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278-289 

278 

 

 

 

Article 

Computer-aided innovation for intelligent product 

design: a text mining and knowledge management 

approach  
S.M. Krishna Ganesh1*, Prolay Ghosh2, Amit S. Tiwari3, Bhushan S. Deore4, Jignesh Hirapara5,  

K. Hema6, Debashis Dev Misra7   

1Department of Computer Science and Engineering: Chennai Institute of Technology, Chennai, India 
2Department of Information Technology JIS College of Engineering, Kalyani, Nadia, West Bengal, India 
3Shah and Anchor Kutchhi Engineering College, India  
4Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Navi Mumbai, India  
5Faculty of Computer Application, Marwadi University Rajkot Gujarat, India 
6Department of Computer Science and Engineering, Koneru Laksmaiah Education, India  
7Assam Down Town University, Guwahati, India 

A R T I C L E   I N F O 
 

Article history: 
Received 25 August 2025  
Received in revised form 
26 October 2025 
Accepted 03 December 2025 
 
Keywords:  
Computer-aided innovation (CAI), Text mining, 
Natural language processing,  
Ontology-based knowledge management, 
Smart product design, Idea generation  
 
*Corresponding author 
Email address: 
krishnaganeshsm@gmail.com 
 
DOI: 10.55670/fpll.futech.5.1.24 

A B S T R A C T 
 

The fast-paced innovation and the growing need for user-centric products hold 
traditional design approaches against the wall in the Industry 4.0 era. This 
research establishes a unified Computer-Aided Innovation (CAI) framework 
based on text mining, ontology-based knowledge management, and TRIZ-based 
reasoning to support intelligent product design. The framework uses natural 
language processing to extract user requirements, technical problems, and 
potential contradictions from unstructured textual content sources such as 
product reviews, patents, and technical information. These insights are then 
structured in a TRIZ-compliant knowledge base to enable the rapid, 
transparent, and traceable generation of concepts. A smart wearable health 
device was used as the case study to evaluate the system's performance, and the 
results showed that the ideation efficiency of all concepts was significantly 
improved, with all concepts produced in less than 20 minutes, and the results 
were balanced across novelty, feasibility, and usability metrics. Compared with 
traditional methods such as brainstorming and Quality Function Deployment 
(QFD), the proposed framework yielded richer insights, greater concept 
diversity, and more evidence-based recommendations. Despite these 
advantages, the approach appears sensitive to textual ambiguity, domain-
specific terminology, and the long-term scalability of the ontology repository. 
Future research will focus on the following areas: leveraging multilingual data 
sources, combining generative AI with digital twin simulations for time-critical 
design exploration, and expanding the framework to other product domains. 
Overall, the proposed CAI framework is part of promoting systematic 
innovation by incorporating AI-assisted reasoning and structured knowledge 
representation in the early stages of product design. 

1. Introduction 

Product innovation has become more difficult as 
companies must cope with fast-changing user expectations 
and with shorter development cycles and a greater need for 
differentiation through advanced technologies. Recent 
researches focus on emphasizing that AI-enhanced 
approaches can be useful to assist early innovation activities 
that identify resources, patterns, and opportunities in large 

information spaces [1]. At the same time, structured design 
methodologies such as TRIZ are still important to order 
contradictions systematically and inventive problem solving 
during the whole product development process [2]. These 
developments lead to the understanding that the combination 
of computational intelligence and systematic design 
knowledge is important for supporting modern innovation 

 

 Open Access Journal 

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February 2026| Volume 05 | Issue 01 | Pages 278-289 

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279 

 

workflows. Artificial intelligence is starting to alter the 
ingrained innovation practices.  

 

 

 

 

 

 

 

 

 

Forecast studies have been conducted to examine how AI 
can be used to gradually automate various steps of the TRIZ-
based innovation process and to determine where it can have 
the greatest impact in real projects [3]. In parallel, new design 
approaches combine generative artificial intelligence models 
with TRIZ tools to facilitate the development of evolutionary 
concepts and early-stage design exploration [4]. These 
studies demonstrate that AI is becoming increasingly capable 
of augmenting human designers, especially in pinpointing the 
design problem and producing ways to improve it. In parallel 
with the development of innovation methodologies, text 
mining and natural language processing have provided new 
opportunities for extracting structured knowledge from large 
unstructured corpora. The use of ontology-linked datasets 
can demonstrate how technical entities can be systematically 
identified and aligned with domain ontologies to support 
downstream reasoning tasks [5]. Deep learning models have 
also continued to increase the accuracy and robustness of text 
classification and terminology extraction, particularly in 
specialized domains [6]. Survey work also shows that NLP 
and text mining are increasingly central to how AI methods 
process and interpret unstructured information in large-scale 
applications [7]. This is especially relevant for innovation 
activities, as idea-mining techniques use machine-driven 
analytics to extract opportunities and design insights from 
patents, publications, and online content [8].  

Hybrid approaches have become more common that 
integrate machine learning and symbolic data structures such 
as ontologies and knowledge graphs. Systematic reviews 
demonstrate that many AI systems now employ both 
inductive and deductive reasoning to improve the quality, 
explainability, and structure of the knowledge they extract 
[9]. In design research, AI is also used to interpret and analyse 
creations in creative combinations that reveal underlying 
relationships in complex design artifacts [10]. 
Complementary research investigates the use of knowledge 
extraction for generative knowledge and graph-based 
reasoning in the context of knowledge discovery and 
conceptual linking in scientific and engineering disciplines 
[11]. Collectively, these advances indicate CAI systems that 
combine data-driven extraction and knowledge-driven 
interpretation. Text mining continues to spread into new 
application areas, with recent work reviewing the changing 
landscape of techniques across different scientific fields and 
highlighting the growing sophistication of the language 
models used for large-scale literature analysis [12]. Within 
the framework of TRIZ research, systematic studies on 
semantic TRIZ and related frameworks investigate how AI 
technologies can enhance TRIZ's elements, identify current 

limitations, and chart the future of research [13]. Domain-
specific text mining investigations demonstrate the capacity 
of large language models to automate information extraction 
in highly technical disciplines [14]. It can be seen that 
effective NLP strategies can work well with specific 
expression sets for specific disciplines. Finally, survey work 
on LLM-augmented knowledge graphs shows how large 
language models can be combined with structured domain 
knowledge to support tasks such as concept generation, 
design reasoning, and process optimization [15].  

Despite these developments, important gaps remain. 
Existing AI — TRIZ and CAI — studies tend to focus on single 
tasks, such as resource mining, forecasting, and generative 
exploration, without offering a unified, reproducible end-to-
end framework that connects user feedback, domain 
literature, ontological knowledge, TRIZ reasoning, and 
concept generation. Additionally, consumer reviews and 
experiential content are underutilized as structured inputs 
for identifying design contradictions, even though they are a 
good source of information about what users expect and their 
pain points. While hybrid AI research shows great potential 
for combining symbolic structures with machine learning, the 
design innovation research domain still lacks detailed, 
transparent, and shareable ontologies and contradiction-
mapping schemes for systematically linking text-derived 
insights to TRIZ concepts.  

To overcome these problems, this paper proposes an 
integrated Computer-Aided Innovation (CAI) framework that 
combines the phenomena of text mining, ontology-based 
knowledge management, and TRIZ-driven reasoning. The 
framework is based on extracting entities, sentiments, and 
design-related topics from heterogeneous textual sources and 
organizing them into an ontology based on TRIZ concepts. 
Contradictions based on user needs and technical limitations 
are mapped to the inventive principles of TRIZ for structured 
concept generation. A case study of smart wearable health 
devices is presented to illustrate the application of the 
framework to systematic innovation in a realistic design 
context. 

1.1 Research objectives 
To build an end-to-end Computer-Aided Innovation 

(CAI) framework to integrate text mining, ontology-based 
knowledge representation, and TRIZ reasoning for intelligent 
product design. 
• To create an ontology-centred contradiction identification 

and mapping approach to increase the transparency, 
traceability, and reusability of the design knowledge 
extracted from heterogeneous textual sources. 

• To test the proposed framework by means of a comparative 
case study, and measure improvements in ideation 
performance (time to first idea, diversity of generated 
concepts, novelty, and feasibility as rated by experts) 
compared to traditional design methods. 

• To place the proposed framework in the context of the 
existing AI-TRIZ and hybrid AI research, identifying the 
relevance of the proposed framework, as well as its 
limitations and opportunities for future integration with 
large language models, knowledge graphs, and structured 
reasoning techniques. 

 
 
 
 
 
 

Abbreviations 

CAI Computer-Aided Innovation 

KM Knowledge Management 

NLP Natural Language Processing 

TRIZ Theory of Inventive Problem Solving 

LDA Latent Dirichlet Allocation 

NER Named Entity Recognition 

QFD Quality Function Deployment 

AI Artificial Intelligence 

LLM Large Language Model 

RDF Resource Description Framework 



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2. Literature review 

2.1 Computer-aided innovation (CAI) and   systematic 
innovation 
Contemporary innovation practices increasingly rely on 

computational assistance to manage complexity in 
engineering workflows. Ontology-driven conceptual 
modelling is part of systematic knowledge formalization and 
helps CAI environments that demand structured reasoning 
capabilities in design activities [16]. Hybrid approaches that 
combine large language models (LLMs) and knowledge 
graphs demonstrate the potential of combining symbolic and 
data-driven approaches to jointly improve creativity, 
reasoning, and problem-solving in product innovation [17]. 
Benchmark work on ontology-guided knowledge graph 
generation further makes the case for machine-readable 
knowledge representation structures to support automated 
innovation tasks [18]. Surveys on automatic knowledge graph 
construction focus on how structured domain knowledge 
allows the scales of semantically consistent innovation 
workflows [19]. Machine learning-biasing analyses of 
knowledge graph construction underpin the need for the 
convergence of AI and semantic frameworks to support CAI 
processes [20]. Collectively, these works provide evidence 
that CAI is on the path to AI-augmented, ontology-based, 
knowledge-graph-driven systems that can structure, 
interpret, and reuse design knowledge systematically. 

2.2 Text mining for insight extraction in product 
innovation 
The emergence of text mining techniques has led to a 

major improvement in the ability to extract high-value 
insights from large volumes of unstructured data. Technology 
roadmap research shows the combined use of TRIZ and text 
mining for the morphological analysis and strategic planning 
in product innovation [21]. Ontology learning methods from 
text demonstrate how NLP can be applied to build domain-
specific conceptual structures that are required for design 
and innovation processes [22]. Studies on TRIZ inventive 
principles emphasize that the textual knowledge must be 
transformed into systematic design actions through 
structured interpretation [23]. Applications of computer-
aided design (CAD) in the innovation workflow demonstrate 
how text-based insights can directly affect concept generation 
and refinement activities [24]. Recent AI-driven ideation 
tools, such as automatic TRIZ ideation systems, are further 
examples of how text data can be converted into structured 
design contradictions and possible inventive directions [25]. 
The development of multi-agent LLM systems for TRIZ-based 
innovation confirms the growing role of advanced NLP for 
automatic creativity support [26]. LLM-augmented problem-
solving frameworks demonstrate how the structured rules of 
design can be extracted and operationalized from text [27]. 
Together, these studies outline a clear trend toward text-
driven, TRIZ-informed design support systems. 

2.3 Knowledge management in product design 
Knowledge management is an important basis of 

innovation, particularly as product design becomes more 
data-driven. Distributed and collaborative knowledge 
management models are particularly useful as frameworks 
for supporting complex engineering design tasks involving 
multiple stakeholders [28]. Systematic literature reviews on 
idea mining show that machine-driven analytics can support 
structured idea generation, providing more objective and 
scalable solutions than manual ideation [29]. Ontological 
modelling in collaborative design environments 

demonstrates how structured semantic representations can 
help to improve knowledge reuse, consistency, and decision-
making [30]. These works, taken together, highlight the need 
for explicit knowledge representation, structured retrieval, 
and semantic reasoning for CAI and innovation-centric 
decision-making processes. 

2.4 TRIZ methodology and AI-driven innovation 
approaches 
Recent research shows a strong trend to combine AI and 

TRIZ-based innovation methodologies. Systematic studies of 
the evolution of TRIZ and its application in modern problem-
solving with the focus on its relevance in structured 
innovation [23]. Automated ideation tools like AutoTRIZ 
demonstrate how AI can implement the tenets of TRIZ and 
serve as a guide for early-stage design [25]. Multi-agent LLM-
based TRIZ systems are a proof of concept of how distributed 
AI agents can be used together to increase creativity and 
produce quality concepts [26]. LLM-augmented TRIZ 
methods also exhibit interesting applications in automated 
reasoning, contradiction identification, and solution synthesis 
[27]. Studies combining TRIZ logic with large-scale text 
analysis of patent literature demonstrate the potential of 
computational methods to enhance TRIZ's effectiveness for 
real engineering applications [26]. Together, these findings 
suggest that TRIZ is moving from a manually applied 
methodology to a digitally augmented, AI-integrated 
innovation methodology. 

2.5 Idea mining and technology intelligence 
Idea mining has become a strategic approach for 

identifying new opportunities and detecting technological 
change early. Technology intelligence research is a method 
for evaluating new opportunities, forecasting trends, and 
extracting future-oriented information from technical 
sources [28]. Machine-driven analytics for idea generation. 
This study shows how systematic text analysis can identify 
new product opportunities and eliminate the need for 
subjective, manual brainstorming [29]. Collaborative design 
ontologies are another example of how structured knowledge 
can be integrated with text-based derived knowledge to 
facilitate more strategic, future-aware innovation activities 
[30]. These research studies validate the growing importance 
of automated, data-driven approaches for identifying 
opportunities and making strategic decisions in product 
innovation. 

2.6 Comparative summary of existing work 
 Prior research, taken together, demonstrates substantial 

progress in TRIZ-based innovation, text mining, ontology-
based knowledge integration, and AI-based ideation. Table 1 
summarizes key contributions and methodological 
advancements, highlighting the growing convergence of NLP, 
knowledge engineering, and TRIZ reasoning in CAI systems.  

3. Proposed framework 

3.1 Architecture overview 
The proposed framework was built as a three-tier 

architecture to transform unstructured textual data into 
structured innovation insights and TRIZ-driven design 
recommendations. The first tier, the text mining layer, was 
responsible for data ingestion, preprocessing, topic 
modelling, entity extraction, and sentiment evaluation. The 
second tier, the knowledge management layer, was an 
ontology-based repository for storing the extracted design 
features, functional attributes, contradictions, and TRIZ-
related knowledge in a structured, semantically consistent 



SMK. Ganesh et al. /Future Technology                                                                            February 2026| Volume 05 | Issue 01 | Pages 278-289 

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format. The third tier, the CAI dashboard, was an interactive 
environment where designers could visualize extracted 
insights, investigate the contradictions, and generate solution 
strategies. These layers have been linked through a well-
defined data flow that started with raw text, transformed into 
processed linguistic features, then into semantic knowledge 
units, and finally into structured reasoning outputs for design 
support. Using a modular architecture, the pipeline was 
ensured to be interoperable, traceable, and extensible 
throughout. This structure enabled steady progress from 
textual evidence gathered from user reviews, patents, and 
technical blogs to systematic reasoning tasks such as 
contradiction identification, inventive principle selection, and 
the provision of actionable design guidance. 

Table 1. Selected key contributions in innovation research 

Reference Core Contribution 

[23] Detailed analysis of TRIZ inventive 

principles and systematic reasoning. 

[26] Integration of TRIZ logic with large-scale 

text analysis. 

[25] Introduction of AutoTRIZ for AI-assisted 

idea generation. 

[19] Comprehensive survey on automatic 

knowledge graph construction. 

[21] Joint application of morphology analysis, 

TRIZ, and text mining for strategic planning. 

[20] Systematic analysis of machine learning–

based KG construction methods. 

[29] Review of machine-driven analytics for idea 

generation. 

[24] Analysis of CAD-supported innovation 

workflows. 

[28] Technology intelligence methods for 

forecasting innovation opportunities. 

[30] Ontological modelling for collaborative 

design knowledge. 
 

3.2 Functional modules 
The text mining engine served as the analytical center of 

the framework. It was based on processing textual data using 
a suite of natural language processing operations, starting 
with pre-processing steps such as tokenization, 
normalization, lemmatization, and domain-specific stop-
word removal. Topic modeling was applied to identify 
recurring themes in product functions, performance issues, 
and user expectations. Named entity recognition was used to 
extract relevant features, components, materials, and 
operational contexts, and sentiment analysis was used to 
classify users' attitudes towards each extracted feature to 
identify strengths, weaknesses, and pain points. The engine 
also associated extracted entities with their sentiment 
polarity, resulting in meaningful feature-sentiment 
relationships that were later used for contradiction detection. 
The result of these was structured design-related 
information, including user requirements, technical 
problems, performance descriptors, and candidate features 
for improvement. 

The knowledge repository stored all extracted 
information in a structured ontology comprising well-defined 
classes, relationships, and semantic constraints. The ontology 
included captured product features, user requirements, 
technical problems, sentiments, functional relationships, 

contradictions, and TRIZ concepts, and it enabled each 
element to be represented consistently and interconnected. 
All the text-mining outputs were transformed into knowledge 
graph triples, which can be queried semantically and used for 
rule-based reasoning. The ontology layer served as the 
central reasoning engine, responsible for detecting 
contradictions using predefined logic, establishing links 
between design issues and TRIZ strategies, and retrieving 
relevant past examples or technical analogies. By structuring 
knowledge semantically, this module ensured transparency 
and reusability in the innovation process and supported 
systematic decision-making. 

The CAI dashboard offered an easy-to-use interface that 
allowed designers to work with the knowledge base and 
apply TRIZ-guided innovation tools. It graphically displayed 
topic distributions, sentiment trends, and emerging design 
themes derived from textual data. The dashboard contained 
what was called a contradiction viewer that presented 
automatically found conflicts and their mapping to 
corresponding TRIZ categories. It also provided some 
creative suggestions for the principal recommendations 
based on the type of contradiction and the contextual 
information in the ontology. Additional features enabled 
designers to browse design knowledge, explore relevant 
ontology nodes, inspect previous solutions, and examine 
technical analogies. The dashboard served as the final layer of 
the system, enabling designers to efficiently interpret insights 
and consider potential innovation directions. 

3.3 Workflow 
The system followed a structured workflow that began 

with the collection of text data from user reviews, patent 
abstracts, technical blog posts, and product descriptions. All 
incoming text underwent preprocessing: cleaning, 
lemmatisation, and domain-specific refinement of stop 
words. Preprocessing, including topic modelling, named 
entity recognition, and sentiment analysis, was used to 
extract features from the raw data, yielding structured 
representations of product features and user perceptions. 
These extracted entities and relationships were then 
populated into the ontology, where they were converted to 
knowledge graph triples. Once stored in the knowledge base, 
heuristic rules and mechanisms for semantic reasoning 
identified potential contradictions, such as conflicts between 
comfort and durability, or between making components 
smaller and allowing batteries to have greater capacity. These 
contradictions were then mapped to corresponding TRIZ 
engineering parameters to identify applicable inventive 
principles. The system produced contextualised design 
suggestions that were consistent with these principles. 
Finally, all results, including contradictions, recommended 
principles, and concept suggestions, were presented on the 
CAI dashboard for analysis and refinement. A high-level 
picture of such a pipeline is given in Figure 1.  

3.4 TRIZ integration 
The TRIZ integration module combined the extracted 

contradictions and converted them into structured, 
operational design guidance. After a contradiction is 
identified, the system correlates the contradictory elements 
with the corresponding TRIZ engineering parameters and 
queries the contradiction matrix to retrieve the relevant 
inventive principles. It then generated context-specific 
solution strategies that reflected the extracted user needs, 
and the technical constraints present in the knowledge base. 
These strategies comprised potential material alternatives, 
structural redesigns, parameter changes, and functional 



SMK. Ganesh et al. /Future Technology                                                                            February 2026| Volume 05 | Issue 01 | Pages 278-289 

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reorganizations. By integrating TRIZ logic into the reasoner 
and connecting it to the real information from the text, the 
framework ensured that its recommendations were both 
systematic and grounded in actual user experience. The 
combination of this logic enabled traceable, reproducible 
decision-making, allowing designers to explore structured 
and creative pathways to solutions in a transparent 
innovation-support environment. 

4. Methodology 

4.1 Data sources and selection criteria 
The data set used for the study included consumer 

reviews, patent abstracts, and technical blog posts related to 
smart wearable health devices. Consumer reviews were 
gathered from major e-commerce platforms and filtered to 
ensure they were relevant to the specific product category. To 
reflect the current user expectations, only English-language 
reviews published within the last three years were included. 
Patent abstracts were searched for with keyword 
combinations including "wearable," "health monitoring," 
"sensor device," "bio-signal" and "smart band." Technical 
blogs and expert articles were taken from verified technology 
news outlets and design-oriented websites. To ensure 
consistency, three inclusion criteria were applied: 
• The text must explicitly describe a feature, function, or 

performance attribute of a wearable device; 
• The content shall present sufficient evaluative or 

descriptive detail so that design-relevant information can 
be extracted from it; 

• The text should be a minimum of 50 words to minimize 
noise. 

After filtering, 5,000 consumer reviews, 1,000 patent 
abstracts, and 180 technical articles were kept for analysis. 

 

 

 

 

Figure 1. CAI framework workflow  

4.2 Preprocessing and NLP pipeline 
All the textual data went through a generalised 

preprocessing pipeline. Each document was normalized using 
tokenization, lower case transformation, punctuation 
cleaning, and removal of non-informative stop-words. A 
domain-specific stop-word list was created to address such 
frequent but meaningless terms related to wearable devices 
("device" in general, "band", "watch", when used generically). 
Lemmatization was used to morph variant word forms; for 
biomedical and sensor-related terminology, customized rules 
were added. Sentence segmentation guaranteed the accuracy 
of extracting feature-sentiment pairs and enhanced topic 
model coherence. Noise reduction techniques were used to 
remove irrelevant pieces of information, such as promotional 
phrases, duplicate content, and incomplete sentences. This 
processing ensured that our downstream extraction models 
ran on clean, consistent inputs in terms of structure. 

4.3 Topic modeling design and parameters 
Latent Dirichlet Allocation (LDA) was used to detect 

recurring themes and use contexts among the textual dataset. 
Several candidate topic numbers were tested, from 10 to 40 
topics, and coherence scores were tested to find the best 
configuration. The final model resulted in a c_v coherence 
score of 0.53, which resulted in the best balance between 
interpretability and thematic granularity. One configuration 
that was chosen was: 
• Number of topics: 20 
• Dirichlet prior α: asymmetric, optimized by the model 
• Dirichlet prior η (β): 0.01 
• Number of passes: 50 
• Iterations: 500 
The optimized model generated clear, semantically 
understandable topics that represented categories such as 
comfort, durability of the straps, battery life, sensor accuracy, 
skin irritation, waterproofing issues, and reliable 
connectivity.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 



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These topics were later assigned to ontology classes and 
helped to find contradictions. 

4.4 Named entity recognition and sentiment analysis 
A named entity recognition (NER) model was 

implemented to extract structured entities, e.g., product 
components, materials, functional actions, measurement 
parameters, and usage scenarios. The model was trained on a 
specialized annotated dataset on wearable-related 
terminology to enhance the recognition of wearable-related 
terminology. Performance validation was performed with an 
80/20 train-test split, and entity-level accuracy, precision, 
and recall were evaluated. Named Entity Recognition (NER) 
was conducted by means of a fine-tuned (spaCy 
en_core_web_trf) transformer model, which has been trained 
on 1,200 manually annotated review sentences with regard to 
components, materials, performance descriptors, and usage 
contexts. The final model achieved a precision of 0.89, a recall 
of 0.85, and an F1-score of 0.87, identifying a total of 4,612 
unique entities of 28 defined entity types. Some of the more 
common error patterns were misclassifications in which 
activity-related terms such as "running mode" or "workout 
session" were misclassified as components of the device, and 
confusions between metaphorical descriptions, such as 
"smooth performance," and literal skin-related descriptions 
of comfort. For sentiment analysis, the framework was set up 
to use a pre-trained CardiffNLP RoBERTa-base sentiment 
classifier (cardiffnlp/twitter-roberta-base-sentiment-latest), 
which had been further adapted with a pre-curated dataset of 
20,000 sentences from product reviews. The accuracy of the 
adapted model was 0.91, and the macro-F1 was 0.88. Most of 
the errors involving sentiment occurred in reviews 
containing sarcasm, mixed sentiment within the same 
sentence, or indirect expressions of dissatisfaction, such as "I 
wish the strap didn't irritate my skin." The model gave each 
extracted feature a sentiment score on a continuous scale 
between -1 (strongly negative) and +1 (strongly positive). 
Sentiment polarity was used to categorize features into 
strengths, weaknesses, and pain points, which in turn 
considered contradiction identification. 

4.5 Ontology construction and knowledge graph 
population 
An ontology specific to wearable device design was 

developed using a hierarchical schema with classes for 
product features and functions, user needs, performance 
attributes, technical problems, and TRIZ engineering 
parameters. Object properties included such relationships as 
"improves," "reduces," "depends_on," "causes," and 
"contradicts." Extracted entities and relations were taken 
from the text mining layer, transformed into RDF triples, and 
added to the ontology. SPARQL queries were applied to 
validate semantic consistency and make sure that each 
element that was extracted mapped to its respective class. 
Contradictions were represented as nodes in the knowledge 
graph between conflicting properties or requirements. 

4.6 Contradiction identification rules 
Contradictions were detected by means of a hybrid rule-

based approach using linguistic cues, polarity patterns, and 
ontology reasoning. Three types of contradiction were 
defined: 
• Feature–requirement conflicts: Example: “thin strap 

improves comfort but reduces durability.” 
• Performance trade-offs: Example: “smaller size lowers 

battery capacity.” 

• Contextual conflicts: Example: "tight fit - increases 
accuracy, causes irritation to skin.” 

Each contradiction was translated into the nearest TRIZ 
engineering parameters using a predefined mapping table. 
Mapping results led to the retrieval of inventive principles 
during the TRIZ reasoning stage. 

A typical consumer review said the following: "The band 
is comfortable when first worn, but after a few hours it will 
irritate my skin, especially if I am sweating." From this 
sentence, the system extracted three important entities - band 
comfort (positive sentiment), skin irritation (negative 
sentiment), and sweating condition (contextual modifier). 
These characteristics resulted in a clear conflict: the user 
needs comfort over long periods, but the material irritates in 
the presence of moisture. This was classed against the 
contradiction between user comfort and material stability. 
The extracted features were then mapped to TRIZ 
engineering parameters: comfort to Parameter 33 (Ease of 
Operation) and irritation to Parameter 10 (Stability of 
Substance). Querying the contradiction matrix yielded 
inventive principles such as Principles 30 (Flexible Shells), 31 
(Porous Materials), and 40 (Composite Materials). These 
principles were used to develop the idea of Skin-Friendly 
Coating, which contained breathable, hypoallergenic 
composite layers to minimize irritation while preserving 
durability. 

4.7 Experimental design 
A controlled experiment was carried out to test the 

effectiveness of the proposed framework. Participants were 
grouped into two groups: 
• Control Group: used traditional brainstorming and QFD-

based ideation methods. 
• Experimental Group: used the proposed CAI framework. 
Each group had 12 participants with engineering or product 
design backgrounds, so that expertise was similar. Both 
groups were given the same design brief and a time limit of 2 
hours to come up with concepts. The CAI system 
automatically recorded timestamps of each concept that was 
accepted; time-to-first-concept was defined as the time 
elapsed from the task's onset until the first concept was saved 
in the system's dashboard. The experimental group used 
topics, contradictions, and inventive principles 
recommendations generated by the system through the 
dashboard, whereas the control group used only manual 
analysis of given textual materials. 

4.8 Expert evaluation protocol 
A panel of five experts in the domain independently 

reviewed the generated concepts based on 3 dimensions: 
• Novelty (how original is the idea in comparison to existing 

products) 
• Feasibility (realism and practicality of Engineering) 
• Usability (improvement in expected user experience) 
A 10-point scoring scale was used, and evaluators followed 
written scoring guidelines to be consistent. The interrater 
reliability was calculated with Fleiss' Kappa for categorical 
agreement and Intraclass Correlation Coefficient (ICC) for the 
continuous scoring consistency. 

4.9 Statistical analysis 
Statistical tests were used to compare the two groups' 

performance on the tests. The normality of the score 
distributions was tested with the Shapiro--Wilk test. If 
normally distributed, independent-samples t-tests were 
used; otherwise, the Wilcoxon rank-sum test was used. For 
the ideation time and the concept quality scores, standard 



SMK. Ganesh et al. /Future Technology                                                                            February 2026| Volume 05 | Issue 01 | Pages 278-289 

284 

 

deviations and confidence intervals were computed. The 
magnitude of the difference between groups was calculated 
for effect sizes. This methodology helped to deliver a rigorous, 
reproducible methodology to assess the effectiveness and 
efficiency of the proposed CAI system. 

4.10 Ethical and data compliance statement 
All textual datasets used in this study were obtained 

and processed in accordance with legal, ethical, and platform-
specific guidelines. Consumer reviews were gathered from 
publicly available datasets or from platforms that explicitly 
permitted research access under their Terms of Service; 
nothing was automatically scraped from restricted web 
interfaces, and no personal identifying information (PII) was 
collected, stored, or analyzed at any stage. Patent abstracts 
were obtained from patent open-access databases that 
support text mining for scholarly research. All experimental 
procedures involving human participants were in accordance 
with standard ethical research practices: voluntary 
participation was obtained, informed consent was obtained 
before data collection, and no sensitive personal data were 
recorded. All data were anonymized and analyzed in 
aggregate, ensuring full confidentiality and privacy 
protection. The study followed the principles of responsible 
research, transparent reporting, and ethical data handling 
throughout the methodology. 

5. Results 

5.1 Case study implementation 
The proposed framework was tested through a case 

study on smart wearable health devices. Textual data 
gathered from consumer reviews, patent abstracts, and 
technical blogs were fed into the text mining engine, yielding 
entities, topics, sentiments, and contradictions, which were 
incorporated into the knowledge repository built on the 
ontology. This structured information influenced the TRIZ 
reasoning module that produced a series of candidate 
inventive solutions. The CAI dashboard offered these 
solutions to participants in the experimental group in the 
controlled design study. The system was able to extract 
design-relevant information and generate candidate concepts 
to address issues identified in the textual data, such as 
comfort, durability, sensor accuracy, user interface 
adaptability, and material safety. This confirmed that the 
framework could successfully translate real-world textual 
evidence into structured innovation support. 

5.2 Text mining output and knowledge extraction 
The text mining module provided coherent and 

semantically interpretable insights. Topic modelling 
identified key design issues related to wearable devices, 
including battery life, comfort of wear, sensor reliability, skin 
irritation, user interface responsiveness, and device 
connectivity. Sentiment analysis revealed which features 
users appreciated most and which they needed to improve. 
Named entity recognition has been used to extract frequent 
component references, materials, and functional attributes. 
The pipeline was able to identify a large number of feature-
sentiment pairs, which serve as direct input to the 
contradiction mapping. These extracted insights filled the 
ontology and served as the basis for contradiction and idea 
generation in the TRIZ module. 

5.3 Ontology reasoning and contradiction mapping 
The ontology-based reasoning engine successfully 

organized the extracted entities and relationships into 
structured knowledge graphs. Using predefined semantic 

rules, the system identified a series of contradictions, e.g., 
comfort vs. durability, compactness vs. battery capacity, fit 
accuracy vs. skin irritation, and feature-richness vs. interface 
complexity. Each contradiction was mapped to its 
corresponding TRIZ parameter pair, enabling the system to 
retrieve inventive principles for a particular context. Based on 
these mappings, the system generated six final concept 
proposals, each focused on a specific set of user needs and 
technical constraints. These concepts were shared with the 
experimental design group for further development and 
expert evaluation. Figure 2 shows how six inventive 
principles of TRIZ helped to the final concepts. Principles like 
"Flexible Shells" and "Composite Material" had the highest 
impact indicating a close match to contradictions found 
during the text-based analysis. 

 
Figure 2. TRIZ principles and their conceptual impact  

5.4 Generated concepts and expert evaluation 
Experts rated six system-generated concepts on novelty, 

feasibility, and usability on a 10-point scale. The evaluation 
scores are summarized in Table 2. The quadrant plot in Figure 
3 emphasizes the consideration of feasibility and usability for 
the proposed concepts, with "Stretchable Strap" and "Skin-
Friendly Coating" falling in the upper-right quadrant, 
indicating good performance on both criteria. 

The six concepts generated addressed specific 
contradictions identified from the user needs and technical 
constraints. The Modular Sensor Core proposed a detachable 
sensing unit that allows quick component replacement and 
multi-function use, and solves the contradiction between 
sensor precision and device compactness through the use of 
TRIZ Principles 1 (Modularity) and 2 (Segmentation). The 
Stretchable Strap used a combination of elastomeric 
materials to improve comfort during movement while 
maintaining structural stability, addressing the contradiction 
between flexibility and mechanical strength through 
Principles 15 (Dynamics) and 30 (Flexible Shells). The 
Motion-Adaptive UI introduced an interface that adapts its 
layout to the intensity of user movement, mitigating the 
visibility limitations imposed by the small screen size, 
following Principles 17 (Another Dimension) and 23 
(Feedback). The Thermal Energy Charging concept uses a 
micro-thermoelectric system that harnesses body heat to 
prolong battery life without adding weight to the device, 
based on the application of Principles 22 (Energy Recycling) 
and 37 (Thermal Expansion). The Dual App Integration 
design provided a two-layer software architecture that 
balances ease of use for beginners with analytics for 
experienced users, made possible by Principles 6 
(Universality) and 7 (Nested Doll). Finally, the Skin-Friendly 



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Coating employed hypoallergenic, breathable composite 
materials to minimize skin irritation while maintaining 
durability, resolving the contradiction between comfort and 
robustness through Principles 40 (Composite Materials) and 
31 (Porous Materials). 

5.5 Quantitative comparison between CAI and 
traditional methods 
Inter-rater reliability for the expert evaluations was 

determined using Fleiss' Kappa and the Intra-class 
correlation Coefficient (ICC) to assess agreement across 
categorical judgments and interval-scale scoring, 
respectively. There was considerable agreement between the 
experts, Fleiss' k = 0.74, and high scoring consistency, ICC(3,k) 
= 0.81. Variation across key evaluation metrics was reported 
as standard deviations: ideation time for the CAI system was 
18.0 minutes with a standard deviation of 3.4 minutes, while 
the traditional ideation group was 31.0 minutes with a 
standard deviation of 6.2 minutes. Concept quality also 
showed moderate variability, with novelty ratings of 8.2 (0.6), 
feasibility ratings of 7.9 (0.5), and usability ratings of 8.5 (0.4). 
The corresponding standard deviations for individual 
concept ratings are provided in Table 3 to provide some 
further granularity about variability in expert scoring. A 
performance comparison between the CAI framework and 
traditional brainstorming/QFD-based design was conducted 
with 12 participants per group. Quantitative results are given 
in Table 3. 

Table 2. Design evaluation scores 

 

 
Figure 3. Design concept mapping: usability vs. feasibility 

The CAI system led to higher average viable concept 
counts, faster ideation, and better novelty and feasibility 
scores. Participants using the CAI dashboard came up with 
ideas nearly 2 times faster than those using traditional 
methods, thereby confirming the framework's efficiency 
benefits. Figure 4 shows the time required to formulate each 
of the six concepts using the CAI system. All concepts were 
generated in less than 20 minutes with an average generation 
time of 18 minutes. 

5.6 Statistical analysis and reliability 
Reliability of agreement of expert evaluations was 

calculated (inter-rater reliability). Fleiss' k showed 
considerable agreement between evaluators on categorical 
judgments, while the Intraclass Correlation Coefficient (ICC) 
was used to confirm good consistency in numerical scoring 
between panel members. Correlation analysis was performed 
to examine the relationships among evaluation metrics (Table 
4).  

Table 3. Comparison of CAI vs. traditional methods 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Concept Novelty Feasibility Usability 

Modular Sensor Core 9.1 7.8 8.5 

Stretchable Strap 8.6 8.5 9.0 

Motion-Adaptive UI 8.3 8.2 8.6 

Thermal Energy Charging 7.9 7.5 7.8 

Dual App Integration 8.0 7.9 8.2 

Skin-Friendly Coating 7.7 8.4 8.9 

Evaluation 

Metric 

CAI Framework 

(Simulated) 

Traditional 

Methods 

(Estimated) 

Viable design 

ideas generated 

6.1 3.4 

Time to first 

concept (minutes) 

18 31 

Novelty score (out 

of 10) 

8.2 6.1 

Feasibility score 

(out of 10) 

7.9 6.4 

User satisfaction 

(5-point Likert) 

4.3 3.2 



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Figure 4. Ideation time across concepts 

Table 4. Correlation between evaluation metrics 

 

These correlations indicate that the concepts rated 
highly for usability tended to be rated as feasible, and that 
novelty correlated moderately with both feasibility and 
usability. Independent-samples t-tests (or Wilcoxon tests in 
case of non-normality) confirmed statistically significant 
differences between the CAI and traditional groups on the 
novelty, feasibility, and time-to-first-concept metrics. Effect 
size calculations also showed that the CAI framework led to a 
meaningful improvement in ideation performance. The 
heatmap in Figure 5 shows the strength of relationships 
among the evaluation metrics, with feasibility and usability 
showing the strongest correlation, indicating that highly 
feasible ideas were also perceived as highly usable. 

6. Discussion 

The proposed CAI framework demonstrated clear 
benefits over traditional ideation and decision-support 
approaches by combining text mining, ontology-based 
reasoning, and TRIZ-based contradiction analysis within a 
unified innovation pipeline. One of the most significant was 
the increase in ideation efficiency. As shown in Figure 4, the 
system produced all six design concepts in less than 20 
minutes, with an average ideation time of 18 minutes. This 
efficiency is much higher than that of typical brainstorming 
workshops or QFD sessions, which typically require several 
hours of manual deliberation, subjective prioritization, and 
iterative refinement. Beyond efficiency, the system 
demonstrated strong performance in the quality of the design 
concepts it generates, with high novelty, feasibility, and 
usability. Table 2 presents an overview of these results, 
whereas Figure 3 presents a feasibility-usability quadrant 
visualization. The concepts "Stretchable Strap" and "Skin-
Friendly Coating" fall in the upper-right quadrant of Figure 3, 
indicating good, balanced performance across both 
evaluation dimensions. This outcome reflects the system's 
semantic extraction efficiency and the structured TRIZ-based 
reasoning involved in the concept synthesis. The framework 
also improved the traceability and interpretability of the 
design process.  

 
Figure 5. Correlation heatmap between evaluation metrics 

Unlike the traditional QFD approaches in which the user 
requirements are fixed early on, and subjective weighting is 
inevitable, this CAI system continuously extracts the real-
world user needs in an unstructured textual source such as a 
review, patent document, and blog. This dynamic extraction 
is enabling designers to more objectively and at scale detect 
latent requirements, recurring pain points and changing 
contextual expectations. The approach is in line with the 
overall movement towards text-driven product intelligence 
and grounded design recommendations. A major strength of 
the framework is the ontology-based knowledge repository, 
which organizes design knowledge into structured entities to 
represent features, relationships, contradictions, and TRIZ 
parameters. The visual effect of TRIZ inventive principles on 
concept performance is illustrated in Figure 2, which shows 
that some principles, such as Flexible Shells, Composite 
Material, and Dynamization, have the highest influence scores 
across the six concepts that were evaluated. This mapping 
shows that the contradiction identification module captured 
meaningful conflicts and matched them with suitable 
inventive strategies. Further analytical insights emerged from 
the relationships among the evaluation metrics. Figure 5 
shows the correlations between the novel, feasible, and 
usable.  

The highest correlation was found between feasibility 
and usability, on the one hand, indicating that design ideas 
perceived as technically feasible were also perceived as easy 
to implement or integrate. Novelty correlated moderately 
with usability and less so with feasibility, suggesting that 
coming up with highly creative concepts may still result in 
some trade-offs in terms of technical feasibility - an expected 
pattern in early-stage innovation. Despite these strengths, 
there are still a number of challenges. User-generated textual 
data is likely to be ambiguous, not only because of domain-
specific terminology but also because sentiment is not always 
consistent, making NER, topic modelling, and sentiment 
analysis less accurate. Ontology scalability is also an issue as 
the knowledge repository grows; computational efficiency 
must be balanced with expressive depth, and thus ontology 
evolution should be carefully planned. These limitations are 
important factors for the future development of CAI systems. 
From an applied perspective, the framework offers significant 
benefits for a range of roles in product development. 
Designers benefit from a faster, more effective route to 
creative solutions. Product managers benefit from insights 

Metric Pair Correlation (r) 

Novelty & Feasibility 0.64 

Novelty & Usability 0.59 

Feasibility & Usability 0.72 



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287 

 

based on real user evidence, rather than subjective 
interpretations of users. Innovation leaders can use the 
system to institutionalize creativity, making idea generation 
more systematic, repeatable, and knowledge-driven. Overall, 
the study shows that combining text mining, TRIZ-based 
reasoning, and ontology-based knowledge structures within 
a single CAI framework can greatly improve the speed, 
quality, and relevance of design ideation. While further work 
is required, especially for the development of semantic 
extraction, scaling the ontology, and validating the framework 
across more product categories, the results provide a clear 
case for the role of hybrid AI-TRIZ approaches in supporting 
systematic innovation. 

7. Limitations 

While the proposed CAI framework shows great 
potential to boost early-stage product innovation at a time 
when this is most needed, there are some limitations to be 
aware of. These limitations concern the quality of textual data, 
the effectiveness of natural language processing techniques, 
the complexity of ontology management, and the limitations 
of the evaluation in the experiment. One of the main 
limitations comes from the nature of user-generated text. 
Consumer reviews and online discussions are likely to 
contain informal language, abbreviations, sarcasm, and 
inconsistent terminology. Such ambiguity can lead to 
inaccuracies in feature extraction, sentiment classification, 
and topic identification. Although preprocessing and domain-
specific refinement work were applied, the system can be at 
fault for the possibility of being misled by less pronounced 
expressions or context-dependent meanings. This is 
especially important in areas such as wearable health devices, 
where terms can be used interchangeably across clinical, 
lifestyle, and general consumer contexts. A second limitation 
lies in the performance of NLP models used for entity 
recognition, sentiment detection, and topic modelling. Even 
domain-adaptation models may produce errors when 
handling highly technical descriptions or rare terms. Topic 
modelling, for example, can sometimes produce topics that 
overlap semantically, leading to redundant or vague 
representations. Similarly, finding component names 
confuses NER models, leading them to treat functional 
descriptions or the naming of new technology terms as 
components. The framework also suffers from ontology 
scalability and maintenance problems. As more entities, 
relationships, and contradictions are introduced, the ontology 
may become complex and not as easily queried efficiently. It 
might be necessary to manually curate the ontology to ensure 
conceptual accuracy and maintain a manageable structure. 
Without careful governance, there would be inconsistencies 
or orphaned nodes that would cause poor reasoning 
performance or contradictions in interpretation. Additionally, 
as the system is expanded to new product categories, the 
ontology will need domain-specific extensions, which may 
add labor and complexity. Another limitation is in the TRIZ-
based reasoning part. Although TRIZ offers the structured 
inventive principles, the way of mapping the contradictions to 
the parameters of TRIZ is partly based on heuristic rules and 
expert-informed assumptions. Some of the contradictions 
may be difficult to fit into predefined parameter pairs, and 
highly novel design situations may require combinations or 
adaptations that are not accommodated in the regular 
mapping table. As a result, the solutions created can be too 
generic at times or require designer intervention to be 
meaningful. The experimental evaluation also has 
shortcomings. Although the controlled study involved two 

balanced groups of people, the number of people was small. A 
larger participant pool would be stronger in terms of 
statistical power and better for generalizing the findings. 
Furthermore, the participants came from engineering and 
design backgrounds; testing the system with interdisciplinary 
teams, professionals from various industries, or 
inexperienced users may produce different results. The 
evaluation period was restricted to a single design session, 
which did not account for long-term learning effects or the 
integration of the CAI system into a long product development 
cycle. A further limitation is that of the domain specificity of 
the current implementation. The framework was evaluated 
on smart wearable health devices in an area with well-defined 
components and rich user feedback. Its effectiveness in highly 
complex or less user-centric institutions — such as industrial 
machinery, aerospace systems, or business process 
innovation — still needs to be studied. Different industries 
might demand specialized ontologies, specific parameters of 
the TRIZ method, or entirely different textual data sources. 
Lastly, the system's performance depends on the availability 
and quality of the data. Domains with little user feedback, 
little patent activity, or very proprietary knowledge, for 
example, may not provide sufficient textual data for 
meaningful text mining. Additionally, more complex 
multimodal inputs, such as images or sensor data, may be 
necessary within the framework in the future to support a 
more comprehensive understanding of design problems. 
Overall, while the limitations are essential constraints, they 
also indicate possible future improvements, such as improved 
domain adaptation for NLP models, automated ontology 
evolution, hybrid TRIZ-machine learning mapping, and 
broader validation experiments. Dealing with these 
challenges will increase the robustness and generalizability of 
the CAI framework in the real-world of design environments. 

8. Conclusion 

This research proposed a unified Computer-Aided 
Innovation (CAI) framework, combining text mining, 
ontology-based knowledge management, and TRIZ-driven 
reasoning that can be used to support systematic and data-
guided product ideation. By leveraging natural language 
processing to extract user needs and technical issues from 
massive unstructured data sources, the framework enables a 
transparent, repeatable process from raw textual data to 
structured design insights. A further application of TRIZ 
principles in the reasoning engine is for grounded, traceable 
solution generation. The framework was applied to the 
domain of smart wearable health devices; the evaluation 
results showed significant improvements in both efficiency 
and concept quality. All six concepts were generated in less 
than 20 minutes (Figure 4), which is significantly less time 
than is typically required for early-stage ideation. The 
generated concepts also showed strong performance in 
novelty, feasibility, and usability, with two concepts 
(Stretchable Strap and Skin-Friendly Coating) showing the 
highest balance across criteria (Figure 3, Table 2). The TRIZ 
influence visualization (Figure 2) and correlation analysis 
(Figure 5) revealed further insights into the role of inventive 
principles in shaping concept outcomes and the 
interrelationships among evaluation metrics. Despite the 
promising results, a number of limitations need to be 
recognised. The quality of extracted insights remains bound 
to the clarity and consistency of user-generated text, and 
domain-specific language may pose a challenge for existing 
NLP techniques. Ontology scalability also raises long-term 



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288 

 

maintenance issues as the knowledge base grows in size and 
complexity. These limitations, therefore, reflect opportunities 
for refinement through better domain adaptation, automatic 
ontology evolution, and improved contradiction 
interpretation. In future work, the applicability of the 
framework to different product categories and design 
situations will be expanded. Incorporating multilingual data 
sources would increase global relevance, while integrating 
generative AI models, multimodal data inputs, or digital twin 
simulations could enable richer, interactive design 
exploration. Testing the framework on other industries - 
including industrial equipment, consumer appliances, and 
assistive technologies - will further validate the robustness 
and generalizability of the framework. Overall, this study 
advances the development of systematic innovation by 
demonstrating that the outputs of data-driven intelligence, 
semantic knowledge representation, and TRIZ-based 
reasoning can be integrated into a coherent CAI system. The 
results show that the potential of such hybrid approaches for 
early-stage design is strong, as it could transform the current 
design process from a time-consuming, often uninformed, 
and essentially guesswork process into a more informed, 
faster, and more user-centred process. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically regarding authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with research ethics policies. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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