>\n

\n If Concept Map is the tool for building and exploring conceptual structure models,\n Auto Research is the upstream tool for preparing the text chunks that go into those models.\n <\/p>\n

\n The data created here can also become foundational input for Concept Index,\n Knowledge Base Builder, and other parts of the ConceptMiner ecosystem.\n <\/p>\n <\/div>\n <\/div>\n <\/section>\n\n\n

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Turn a research theme into data that can be concept-mapped.<\/h2>\n

\n Organize markets, competitors, news, papers, patents, free-form ideas, and interview records\n as text chunks that can be explored in Concept Map.\n Auto Research is the entry point connecting research and conceptual structure modeling.\n <\/p>\n

\n Try Auto Research<\/a>\n Contact Us<\/a>\n <\/div>\n

\n \u203b Please adjust the contact-page URL to match the actual English page.\n <\/p>\n <\/div>\n <\/div>\n <\/section>\n\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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