How ChatGPT Is Shaping Next-Generation Patent Solutions
With the rapid advancement of artificial intelligence, large language models (LLMs) such as ChatGPT have garnered increasing attention in the field of text generation. However, when applied to patent drafting in the architectural domain, these models often produce verbose texts and omit critical ele...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/13/2273 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849428815728607232 |
|---|---|
| author | Chong Liu Nikita Igorevich Fomin Shuoting Xiao Guofeng Sun Yuelong Lyu |
| author_facet | Chong Liu Nikita Igorevich Fomin Shuoting Xiao Guofeng Sun Yuelong Lyu |
| author_sort | Chong Liu |
| collection | DOAJ |
| description | With the rapid advancement of artificial intelligence, large language models (LLMs) such as ChatGPT have garnered increasing attention in the field of text generation. However, when applied to patent drafting in the architectural domain, these models often produce verbose texts and omit critical elements, leading to issues such as limited claim scope and insufficient disclosure in the specifications. To address these challenges, this study conducted experimental research on the application of AI technologies, including GPT-4o, in the drafting and publication of patents related to prefabricated integral buildings. The results indicate that GPT-4o performs well in generating claims and technical descriptions. Nevertheless, problems such as repetitive descriptions of technical achievements, ambiguity in result interpretation, and a lack of detailed implementation methods were observed during the generation process. Therefore, we recommend incorporating expert review mechanisms when using GPT-4o for patent drafting, to enhance the accuracy and professionalism of the output. Moreover, enriching training datasets and enforcing a rigorous textual review workflow can further optimize model outputs, aligning them more closely with the high standards required in patent documentation. |
| format | Article |
| id | doaj-art-ef18eab024574f929eb3df9fdd3878cc |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-ef18eab024574f929eb3df9fdd3878cc2025-08-20T03:28:33ZengMDPI AGBuildings2075-53092025-06-011513227310.3390/buildings15132273How ChatGPT Is Shaping Next-Generation Patent SolutionsChong Liu0Nikita Igorevich Fomin1Shuoting Xiao2Guofeng Sun3Yuelong Lyu4Institute of Civil Engineering and Architecture, Ural Federal University, Yekaterinburg 620002, RussiaInstitute of Civil Engineering and Architecture, Ural Federal University, Yekaterinburg 620002, RussiaInstitute of Civil Engineering and Architecture, Ural Federal University, Yekaterinburg 620002, RussiaInstitute of Civil Engineering and Architecture, Ural Federal University, Yekaterinburg 620002, RussiaInstitute of Civil Engineering and Architecture, Ural Federal University, Yekaterinburg 620002, RussiaWith the rapid advancement of artificial intelligence, large language models (LLMs) such as ChatGPT have garnered increasing attention in the field of text generation. However, when applied to patent drafting in the architectural domain, these models often produce verbose texts and omit critical elements, leading to issues such as limited claim scope and insufficient disclosure in the specifications. To address these challenges, this study conducted experimental research on the application of AI technologies, including GPT-4o, in the drafting and publication of patents related to prefabricated integral buildings. The results indicate that GPT-4o performs well in generating claims and technical descriptions. Nevertheless, problems such as repetitive descriptions of technical achievements, ambiguity in result interpretation, and a lack of detailed implementation methods were observed during the generation process. Therefore, we recommend incorporating expert review mechanisms when using GPT-4o for patent drafting, to enhance the accuracy and professionalism of the output. Moreover, enriching training datasets and enforcing a rigorous textual review workflow can further optimize model outputs, aligning them more closely with the high standards required in patent documentation.https://www.mdpi.com/2075-5309/15/13/2273large language modelspatentprefabricated integral buildingsmultiple linear regression |
| spellingShingle | Chong Liu Nikita Igorevich Fomin Shuoting Xiao Guofeng Sun Yuelong Lyu How ChatGPT Is Shaping Next-Generation Patent Solutions Buildings large language models patent prefabricated integral buildings multiple linear regression |
| title | How ChatGPT Is Shaping Next-Generation Patent Solutions |
| title_full | How ChatGPT Is Shaping Next-Generation Patent Solutions |
| title_fullStr | How ChatGPT Is Shaping Next-Generation Patent Solutions |
| title_full_unstemmed | How ChatGPT Is Shaping Next-Generation Patent Solutions |
| title_short | How ChatGPT Is Shaping Next-Generation Patent Solutions |
| title_sort | how chatgpt is shaping next generation patent solutions |
| topic | large language models patent prefabricated integral buildings multiple linear regression |
| url | https://www.mdpi.com/2075-5309/15/13/2273 |
| work_keys_str_mv | AT chongliu howchatgptisshapingnextgenerationpatentsolutions AT nikitaigorevichfomin howchatgptisshapingnextgenerationpatentsolutions AT shuotingxiao howchatgptisshapingnextgenerationpatentsolutions AT guofengsun howchatgptisshapingnextgenerationpatentsolutions AT yuelonglyu howchatgptisshapingnextgenerationpatentsolutions |