Identification of Product Innovation Path Incorporating the FOS and BERTopic Model from the Perspective of Invalid Patents
Abstract
:1. Introduction
2. Theoretical Background
2.1. Invalid Patents
2.2. FOS & TRIZ Theory
2.3. BERTopic Model
2.4. The Best-Worst Method
3. Methodology
3.1. Identify Problems and Functional-Oriented Search
3.1.1. Identify the Target Product
3.1.2. Identify Problems with Function Analysis
3.1.3. Functional-Oriented Search
3.2. Identifying Core Invalid Patents and Extracting Technical Thematic Elements
3.2.1. Screening for Invalid Patents
3.2.2. Identifying Core Invalid Patents
3.2.3. Extracting Technical Thematic Elements with BERTopic Model
3.3. Innovation Solutions’ Generation and Evaluation
3.3.1. Generating Innovative Solutions with TRIZ
3.3.2. Evaluating Innovative Solutions with BWM
- (1)
- Determine a set of criterion sets, and select the best and worst criteria CW in the criterion sets {𝑐1, 𝑐2,…, 𝑐𝑛}.
- (2)
- The numerical scoring used determines the degree of preference of the optimal criterion over all other criteria, and we constructed a comparison vector AB = (aB1, aB2,…, aBn) in which the degree of preference of the optimal criterion compared to the criteria is indicated.
- (3)
- The numerical scoring used to determine the degree of preference of all other criteria over the worst criterion, and the construction of a comparison vector AW = (a1W, a2W,…, anw)T in which the degree of preference of the criterion over the worst criterion is represented.
- (4)
- A mathematical programming problem is constructed and solved to derive the optimal weights .
4. Empirical Analysis
4.1. Identify Problems and Functional-Oriented Search
4.1.1. Identify the Target Product
4.1.2. Identify Problems with Function Analysis
4.1.3. Functional-Oriented Search
4.2. Identifying Core Invalid Patents and Extracting Technical Thematic Elements
4.2.1. Screening for Invalid Patents
4.2.2. Identifying Core Invalid Patents
4.2.3. Extracting Technical Thematic Elements with BERTopic Model
4.3. Solving the Problem and Evaluating Solutions
4.3.1. Generating Innovative Solutions with TRIZ
4.3.2. Evaluating Innovative Solutions with BWM
- Step 1:
- Determine the best and worst option. According to the experts’ discussion, solution 1 is the optimal solution and solution 3 is the worst solution.
- Step 2:
- The optimal solution is compared with the remaining solutions. Option 1 is compared with the remaining options separately and the result is BO = (1,3,8,4).
- Step 3:
- The remaining options are compared with the worst option. Compare each of the remaining solutions with option 3 and the result is OW = (9,7,1,3)T.
- Step 4:
- Construct the mathematical planning problem and solve it. The following programming is solved and standard weights are determined via Lingo:
5. Conclusions
5.1. Implications for Theory and Practice
5.1.1. Theoretical Implications
5.1.2. Practical Implications
5.2. Limitations and Future Research Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Han, X.; Zhu, D.; Wang, X.; Daim, T.; Qiao, Y. Discovering technology opportunities based on the linkage between technology and business areas: Matching patents and trademarks. Technol. Anal. Strat. Manag. 2021, 2021, 2003773. [Google Scholar] [CrossRef]
- Zhao, X.; Zhang, X.; Emmanuel, A. Research and demonstration of technology opportunity identification model based on text classification and core patents. Comput. Ind. Eng. 2022, 171, 108403. [Google Scholar] [CrossRef]
- Li, M.; Ming, X.; He, L.; Zheng, M.; Xu, Z. A TRIZ-based Trimming method for Patent design around. Comput. Des. 2015, 62, 20–30. [Google Scholar] [CrossRef]
- Li, H.; Yuan, J.; Tan, R.; Peng, Q. Design around Bundle Patent Portfolio Based on Technological Evolution. Chin. J. Mech. Eng. 2019, 32, 86. [Google Scholar] [CrossRef] [Green Version]
- Chen, B.P.-H. A Design-Around Solution for a MMX-Based Tablet Drug Comprising Mesalazine—A Lesson from Shire Development, LLC v. Watson Pharmaceuticals, Inc. Biotechnol. Law Rep. 2020, 39, 25–32. [Google Scholar] [CrossRef] [Green Version]
- Chen, W.-C.; Chen, J.L. Innovative method by design-around concepts with integrating the algorithm for inventive problem solving. J. Mech. Sci. Technol. 2014, 28, 201–211. [Google Scholar] [CrossRef]
- Wang, S.-J. Design-around biotechnology patents: An analysis of US Federal Circuit decisions shows the possibility of designing around biotechnology patents. Hum. Vaccines 2011, 7, 125–128, Editorial Material. [Google Scholar] [CrossRef] [Green Version]
- Yun, S.; Song, K.; Kim, C.; Lee, S. From stones to jewellery: Investigating technology opportunities from expired patents. Technovation 2021, 103, 102235. [Google Scholar] [CrossRef]
- Dong, H.R.; Chen, D.Z.; Huang, M.H. Performance Gap Between Valid and Invalid Patents in Six Technology Fields. In Proceedings of the 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, 15–18 December 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1058–1061. [Google Scholar]
- Kim, J.; Lee, S. Forecasting and identifying multi-technology convergence based on patent data: The case of IT and BT industries in 2020. Scientometrics 2017, 111, 47–65. [Google Scholar] [CrossRef]
- Clancy, M.S. Inventing by combining pre-existing technologies: Patent evidence on learning and fishing out. Res. Policy 2018, 47, 252–265. [Google Scholar] [CrossRef]
- Han, X.; Zhu, D.; Lei, M.; Daim, T. R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data. Technol. Forecast. Soc. Chang. 2021, 167, 120691. [Google Scholar] [CrossRef]
- Choi, S.; Kang, D.; Lim, J.; Kim, K. A fact-oriented ontological approach to SAO-based function modeling of patents for implementing Function-based Technology Database. Expert Syst. Appl. 2012, 39, 9129–9140. [Google Scholar] [CrossRef]
- Moehrle, M.G. How combinations of TRIZ tools are used in companies—Results of a cluster analysis. R&D Manag. 2005, 35, 285–296. [Google Scholar] [CrossRef]
- Masin, I.; Petru, M. Conceptual Design of Mechanical Flood Barrier. In Proceedings of the 7th International Conference on Mechanics and Materials in Design (M2D), Albufeira, Portugal, 11–15 June 2017. [Google Scholar]
- Tanoyo, T.; Harlim, J. TRIZ training within a continuous improvement (Kaizen) event–exploration and evaluation. In Systematic Innovation Partnerships with Artificial Intelligence and Information Technology, Proceedings of the 22nd International TRIZ Future Conference, TFC 2022, Warsaw, Poland, 27–29 September 2022; International TRIZ Future Conference; Springer International Publishing: Cham, Switzerland, 2022; pp. 458–469. [Google Scholar]
- Wang, J.; Zhang, Z.; Feng, L.; Lin, K.-Y.; Liu, P. Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ. Technol. Forecast. Soc. Chang. 2023, 191, 122481. [Google Scholar] [CrossRef]
- Lepšík, P.; Petrů, M.; Novák, O. Innovation of car seat construction using TRIZ-based tool: Function-oriented search. In Modern Methods of Construction Design: Proceedings of ICMD 2013; Springer International Publishing: Cham, Switzerland, 2014; pp. 459–470. [Google Scholar]
- Shaharuzaman, M.A.; Sapuan, S.M.; Mansor, M.R.; Zuhri, M.Y.M. Conceptual Design of Natural Fiber Composites as a Side-Door Impact Beam Using Hybrid Approach. J. Renew. Mater. 2020, 8, 549–563. [Google Scholar] [CrossRef]
- Grootendorst, M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv 2022, arXiv:2203.05794. [Google Scholar]
- Zankadi, H.; Idrissi, A.; Daoudi, N.; Hilal, I. Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques. Educ. Inf. Technol. 2022, 28, 5567–5584. [Google Scholar] [CrossRef] [PubMed]
- Ogunleye, B.; Maswera, T.; Hirsch, L.; Gaudoin, J.; Brunsdon, T. Comparison of Topic Modelling Approaches in the Banking Context. Appl. Sci. 2023, 13, 797. [Google Scholar] [CrossRef]
- Meaney, C.; Escobar, M.; Stukel, T.A.; Austin, P.C.; Jaakkimainen, L. Comparison of Methods for Estimating Temporal Topic Models from Primary Care Clinical Text Data: Retrospective Closed Cohort Study. Jmir Med. Inform. 2022, 10, e40102. [Google Scholar] [CrossRef]
- Jeon, E.; Yoon, N.; Sohn, S.Y. Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa. Technol. Forecast. Soc. Chang. 2023, 186, 122130. [Google Scholar] [CrossRef]
- Liang, F.; Verhoeven, K.; Brunelli, M.; Rezaei, J. Inland terminal location selection using the multi-stakeholder best-worst method. Int. J. Logist. Res. Appl. 2021, 2021, 1885634. [Google Scholar] [CrossRef]
- Van de Kaa, G.; Rezaei, J.; Taebi, B.; van de Poel, I.; Kizhakenath, A. How to Weigh Values in Value Sensitive Design: A Best Worst Method Approach for the Case of Smart Metering. Sci. Eng. Ethic 2020, 26, 475–494. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, J.; Niu, L.-L.; Chen, Q.; Wang, Z.-X. Approaches for multicriteria decision-making based on the hesitant fuzzy best–worst method. Complex Intell. Syst. 2021, 7, 2617–2634. [Google Scholar] [CrossRef]
- Ali, A.; Rashid, T. Best–worst method for robot selection. Soft Comput. 2021, 25, 563–583. [Google Scholar] [CrossRef]
- Flynn, T.N.; Louviere, J.J.; Peters, T.J.; Coast, J. Best–worst scaling: What it can do for health care research and how to do it. J. Health Econ. 2007, 26, 171–189. [Google Scholar] [CrossRef]
- Fallahpour, A.; Wong, K.Y.; Rajoo, S.; Fathollahi-Fard, A.M.; Antucheviciene, J.; Nayeri, S. An integrated approach for a sustainable supplier selection based on Industry 4.0 concept. Environ. Sci. Pollut. Res. 2021, 1–19. [Google Scholar] [CrossRef]
- Moslem, S. A Novel Parsimonious Best Worst Method for Evaluating Travel Mode Choice. IEEE Access 2023, 11, 16768–16773. [Google Scholar] [CrossRef]
- Chandel, S.; Singh, S.N.; Seshadri, V. A Comparative Study on the Performance Characteristics of Centrifugal and Progressive Cavity Slurry Pumps with High Concentration Fly Ash Slurries. Part. Sci. Technol. 2011, 29, 378–396. [Google Scholar] [CrossRef]
- Seo, Y.G.; Park, J.G.; Elaiyaraju, P. Effects of pump-induced particle agglomeration during chemical mechanical planarization (CMP). In Proceedings of the International Conference on Planarization/CMP Technology 2014, Kobe, Japan, 19–21 November 2014; pp. 254–258. [Google Scholar]
- Sun, S.; Tse, P.W.; Tse, Y.L. An Enhanced Factor Analysis of Performance Degradation Assessment on Slurry Pump Impellers. Shock. Vib. 2017, 2017, 1524840. [Google Scholar] [CrossRef] [Green Version]
- Wang, D.; Tse, P.W. Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method. Mech. Syst. Signal Process. 2015, 56–57, 213–229. [Google Scholar] [CrossRef]
Functions | Generalized Functions | Search Expressions | Number of Patents in All Areas |
---|---|---|---|
Function 1 | Function description 1 | Expression 1 | Number 1 |
Function 2 | Function description 2 | Expression 2 | Number 2 |
… | … | … | … |
Function n | Function description n | Expression n | Number n |
Indicator Name | Calculation Method | Indicator Meaning |
---|---|---|
Legal quality indicator | A higher number of claims means a deeper understanding of the technology and a higher legal quality of the relevant technical patent documents. | |
Technical quality indicator | A higher number of positive citations means that the patent exhibits a higher level of technical sophistication. | |
Economic qualitative indicator | A higher number of homologues means a higher economic quality of the patent. |
Functions | Generalized Functions | Search Expressions | Number of Patents in All Areas |
---|---|---|---|
Transport | Conveying the material to the designated position | TS = (material AND conveying AND impeller) | 2465 |
No. | Legal Quality Indicator | Technical Quality Indicator | Technical Quality Indicator |
---|---|---|---|
1 | 14 | 13 | 11 |
2 | 8 | 2 | 29 |
3 | 9 | 37 | 1 |
4 | 6 | 12 | 2 |
5 | 41 | 9 | 1 |
6 | 26 | 10 | 2 |
7 | 5 | 12 | 2 |
8 | 4 | 2 | 1 |
…… | …… | …… | …… |
853 | 4 | 9 | 5 |
Average value | 7.4 | 6.5 | 3 |
Topics | Topic Tags | Technology Keywords |
---|---|---|
0 | High temperature conveying | Thermocouples, insulation, water cooling systems, high temperature alloys, frequency control, heat loss |
1 | Pneumatic conveying | Injectors, pneumatic buffer tanks, separators, airflow handling, gas circuit design, sensor control |
2 | Gas conveying | Pressure vessels, piping systems, pumping station equipment, regulating valves, gas detection, safety testing |
3 | Mechanical conveying | Belt conveyors, spiral conveyors, drum conveyors, chain conveyors, vibratory conveyors, unmanned control |
4 | Hydrodynamic conveying | Pipelines, pumps, hydropower, regulating ponds, hydraulic turbines, hydraulic calculations |
5 | Magnetic conveying | Superconductor technology, intelligent control, permanent magnet motors, sensors, high-speed motion balancing, magnetic levitation |
6 | Vacuum conveying | Vacuum pumps, seals, material recovery, anti-clogging technology, automatic control, vibration |
7 | Gravity conveying | Bucket lifting, cylinder lifting, automatic control, sensor detection, conveyor line planning, safety protection devices |
Innovation Dimension | Elements of Technological Innovation |
---|---|
Structural dimension | Belt conveying, screw conveying, drum conveying, chain plate conveying, bucket lifting, barrel lifting, safety protection devices, thermocouples, pressure vessels, pumping station equipment, regulating valves, permanent magnet motors, sensors, seals |
Functional dimension | Ejectors, pneumatic buffer tanks, separators, water cooling systems, material recovery, sensor detection, frequency control, gas circuit design, sensor control, gas detection, safety detection, vacuum pumps, anti-clogging technology, vibration |
Mechanistic dimension | Vibratory conveying, magnetic levitation, airflow handling, hydraulic calculations, superconductivity technology |
Spatial dimension | Piping systems, conveying line planning, high speed motion balancing |
Environmental dimension | Pipelines, pumps, hydropower, regulating ponds, hydraulic turbines, heat loss |
Material dimension | High temperature alloys, thermal insulation |
Human-machine dimension | Intelligent control, unmanned control, automatic control |
Innovation Dimension | Principle of Invention | Description of Technical Opportunities |
---|---|---|
Human-machine dimension (Intelligent control, unmanned control, automatic control) | 15.Dynamicity 25.Self-service | A remote control system for slurry pumps is proposed, which combines cloud computing and Internet of Things technology to enable remote monitoring, data analysis and predictive maintenance of slurry pumps. At the same time, the operating experience of the human-machine interface is optimised to further improve the operability and ease of use of the slurry pump. |
Functional dimension (Vibration, anti-clogging technology) | 3. Local quality 30. Flexible shells or films | A system for enhancing the stability of slurry pumps is proposed. By introducing rotary commutators, pneumatic vibrators etc. to solve the problems such as blockage encountered by slurry pumps in the conveying process. At the same time, the noise and vibration of the slurry pump is reduced by using flexible connections, rubber shock absorbers, etc., thus improving its stability and reliability. |
Structural dimension (Valve adjustment) Spatial dimension (Piping system) | 1.Separation | A streamlining process is proposed. By streamlining and optimising unnecessary piping systems and valves in slurry pumps, pipeline resistance and energy consumption are reduced. At the same time, advanced materials and manufacturing processes are used to further reduce the gravity of the slurry pump itself in order to further reduce transport and installation costs. |
Material dimension (Insulation materials) Human-machine dimension (Intelligent control) | 3.Local quality 25.Self-service | Proposes an energy saving and emission reduction system. The conveying flow is optimised by intelligent control in order to reduce energy consumption and wastewater emissions. Insulation materials and energy efficient equipment are also used to reduce energy consumption and greenhouse gas emissions in the production process. |
Development Costs | Novelty | Feasibility | Expected Benefits | Weighted Score | Ranking | |
---|---|---|---|---|---|---|
Solution1 | 0.5388 | 0.4389 | 0.3876 | 0.6351 | 0.5001 | 1 |
Solution2 | 0.2374 | 0.2967 | 0.2581 | 0.3236 | 0.2790 | 2 |
Solution3 | 0.0457 | 0.0981 | 0.1413 | 0.2099 | 0.1238 | 4 |
Solution4 | 0.1781 | 0.1652 | 0.2987 | 0.3457 | 0.2469 | 3 |
Innovation Dimension | Principle of Invention | Description of Technical Opportunities |
---|---|---|
Material dimension (Ceramic materials, polymer composites) Structure dimension (Filter) | 3. Local quality 40. Composite materials | A design method to improve the lifespan of slurry pumps is proposed. By introducing the high-performance ceramics, polymer composites to improve their wear resistance and anti-clogging performance, thereby increasing their service life effectively. |
Functional dimension (Cleanse) Structure dimension (Filter) Human-machine dimension (Sensor) | 5. Consolidation 25. Self-service | A function for cleaning the slurry pumps is proposed. By introducing the specialized cleaning robots to regularly clean filters to reduce their impact on equipment. These robots can easily enter pump rooms or other hard-to-reach areas and use high-pressure water guns for cleaning. Additionally, it may be considered to integrate the automatic cleaning robots with sensors and control systems, so that they can identify the filters that need to be cleaned on their own and perform cleaning at appropriate times. |
Function dimension (Electron beam, ultrasonic waves) | 3. Local quality 5. Consolidation | A filter system is proposed. By introducing electron beam emitters to process the slurry inside the pump. Electron beams form efficient chemical reactions by interacting with water molecules, producing free oxygen ions and killing bacteria, viruses and other microorganisms. Similarly, ultrasonic sensors can be used to generate ultrasonic waves to disperse particulate matter and suspend matter into smaller particles and more uniform mixtures. This can improve filtration efficiency and reduce the risk of equipment damage and blockage. |
Innovation Dimension | Principle of Invention | Description of Technical Opportunities |
---|---|---|
Structure dimension (Blade) | 1. Segmentation 15. Dynamicity | An adjustable mixing blade design system is proposed. By introducing the electric motor control system to achieve automatic adjustment, thereby improves mixing efficiency and operational convenience. Additionally, a multi-stage adjustable mixing blade design can be considered. This design can divide the mixing blades into multiple parts, each of which can be independently adjusted to meet the mixing requirements of different materials. |
Structure dimension (Blade) Function dimension (Ultrasonic waves) | 1. Segmentation 5. Consolidation | A swirling mixing technology is proposed. Introducing special mixing blades or a multi-stage cyclone to rotate rapidly within the pump and separate the suspended material through centrifugal force, achieves a more efficient and thorough mixing effect. Additionally, the swirling mixing technology can also be combined with other technologies to improve its effectiveness and stability. Ultrasonic waves can be used to enhance the stirring effect. |
Function dimension (Stir) Human-machine dimension (Sensor) | 5. Consolidation 15. Dynamicity | A monitoring and analysis system is proposed. By introducing multiple sensors and instruments to monitor the flow, temperature, concentration, and pressure parameters of materials. Based on different material and process characteristics, appropriate control algorithms and models can be selected for adaptive adjustment to achieve the best stirring effect. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, D.; Wu, X.; Liu, P.; Qin, H.; Zhou, W. Identification of Product Innovation Path Incorporating the FOS and BERTopic Model from the Perspective of Invalid Patents. Appl. Sci. 2023, 13, 7987. https://doi.org/10.3390/app13137987
Zhang D, Wu X, Liu P, Qin H, Zhou W. Identification of Product Innovation Path Incorporating the FOS and BERTopic Model from the Perspective of Invalid Patents. Applied Sciences. 2023; 13(13):7987. https://doi.org/10.3390/app13137987
Chicago/Turabian StyleZhang, Dingtang, Xuan Wu, Peng Liu, Hao Qin, and Wei Zhou. 2023. "Identification of Product Innovation Path Incorporating the FOS and BERTopic Model from the Perspective of Invalid Patents" Applied Sciences 13, no. 13: 7987. https://doi.org/10.3390/app13137987