ConceptMiner R&D is a research and development platform that takes you from technology-theme discovery through literature research, concept structuring, knowledge-base building, and the creation of hypotheses and applications—all in one environment. The features below follow the order of the sidebar menu.
1. R&D Theme Explore
Most traditional research starts with keywords. Keyword search, however, tends to circle around words, fields, and prior art you already know, so exploration stays inside a known information space. Even when you intend to look for what you do not know, you often get back only extensions of what you already know.
R&D Theme Explore is designed to overcome that limit. Instead of starting from keywords, it explores themes from the question: “What value do you want to deliver?” Using shifts in characteristics (higher performance, miniaturization, energy saving, and so on) together with directions of social value and need, it surfaces technology themes worth pursuing next.
The key point is that AI makes information exploration possible without depending on keywords you already have. You can reach domains whose vocabulary you do not yet know, and intersections outside your specialty where value may emerge—guided by the lens of value rather than by familiar search terms. It is the entry point for conceptual investigation before you dig into any single paper: opening up what kind of theme you should aim for.
Typical uses
- Find theme candidates from the value you want to provide, without being locked to keywords
- Explore unknown intersections by combining characteristic change with social challenges
- See how themes span industries and technology fields
- Set the starting point for later cross-domain exploration and Auto Research
2. Cross-Domain Explore
This is the core pipeline: from technical documents such as papers, patents, and technical reports, it extracts the essence of a technology, confronts it with knowledge from other domains, and generates new R&D hypotheses and application candidates. It does not stop at a one-shot summary. Extraction, abstraction, exploration, principle extraction, and hypothesis generation proceed in stages, so you can review intermediate results as you go.
Rather than searching for similar technologies by keyword, it steps into other domains through abstracted lenses—problem structure, function, operating principle, constraints, and design trade-offs—making it easier to encounter principles and cases outside your known vocabulary.
2-1. R&D Hypothesis Generation
Bring design principles found in other domains back to your original research theme and turn them into testable R&D hypotheses.
Flow: extract technical elements → abstract → cross-domain exploration → extract design principles → reapply to the original theme → R&D hypotheses
2-2. Cross-Domain Application Explore
Ask where an existing technology or principle could be used beyond the field where it is used today. The focus is on discovering candidate applications in new fields, products, and industries.
Flow: extract technical elements → (optional low-level abstraction) → application exploration → cross-domain application candidates
Semi-automatic exploration
Full enumeration is not the default. Beam search and step budgets deepen only promising branches. You can save midstream, resume, and accept or reject candidates at each stage. The intended work style is not “invent everything automatically,” but to craft sharp angles of attack and dig only as far as needed.
3. Auto Research
Automatically collect and organize data from multiple sources—markets, competitors, papers, patents, and more. Instead of assembling research materials only by hand, an agent gathers information, carries out concept extraction and organization, and prepares structured input for concept-map building.
This is the stage where you make investigation concrete, using the “direction of value” and “principle angles” uncovered in Theme Explore and Cross-Domain Explore as clues for related literature and market information.
Typical uses
- Collect market and competitive intelligence
- Survey academic papers and patents
- Extract concepts from books, papers, and other text
- Organize collected data and save it by project
4. File Management
List, merge, and edit CSVs and other files saved from Auto Research and exploration, organized by project. It is the workbench for keeping results from scattering and for reusing them as input to concept maps and knowledge bases.
Typical uses
- Review, preview, and download files in a project
- Merge and edit multiple CSVs
- Prepare data for concept-map construction
5. Concept Maps
Turn collected text and research data into a visual concept-structure model. Connections between concepts and the structure of a domain—often invisible when reading documents one by one—become an interactive map you can survey.
Gaps and adjacent regions that a keyword list would miss can become visible as structure, helping you notice places that are not yet named but may hold value.
Typical uses
- Structure scattered information at the concept level
- Grasp span and blank spots in a theme via clusters and relations
- Visualize “invisible links” among strategy, research, and the field
- Reuse saved models as a foundation for wiki generation and sharing
ConceptMiner emphasizes finding the structure of concepts in text—not only finding answer fragments the way RAG does.
6. Knowledge Base
Convert papers, technical materials, and internal documents into a human-readable wiki-style knowledge base. You do not need to read every volume or the entire corpus cover to cover. Summarized articles, search, and chat let you confirm content and grasp it across documents efficiently.
Typical uses
- Auto-generate a summarized wiki from papers, PDFs, web documents, and more
- Search and compare to grasp the big picture before full close reading
- Ask questions in chat and get answers grounded in the knowledge base
- Explore articles across the corpus with concept-map linkage
- Embed and share on internal portals or websites
The knowledge base is not vector search alone. It first builds a readable article layer, then uses chat and concept maps on top of that. It fits the use case of deciding what is written and what deserves deeper reading—before you try to read everything.
7. AI Chat
Multi-model AI chat for evaluating exploration results and consulting on next steps. Deepen, compare, and organize validation plans for candidates from R&D Theme Explore and Cross-Domain Explore through dialogue.
Typical uses
- Review and pressure-test hypotheses and application candidates
- Evaluate results with multiple models
- Discuss next moves for research and exploration
How the features connect (sidebar order)
- R&D Theme Explore opens theme directions from the value you want to deliver—without closing into keywords
- Cross-Domain Explore generates hypotheses and cross-domain applications from papers and related sources
- Auto Research gathers related market, competitive, and literature material
- File Management organizes results for reuse
- Concept Maps survey structure and blank spots in scattered information
- Knowledge Base turns document sets into a readable wiki for efficient search and review via chat
- AI Chat supports evaluation of results and advice on next steps
The flow—open themes from value → create hypotheses and applications → research → organize → structure → efficient content grasp → conversational review—runs in one environment.
In one sentence
ConceptMiner R&D is an R&D foundation for stepping beyond the known information space that keyword search tends to close you into—starting from “What value do you want to deliver?” and using AI to reach unknown themes, principles, and applications. Insights from exploration can be accumulated as research data, concept maps, and a knowledge base you can confirm and search without reading everything end to end.