Cross-Domain Exploration is an approach to discovering knowledge and solutions in different academic disciplines, industries, technological fields, and natural phenomena, and applying those discoveries to problems and technical challenges in another domain. It can be used in research and development, new product development, business creation, technology strategy, and problem-solving.
In many R&D activities, researchers and engineers investigate scientific literature, patents, and technical information within their areas of expertise to improve existing technologies or develop new solutions. However, a problem that is difficult to solve within one field may already have been addressed in another.
For example, when an industry faces challenges in developing new materials, manufacturing technologies or material structures used in another industry may provide useful insights. Industrial systems may also draw inspiration from biological functions or natural phenomena.
However, such knowledge can be difficult to discover through conventional information gathering organized around established disciplines.
Different fields may use entirely different names and technical terminology for technologies that perform similar functions or share structural characteristics.
Cross-Domain Exploration therefore involves reconsidering technologies and problems not only through their names and applications but also through their characteristics, functions, operating principles, structures, relationships, and patterns.
For example, instead of searching only for products called “vibration sensors,” we can abstract the underlying function as “observing changes in an object’s state and detecting deviations from normal conditions.”
This makes it possible to consider anomaly detection technologies beyond vibration measurement, as well as biological sensing mechanisms and other phenomena.
However, discovering a similar technology in another domain does not mean that it can be transferred directly.
Differences in operating conditions, scale, materials, environments, costs, and other constraints must be examined to determine whether practical application is possible.
The essence of Cross-Domain Exploration is not simply collecting information from other industries, but discovering knowledge and solution principles that remain hidden within established disciplinary boundaries and connecting them to new possibilities for problem-solving.
Theoretical Foundations of Cross-Domain Exploration
The use of knowledge from different disciplines to solve problems and develop innovations has been studied in cognitive science, management research, technology management, and creativity research.
In cognitive science, Analogy and Analogical Transfer explain how knowledge from one domain can be used to understand or solve problems in another.
In his 1983 paper “Structure-Mapping: A Theoretical Framework for Analogy,” Dedre Gentner emphasized the importance of correspondences between relational structures rather than similarities in superficial attributes.
Mary Gick and Keith Holyoak demonstrated in their 1983 paper “Schema Induction and Analogical Transfer” that extracting common problem-solving structures from multiple examples can facilitate analogical transfer to new problems under certain conditions.
Innovation research has also investigated the importance of transferring and combining knowledge across industrial and technological boundaries.
In their 1997 paper “Technology Brokering and Innovation in a Product Development Firm,” Andrew Hargadon and Robert Sutton examined the product development company IDEO.
IDEO accumulated technological knowledge through its work with clients from many industries and applied solutions familiar in one industry to problems encountered in another.
This activity is described as Technology Brokering.
Lee Fleming examined the uncertainty associated with new combinations of technological components in his 2001 paper “Recombinant Uncertainty in Technological Search.”
His research showed that unfamiliar technological combinations can increase uncertainty and the likelihood of failure while also creating possibilities for exceptional outcomes.
Lars Bo Jeppesen and Karim Lakhani investigated external problem-solving competitions in their 2010 paper “Marginality and Problem-Solving Effectiveness in Broadcast Search.”
Using data involving 166 scientific and technical challenges and more than 12,000 participants, their study found that individuals whose expertise was relatively distant from the problem domain sometimes submitted successful solutions.
However, this was a study of a particular form of problem-solving competition. It does not establish a general rule that more distant expertise always produces better solutions.
These studies address different aspects of Cross-Domain Exploration.
| Research Area | Main Perspective |
|---|---|
| Analogy research | Discovering common relationships and structures between different objects |
| Concept Formation and Abstraction | Extracting characteristics and concepts applicable beyond individual examples |
| Technology Transfer | Applying technologies developed in one field to another |
| Technology Brokering | Connecting knowledge from different domains to problem-solving |
| Recombinant Innovation | Creating new combinations of existing technological components |
| Open Innovation | Incorporating knowledge and technologies from outside an organization |
| Cross-Domain Concept Exploration | Searching for unfamiliar technologies and objects through functions, structures, and patterns |
Cross-Domain Exploration treats the discovery and application of knowledge across disciplinary boundaries as a practical approach to thinking and problem-solving.
Why Is Knowledge from Other Domains Difficult to Discover?
Researchers and engineers often possess advanced knowledge within their own disciplines.
However, specialized knowledge is typically organized around particular research subjects, theories, technologies, methods, and terminology.
For example, a materials scientist and a biologist may investigate structurally similar phenomena while using very different terminology and explanatory frameworks.
Technologies performing the same function may also have different names in different industries.
For example, mechanisms for delivering substances to required locations may be discussed as fluid transportation, drug delivery, logistics networks, or nutrient transport, depending on the field.
Their materials and specific operating mechanisms may differ, but they share an abstract function: delivering required substances to required locations.
When searching with specialized terminology, however, the results tend to concentrate on the domains where that terminology is commonly used.
Scientific publications and patents are also organized through disciplinary classification systems and citation relationships, which do not always make structurally similar solutions across fields easy to discover.
At least three challenges are involved.
First, we may not know that relevant knowledge exists. If a useful technology is already available in another domain but we are unaware of it, we may never consider it as a candidate solution.
Second, terminology and categories may differ. Similar functions and structures may be described using entirely different specialized vocabularies.
Third, we may fail to recognize the underlying similarity. Even if we encounter a technology from another domain, we may not recognize how it relates to the original problem.
Cross-Domain Exploration therefore involves more than gathering information. It requires discovering relationships that cross the boundaries of established knowledge.
What Constitutes a Domain Boundary?
In Cross-Domain Exploration, a “domain” does not necessarily mean an academic discipline or industry category.
Even within the automotive industry, materials development, mechanical engineering, control technology, software, and manufacturing may involve different bodies of specialized knowledge.
Conversely, different industries may use similar materials or control technologies.
It is therefore important to clarify what kind of boundary is being crossed.
| Type of Boundary | Examples |
|---|---|
| Academic discipline | Physics, chemistry, biology, information science |
| Industry | Automotive, healthcare, food, construction, electronics |
| Technological field | Materials, measurement, control, communications, processing |
| Application or function | Transportation, separation, detection, protection, repair |
| Scale of the target | Molecules, materials, components, equipment, social systems |
| Organization or business function | Research, manufacturing, quality management, logistics, maintenance |
For example, applying a biological transportation mechanism to industrial materials development crosses both disciplinary and technological boundaries.
Applying a quality management method from the automotive industry to food manufacturing primarily crosses an industrial boundary.
The important question is not simply whether two subjects belong to different categories.
It is whether knowledge from another domain provides new possibilities for understanding or solving the original problem.
Two Directions of Cross-Domain Exploration
Cross-Domain Exploration can be organized into two major directions.
The first begins with an existing technology or body of knowledge and searches for new applications.
The second begins with a problem or desired function and searches for technologies or mechanisms capable of addressing it.
| Direction | Starting Point | Main Objective |
|---|---|---|
| Technology-Push Exploration | Existing technologies, knowledge, or capabilities | Discover new applications and markets |
| Problem- or Need-Driven Exploration | Problems or required functions | Discover solutions from other domains |
For example, consider a company that possesses advanced surface processing technology.
By abstracting the technology’s functions and characteristics, the company may search for applications in other industries requiring similar capabilities.
This is Technology-Push Exploration.
Now consider a company that wants to inspect the interior of a compact device without damaging it.
The company might investigate nondestructive testing methods used in other industries, medical imaging technologies, or optical measurement techniques.
This is Problem-Driven Exploration.
In both cases, abstraction of functions and structures can be more useful than relying exclusively on specific technology names.
However, Technology-Push Exploration must also establish whether the newly identified application creates customer value and satisfies market requirements.
Problem-Driven Exploration must determine whether the discovered technology can satisfy the required performance and operating constraints.
Differences Between Keyword Search and Cross-Domain Exploration
Conventional information retrieval specifies search targets through keywords.
For example, searching for “vibration sensors,” “anomaly detection,” or “equipment maintenance” can retrieve relevant technologies, products, research papers, and patents.
However, these keywords are based on technologies and applications that are already known.
If a technology performing the same function exists in another domain under unfamiliar terminology, it may be difficult to include that technology in the search.
Cross-Domain Exploration therefore uses functions, operating principles, structures, and relationships to formulate search conditions without being restricted to established names or categories.
| Search Approach | Example Search Condition |
|---|---|
| Product name | Vibration sensor |
| Technology category | Non-contact anomaly detection technology |
| Function | Mechanisms that detect changes in an object’s state |
| Operating principle | Mechanisms that convert small physical variations into measurable signals |
| Structure | Systems that observe states, compare them with references, and detect deviations |
| Pattern | Methods that identify abnormalities early through changes from normal conditions |
However, this does not mean that keyword search cannot discover cross-domain knowledge.
When functions and structures are expressed using appropriate terms, conventional search engines and academic databases can also retrieve information from other domains.
If relevant terminology from multiple fields is known, search queries can be expanded accordingly.
The important distinction is therefore not simply the type of search system used.
It is how the search problem is represented and how the scope of exploration is expanded.
Abstraction Changes the Scope of Exploration
Abstraction plays an important role in Cross-Domain Exploration.
For example, beginning with the function “detecting equipment vibration” may concentrate the search on vibration measurement technologies.
Abstracting the function to “observing changes in the state of an object” broadens the search to measurement methods beyond vibration.
Further abstraction to “detecting deviations from normal conditions” makes it possible to consider physical measurement systems, software monitoring, and biological sensing mechanisms.
However, increasing the degree of abstraction also expands the search scope and may introduce many irrelevant candidates.
For example, the expression “observing states and responding to changes” applies to an enormous variety of systems.
It is therefore necessary to determine which details should be omitted and which constraints should be preserved.
When searching for methods of detecting changes in an object, relevant constraints may include the target phenomenon, required accuracy, response time, or non-contact operation.
On the other hand, if the purpose is to discover alternative sensing mechanisms, retaining the original sensor technology as a fixed requirement may unnecessarily restrict the search.
Cross-Domain Exploration requires combining Abstraction, which expands the search scope, with constraints that help identify appropriate candidates.
Searching Through Characteristics, Functions, Structures, and Patterns
Cross-Domain Exploration can use several different perspectives for abstracting a technology or problem.
| Exploration Perspective | Key Question |
|---|---|
| Characteristics | What properties does the technology possess? |
| Functions | What does the mechanism accomplish? |
| Operating principles | What phenomena or principles make the function possible? |
| Structures | Which elements constitute the system, and how are they related? |
| Patterns | What changes or interactions occur repeatedly? |
| Constraints | Under what conditions must the system operate? |
For example, the function “supplying substances to required locations” appears in many fields.
A more specific structural description might be “repeatedly delivering substances from a limited supply source to distributed targets.”
Adding the pattern “adjusting routes and delivery quantities according to the needs of each destination” creates a more detailed representation.
By describing not only general functions but also relationships among elements and patterns of operation, it may become possible to discover structural similarities among otherwise unrelated fields.
However, functional or structural similarity does not establish technological transferability.
Biological transportation mechanisms and industrial piping systems, for example, may share certain structures while differing substantially in materials, dimensions, fluid characteristics, and control methods.
Exploration should therefore identify similarities, while evaluation should examine differences and constraints.
Analogy and Structure-Mapping Theory
An important cognitive foundation of Cross-Domain Exploration is Analogy and Structure-Mapping Theory.
Analogy uses knowledge of one object or situation to understand another or solve a different problem.
Structure-Mapping Theory emphasizes correspondences between the relational structures of different objects.
For example, biological circulatory systems and industrial fluid distribution networks differ in their materials and specific mechanisms.
Nevertheless, correspondences may exist among supply sources, transportation pathways, and distribution destinations.
Abstracting these relationships and applying them to another problem can suggest new solutions.
However, similarity alone is insufficient to establish a meaningful analogy.
Two systems may both have network structures while differing significantly in how substances or information are transferred and what constraints govern their operation.
Structure Mapping emphasizes not only individual correspondences but also the consistency of the larger relational structure.
After discovering a candidate technology from another domain, it is therefore necessary to examine which structural correspondences exist between that technology and the original problem.
Case Study 1: Fujifilm’s Expansion from Photography to Cosmetics
One representative example of technological expansion across industries is Fujifilm’s cosmetics business.
Fujifilm accumulated expertise in collagen, nanotechnology, antioxidant technologies, and light analysis and control through research and development related to photographic film.
Photographic film uses gelatin derived from collagen, and advanced materials technologies were developed to ensure film stability and performance.
Research into preventing photographic fading also contributed to technological knowledge concerning oxidation and the effects of ultraviolet light.
As digitalization reduced demand for photographic film, Fujifilm applied these technological assets to new business areas.
In 2007, the company launched its skincare brand ASTALIFT, incorporating technologies developed through its photographic research.
According to Fujifilm’s official materials, its cosmetics development draws on collagen research, nanotechnology, antioxidant and ultraviolet protection technologies, and technologies for analyzing and controlling light.
Although photographic film and cosmetics belong to very different product categories, they share relevant technical characteristics involving materials, dispersion and stabilization, oxidation, and light control.
The case can therefore be interpreted as an example of moving beyond the established category of photographic film and reconsidering the underlying functions and operating principles of accumulated technologies.
| Technological Assets from Photography | Applications in Cosmetics |
|---|---|
| Knowledge of collagen | Research into skin-related materials and ingredients |
| Nanotechnology | Stabilization, dispersion, and formulation of ingredients |
| Antioxidant and ultraviolet protection technologies | Skin protection and skincare development |
| Light analysis and control | Skin analysis and related cosmetics technologies |
However, Fujifilm’s success cannot be explained by Abstraction alone.
Research capabilities, quality management, manufacturing technology, business investment, product development, and distribution also played important roles.
Discovering technical similarities may provide the starting point for cross-domain expansion, but establishing a successful business requires additional capabilities and conditions.
Case Study 2: Self-Healing Materials and Biological Supply Networks
Research on supply networks for self-healing materials provides another example of using mechanisms from different domains.
Some self-healing materials incorporate capsules containing repair agents.
When damage occurs, the capsules rupture and release their contents.
However, because the locally stored repair agent is consumed, repeated repair at the same location can be difficult.
Instead of defining the problem as “improving the performance of capsules,” it can be reframed as “repeatedly supplying repair agents to damaged locations.”
This abstraction makes it possible to investigate continuous supply mechanisms from other fields.
In 2007, Kathleen Toohey and colleagues published “Self-Healing Materials with Microvascular Networks” in Nature Materials.
The study reported a self-healing system incorporating three-dimensional microvascular networks inspired by biological vascular systems, allowing repair agents to be supplied for repeated healing.
| Original Problem | Relevant Cross-Domain Structure | Investigated Solution |
|---|---|---|
| One-time local storage limits repeated repair | Continuous supply networks resembling biological vascular systems | Supplying repair agents through internal microvascular pathways |
The important point is that the biological vascular system was not directly transplanted into an artificial material.
Instead, its function and structure as a continuous supply mechanism provided inspiration for a technology adapted to the conditions of artificial materials.
This is an established research example rather than a newly proposed invention.
When knowledge is transferred across domains, discovering a structural correspondence must be distinguished from developing a technically feasible implementation.
A Practical Procedure for Cross-Domain Exploration
A practical Cross-Domain Exploration process can be organized as follows.
- Clarify the exploration objective. Determine whether the goal is to identify new applications for existing technologies, discover solutions to technical problems, or generate new R&D themes.
- Organize existing knowledge. Document the original technology or problem, known information, operating conditions, constraints, and unresolved issues.
- Extract technological elements and characteristics. Distinguish functions, operating principles, structures, relationships, and patterns.
- Abstract the problem for exploration. Represent the technology or problem without relying exclusively on product names or specialized terminology.
- Search for candidates across domains. Use scientific publications, patents, technical documents, case studies, and expert knowledge.
- Analyze similarities and differences. Examine how the discovered technologies correspond to the original problem in function and structure.
- Form application hypotheses. Consider direct transfer, structural modification, or combinations with other technologies.
- Examine existing technologies and feasibility. Investigate prior research, technical constraints, required modifications, and verification methods.
- Repeat the exploration when necessary. Revise search conditions using newly discovered characteristics or questions.
This is not intended to represent a single universally established standard procedure.
It is a practical organization of ideas informed by cognitive science and technology management research.
The original objective or search conditions may also need to be revised when unexpected problems or possibilities emerge.
Cross-Domain Exploration should therefore be regarded as an iterative process in which discoveries create opportunities for further investigation, rather than a single search that immediately produces a final answer.
Example: Exploring Equipment Anomaly Detection Across Domains
Consider a company attempting to detect early abnormalities in existing manufacturing equipment while minimizing the installation of additional sensors.
Conventional technical research might focus on vibration sensors, acoustic sensors, electrical current measurement, and AI-based anomaly detection.
Cross-Domain Exploration allows the problem to be reframed:
“How can changes in externally observable signals be used to detect early signs of abnormalities when the internal condition of a target cannot be observed directly?”
This question makes it possible to explore similar problems outside equipment maintenance.
Potentially relevant fields include medical diagnosis, electrical infrastructure monitoring, structural health monitoring, and communication network monitoring.
| Domain | Potentially Relevant Principle |
|---|---|
| Medical diagnosis | Estimating internal conditions from externally observable signals |
| Electrical equipment | Inferring equipment conditions from changes in current or voltage |
| Structural health monitoring | Evaluating internal abnormalities through changes in vibration or structural response |
| Communication networks | Detecting deviations from normal communication patterns |
| Biological sensing | Detecting and responding to changes in environmental or internal conditions |
This is a hypothetical illustration of exploration directions, not a verified assessment of the transferability of specific technologies.
The purpose is not necessarily to identify the same technology in every domain.
Medical diagnosis may offer approaches for estimating hidden states from multiple signals.
Communication network monitoring may provide methods for detecting deviations from expected patterns.
However, the targets, available observations, accuracy requirements, and frequencies of abnormal events differ.
Methods cannot necessarily be transferred directly.
Cross-Domain Exploration examines these similarities and differences to determine which kinds of knowledge may contribute to the original problem.
Should Exploration Focus on Nearby or Distant Domains?
Cross-Domain Exploration may investigate fields technically close to the original problem or fields that are much more distant.
Nearby fields may share relevant knowledge, operating principles, and constraints, making their technologies comparatively easy to understand and transfer.
Distant fields may provide unfamiliar technologies and alternative ways of framing a problem.
However, more distant domains do not necessarily produce better ideas.
Greater distance may also involve larger differences in terminology, underlying phenomena, specialized knowledge, and technical requirements.
This can make discoveries more difficult to understand and implement.
Fleming’s 2001 research showed that unfamiliar technological elements and new combinations can increase variability in innovation outcomes.
The 2016 study “Where and How to Search? Search Paths in Open Innovation” also examined exploration in terms of both the proximity of the knowledge being searched and the methods used to conduct that search.
The important questions are therefore not only how distant a domain is, but also what is being used as the basis for exploration and how the discovered knowledge is understood and transferred.
In practice, it may be useful to explore nearby and distant fields in parallel rather than assuming that one is always preferable.
Distinguishing Common Functions from Necessary Operating Conditions
After discovering a cross-domain candidate, the first step is to examine common functions and structures.
However, this alone is insufficient for assessing transferability.
For example, two systems may both perform the function of “transporting substances,” while differing in the substances involved, dimensions, temperatures, pressures, and required speeds.
Similarly, systems sharing a control structure may have very different requirements concerning response time and stability.
Candidate evaluation should therefore distinguish several dimensions.
| Evaluation Dimension | Key Question |
|---|---|
| Functional correspondence | Do the systems accomplish similar objectives? |
| Structural correspondence | Are there meaningful similarities in relationships among components? |
| Operating principles | Do they use the same principle, or different principles that achieve equivalent functions? |
| Necessary conditions | Can the required physical and technical conditions be satisfied? |
| Scale and environment | Are dimensions, temperatures, pressures, speeds, and loads compatible? |
| Required modifications | Which elements would need to be improved or redesigned? |
| Novelty | Has a similar application already been investigated? |
| Usefulness | How much could the technology contribute to solving the original problem? |
| Verifiability | Can its potential be tested through research or experimentation? |
In R&D, it is particularly important not to confuse technological similarity with feasibility.
Even when knowledge from another domain is relevant, it may not be directly usable.
Modifications or combinations with other technologies may be required.
Developing those modifications or combinations can itself become a new R&D theme.
Discovery Is Not the Same as Invention
Cross-Domain Exploration may reveal solutions described in existing publications or technical information that were previously unknown to the investigator.
However, such knowledge may already be established in another field.
A discovery that is new to the investigator is therefore not necessarily a scientifically or technologically novel invention.
If an existing technology can be transferred directly, the result may be valuable as a practical application of Technology Transfer.
If applying the technology requires developing new structures or operating principles, the adaptation may create opportunities for invention.
Combining technologies from different fields may also produce previously unavailable functions.
These outcomes should be distinguished.
| Stage | Main Outcome |
|---|---|
| Discovery of cross-domain knowledge | Identification of an existing but previously unfamiliar technology or phenomenon |
| Discovery of correspondences | Understanding functional or structural similarities with the original problem |
| Identification of application potential | Recognition of possible transfer, adaptation, or improvement |
| Hypothesis formation | Development of a testable proposal involving a new structure or combination |
| Technical verification | Investigation of feasibility and effectiveness through experiments or analysis |
| Invention and implementation | Development into a new technology or practical application when appropriate |
These distinctions are important when evaluating Cross-Domain Exploration.
Even when no new R&D theme is produced, solving a problem through the transfer of an existing technology can have significant value.
Conversely, an interesting analogy may not justify further development if it does not contribute to a meaningful solution.
Relationship with Conceptual Combination and Conceptual Blending
Knowledge discovered through Cross-Domain Exploration may sometimes be applied directly.
In other cases, it may provide the basis for forming a new technological concept.
This is where Conceptual Combination and Conceptual Blending become relevant.
Conceptual Combination refers to the cognitive process of combining concepts to form new meanings or functions.
Conceptual Blending is a theory explaining how elements and relationships from different conceptual spaces can be selectively integrated to form new meanings and structures.
For example, suppose Cross-Domain Exploration identifies a supply network with a structure different from that of the original technology.
If the network can be used without significant modification, the result may be understood as Technology Transfer.
However, if the network structure is integrated with the characteristics of the original technology to create a new supply mechanism, Conceptual Combination or Conceptual Blending may provide useful perspectives.
Cross-Domain Exploration is primarily concerned with discovering knowledge.
Conceptual Combination and Conceptual Blending are concerned with how such knowledge can contribute to forming new concepts.
In actual thinking, however, these processes may not be clearly separated.
Discovering unfamiliar knowledge may change the definition of the original problem, leading to another round of exploration.
Differences from Technology Intelligence
Technology Intelligence involves collecting and analyzing information about technological trends, research activities, patents, competitive environments, and technological maturity to support technology strategy and R&D decisions.
Cross-Domain Exploration also uses technological information, but its objectives are not necessarily the same.
For example, Technology Intelligence may investigate major research organizations, companies, patent trends, and development trajectories within a particular technological field.
Cross-Domain Exploration instead asks whether functions or structures relevant to the current problem exist in unfamiliar domains.
| Aspect | Technology Intelligence | Cross-Domain Exploration |
|---|---|---|
| Main objective | Understand technological trends and competitive environments | Discover unfamiliar technologies and solution principles |
| Typical starting point | Technological fields, companies, products, research areas | Problems, functions, structures, patterns |
| Main subjects of analysis | Patents, publications, companies, technology roadmaps | Technologies, functions, mechanisms, and analogous structures across domains |
| Typical outcomes | Technology trends, competitive analysis, strategic information | Application candidates, technological analogies, R&D hypotheses |
However, Technology Intelligence is not necessarily restricted to established disciplinary boundaries.
Technology Intelligence systems and analyses may investigate patents and publications from different fields and identify opportunities for combining technologies.
The difference lies more in the objective of the investigation than in the information sources themselves.
Technology Intelligence may help identify candidates, while Cross-Domain Exploration can help examine their relevance and possible applications.
Relationship with Open Innovation
Open Innovation emphasizes the use of knowledge and technologies from outside an organization and, when appropriate, the external application of internally developed technologies.
Cross-Domain Exploration is related to Open Innovation because both can involve discovering external knowledge.
For example, a company may publish a technical challenge and invite solutions from experts outside the organization.
A useful solution may come from a discipline that the company had not originally considered.
However, Open Innovation includes a broader range of activities such as collaborative research, technology acquisition, licensing, and partnerships.
Cross-Domain Exploration focuses more specifically on discovering relevant knowledge and possible solutions.
A technology discovered through Cross-Domain Exploration may subsequently lead to licensing or collaboration with an external organization.
However, Cross-Domain Exploration does not necessarily require external partnerships.
For example, a company may develop a new application hypothesis using publicly available scientific literature.
Cross-Domain Exploration in the Age of AI
The development of LLMs is changing how Cross-Domain Exploration can be supported.
Traditionally, identifying technologies from unfamiliar fields often required knowledge of the terminology and literature used within those fields.
LLMs make it possible to describe technical characteristics or functions in natural language and ask whether similar mechanisms exist in different domains.
For example, instead of searching only for “equipment anomaly detection technology,” a user can describe a function as:
“Methods for estimating an internal state that cannot be directly observed by analyzing changes in signals available from outside the system.”
Such questions may help surface knowledge beyond the original discipline.
LLMs can also reformulate technological characteristics and structures at different levels of abstraction, generating alternative conditions for exploration.
However, AI-assisted Cross-Domain Exploration has important limitations.
First, a technology or example proposed by an LLM may not actually exist.
Second, descriptions may appear semantically similar even when their operating principles or necessary conditions differ.
Third, LLMs depend on their available knowledge and information sources, which may be incomplete or biased toward familiar examples.
Fourth, it is necessary to distinguish established examples from newly proposed application hypotheses.
LLM responses should therefore be verified through scientific publications, patents, technical documents, and other appropriate sources.
LLMs can provide powerful support for exploring unfamiliar technologies, but they do not guarantee the accuracy, novelty, or feasibility of the results.
Designing LLM Prompts for Cross-Domain Exploration
Simply asking an LLM to “find technologies that can be applied across different fields” may produce a list of familiar examples.
It is more useful to clarify the exploration objective and organize relevant functions, structures, and constraints before formulating the prompt.
For example:
Search for mechanisms capable of repeatedly supplying substances to required locations, rather than relying on one-time local storage. Do not restrict the search to a particular industry. Investigate real examples from industrial technology, biology, materials science, fluid systems, and other relevant fields. For each example, identify the supply source, transportation pathways, destinations, operating principles, and mechanisms enabling repeated supply. Explain similarities and differences relative to the original problem, and clearly distinguish documented examples from hypothetical applications.
This prompt specifies functions and structures rather than depending on a particular technology name.
It also defines the information needed to evaluate how each discovered example relates to the original problem.
However, a detailed prompt does not automatically ensure successful exploration.
Initial results may reveal new functions or structures that were not anticipated.
These discoveries can provide the basis for further searches.
Similarly, restricting the search scope too narrowly from the beginning may exclude unexpected candidates.
Cross-Domain Exploration therefore requires both clearly formulated search conditions and the flexibility to revise them.
Supporting Exploration with Embeddings and Concept Maps
When analyzing large collections of scientific publications or patents, Embedding Vectors can be used to examine semantic similarity among texts.
Dimensionality reduction methods such as UMAP and structural learning methods such as Growing Neural Gas (GNG) can also help organize information and visualize its distribution in conceptual maps.
These approaches are useful for organizing large information collections and identifying potentially related technologies or documents.
However, semantic similarity does not always correspond to similarity in technological functions or structures.
For example, similar technologies discussed in different industries may use very different terminology and may not be represented as sufficiently close in embedding space.
Conversely, technologies using similar terminology may rely on different operating principles.
Embedding-based similarity analysis alone is therefore insufficient for Cross-Domain Exploration.
An alternative is to explicitly extract technological characteristics, functions, structures, and relationships and use them as conditions for exploration.
Discovered candidates can also be organized into concept maps to examine the overall distribution of technologies and application hypotheses.
However, positions and clusters in a concept map should not automatically be interpreted as measures of technical relevance or feasibility.
Information organization and conceptual exploration should be combined while preserving this distinction.
How Should Cross-Domain Exploration Be Evaluated?
The results of Cross-Domain Exploration cannot be evaluated solely by counting the number of candidates discovered.
For example, collecting numerous examples from different industries may produce little value if their similarities to the original problem are superficial.
Conversely, a small number of candidates may be highly valuable if they reveal previously unrecognized solution principles.
Several evaluation dimensions can be considered.
| Evaluation Criterion | Key Question |
|---|---|
| Novelty of Discovery | Was the knowledge unfamiliar within the original field? |
| Information Reliability | Can the example be verified through existing technologies or research? |
| Functional Relevance | Does it address a function similar to that required by the original problem? |
| Structural Correspondence | Are there meaningful similarities in relationships among elements? |
| Technical Feasibility | Could the necessary operating conditions be satisfied? |
| Application Potential | Could the technology be transferred, adapted, or combined with other technologies? |
| New Research Questions | Does the discovery suggest worthwhile investigations or experiments? |
| Diversity of Exploration | Are the candidates distributed across different industries or technological fields? |
These criteria do not necessarily need to be combined into a single numerical score.
For example, a technically feasible candidate with limited novelty and a highly novel candidate with uncertain feasibility may occupy different positions within an R&D portfolio.
During early exploration, sufficient information may not be available to evaluate feasibility rigorously.
Rather than immediately rejecting such candidates, it may be more useful to identify what additional research is needed.
Limitations of Cross-Domain Exploration
Cross-Domain Exploration has several important limitations.
First, even when similar functions or structures exist in different fields, they may not be applicable to the original problem.
Second, the results depend on how the problem is abstracted. Omitting too many important conditions may produce large numbers of weakly related candidates.
Third, specialized knowledge is often required to understand differences between domains. Evaluating meaningful correspondences may require expertise from several disciplines.
Fourth, it is impossible to search all unknown knowledge comprehensively. Available information sources, languages, access restrictions, and publication practices constrain what can be discovered.
Fifth, discovering a technology from another field is not the same as inventing a new technology. The discovery or transfer of existing knowledge does not necessarily establish technological novelty.
Sixth, greater disciplinary distance does not automatically lead to better outcomes. Technical relevance, difficulty of understanding, and implementation costs must be considered.
Seventh, evaluation depends on the analyst’s knowledge and objectives. The usefulness of the same candidate may differ depending on which functions or structures are emphasized.
Eighth, AI does not eliminate the need for verification. Source reliability, technological feasibility, and applicability must still be examined.
Cross-Domain Exploration is therefore not a universal method that automatically discovers unknown technologies and turns them into inventions.
Its value lies in expanding the range of knowledge and alternatives that can be considered beyond established disciplinary boundaries.
Cross-Domain Exploration in ThinkNavi
ThinkNavi regards Cross-Domain Exploration as a thinking method that identifies common functions, structures, and patterns among technologies and knowledge originating in different domains and uses those discoveries to support new approaches to problem-solving.
Cross-Domain Exploration is closely related to thinking methods such as Abstraction, Analogy, Structure-Mapping Theory, Conceptual Combination, and Conceptual Blending.
Abstraction extracts functions and structures from concrete technologies and products.
Analogy and Structure Mapping identify correspondences between different objects.
Conceptual Combination and Conceptual Blending can help develop the discovered knowledge into new configurations and meanings.
ThinkNavi Free could provide worksheets for organizing the characteristics of a technology or problem and creating multiple abstract representations before conducting Cross-Domain Exploration.
For example, users could manually enter functions, operating principles, structures, and constraints, then develop abstract descriptions suitable for exploring other fields.
Users could also record technologies and examples they discover independently and compare their similarities, differences, and potential application hypotheses.
An LLM-assisted version could support technological abstraction, generation of alternative search conditions, identification of cross-domain candidates, analysis of correspondences, and organization of questions requiring further research.
However, comprehensive Cross-Domain Exploration may require collecting and analyzing large amounts of information using multiple search strategies.
Automating this process and analyzing large conceptual structures should be distinguished from ThinkNavi’s basic thinking-support functions.
Relationship with ConceptMiner R&D
ConceptMiner R&D is a system developed by Mindware Research Institute to support Cross-Domain Concept Exploration, building on the company’s long-standing research into Concept Exploration.
ConceptMiner R&D extracts technological elements from research papers and other technical documents and uses their characteristics and patterns to explore technologies and phenomena from different fields.
Its objective is to support exploration beyond known keywords and established disciplines and connect unfamiliar knowledge to the development of new R&D themes and hypotheses.
For example, multiple important technological elements can be extracted from an existing technology and abstracted from different perspectives.
Cross-domain exploration can then be performed for each element, allowing the discovered candidates to be compared.
The results can be examined to identify structural correspondences and possible applications relevant to the original technology.
Organizing multiple exploration results into concept maps can also make it easier to understand the overall distribution of candidates and distinguish different technological directions.
Furthermore, preserving relationships between the extracted technological elements and the generated R&D hypotheses would make it possible to analyze which characteristics contributed to particular hypotheses.
Such information could support the integration, evaluation, and prioritization of hypotheses, as well as the development of research plans.
However, not every discovered technology or generated hypothesis will possess novelty or technical feasibility.
ConceptMiner R&D results must still be checked against source documents, operating conditions, and existing research.
Whether AI-generated hypotheses develop into promising R&D themes should also be evaluated through concrete cases and verification.
ThinkNavi explains Cross-Domain Exploration as a general thinking method.
ConceptMiner R&D focuses on implementing a practical process that combines technological element extraction, analysis of conceptual structures, generation of exploration conditions, LLM-assisted cross-domain exploration, and organization of hypotheses.
The importance of Cross-Domain Exploration is not that it rejects specialized expertise, but that it reveals alternatives that specialized knowledge alone may not bring into view and makes it possible to apply that knowledge to new problems.
Discovering new R&D themes requires more than deepening knowledge within established disciplines.
It also requires reconsidering problems and technologies in terms of abstract functions, structures, and patterns and investigating their relationships with knowledge from other fields.
By returning from discovered knowledge to concrete technological and practical conditions, Cross-Domain Exploration can become a meaningful part of research, development, and innovation.
Primary English Term: Cross-Domain Exploration
Related Terms: Cross-Domain Search, Cross-Industry Innovation, Analogical Search, Knowledge Transfer, Technology Brokering, Recombinant Innovation, Boundary Spanning, Open Innovation, Functional Abstraction, Structural Analogy, Cross-Domain Concept Exploration
Related Thinking Methods:
- Abstraction | Extracting Features, Functions, and Structures from Concrete Phenomena to Discover New Possibilities
- Concept Formation and Categorization | Change the Categories, Change What You See
- Analogy — Connecting Knowledge Across Domains to Generate New Ideas and Hypotheses
- Structure-Mapping Theory | Understanding Relational Correspondences and Generating New Inferences Across Domains
- Conceptual Combination | Creating New Meanings, Functions, and Possibilities by Combining Concepts
- Conceptual Blending | Integrating Different Conceptual Worlds to Create New Meanings and Structures
- SCAMPER | Generating New Ideas Through Seven Perspectives
- TRIZ | A Systematic Approach to Inventive Problem Solving Through Technical Contradictions
- Morphological Analysis | Systematically Exploring New Possibilities Through Combinations of Elements
- Systems Thinking — Understanding Interdependence, Feedback, and Change Over Time
- Horizon Scanning | Discovering Early Signals of Change and Exploring Future Possibilities
- Concept Maps — Representing Relationships Between Concepts and Understanding Knowledge Structures
- Concept Exploration | Discovering Emerging Concepts and Unknown Possibilities
- R&D Theme Exploration and Generation,
- Technology Intelligence
References and Further Reading
- Dedre Gentner, “Structure-Mapping: A Theoretical Framework for Analogy,” Cognitive Science, Vol. 7, No. 2, pp. 155–170, 1983. A foundational study of Structure-Mapping Theory and relational correspondences across domains.
- Mary L. Gick and Keith J. Holyoak, “Schema Induction and Analogical Transfer,” Cognitive Psychology, Vol. 15, No. 1, pp. 1–38, 1983. Research on extracting common problem-solving structures and transferring them through analogy.
- Andrew Hargadon and Robert I. Sutton, “Technology Brokering and Innovation in a Product Development Firm,” Administrative Science Quarterly, Vol. 42, No. 4, pp. 716–749, 1997. An analysis of IDEO and the transfer and recombination of technological knowledge across industries.
- Andrew Hargadon, “Firms as Knowledge Brokers: Lessons in Pursuing Continuous Innovation,” California Management Review, Vol. 40, No. 3, 1998. Research on innovation through brokering technological knowledge across fields.
- Lee Fleming, “Recombinant Uncertainty in Technological Search,” Management Science, Vol. 47, No. 1, pp. 117–132, 2001. Research on uncertainty associated with new combinations of technological components.
- Kathleen S. Toohey, Nancy R. Sottos, Jennifer A. Lewis, Jeffrey S. Moore, and Scott R. White, “Self-Healing Materials with Microvascular Networks,” Nature Materials, Vol. 6, pp. 581–585, 2007. Research demonstrating repeated healing through internal microvascular supply networks.
- Lars Bo Jeppesen and Karim R. Lakhani, “Marginality and Problem-Solving Effectiveness in Broadcast Search,” Organization Science, Vol. 21, No. 5, pp. 1016–1033, 2010. Research examining external problem-solving and the relationship between domain distance and solution effectiveness.
- “Where and How to Search? Search Paths in Open Innovation,” Research Policy, Vol. 45, No. 1, pp. 125–136, 2016. Research examining search paths in Open Innovation in relation to knowledge distance and search methods.
- Fujifilm Corporation, “ASTALIFT.” Official information on applying photographic technologies, including collagen research, nanotechnology, and antioxidant technologies, to skincare products. (Japanese)
- Fujifilm, “History of ASTALIFT.” Official information about Fujifilm’s expansion into cosmetics and the launch of ASTALIFT in 2007. (Japanese)