System and method

Abstract

To provide a system that efficiently creates and corrects a patent application manuscript.SOLUTION: A system according to an embodiment comprises an information acquisition part, a generation part, a presentation part and a correction part. The information acquisition part acquires information showing an idea of an inventor. The generation part inputs the idea of the inventor acquired by the information acquisition part to generative AI, and creates a patent application manuscript. The presentation part presents the patent application manuscript created by the generation part to a user. The correction part receives a correction from the user to the patent application manuscript presented by the presentation part, and corrects the manuscript.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The techniques of this disclosure relate to systems and methods. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] An object of the present invention is to efficiently prepare and modify patent application manuscripts. [Means for solving the problem]

[0005] The system according to the embodiment includes an information acquisition unit, a generation unit, a presentation unit, and a correction unit. The information acquisition unit acquires information indicating an inventor's idea. The generation unit inputs the inventor's idea acquired by the information acquisition unit into a generation AI to create a patent application manuscript. The presentation unit presents the patent application manuscript created by the generation unit to a user. The correction unit accepts and corrects corrections from the user to the patent application manuscript presented by the presentation unit. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0015] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0016] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0017] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0018] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicative of the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicative of the user input.

[0019] The output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0020] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0021] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0022] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0023] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0024] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0025] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user. Next, the identification process by the identification processing unit 290 of the data processing device 12 will be described.

[0026] (Example 1) The patent application support system according to the embodiment of the present invention is a system that efficiently converts an inventor's idea into a patent application manuscript and reflects user corrections. This patent application support system includes an information acquisition unit that acquires information indicating the inventor's idea, a generation unit that inputs the acquired inventor's idea into a generation AI to create a patent application manuscript, a presentation unit that presents the created patent application manuscript to a user, and a correction unit that accepts and corrects corrections from the user to the presented patent application manuscript. As a result, the patent application support system can improve the efficiency and accuracy of patent applications by efficiently converting the inventor's idea into a patent application manuscript and reflecting user corrections.

[0027] The patent application support system according to the embodiment includes an information acquisition unit, a generation unit, a presentation unit, and a correction unit. The information acquisition unit acquires information indicating an inventor's idea, for example, from the inventor's terminal device. For example, the information acquisition unit acquires text information provided by the inventor, for example, from the inventor's terminal device. The information acquisition unit can also acquire drawings provided by the inventor, for example, from the inventor's terminal device. The information acquisition unit can also record the inventor's oral explanation and acquire it as audio data. The generation unit inputs the inventor's idea acquired by the information acquisition unit into a generation AI to create a patent application manuscript. For example, the generation unit uses information including information indicating the inventor's idea and instruction information that is information instructing the creation of a patent application manuscript from the inventor's idea as input information, and causes the generation AI (for example, a large-scale language model) to output a patent application manuscript. The generation unit can input information including an inventor's idea or information of a part of the patent application manuscript generated by the generation AI as input information for each part of the patent application manuscript (e.g., background art, problem of the invention, embodiment, scope of claims, abstract), and can output information for each part of the patent application manuscript to the generation AI. The generation unit can also create a new patent application manuscript by referring to a past patent application manuscript. For example, the generation AI may be a generation AI that has been fine-tuned using a data set of ideas and patent application manuscripts, and in this case, the information input to the generation AI may not include instruction information. The presentation unit presents the patent application manuscript created by the generation unit to a user (e.g., a patent attorney, an intellectual property officer, or an inventor) by transmitting the patent application manuscript created by the generation unit to the device of the user. For example, the presentation unit digitally displays the patent application manuscript. The presentation unit can also print and present the patent application manuscript. The presentation unit can also send the patent application manuscript by email. The correction unit accepts and corrects corrections from the user to the patent application manuscript presented by the presentation unit. For example, the correction unit receives corrections input by the user from the user's device or the like and reflects the received corrections in the patent application manuscript. The correction unit can also scan and reflect corrections made by the user in handwriting. The correction unit can also reflect corrections made by the user through voice.As a result, the patent application support system according to the embodiment can efficiently convert the inventor's idea into a patent application manuscript and reflect the user's corrections, thereby improving the efficiency and accuracy of patent applications. The patent application support system obtains information from the user and presents the user's information through communication with the user's device, etc., but is not limited to such an example.

[0028] Furthermore, the patent application support system includes a search unit that searches for related existing patents based on the inventor's idea, and a summarization unit that summarizes the searched existing patents and presents them to a user (e.g., an inventor, an intellectual property officer, a new business development officer, an investment officer, etc.). The search unit, for example, searches for existing patents using keywords related to the inventor's idea. The search unit can also search for existing patents using classification codes. The search unit can also preferentially search for existing patents that are highly related based on the inventor's idea. The summarization unit, for example, extracts and summarizes the main points of the searched existing patents. The summarization unit can also generate a summary of the existing patent. The summarization unit can also highlight important points when presenting the summary to the user. The summarization unit presents the summary to the user, for example, by transmitting the summary to a user's device. This makes it possible to quickly search for existing patents related to the inventor's idea and provide a summary to support the inventor's idea in brushing up the inventor's idea.

[0029] Furthermore, the patent application support system includes an analysis unit that uses a generation AI to analyze the relationship between the inventor's idea and the existing patent, and a proposal unit that presents the analysis result and improvement proposals to a user (e.g., an inventor, an intellectual property officer, a patent attorney, etc.). The analysis unit, for example, analyzes the similarity between the inventor's idea and the existing patent. The analysis unit can also analyze the technical relevance between the inventor's idea and the existing patent. The analysis unit can also analyze the relationship between the inventor's idea and the existing patent and generate improvement proposals. The proposal unit presents the analysis result to the user, for example, by transmitting information indicating the analysis result to a user's device or the like. The summarization unit presents the summary to the user, for example, by transmitting a summary to the user's device or the like. The proposal unit can also present improvement proposals to the user. The summarization unit presents improvement proposals to the user, for example, by transmitting information indicating the improvement proposals to the user's device or the like. The proposal unit can also modify the proposal content based on user feedback. This allows the quality of an inventor's ideas to be improved by analyzing the relationship between the inventor's ideas and existing patents and proposing improvements.

[0030] Furthermore, the patent application support system includes a drawing creation unit that automatically creates drawings for patent application documents using image generation AI or multimodal AI. The drawing creation unit automatically generates drawings for patent application documents using, for example, deep learning technology. The drawing creation unit can also automatically generate drawings using a generative adversarial network (GAN). The drawing creation unit can also automatically generate drawings for patent application documents by performing integrated analysis of text and images. This makes it possible to improve the efficiency and accuracy of drawing creation by automatically creating drawings for patent application documents.

[0031] The information acquisition unit can optimize the acquisition of new ideas by referring to the inventor's past idea history and analyzing patterns. For example, the information acquisition unit acquires the inventor's past idea history from a database and analyzes patterns using text mining technology. For example, frequently occurring keywords and phrases are extracted and used to acquire the next idea. The information acquisition unit also builds a system in which AI learns features based on the history of ideas submitted by the inventor in the past and acquires new ideas more efficiently. For example, it recommends new ideas similar to successful ideas in the past. The information acquisition unit also refers to the inventor's past idea history and finds specific patterns and trends. For example, if there are many ideas related to a specific theme or technical field, it prioritizes the acquisition of new ideas related to that field. This makes it possible to support efficient idea generation by optimizing the acquisition of new ideas based on the inventor's past idea history.

[0032] The information acquisition unit can convert the inventor's oral explanation into text using voice recognition technology and automatically extract the main points. For example, when the inventor orally explains an idea, the information acquisition unit converts the information into text in real time using voice recognition software. For example, the inventor's explanation content is converted into text as is and the main points are automatically extracted. The information acquisition unit also records the inventor's oral explanation and converts the voice data into text later using voice recognition technology. For example, important keywords and phrases are automatically highlighted and the main points are extracted. The information acquisition unit also analyzes the voice data when the inventor explains the idea and extracts the main points using natural language processing technology. For example, important parts are automatically picked up from the explanation content and saved in text format. This makes it possible to support efficient idea acquisition by converting the inventor's oral explanation into text and automatically extracting the main points.

[0033] The information acquisition unit can incorporate perspectives from different fields by holding discussions with experts in other fields in real time when an inventor acquires an idea. For example, when an inventor acquires an idea, the information acquisition unit holds discussions with experts in different fields in real time through a video conference system. For example, a technical inventor exchanges opinions with a design expert. In addition, when an inventor acquires an idea, the information acquisition unit exchanges opinions with experts in different fields in real time using a chat function. For example, a technical perspective is shared with a marketing expert. In addition, when an inventor acquires an idea, the information acquisition unit holds an online workshop for collaboration with experts in different fields. For example, a medical expert and a technical expert jointly consider a new idea. This allows the quality of the inventor's idea to be improved by incorporating perspectives from different fields.

[0034] The information acquisition unit provides an interactive visual tool for the inventor to draw an idea, and can visually materialize the idea. The information acquisition unit, for example, provides an interactive visual tool for the inventor to draw an idea. For example, the information acquisition unit visualizes the idea by combining elements of the idea by drag and drop. The information acquisition unit also provides a tool for the inventor to visually materialize the idea, and enables the inventor to easily create diagrams and graphs. For example, the information acquisition unit provides a function for creating a flow chart or a mind map. The information acquisition unit also uses an interactive visual editor to visually organize the structure and elements of the idea when the inventor draws the idea. For example, each element of the idea is arranged as a block, and the relationship is connected by a line. This allows the inventor to visually materialize the idea, thereby improving the quality of the idea.

[0035] The generation unit can cause the generation AI to create the application manuscript in an appropriate tone and style based on the emotional state of the inventor. For example, the generation unit uses a casual tone when the inventor is relaxed, and a formal tone when the inventor is nervous. The generation unit can also select a technical style or a business style depending on the emotional state of the inventor. The generation unit can also create the application manuscript in an optimal tone and style based on the emotional data of the inventor. This makes it possible to more accurately reflect the inventor's intentions by creating the application manuscript in an appropriate tone and style based on the emotional state of the inventor.

[0036] The generation unit can have the generation AI learn from past success cases and create a manuscript with a high probability of success. For example, the generation unit aims to achieve similar success by having the AI ​​learn from past successful cases of patent acquisition. The generation unit can also optimize the structure and content of the patent application manuscript based on past successful cases. The generation unit can also extract the characteristics of successful cases and create a manuscript that reflects them. In this way, by having the generation AI learn from past successful cases, it is possible to create a patent application manuscript with a high probability of success.

[0037] The generation unit can reflect the latest trends and regulatory information for patent applications in the generation AI in real time. For example, the generation unit aims to make applications more likely to be successful by reflecting the latest legal regulations and industry trends. The generation unit can also collect the latest information on patent applications and create manuscripts based on that information. The generation unit can also increase the success rate of patent applications by reflecting regulatory information that is updated in real time. This makes it possible to increase the success rate of patent applications by reflecting the latest trends and regulatory information.

[0038] The generation unit can automatically generate multiple variations using the generation AI based on the information input by the inventor and select the optimal one. The generation unit generates variations of, for example, different technical approaches or writing styles and selects the optimal one. The generation unit can also present multiple candidates to select the variation that best suits the inventor's intention. The generation unit can also set criteria for comparing the generated variations and selecting the optimal one. In this way, it is possible to create a patent application manuscript that best suits the inventor's intention by automatically generating multiple variations and selecting the optimal one.

[0039] The generation unit can automatically translate the manuscript created by the generation AI into different languages ​​to support multilingual patent applications. The generation unit supports international patent applications by automatically translating into, for example, English, Chinese, Spanish, etc. The generation unit can also check the quality of the translated manuscript and correct it as necessary. The generation unit can also perform translations that take into account the patent requirements of each country in order to support patent applications in different languages. This makes it possible to support global patent applications by automatically translating into different languages.

[0040] The presentation unit can reflect the user's feedback on the presented content in real time and immediately present a revision proposal. The presentation unit can reflect the user's feedback in real time, for example, by using a live preview or an instant update function. The presentation unit can also immediately reflect the revision content input by the user. The presentation unit can also automatically correct the presented content based on the user's feedback. In this way, the quality of the patent application manuscript can be improved by reflecting the user's feedback in real time and immediately presenting a revision proposal.

[0041] The presentation unit can display related existing patents and reference materials at the same time as presenting the manuscript, making it easier for the user to compare and consider. For example, the presentation unit can also display materials that are technically or industry related, making it easier for the user to make comparisons. The presentation unit can also display summaries of related existing patents. The presentation unit can also visually display reference materials, allowing the user to intuitively understand. In this way, by displaying related existing patents and reference materials together, it becomes easier for the user to compare and consider.

[0042] The presentation unit can be equipped with an interactive comment function to realize collaborative editing when users check a manuscript. The presentation unit can facilitate collaborative editing, for example, by being equipped with a real-time comment or threaded comment function. The presentation unit can also provide an interface that makes it easy for users to add comments. The presentation unit can also promote communication between users through the comment function. As a result, the interactive comment function makes it easier for users to edit collaboratively.

[0043] The presentation unit can present the manuscript and also display it in a visual note or mind map format to aid visual understanding. The presentation unit displays, for example, a handwritten visual note or a mind map using a digital tool. The presentation unit can also provide a tool for visually organizing the main points of the manuscript. The presentation unit can also display the manuscript in a format that is visually easy for the user to understand. Thus, by displaying in a visual note or mind map format, the user can easily understand visually.

[0044] The correction unit can analyze the contents of the user's corrections and automatically understand and reflect the intention and background of the corrections. For example, the correction unit builds a system that analyzes the contents of the corrections made by the user to the patent application manuscript and automatically understands the intention and background. For example, the intention is estimated based on the user's correction history. The correction unit can also analyze the contents of the user's corrections in real time, understand the intention and background, and reflect them. For example, the purpose of the corrections made by the user is automatically estimated. The correction unit also develops a system that analyzes the contents of the corrections and automatically understands the intention and background of the user and reflects them in the patent application manuscript. For example, the changes desired by the user are immediately reflected. In this way, the quality of the patent application manuscript can be improved by analyzing the contents of the user's corrections, understanding the intention and background, and reflecting them.

[0045] The correction unit can track the correction history, learn the correction patterns, and use them in the next proposal. The correction unit, for example, tracks the correction history made by the user and builds a system that learns the patterns. For example, it detects a specific correction pattern and reflects it in the next proposal. The correction unit can also analyze the correction history, learn a specific pattern, and use it in creating the next patent application manuscript. For example, it reflects the user's preferred style of correction. The correction unit also develops a system that tracks the user's correction history, learns the patterns, and reflects them in the next proposal. For example, it predicts frequent corrections and suggests them in advance. In this way, it is possible to track the correction history and learn the patterns, which can be used in the next proposal and improve the quality of the patent application manuscript.

[0046] The correction unit can integrate corrections from multiple users in real time and present an optimal correction proposal. The correction unit, for example, collects corrections from multiple users in real time and builds a system that integrates the corrections. For example, even if multiple users make corrections at the same time, they are consolidated into one correction proposal. The correction unit can also develop a system that analyzes corrections from multiple users and automatically presents an optimal correction proposal. For example, the most appropriate correction content is reflected preferentially. The correction unit can also develop a system that integrates corrections from multiple users in real time and presents an optimal correction proposal based on the results. For example, the opinions of users are aggregated to make optimal corrections. In this way, the quality of patent application manuscripts can be improved by integrating corrections from multiple users in real time and presenting an optimal correction proposal.

[0047] The correction unit can share the correction content with experts in different fields and obtain cross-feedback. The correction unit, for example, builds a system for sharing the correction content from the user with experts in different fields and obtaining cross-feedback. For example, a correction in the technical field is checked by an expert in the design field. The correction unit can also develop a system for sharing the correction content with experts in different fields and aggregating feedback from each field. For example, a marketing expert and a technical expert make corrections jointly. The correction unit can also build a system for sharing the correction content made by the user with experts in different fields and presenting optimal correction proposals based on cross-feedback. For example, the opinions of multiple experts are reflected. In this way, the quality of the patent application manuscript can be improved by sharing the correction content with experts in different fields and obtaining cross-feedback.

[0048] The search unit can filter the search results of existing patents based on the inventor's field of expertise and past research results. The search unit, for example, builds a system that filters the search results of existing patents based on the inventor's field of expertise and past research results. For example, patents related to the inventor's specialized knowledge are preferentially displayed. The search unit can also filter the search results of existing patents based on the inventor's field of expertise and past research results. For example, the search results are displayed by narrowing down to a specific technical field. The search unit can also develop a system that filters the search results of existing patents according to the inventor's field of expertise and past research results, and presents the most relevant patents. For example, patents related to projects that the inventor has worked on in the past are displayed. In this way, by filtering based on the inventor's field of expertise and past research results, it is possible to provide highly relevant existing patents.

[0049] The search unit can emphasize important points when summarizing search results, so that the inventor can intuitively understand. For example, the search unit constructs a system that emphasizes and displays important points when generating a summary of the search results. For example, the main points and important features of the patent are highlighted. The search unit can also summarize the search results of existing patents and visually emphasize them so that the inventor can intuitively understand. For example, graphs and figures are displayed in addition to the summary. The search unit can also develop a system that emphasizes important points when summarizing search results, so that the inventor can intuitively understand. For example, highly relevant parts of the patent are displayed in different colors. As a result, when generating a summary of the search results, important points are emphasized and displayed, so that the inventor can intuitively understand.

[0050] The search unit can automatically translate search results into different languages ​​and evaluate the relevance from a global perspective. The search unit, for example, builds a system that automatically translates search results of existing patents into different languages ​​and evaluates the relevance from a global perspective. For example, it translates into English, Japanese, French, etc. The search unit can also automatically translate search results into multiple languages ​​and integrate patent information from different language areas to evaluate the relevance. For example, it displays international patents related to a specific technical field. The search unit can also automatically translate search results of existing patents and develop a system that evaluates the relevance from a global perspective. For example, it performs translation according to patent requirements for each region. As a result, it is possible to evaluate the relevance from a global perspective by automatically translating search results into different languages.

[0051] The search unit can display the search results of existing patents in a visual note or mind map format to make it easier to understand visually. The search unit, for example, builds a system that displays the search results of existing patents in a visual note or mind map format. For example, the main points of the patents are shown in diagrams and graphs to make it easier to understand visually. The search unit can also display the search results in a visual note or mind map format to make it easier for the inventor to intuitively understand. For example, related patents are displayed as nodes and the relationships are connected by lines. The search unit can also develop a system that visually displays the search results of existing patents to make it easier for the inventor to understand. For example, the main points are shown in pictograms or icons. In this way, the search results of existing patents can be displayed in a visual note or mind map format to make it easier for the inventor to intuitively understand.

[0052] The summarization unit can refer to related industry news and trend information in order to improve the accuracy of the summary sentence. The summarization unit, for example, builds a system that automatically collects related industry news and trend information in order to improve the accuracy of the summary sentence, and reflects the information in the summary sentence. For example, the latest technology trends and market needs are incorporated into the summary sentence. The summarization unit can also refer to related industry news and trend information for the presented summary sentence to improve its accuracy. For example, the latest technology trends and market trends are reflected in the summary sentence. The summarization unit can also develop a system that automatically collects industry news and trend information, and generates a summary sentence with high accuracy based on that information. For example, the latest technology and market information is reflected in the summary sentence. In this way, the accuracy of the summary sentence can be improved by referring to related industry news and trend information.

[0053] The summarization unit can visualize the summary results and generate graphs and charts so that the user can intuitively understand. The summarization unit builds a system that visualizes the summary results of existing patents and generates graphs and charts so that the user can intuitively understand. For example, the main points of the patents are displayed in bar graphs and pie charts. The summarization unit can also visualize the summary results so that the user can easily understand. For example, important points are displayed in pictograms and icons. The summarization unit can also develop a system that visually displays the summary results so that the user can intuitively understand. For example, the main points of the patents are displayed in infographics. In this way, the summary results can be visualized to make it easier for the user to intuitively understand.

[0054] The summarization unit may, for example, construct a system that automatically translates the abstracts of existing patents into different languages ​​and obtains feedback from an international perspective. For example, the summarization unit may translate into English, Japanese, French, and the like. The summarization unit may also automatically translate the abstracts into multiple languages ​​and collect feedback from users in different language areas. For example, the patent summaries may be displayed in different languages ​​and feedback may be obtained. The summarization unit may also develop a system that automatically translates the abstracts of existing patents and obtains feedback from an international perspective. For example, the summarization unit may perform translation according to patent requirements for each region. This allows the automatic translation of the abstracts into different languages ​​to obtain feedback from an international perspective.

[0055] The summarizing unit can convert the summary sentence into a visual note or a mind map to make it easier to understand visually. The summarizing unit can, for example, convert the summary sentence of an existing patent into a visual note or a mind map to build a system that makes it easier to understand visually. For example, the main points are shown in a diagram or graph. The summarizing unit can also convert the summary sentence into a visual note or a mind map to make it easier for the user to understand intuitively. For example, related items are visually organized. The summarizing unit can also develop a system that visually displays the summary sentence of an existing patent to make it easier for the user to understand. For example, the main points are shown in a pictogram or an icon. In this way, by converting the summary sentence into a visual note or a mind map, it is easier for the user to understand intuitively.

[0056] The analysis unit can present the points where the inventor's idea has the most advantage based on the analysis results, emphasizing them. For example, the analysis unit builds a system that, based on the analysis results, emphasizes and presents the points where the inventor's idea has the most advantage. For example, the main points of the patent are highlighted. The analysis unit can also highlight and display the points where the inventor's idea has the most advantage based on the analysis results of the generation AI. For example, it shows the advantage compared to highly related patents. The analysis unit can also develop a system that emphasizes and presents the advantages of the inventor's idea, and display the points of advantage based on the analysis results. For example, it emphasizes technical advantages and market demand. As a result, the quality of the inventor's idea can be improved by emphasizing and presenting the points where the inventor's idea has the most advantage based on the analysis results.

[0057] The analysis unit, for example, analyzes the relationship between an inventor's idea and existing patents, and builds a system to visualize the results. For example, it generates interactive graphs and charts. The analysis unit can also visualize the analysis results using the generation AI and display them interactively so that the user can intuitively understand them. For example, it shows the relevance of patents in a graph. The analysis unit can also develop a system to visually display the analysis results, so that the user can intuitively understand them. For example, it shows the main points in a graph or chart. In this way, by visualizing the analysis results, it is possible to make it easier for the user to intuitively understand them.

[0058] The analysis unit can share the analysis results with experts in different fields and obtain cross-feedback. The analysis unit, for example, builds a system that shares the results of the analysis by the generation AI with experts in different fields and obtains cross-feedback. For example, the analysis results in the technical field are checked by an expert in the design field. The analysis unit can also develop a system that shares the analysis results with experts in different fields and aggregates feedback from each field. For example, a marketing expert and a technical expert jointly perform the analysis. The analysis unit can also build a system that shares the results of the analysis by the generation AI with experts in different fields and presents optimal analysis results based on cross-feedback. For example, the opinions of multiple experts are reflected. This allows the analysis results to be shared with experts in different fields and cross-feedback to be obtained, thereby improving the quality of the inventor's ideas.

[0059] The analysis unit, for example, constructs a system that automatically translates the results analyzed by the generation AI into different languages ​​and performs evaluation in multiple languages. For example, it translates into English, Japanese, French, etc. The analysis unit can also automatically translate the analysis results into multiple languages ​​and collect evaluations from experts in different language areas. For example, it displays patent summaries in different languages ​​and obtains feedback. The analysis unit can also automatically translate the results analyzed by the generation AI and develop a system that performs evaluation in multiple languages. For example, it performs translation according to the patent requirements for each region. This allows the analysis results to be automatically translated into different languages, allowing evaluation in multiple languages ​​and improving the quality of the inventor's ideas.

[0060] The suggestion unit can optimize the proposed content based on past success cases. For example, when presenting analysis results or improvement proposals, the suggestion unit builds a system that optimizes the proposed content based on past success cases. For example, the suggestion unit analyzes past success cases and reflects the findings. The suggestion unit can also develop a system that optimizes the proposed content based on past success cases to increase the probability of success. For example, the suggestion unit extracts success factors and makes a proposal that reflects the factors. The suggestion unit can also build a system that optimizes the proposed content by referring to past success cases when presenting analysis results or improvement proposals. For example, the suggestion unit makes a proposal based on similar success cases. In this way, the quality of the inventor's idea can be improved by optimizing the proposed content based on past success cases.

[0061] The proposal unit, for example, constructs a system that visualizes the analysis results and improvement proposals and generates interactive graphs and charts so that the user can intuitively understand them. For example, the main points are displayed in a bar graph or a pie chart. The proposal unit can also visualize the proposal contents so that the user can easily understand them. For example, important points are displayed in a pictogram or an icon. The proposal unit can also develop a system that visually displays the proposal contents so that the user can intuitively understand them. For example, the main points are displayed in an infographic. In this way, the proposal contents can be visualized to make it easier for the user to intuitively understand them.

[0062] The proposal department, for example, builds a system that shares the analysis results and improvement proposals with experts in different fields and obtains cross-feedback. For example, the analysis results in the technical field are checked by an expert in the design field. The proposal department can also develop a system that shares the proposal contents with experts in different fields and aggregates feedback from each field. For example, marketing experts and technical experts jointly review the proposal contents. The proposal department can also build a system that shares the analysis results and improvement proposals with experts in different fields and presents optimal proposals based on cross-feedback. For example, the opinions of multiple experts are reflected. In this way, the proposal contents can be shared with experts in different fields and cross-feedback can be obtained, thereby improving the quality of the inventor's ideas.

[0063] The suggestion unit, for example, constructs a system that automatically translates the analysis results and improvement proposals into different languages ​​and performs evaluation in multiple languages. For example, it translates into English, Japanese, French, etc. The suggestion unit can also automatically translate the proposal contents into multiple languages ​​and collect evaluations from experts in different language areas. For example, it displays the proposal contents in different languages ​​and obtains feedback. The suggestion unit can also develop a system that automatically translates the analysis results and improvement proposals and performs evaluation in multiple languages. For example, it performs translation according to patent requirements for each region. As a result, it is possible to automatically translate the proposal contents into different languages, perform evaluation in multiple languages, and improve the quality of the inventor's ideas.

[0064] The drawing creation unit, for example, builds a system that collects feedback on drawings in real time and immediately reflects suggested revisions. For example, it immediately corrects parts pointed out by the user. The drawing creation unit can also analyze feedback in real time, and the generation AI can immediately present suggested revisions. For example, it instantly reflects changes desired by the user. The drawing creation unit can also develop a system that reflects feedback on drawings in real time, and the generation AI can automatically create suggested revisions. For example, it immediately reflects user comments. This makes it possible to improve the quality of drawings by collecting feedback on drawings in real time and immediately reflecting suggested revisions.

[0065] The drawing creation unit, for example, builds a system that visualizes the drawing creation process and enables the user to intuitively understand it. For example, each step of the drawing is displayed by animation. The drawing creation unit can also visually display the drawing creation process so that the user can easily understand it. For example, important points are indicated by pictograms or icons. The drawing creation unit can also develop a system that visualizes the drawing creation process in real time and enables the user to intuitively understand it. For example, each step of the drawing is displayed by an infographic. In this way, the drawing creation process can be visualized to make it easier for the user to intuitively understand.

[0066] The drawing creation unit, for example, constructs a system that automatically generates drawings in different styles and formats and selects the most suitable one. For example, it generates technical drawings and drawings that emphasize design. The drawing creation unit can also automatically generate drawings in different styles and formats and select the most suitable one from among them. For example, it selects a style according to the inventor's preference. The drawing creation unit can also automatically generate drawings in multiple styles and formats and develop a system that allows the user to select the most suitable one. For example, it provides drawings that emphasize technical details and drawings that emphasize design. In this way, it is possible to provide drawings that are most suitable for the inventor's intention by automatically generating drawings in different styles and formats and selecting the most suitable one.

[0067] The drawing creation unit may, for example, build a system that shares automatically generated drawings with experts in different fields and obtains cross-feedback. For example, drawings in the technical field may be checked by an expert in the design field. The drawing creation unit may also develop a system that shares drawings with experts in different fields and consolidates feedback from each field. For example, marketing experts and technical experts may jointly review drawings. The drawing creation unit may also build a system that shares automatically generated drawings with experts in different fields and provides optimal drawings based on cross-feedback. For example, the opinions of multiple experts may be reflected. This allows drawings to be shared with experts in different fields and cross-feedback to be obtained, thereby improving the quality of the drawings.

[0068] Each of the above-mentioned information acquisition unit, generation unit, presentation unit, correction unit, search unit, summary unit, analysis unit, proposal unit, and drawing creation unit is realized by the specific processing unit 290 of the data processing device 12, but is not limited to such an example. The information acquisition unit can acquire the inventor's idea using, for example, the camera 42 or the microphone 38B of the smart device 14. The presentation unit can present the patent application manuscript to the user via the display 40A or the speaker 40B of the smart device 14. The correction unit can accept the user's correction via the touch panel 38A or the microphone 38B of the smart device 14. Note that each of the search unit, summary unit, analysis unit, proposal unit, and drawing creation unit may also be realized by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0069] The information acquisition unit can optimize the acquisition of new ideas by referring to the inventor's past idea history and analyzing patterns. For example, the information acquisition unit acquires the inventor's past idea history from a database and analyzes patterns using text mining technology. For example, frequently occurring keywords and phrases are extracted and used to acquire the next idea. The information acquisition unit also builds a system in which AI learns features based on the history of ideas submitted by the inventor in the past and acquires new ideas more efficiently. For example, it recommends new ideas similar to successful ideas in the past. The information acquisition unit also refers to the inventor's past idea history and finds specific patterns and trends. For example, if there are many ideas related to a specific theme or technical field, it prioritizes the acquisition of new ideas related to that field. This makes it possible to support efficient idea generation by optimizing the acquisition of new ideas based on the inventor's past idea history.

[0070] The information acquisition unit can convert the inventor's oral explanation into text using voice recognition technology and automatically extract the main points. For example, when the inventor orally explains an idea, the information acquisition unit converts the information into text in real time using voice recognition software. For example, the inventor's explanation content is converted into text as is and the main points are automatically extracted. The information acquisition unit also records the inventor's oral explanation and converts the voice data into text later using voice recognition technology. For example, important keywords and phrases are automatically highlighted and the main points are extracted. The information acquisition unit also analyzes the voice data when the inventor explains the idea and extracts the main points using natural language processing technology. For example, important parts are automatically picked up from the explanation content and saved in text format. This makes it possible to support efficient idea acquisition by converting the inventor's oral explanation into text and automatically extracting the main points.

[0071] The information acquisition unit can incorporate perspectives from different fields by holding discussions with experts in other fields in real time when an inventor acquires an idea. For example, when an inventor acquires an idea, the information acquisition unit holds discussions with experts in different fields in real time through a video conference system. For example, a technical inventor exchanges opinions with a design expert. In addition, when an inventor acquires an idea, the information acquisition unit exchanges opinions with experts in different fields in real time using a chat function. For example, a technical perspective is shared with a marketing expert. In addition, when an inventor acquires an idea, the information acquisition unit holds an online workshop for collaboration with experts in different fields. For example, a medical expert and a technical expert jointly consider a new idea. This allows the quality of the inventor's idea to be improved by incorporating perspectives from different fields.

[0072] The information acquisition unit provides an interactive visual tool for the inventor to draw an idea, and can visually materialize the idea. The information acquisition unit, for example, provides an interactive visual tool for the inventor to draw an idea. For example, the information acquisition unit visualizes the idea by combining elements of the idea by drag and drop. The information acquisition unit also provides a tool for the inventor to visually materialize the idea, and enables the inventor to easily create diagrams and graphs. For example, the information acquisition unit provides a function for creating a flow chart or a mind map. The information acquisition unit also uses an interactive visual editor to visually organize the structure and elements of the idea when the inventor draws the idea. For example, each element of the idea is arranged as a block, and the relationship is connected by a line. This allows the inventor to visually materialize the idea, thereby improving the quality of the idea.

[0073] The generation unit can have the generation AI learn from past success cases and create a manuscript with a high probability of success. For example, the generation unit aims to achieve similar success by having the AI ​​learn from past successful cases of patent acquisition. The generation unit can also optimize the structure and content of the patent application manuscript based on past successful cases. The generation unit can also extract the characteristics of successful cases and create a manuscript that reflects them. In this way, by having the generation AI learn from past successful cases, it is possible to create a patent application manuscript with a high probability of success.

[0074] The generation unit can reflect the latest trends and regulatory information for patent applications in the generation AI in real time. For example, the generation unit aims to make applications more likely to be successful by reflecting the latest legal regulations and industry trends. The generation unit can also collect the latest information on patent applications and create manuscripts based on that information. The generation unit can also increase the success rate of patent applications by reflecting regulatory information that is updated in real time. This makes it possible to increase the success rate of patent applications by reflecting the latest trends and regulatory information.

[0075] The generation unit can automatically generate multiple variations using the generation AI based on the information input by the inventor and select the optimal one. The generation unit generates variations of, for example, different technical approaches or writing styles and selects the optimal one. The generation unit can also present multiple candidates to select the variation that best suits the inventor's intention. The generation unit can also set criteria for comparing the generated variations and selecting the optimal one. In this way, it is possible to create a patent application manuscript that best suits the inventor's intention by automatically generating multiple variations and selecting the optimal one.

[0076] The generation unit can automatically translate the manuscript created by the generation AI into different languages ​​to support multilingual patent applications. The generation unit supports international patent applications by automatically translating into, for example, English, Chinese, Spanish, etc. The generation unit can also check the quality of the translated manuscript and correct it as necessary. The generation unit can also perform translations that take into account the patent requirements of each country in order to support patent applications in different languages. This makes it possible to support global patent applications by automatically translating into different languages.

[0077] The process flow of each embodiment will be described below.

[0078] Step 1: The information acquisition unit acquires information that indicates the inventor's idea, for example, text information, drawings, and recordings of oral explanations provided by the inventor.

[0079] Step 2: The search section searches existing patents using keywords and classification codes related to the inventor's idea. It can also prioritize searching for existing patents that are highly relevant.

[0080] Step 3: The abstract section extracts and summarizes the key points of the searched existing patents. It generates an abstract and displays it with the important points highlighted.

[0081] Step 4: The analysis unit analyzes the similarity and technical relevance between the inventor's idea and existing patents. It analyzes the relationship between the inventor's idea and existing patents and generates improvement suggestions.

[0082] Step 5: The suggestion unit presents the analysis results and improvement suggestions to a user (e.g., the inventor). The inventor's idea can also be modified based on the user's feedback. The information acquisition unit acquires information indicating the inventor's idea modified by the suggestion unit based on the user's feedback.

[0083] Step 6: The generation unit inputs the inventor's idea acquired by the information acquisition unit in step 1 or step 5 into the generation AI, and creates a patent application manuscript. For example, the patent application manuscript is created using natural language generation technology or a machine learning model.

[0084] Step 7: The drawing creation department automatically generates drawings for patent application documents using image generation AI or multimodal AI. Drawings are automatically generated using deep learning techniques and generative adversarial networks (GANs).

[0085] Step 8: The presentation unit presents the patent application documents including the patent application manuscript created by the generation unit and the drawings created by the drawing creation unit to the user, for example by digitally displaying, printing, or sending by email.

[0086] Step 9: The correction unit accepts and corrects the corrections made by the user to the patent application document presented by the presentation unit. For example, the corrections made by the user are reflected in the corrections made by input, handwritten, or voice instructions.

[0087] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0088] (Example 2) The patent application support system according to the embodiment of the present invention is a system that efficiently converts an inventor's idea into a patent application manuscript and reflects user corrections. This patent application support system includes an information acquisition unit that acquires information indicating the inventor's idea, a generation unit that inputs the acquired inventor's idea into a generation AI to create a patent application manuscript, a presentation unit that presents the created patent application manuscript to a user, and a correction unit that accepts and corrects corrections from the user to the presented patent application manuscript. As a result, the patent application support system can improve the efficiency and accuracy of patent applications by efficiently converting the inventor's idea into a patent application manuscript and reflecting user corrections.

[0089] The patent application support system according to the embodiment includes an information acquisition unit, a generation unit, a presentation unit, and a correction unit. The information acquisition unit acquires information indicating an inventor's idea, for example, from the inventor's device. For example, the information acquisition unit acquires text information provided by the inventor, for example, from the inventor's device. The information acquisition unit can also acquire drawings provided by the inventor, for example, from the inventor's device. The information acquisition unit can also record the inventor's oral explanation and acquire it as audio data. The generation unit inputs the inventor's idea acquired by the information acquisition unit into a generation AI to create a patent application manuscript. For example, the generation unit uses information including information indicating the inventor's idea and instruction information that is information instructing the creation of a patent application manuscript from the inventor's idea as input information, and causes the generation AI (for example, a large-scale language model) to output a patent application manuscript. The generation unit can input information including an inventor's idea or information of a part of the patent application manuscript generated by the generation AI as input information for each part of the patent application manuscript (e.g., background art, problem of the invention, embodiment, scope of claims, abstract), and can output information for each part of the patent application manuscript to the generation AI. The generation unit can also create a new patent application manuscript by referring to a past patent application manuscript. For example, the generation AI may be a generation AI that has been fine-tuned using a data set of ideas and patent application manuscripts, and in this case, the information input to the generation AI may not include instruction information. The presentation unit presents the patent application manuscript created by the generation unit to a user (e.g., a patent attorney, an intellectual property officer, or an inventor) by transmitting the patent application manuscript created by the generation unit to a device of the user. For example, the presentation unit digitally displays the patent application manuscript. The presentation unit can also print and present the patent application manuscript. The presentation unit can also send the patent application manuscript by email. The correction unit accepts and corrects corrections from the user to the patent application manuscript presented by the presentation unit. For example, the correction unit receives corrections input by the user from the user's device or the like and reflects the received corrections in the patent application manuscript. The correction unit can also scan and reflect corrections made by the user in handwriting. The correction unit can also reflect corrections made by the user through voice.As a result, the patent application support system according to the embodiment can efficiently convert the inventor's idea into a patent application manuscript and reflect the user's corrections, thereby improving the efficiency and accuracy of patent applications. The patent application support system obtains information from the user and presents the user's information through communication with the user's device, etc., but is not limited to such an example.

[0090] Furthermore, the patent application support system includes a search unit that searches for related existing patents based on the inventor's idea, and a summarization unit that summarizes the searched existing patents and presents them to a user (e.g., an inventor, an intellectual property officer, a new business development officer, an investment officer, etc.). The search unit, for example, searches for existing patents using keywords related to the inventor's idea. The search unit can also search for existing patents using classification codes. The search unit can also preferentially search for existing patents that are highly related based on the inventor's idea. The summarization unit, for example, extracts and summarizes the main points of the searched existing patents. The summarization unit can also generate a summary of the existing patent. The summarization unit can also highlight important points when presenting the summary to the user. The summarization unit presents the summary to the user, for example, by transmitting the summary to a user's device. This makes it possible to quickly search for existing patents related to the inventor's idea and provide a summary to support the inventor's idea in brushing up the inventor's idea.

[0091] Furthermore, the patent application support system includes an analysis unit that uses a generation AI to analyze the relationship between the inventor's idea and the existing patent, and a proposal unit that presents the analysis result and improvement proposals to a user (e.g., an inventor, an intellectual property officer, a patent attorney, etc.). The analysis unit, for example, analyzes the similarity between the inventor's idea and the existing patent. The analysis unit can also analyze the technical relevance between the inventor's idea and the existing patent. The analysis unit can also analyze the relationship between the inventor's idea and the existing patent and generate improvement proposals. The proposal unit presents the analysis result to the user, for example, by transmitting information indicating the analysis result to a user's device or the like. The summarization unit presents the summary to the user, for example, by transmitting a summary to the user's device or the like. The proposal unit can also present improvement proposals to the user. The summarization unit presents improvement proposals to the user, for example, by transmitting information indicating the improvement proposals to the user's device or the like. The proposal unit can also modify the proposal content based on user feedback. This allows the quality of an inventor's ideas to be improved by analyzing the relationship between the inventor's ideas and existing patents and proposing improvements.

[0092] Furthermore, the patent application support system includes a drawing creation unit that automatically creates drawings for patent application documents using image generation AI or multimodal AI. The drawing creation unit automatically generates drawings for patent application documents using, for example, deep learning technology. The drawing creation unit can also automatically generate drawings using a generative adversarial network (GAN). The drawing creation unit can also automatically generate drawings for patent application documents by performing integrated analysis of text and images. This makes it possible to improve the efficiency and accuracy of drawing creation by automatically creating drawings for patent application documents.

[0093] The information acquisition unit can monitor the emotional state of the inventor in real time and select a method of acquiring an idea according to the emotion. For example, when the inventor explains an idea, the information acquisition unit monitors the emotional state in real time using emotion analysis software. For example, the information acquisition unit reads the emotion from the tone of voice and facial expression of the inventor and selects a method of acquiring an idea when the inventor is relaxed. In addition, when the inventor inputs an idea, the information acquisition unit measures the emotional state using a device with an emotion sensor and acquires the idea at a timing when positive emotion increases. For example, the heart rate and skin electrodermal response are measured to grasp the emotional state. In addition, the information acquisition unit monitors the emotional state of the inventor and builds a system for acquiring an idea when stress is low. For example, an interface is provided that allows the inventor to input details of an idea when stress is low. This allows the quality of the idea to be improved by selecting the optimal idea acquisition method according to the emotional state of the inventor.

[0094] The information acquisition unit can optimize the acquisition of new ideas by referring to the inventor's past idea history and analyzing patterns. For example, the information acquisition unit acquires the inventor's past idea history from a database and analyzes patterns using text mining technology. For example, frequently occurring keywords and phrases are extracted and used to acquire the next idea. The information acquisition unit also builds a system in which AI learns features based on the history of ideas submitted by the inventor in the past and acquires new ideas more efficiently. For example, it recommends new ideas similar to successful ideas in the past. The information acquisition unit also refers to the inventor's past idea history and finds specific patterns and trends. For example, if there are many ideas related to a specific theme or technical field, it prioritizes the acquisition of new ideas related to that field. This makes it possible to support efficient idea generation by optimizing the acquisition of new ideas based on the inventor's past idea history.

[0095] The information acquisition unit can convert the inventor's oral explanation into text using voice recognition technology and automatically extract the main points. For example, when the inventor orally explains an idea, the information acquisition unit converts the information into text in real time using voice recognition software. For example, the inventor's explanation content is converted into text as is and the main points are automatically extracted. The information acquisition unit also records the inventor's oral explanation and converts the voice data into text later using voice recognition technology. For example, important keywords and phrases are automatically highlighted and the main points are extracted. The information acquisition unit also analyzes the voice data when the inventor explains the idea and extracts the main points using natural language processing technology. For example, important parts are automatically picked up from the explanation content and saved in text format. This makes it possible to support efficient idea acquisition by converting the inventor's oral explanation into text and automatically extracting the main points.

[0096] The information acquisition unit can incorporate perspectives from different fields by holding discussions with experts in other fields in real time when an inventor acquires an idea. For example, when an inventor acquires an idea, the information acquisition unit holds discussions with experts in different fields in real time through a video conference system. For example, a technical inventor exchanges opinions with a design expert. In addition, when an inventor acquires an idea, the information acquisition unit exchanges opinions with experts in different fields in real time using a chat function. For example, a technical perspective is shared with a marketing expert. In addition, when an inventor acquires an idea, the information acquisition unit holds an online workshop for collaboration with experts in different fields. For example, a medical expert and a technical expert jointly consider a new idea. This allows the quality of the inventor's idea to be improved by incorporating perspectives from different fields.

[0097] The information acquisition unit provides an interactive visual tool for the inventor to draw an idea, and can visually materialize the idea. The information acquisition unit, for example, provides an interactive visual tool for the inventor to draw an idea. For example, the information acquisition unit visualizes the idea by combining elements of the idea by drag and drop. The information acquisition unit also provides a tool for the inventor to visually materialize the idea, and enables the inventor to easily create diagrams and graphs. For example, the information acquisition unit provides a function for creating a flow chart or a mind map. The information acquisition unit also uses an interactive visual editor to visually organize the structure and elements of the idea when the inventor draws the idea. For example, each element of the idea is arranged as a block, and the relationship is connected by a line. This allows the inventor to visually materialize the idea, thereby improving the quality of the idea.

[0098] The information acquisition unit can use the emotion estimation function to identify the environment and time period when the inventor is most creative, and acquire ideas under those conditions. The information acquisition unit, for example, uses the emotion estimation function to identify the time period when the inventor is most creative. For example, the emotion data of the inventor is analyzed to find the time period when creativity is enhanced. Furthermore, the information acquisition unit identifies the environment in which the inventor can be most relaxed based on the emotion estimation data, and acquires ideas in that environment. For example, it is determined that creativity is enhanced when the inventor thinks of ideas in a place with natural light. Furthermore, the information acquisition unit monitors the emotional state of the inventor in real time, and identifies the conditions under which creativity is enhanced. For example, it is determined that creativity is enhanced when listening to specific music or when in a specific place. In this way, the quality of ideas can be improved by identifying the environment and time period when the inventor is most creative.

[0099] The generation unit can cause the generation AI to create the application manuscript in an appropriate tone and style based on the emotional state of the inventor. For example, the generation unit uses a casual tone when the inventor is relaxed, and a formal tone when the inventor is nervous. The generation unit can also select a technical style or a business style depending on the emotional state of the inventor. The generation unit can also create the application manuscript in an optimal tone and style based on the emotional data of the inventor. This makes it possible to more accurately reflect the inventor's intentions by creating the application manuscript in an appropriate tone and style based on the emotional state of the inventor.

[0100] The generation unit can have the generation AI learn from past success cases and create a manuscript with a high probability of success. For example, the generation unit aims to achieve similar success by having the AI ​​learn from past successful cases of patent acquisition. The generation unit can also optimize the structure and content of the patent application manuscript based on past successful cases. The generation unit can also extract the characteristics of successful cases and create a manuscript that reflects them. In this way, by having the generation AI learn from past successful cases, it is possible to create a patent application manuscript with a high probability of success.

[0101] The generation unit can reflect the latest trends and regulatory information for patent applications in the generation AI in real time. For example, the generation unit aims to make applications more likely to be successful by reflecting the latest legal regulations and industry trends. The generation unit can also collect the latest information on patent applications and create manuscripts based on that information. The generation unit can also increase the success rate of patent applications by reflecting regulatory information that is updated in real time. This makes it possible to increase the success rate of patent applications by reflecting the latest trends and regulatory information.

[0102] The generation unit can automatically generate multiple variations using the generation AI based on the information input by the inventor and select the optimal one. The generation unit generates variations of, for example, different technical approaches or writing styles and selects the optimal one. The generation unit can also present multiple candidates to select the variation that best suits the inventor's intention. The generation unit can also set criteria for comparing the generated variations and selecting the optimal one. In this way, it is possible to create a patent application manuscript that best suits the inventor's intention by automatically generating multiple variations and selecting the optimal one.

[0103] The generation unit can automatically translate the manuscript created by the generation AI into different languages ​​to support multilingual patent applications. The generation unit supports international patent applications by automatically translating into, for example, English, Chinese, Spanish, etc. The generation unit can also check the quality of the translated manuscript and correct it as necessary. The generation unit can also perform translations that take into account the patent requirements of each country in order to support patent applications in different languages. This makes it possible to support global patent applications by automatically translating into different languages.

[0104] The generation unit can use the emotion estimation function to collect feedback for selecting an application manuscript that the inventor is most satisfied with. The generation unit collects feedback through, for example, questionnaires and interviews, and selects the optimal manuscript. The generation unit can also analyze the emotional reactions of the inventor in real time, and select the most satisfactory manuscript based on the data. The generation unit can also automatically generate a manuscript that the inventor is satisfied with, using the emotion estimation data. This makes it possible to improve the quality of patent applications by selecting an application manuscript that the inventor is most satisfied with.

[0105] The presentation unit can analyze the user's emotional response to the presented manuscript and generate improvement suggestions to elicit a positive response. The presentation unit measures the user's emotional response using, for example, facial expression recognition or voice analysis, and makes improvement suggestions. The presentation unit can also generate specific suggestions to elicit a positive response based on the user's emotional data. The presentation unit can also modify the presentation content based on user feedback. In this way, the quality of the patent application manuscript can be improved by analyzing the user's emotional response and making improvement suggestions to elicit a positive response.

[0106] The presentation unit can reflect the user's feedback on the presented content in real time and immediately present a revision proposal. The presentation unit can reflect the user's feedback in real time, for example, by using a live preview or an instant update function. The presentation unit can also immediately reflect the revision content input by the user. The presentation unit can also automatically correct the presented content based on the user's feedback. In this way, the quality of the patent application manuscript can be improved by reflecting the user's feedback in real time and immediately presenting a revision proposal.

[0107] The presentation unit can display related existing patents and reference materials at the same time as presenting the manuscript, making it easier for the user to compare and consider. For example, the presentation unit can also display materials that are technically or industry related, making it easier for the user to make comparisons. The presentation unit can also display summaries of related existing patents. The presentation unit can also visually display reference materials, allowing the user to intuitively understand. In this way, by displaying related existing patents and reference materials together, it becomes easier for the user to compare and consider.

[0108] The presentation unit can be equipped with an interactive comment function to realize collaborative editing when users check a manuscript. The presentation unit can facilitate collaborative editing, for example, by being equipped with a real-time comment or threaded comment function. The presentation unit can also provide an interface that makes it easy for users to add comments. The presentation unit can also promote communication between users through the comment function. As a result, the interactive comment function makes it easier for users to edit collaboratively.

[0109] The presentation unit can present the manuscript and also display it in a visual note or mind map format to aid visual understanding. The presentation unit displays, for example, a handwritten visual note or a mind map using a digital tool. The presentation unit can also provide a tool for visually organizing the main points of the manuscript. The presentation unit can also display the manuscript in a format that is visually easy for the user to understand. Thus, by displaying in a visual note or mind map format, the user can easily understand visually.

[0110] The presentation unit can use the emotion estimation function to present the manuscript in a format that induces the user's most positive reaction. The presentation unit presents the manuscript in a format that induces the user's most positive reaction, for example, by devising color usage, layout, and font. The presentation unit can also select an optimal display format based on the user's emotion data. The presentation unit can also modify the display format based on user feedback. In this way, the quality of the patent application manuscript can be improved by presenting the manuscript in a format that induces the user's most positive reaction.

[0111] The correction unit can analyze the contents of the user's corrections and automatically understand and reflect the intention and background of the corrections. For example, the correction unit builds a system that analyzes the contents of the corrections made by the user to the patent application manuscript and automatically understands the intention and background. For example, the intention is estimated based on the user's correction history. The correction unit can also analyze the contents of the user's corrections in real time, understand the intention and background, and reflect them. For example, the purpose of the corrections made by the user is automatically estimated. The correction unit also develops a system that analyzes the contents of the corrections and automatically understands the intention and background of the user and reflects them in the patent application manuscript. For example, the changes desired by the user are immediately reflected. In this way, the quality of the patent application manuscript can be improved by analyzing the contents of the user's corrections, understanding the intention and background, and reflecting them.

[0112] The correction unit can track the correction history, learn the correction patterns, and use them in the next proposal. The correction unit, for example, tracks the correction history made by the user and builds a system that learns the patterns. For example, it detects a specific correction pattern and reflects it in the next proposal. The correction unit can also analyze the correction history, learn a specific pattern, and use it in creating the next patent application manuscript. For example, it reflects the user's preferred style of correction. The correction unit also develops a system that tracks the user's correction history, learns the patterns, and reflects them in the next proposal. For example, it predicts frequent corrections and suggests them in advance. In this way, it is possible to track the correction history and learn the patterns, which can be used in the next proposal and improve the quality of the patent application manuscript.

[0113] The correction unit can perform sentiment analysis on the correction content and generate a proposal to improve the part containing negative emotions. The correction unit, for example, performs sentiment analysis on the correction content made by the user and builds a system to identify the part containing negative emotions. For example, detects negative expressions. The correction unit can also perform sentiment analysis on the correction content and generate a specific proposal to improve the part containing negative emotions. For example, makes a proposal to change a negative expression to a positive expression. The correction unit also develops a system that automatically generates a proposal to improve the part containing negative emotions based on the result of the sentiment analysis on the correction content. For example, corrects a part that the user finds unpleasant. In this way, the quality of the patent application manuscript can be improved by performing sentiment analysis on the correction content and making a proposal to improve the part containing negative emotions.

[0114] The correction unit can integrate corrections from multiple users in real time and present an optimal correction proposal. The correction unit, for example, collects corrections from multiple users in real time and builds a system that integrates the corrections. For example, even if multiple users make corrections at the same time, they are consolidated into one correction proposal. The correction unit can also develop a system that analyzes corrections from multiple users and automatically presents an optimal correction proposal. For example, the most appropriate correction content is reflected preferentially. The correction unit can also develop a system that integrates corrections from multiple users in real time and presents an optimal correction proposal based on the results. For example, the opinions of users are aggregated to make optimal corrections. In this way, the quality of patent application manuscripts can be improved by integrating corrections from multiple users in real time and presenting an optimal correction proposal.

[0115] The correction department, for example, builds a system that shares the corrections made by the user with experts in different fields and obtains cross-feedback. For example, corrections in the technical field are checked by an expert in the design field. The correction department can also develop a system that shares the corrections made with experts in different fields and aggregates feedback from each field. For example, a marketing expert and a technical expert make corrections together. The correction department can also build a system that shares the corrections made by the user with experts in different fields and presents optimal correction suggestions based on cross-feedback. For example, the opinions of multiple experts are reflected. In this way, the quality of the patent application manuscript can be improved by sharing the corrections made with experts in different fields and obtaining cross-feedback.

[0116] The correction unit can use the emotion estimation function to automatically generate a revision proposal that satisfies the user. The correction unit, for example, uses the emotion estimation function to build a system that automatically generates a revision proposal that satisfies the user. For example, the correction unit proposes an optimal revision based on the user's emotion data. The correction unit can also analyze the user's emotional response in real time and generate a revision proposal that satisfies the user based on the data. For example, a revision proposal with a strong positive emotion is prioritized. The correction unit can also develop a system that automatically generates a revision proposal that satisfies the user using emotion estimation data. For example, an optimal revision is made based on the user's emotion score. As a result, the quality of the patent application manuscript can be improved by automatically generating a revision proposal that satisfies the user using the emotion estimation function.

[0117] The search unit can search the most relevant existing patents preferentially based on the emotional state of the inventor. The search unit, for example, builds a system that preferentially searches the most relevant existing patents based on the emotional state of the inventor. For example, patents for which the inventor has positive emotions are preferentially displayed. The search unit can also use an emotion estimation function to search for existing patents according to the emotional state of the inventor. For example, highly relevant patents are displayed when the inventor is relaxed. The search unit can also develop a system that searches for the most relevant existing patents based on the emotional data of the inventor and presents the results. For example, patents related to new technology are displayed when the inventor is excited. This makes it possible to improve the quality of the inventor's ideas by preferentially searching the most relevant existing patents based on the emotional state of the inventor.

[0118] The search unit can filter the search results of existing patents based on the inventor's field of expertise and past research results. The search unit, for example, builds a system that filters the search results of existing patents based on the inventor's field of expertise and past research results. For example, patents related to the inventor's specialized knowledge are preferentially displayed. The search unit can also filter the search results of existing patents based on the inventor's field of expertise and past research results. For example, the search results are displayed by narrowing down to a specific technical field. The search unit can also develop a system that filters the search results of existing patents according to the inventor's field of expertise and past research results, and presents the most relevant patents. For example, patents related to projects that the inventor has worked on in the past are displayed. In this way, by filtering based on the inventor's field of expertise and past research results, it is possible to provide highly relevant existing patents.

[0119] The search unit can emphasize important points when summarizing search results, so that the inventor can intuitively understand. For example, the search unit constructs a system that emphasizes and displays important points when generating a summary of the search results. For example, the main points and important features of the patent are highlighted. The search unit can also summarize the search results of existing patents and visually emphasize them so that the inventor can intuitively understand them. For example, graphs and figures are displayed in addition to the summary. The search unit can also develop a system that emphasizes important points when summarizing search results, so that the inventor can intuitively understand them. For example, highly relevant parts of the patent are displayed in different colors. As a result, important points are emphasized when generating a summary of the search results, so that the inventor can easily intuitively understand them.

[0120] The search unit can automatically translate search results into different languages ​​and evaluate the relevance from a global perspective. The search unit, for example, builds a system that automatically translates search results of existing patents into different languages ​​and evaluates the relevance from a global perspective. For example, it translates into English, Japanese, French, etc. The search unit can also automatically translate search results into multiple languages ​​and integrate patent information from different language areas to evaluate the relevance. For example, it displays international patents related to a specific technical field. The search unit can also automatically translate search results of existing patents and develop a system that evaluates the relevance from a global perspective. For example, it performs translation according to patent requirements for each region. As a result, it is possible to evaluate the relevance from a global perspective by automatically translating search results into different languages.

[0121] The search unit can display the search results of existing patents in a visual note or mind map format to make it easier to understand visually. The search unit, for example, builds a system that displays the search results of existing patents in a visual note or mind map format. For example, the main points of the patents are shown in diagrams and graphs to make it easier to understand visually. The search unit can also display the search results in a visual note or mind map format to make it easier for the inventor to intuitively understand. For example, related patents are displayed as nodes and the relationships are connected by lines. The search unit can also develop a system that visually displays the search results of existing patents to make it easier for the inventor to understand. For example, the main points are shown in pictograms or icons. In this way, the search results of existing patents can be displayed in a visual note or mind map format to make it easier for the inventor to intuitively understand.

[0122] The search unit can use the emotion estimation function to preferentially present existing patents that interest the inventor. The search unit, for example, uses the emotion estimation function to build a system that preferentially presents existing patents that interest the inventor. For example, the emotional reaction of the inventor is analyzed and patents of interest are highlighted. The search unit can also monitor the emotional state of the inventor in real time and present existing patents that interest the inventor based on the data. For example, patents that are highly relevant when the inventor is excited are displayed. The search unit can also develop a system that preferentially presents existing patents that interest the inventor using emotion estimation data. For example, patents are selected based on the emotion score of the inventor. As a result, the quality of the inventor's ideas can be improved by preferentially presenting existing patents that interest the inventor using the emotion estimation function.

[0123] The summarization unit can analyze the user's emotional response to the summary sentence and generate improvement proposals to elicit a positive response. The summarization unit, for example, builds a system that analyzes the user's emotional response to the summary sentence of an existing patent and generates improvement proposals to elicit a positive response. For example, the summarization unit changes the expression or style to the user's preference. The summarization unit can also monitor the user's emotional response in real time and make proposals to improve the summary sentence based on the data. For example, it can emphasize points that excite the user. The summarization unit can also analyze the user's emotional response using an emotion estimation function and generate specific improvement proposals to elicit a positive response. For example, it can change negative expressions to positive expressions. In this way, the quality of the summary sentence can be improved by analyzing the user's emotional response to the summary sentence and making improvement proposals to elicit a positive response.

[0124] The summarization unit can refer to related industry news and trend information in order to improve the accuracy of the summary sentence. The summarization unit, for example, builds a system that automatically collects related industry news and trend information in order to improve the accuracy of the summary sentence, and reflects the information in the summary sentence. For example, the latest technology trends and market needs are incorporated into the summary sentence. The summarization unit can also refer to related industry news and trend information for the presented summary sentence to improve its accuracy. For example, the latest technology trends and market trends are reflected in the summary sentence. The summarization unit can also develop a system that automatically collects industry news and trend information, and generates a summary sentence with high accuracy based on that information. For example, the latest technology and market information is reflected in the summary sentence. In this way, the accuracy of the summary sentence can be improved by referring to related industry news and trend information.

[0125] The summarization unit can visualize the summary results and generate graphs and charts so that the user can intuitively understand. The summarization unit builds a system that visualizes the summary results of existing patents and generates graphs and charts so that the user can intuitively understand. For example, the main points of the patents are displayed in bar graphs and pie charts. The summarization unit can also visualize the summary results so that the user can easily understand. For example, important points are displayed in pictograms and icons. The summarization unit can also develop a system that visually displays the summary results so that the user can intuitively understand. For example, the main points of the patents are displayed in infographics. In this way, the summary results can be visualized to make it easier for the user to intuitively understand.

[0126] The summarization unit can automatically translate the abstract into different languages ​​and obtain feedback from an international perspective. The summarization unit, for example, builds a system that automatically translates the abstract of an existing patent into different languages ​​and obtains feedback from an international perspective. For example, it translates into English, Japanese, French, etc. The summarization unit can also automatically translate the abstract into multiple languages ​​and collect feedback from users in different language areas. For example, it displays the patent abstract in different languages ​​and obtains feedback. The summarization unit can also develop a system that automatically translates the abstract of an existing patent and obtains feedback from an international perspective. For example, it performs translation according to patent requirements for each region. As a result, it is possible to obtain feedback from an international perspective by automatically translating the abstract into different languages.

[0127] The summarizing unit can convert the summary sentence into a visual note or a mind map to make it easier to understand visually. The summarizing unit can, for example, convert the summary sentence of an existing patent into a visual note or a mind map to build a system that makes it easier to understand visually. For example, the main points are shown in a diagram or graph. The summarizing unit can also convert the summary sentence into a visual note or a mind map to make it easier for the user to understand intuitively. For example, related items are visually organized. The summarizing unit can also develop a system that visually displays the summary sentence of an existing patent to make it easier for the user to understand. For example, the main points are shown in a pictogram or an icon. In this way, by converting the summary sentence into a visual note or a mind map, it is easier for the user to understand intuitively.

[0128] The summarization unit can use the emotion estimation function to collect the user's emotional reaction to the summary sentence, and improve the accuracy of the summary based on the collected data. The summarization unit can, for example, use the emotion estimation function to collect the user's emotional reaction to the summary sentence of the existing patent, and build a system to improve the accuracy of the summary based on the collected data. For example, the summarization unit improves the summary sentence based on the user's emotion data. The summarization unit can also analyze the user's emotional reaction in real time, and improve the accuracy of the summary sentence based on the data. For example, the summarization unit preferentially adopts a summary sentence with a strong positive emotion. The summarization unit can also use the emotion estimation data to collect feedback on the summary sentence of the existing patent, and develop a system to improve the accuracy of the summary. For example, the summarization sentence is modified based on the user's emotion score. This allows the emotion estimation function to collect the user's emotional reaction to the summary sentence, and improve the accuracy of the summary based on the collected data.

[0129] The analysis unit can provide analysis results according to the emotion, taking into account the emotional state of the inventor. The analysis unit, for example, builds a system that provides analysis results according to the emotion, taking into account the emotional state of the inventor. For example, when the inventor is relaxed, detailed analysis results are displayed. The analysis unit can also generate analysis results according to the emotional state of the inventor using an emotion estimation function. For example, when the inventor is excited, concise analysis results are displayed. The analysis unit can also develop a system that provides analysis results according to the emotion, based on the emotion data of the inventor. For example, detailed data is displayed when the inventor is calm. This allows the quality of the inventor's ideas to be improved by taking into account the emotional state of the inventor and providing analysis results according to the emotion.

[0130] The analysis unit can present the points where the inventor's idea has the most advantage based on the analysis results, emphasizing them. For example, the analysis unit builds a system that, based on the analysis results, emphasizes and presents the points where the inventor's idea has the most advantage. For example, the main points of the patent are highlighted. The analysis unit can also highlight and display the points where the inventor's idea has the most advantage based on the analysis results of the generation AI. For example, it shows the advantage compared to highly related patents. The analysis unit can also develop a system that emphasizes and presents the advantages of the inventor's idea, and display the points of advantage based on the analysis results. For example, it emphasizes technical advantages and market demand. As a result, the quality of the inventor's idea can be improved by emphasizing and presenting the points where the inventor's idea has the most advantage based on the analysis results.

[0131] The analysis unit can generate interactive graphs and charts to visualize the analysis results and enable users to intuitively understand. The analysis unit, for example, analyzes the relationship between the inventor's idea and existing patents and builds a system to visualize the results. For example, it generates interactive graphs and charts. The analysis unit can also visualize the analysis results using the generation AI and display them interactively so that users can intuitively understand. For example, it shows the relevance of patents in a graph. The analysis unit can also develop a system to visually display the analysis results so that users can intuitively understand. For example, it shows the main points in a graph or chart. In this way, the analysis results can be visualized to make it easier for users to intuitively understand.

[0132] The analysis unit can share the analysis results with experts in different fields and obtain cross-feedback. The analysis unit, for example, builds a system that shares the results of the analysis by the generation AI with experts in different fields and obtains cross-feedback. For example, the analysis results in the technical field are checked by an expert in the design field. The analysis unit can also develop a system that shares the analysis results with experts in different fields and aggregates feedback from each field. For example, a marketing expert and a technical expert jointly perform the analysis. The analysis unit can also build a system that shares the results of the analysis by the generation AI with experts in different fields and presents optimal analysis results based on cross-feedback. For example, the opinions of multiple experts are reflected. This allows the analysis results to be shared with experts in different fields and cross-feedback to be obtained, thereby improving the quality of the inventor's ideas.

[0133] The analysis unit can automatically translate the analysis results into different languages ​​and perform multilingual evaluation. The analysis unit, for example, builds a system that automatically translates the results analyzed by the generation AI into different languages ​​and performs multilingual evaluation. For example, it translates into English, Japanese, French, etc. The analysis unit can also automatically translate the analysis results into multiple languages ​​and collect evaluations from experts in different language areas. For example, it displays patent summaries in different languages ​​and obtains feedback. The analysis unit can also automatically translate the results analyzed by the generation AI and develop a system that performs multilingual evaluation. For example, it performs translation according to the patent requirements for each region. This allows the analysis results to be automatically translated into different languages, allowing evaluations to be performed in multiple languages ​​and improving the quality of the inventor's ideas.

[0134] The analysis unit can use the emotion estimation function to preferentially present the analysis result in which the user is most interested. The analysis unit, for example, uses the emotion estimation function to build a system that preferentially presents the analysis result in which the user is most interested. For example, the emotion estimation function is used to analyze the user's emotional reaction and highlight the analysis result in which the user is interested. The analysis unit can also monitor the user's emotional state in real time and present the analysis result in which the user is most interested based on the data. For example, when the user is excited, the analysis unit displays the analysis result that is highly relevant. The analysis unit can also develop a system that preferentially presents the analysis result in which the user is most interested using emotion estimation data. For example, the analysis result is selected based on the user's emotion score. This allows the quality of the inventor's idea to be improved by preferentially presenting the analysis result in which the user is most interested using the emotion estimation function.

[0135] The suggestion unit can analyze the user's emotional response to the proposed content and generate an improvement proposal to elicit a positive response. The suggestion unit, for example, builds a system that analyzes the user's emotional response to the analysis result and the improvement proposal and generates an improvement proposal to elicit a positive response. For example, the suggestion unit changes the tone or style to the user's preference. The suggestion unit can also monitor the user's emotional response in real time and make an improvement proposal based on the data. For example, the suggestion unit can emphasize points that excite the user. The suggestion unit can also use an emotion estimation function to analyze the user's emotional response and generate a specific improvement proposal to elicit a positive response. For example, a negative expression is changed to a positive expression. This allows the quality of the inventor's idea to be improved by analyzing the user's emotional response to the proposed content and making an improvement proposal to elicit a positive response.

[0136] The suggestion unit can optimize the proposed content based on past success cases. For example, when presenting analysis results or improvement proposals, the suggestion unit builds a system that optimizes the proposed content based on past success cases. For example, the suggestion unit analyzes past success cases and reflects the findings. The suggestion unit can also develop a system that optimizes the proposed content based on past success cases to increase the probability of success. For example, the suggestion unit extracts success factors and makes a proposal that reflects the factors. The suggestion unit can also build a system that optimizes the proposed content by referring to past success cases when presenting analysis results or improvement proposals. For example, the suggestion unit makes a proposal based on similar success cases. In this way, the quality of the inventor's idea can be improved by optimizing the proposed content based on past success cases.

[0137] The proposal unit can visualize the proposal contents and generate interactive graphs and charts so that the user can intuitively understand them. The proposal unit builds a system that visualizes, for example, analysis results and improvement proposals and generates interactive graphs and charts so that the user can intuitively understand them. For example, the main points are displayed in a bar graph or a pie chart. The proposal unit can also visualize the proposal contents so that the user can easily understand them. For example, important points are displayed in a pictogram or an icon. The proposal unit can also develop a system that visually displays the proposal contents so that the user can intuitively understand them. For example, the main points are displayed in an infographic. In this way, the proposal contents can be visualized to make it easier for the user to intuitively understand them.

[0138] The proposal section can share the proposal contents with experts in different fields and obtain cross-feedback. The proposal section, for example, builds a system for sharing analysis results and improvement proposals with experts in different fields and obtaining cross-feedback. For example, the analysis results in the technical field are checked by an expert in the design field. The proposal section can also develop a system for sharing the proposal contents with experts in different fields and aggregating feedback from each field. For example, a marketing expert and a technical expert jointly consider the proposal contents. The proposal section can also build a system for sharing analysis results and improvement proposals with experts in different fields and presenting optimal proposals based on cross-feedback. For example, the opinions of multiple experts are reflected. In this way, the proposal contents can be shared with experts in different fields and cross-feedback can be obtained, thereby improving the quality of the inventor's ideas.

[0139] The suggestion unit can automatically translate the proposed content into different languages ​​and perform evaluation in multiple languages. The suggestion unit, for example, builds a system that automatically translates the analysis results and improvement proposals into different languages ​​and performs evaluation in multiple languages. For example, it translates into English, Japanese, French, etc. The suggestion unit can also automatically translate the proposed content into multiple languages ​​and collect evaluations from experts in different language areas. For example, it displays the proposed content in different languages ​​and obtains feedback. The suggestion unit can also automatically translate the analysis results and improvement proposals and develop a system that performs evaluation in multiple languages. For example, it performs translation according to patent requirements for each region. This allows the proposed content to be automatically translated into different languages, allowing evaluation in multiple languages ​​to be performed and improving the quality of the inventor's ideas.

[0140] The suggestion unit can use the emotion estimation function to automatically generate proposal content to which the user has the most positive reaction. The suggestion unit, for example, uses the emotion estimation function to build a system that automatically generates proposal content to which the user has the most positive reaction. For example, the suggestion unit makes an optimal proposal based on the user's emotion data. The suggestion unit can also analyze the user's emotion reaction in real time and generate proposal content that elicits the most positive reaction based on the data. For example, a proposal with a strong positive emotion is prioritized. The suggestion unit can also use the emotion estimation data to develop a system that automatically generates proposal content that satisfies the user the most. For example, the suggestion unit makes an optimal proposal based on the user's emotion score. As a result, the quality of the inventor's idea can be improved by automatically generating proposal content to which the user has the most positive reaction using the emotion estimation function.

[0141] The drawing creation unit can reflect the emotional state of the inventor in real time when creating the drawing, and generate a design according to the emotion. The drawing creation unit, for example, monitors the emotional state of the inventor in real time when creating the drawing, and constructs a system that generates a design according to the emotion. For example, when the inventor is relaxed, a soft design is generated. The drawing creation unit can also automatically generate a drawing according to the emotional state of the inventor using an emotion estimation function. For example, when the inventor is excited, a dynamic design is generated. The drawing creation unit can also develop a system that generates a design according to the emotion in real time based on the emotion data of the inventor. For example, when the inventor is calm, a simple design is generated. In this way, the emotional state of the inventor is reflected when creating the drawing, and a design that best suits the inventor's intention can be generated.

[0142] The drawing creation unit can collect feedback on the drawings in real time and instantly reflect proposed revisions. The drawing creation unit, for example, builds a system that collects feedback on the drawings in real time and instantly reflects proposed revisions. For example, it immediately corrects the parts pointed out by the user. The drawing creation unit can also analyze the feedback in real time, and the generation AI can instantly present proposed revisions. For example, it instantly reflects the changes desired by the user. The drawing creation unit can also develop a system that reflects feedback on the drawings in real time and automatically creates proposed revisions with the generation AI. For example, it immediately reflects the user's comments. This allows the quality of the drawings to be improved by collecting feedback on the drawings in real time and instantly reflecting proposed revisions.

[0143] The drawing creation unit can visualize the drawing creation process so that the user can intuitively understand it. The drawing creation unit, for example, builds a system that visualizes the drawing creation process so that the user can intuitively understand it. For example, each step of the drawing is displayed by animation. The drawing creation unit can also visually display the drawing creation process so that the user can easily understand it. For example, important points are indicated by pictograms or icons. The drawing creation unit can also develop a system that visualizes the drawing creation process in real time so that the user can intuitively understand it. For example, each step of the drawing is displayed by an infographic. In this way, the drawing creation process can be visualized to make it easier for the user to intuitively understand.

[0144] The drawing creation unit can automatically generate drawings in different styles and formats and select the most suitable one. The drawing creation unit, for example, builds a system that automatically generates drawings in different styles and formats and selects the most suitable one. For example, it generates technical drawings and drawings that emphasize design. The drawing creation unit can also automatically generate drawings in different styles and formats and select the most suitable one from among them. For example, it selects a style according to the inventor's preference. The drawing creation unit can also automatically generate drawings in multiple styles and formats and develop a system that allows the user to select the most suitable one. For example, it provides drawings that emphasize technical details and drawings that emphasize design. This makes it possible to automatically generate drawings in different styles and formats and select the most suitable one to provide drawings that are most suitable for the inventor's intentions.

[0145] The drawing creation unit can share the drawings with experts in different fields and obtain cross-feedback. The drawing creation unit, for example, builds a system for sharing automatically generated drawings with experts in different fields and obtaining cross-feedback. For example, a drawing in the technical field is checked by an expert in the design field. The drawing creation unit can also develop a system for sharing drawings with experts in different fields and aggregating feedback from each field. For example, a marketing expert and a technical expert jointly review the drawings. The drawing creation unit can also build a system for sharing automatically generated drawings with experts in different fields and providing optimal drawings based on cross-feedback. For example, the opinions of multiple experts are reflected. This allows the quality of the drawings to be improved by sharing drawings with experts in different fields and obtaining cross-feedback.

[0146] The drawing creation unit can use the emotion estimation function to automatically generate a drawing that satisfies the user the most. The drawing creation unit uses, for example, the emotion estimation function to build a system that automatically generates a drawing that satisfies the user the most. For example, an optimal drawing is generated based on the user's emotion data. The drawing creation unit can also analyze the user's emotional response in real time and generate a drawing that satisfies the user the most based on the data. For example, a drawing with strong positive emotions is given priority. The drawing creation unit can also use emotion estimation data to develop a system that automatically generates a drawing that satisfies the user the most. For example, an optimal drawing is generated based on the user's emotion score. This allows the quality of the drawing to be improved by automatically generating a drawing that satisfies the user the most using the emotion estimation function.

[0147] Each of the above-mentioned information acquisition unit, generation unit, presentation unit, correction unit, search unit, summary unit, analysis unit, proposal unit, and drawing creation unit is realized by the specific processing unit 290 of the data processing device 12, but is not limited to such an example. The information acquisition unit can acquire the inventor's idea using, for example, the camera 42 or the microphone 38B of the smart device 14. The presentation unit can present the patent application manuscript to the user via the display 40A or the speaker 40B of the smart device 14. The correction unit can accept the user's correction via the touch panel 38A or the microphone 38B of the smart device 14. Note that each of the search unit, summary unit, analysis unit, proposal unit, and drawing creation unit may also be realized by the specific processing unit 290 of the data processing device 12. In addition, the emotion estimation function, which is a function for estimating emotions, is realized by, for example, an emotion engine (for example, the emotion identification model 59), but is not limited to such an example, and may be realized by, for example, a generation AI. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] The information acquisition unit can optimize the acquisition of new ideas by referring to the inventor's past idea history and analyzing patterns. For example, the information acquisition unit acquires the inventor's past idea history from a database and analyzes patterns using text mining technology. For example, frequently occurring keywords and phrases are extracted and used to acquire the next idea. The information acquisition unit also builds a system in which AI learns features based on the history of ideas submitted by the inventor in the past and acquires new ideas more efficiently. For example, it recommends new ideas similar to successful ideas in the past. The information acquisition unit also refers to the inventor's past idea history and finds specific patterns and trends. For example, if there are many ideas related to a specific theme or technical field, it prioritizes the acquisition of new ideas related to that field. This makes it possible to support efficient idea generation by optimizing the acquisition of new ideas based on the inventor's past idea history.

[0149] The information acquisition unit can convert the inventor's oral explanation into text using voice recognition technology and automatically extract the main points. For example, when the inventor orally explains an idea, the information acquisition unit converts the information into text in real time using voice recognition software. For example, the inventor's explanation content is converted into text as is and the main points are automatically extracted. The information acquisition unit also records the inventor's oral explanation and converts the voice data into text later using voice recognition technology. For example, important keywords and phrases are automatically highlighted and the main points are extracted. The information acquisition unit also analyzes the voice data when the inventor explains the idea and extracts the main points using natural language processing technology. For example, important parts are automatically picked up from the explanation content and saved in text format. This makes it possible to support efficient idea acquisition by converting the inventor's oral explanation into text and automatically extracting the main points.

[0150] The information acquisition unit can incorporate perspectives from different fields by holding discussions with experts in other fields in real time when an inventor acquires an idea. For example, when an inventor acquires an idea, the information acquisition unit holds discussions with experts in different fields in real time through a video conference system. For example, a technical inventor exchanges opinions with a design expert. In addition, when an inventor acquires an idea, the information acquisition unit exchanges opinions with experts in different fields in real time using a chat function. For example, a technical perspective is shared with a marketing expert. In addition, when an inventor acquires an idea, the information acquisition unit holds an online workshop for collaboration with experts in different fields. For example, a medical expert and a technical expert jointly consider a new idea. This allows the quality of the inventor's idea to be improved by incorporating perspectives from different fields.

[0151] The information acquisition unit provides an interactive visual tool for the inventor to draw an idea, and can visually materialize the idea. The information acquisition unit, for example, provides an interactive visual tool for the inventor to draw an idea. For example, the information acquisition unit visualizes the idea by combining elements of the idea by drag and drop. The information acquisition unit also provides a tool for the inventor to visually materialize the idea, and enables the inventor to easily create diagrams and graphs. For example, the information acquisition unit provides a function for creating a flow chart or a mind map. The information acquisition unit also uses an interactive visual editor to visually organize the structure and elements of the idea when the inventor draws the idea. For example, each element of the idea is arranged as a block, and the relationship is connected by a line. This allows the inventor to visually materialize the idea, thereby improving the quality of the idea.

[0152] The information acquisition unit can use the emotion estimation function to identify the environment and time period when the inventor is most creative, and acquire ideas under those conditions. The information acquisition unit, for example, uses the emotion estimation function to identify the time period when the inventor is most creative. For example, the emotion data of the inventor is analyzed to find the time period when creativity is enhanced. Furthermore, the information acquisition unit identifies the environment in which the inventor can be most relaxed based on the emotion estimation data, and acquires ideas in that environment. For example, it is determined that creativity is enhanced when the inventor thinks of ideas in a place with natural light. Furthermore, the information acquisition unit monitors the emotional state of the inventor in real time, and identifies the conditions under which creativity is enhanced. For example, it is determined that creativity is enhanced when listening to specific music or when in a specific place. In this way, the quality of ideas can be improved by identifying the environment and time period when the inventor is most creative.

[0153] The generation unit can cause the generation AI to create the application manuscript in an appropriate tone and style based on the emotional state of the inventor. For example, the generation unit uses a casual tone when the inventor is relaxed, and a formal tone when the inventor is nervous. The generation unit can also select a technical style or a business style depending on the emotional state of the inventor. The generation unit can also create the application manuscript in an optimal tone and style based on the emotional data of the inventor. This makes it possible to more accurately reflect the inventor's intentions by creating the application manuscript in an appropriate tone and style based on the emotional state of the inventor.

[0154] The generation unit can have the generation AI learn from past success cases and create a manuscript with a high probability of success. For example, the generation unit aims to achieve similar success by having the AI ​​learn from past successful cases of patent acquisition. The generation unit can also optimize the structure and content of the patent application manuscript based on past successful cases. The generation unit can also extract the characteristics of successful cases and create a manuscript that reflects them. In this way, by having the generation AI learn from past successful cases, it is possible to create a patent application manuscript with a high probability of success.

[0155] The generation unit can reflect the latest trends and regulatory information for patent applications in the generation AI in real time. For example, the generation unit aims to make applications more likely to be successful by reflecting the latest legal regulations and industry trends. The generation unit can also collect the latest information on patent applications and create manuscripts based on that information. The generation unit can also increase the success rate of patent applications by reflecting regulatory information that is updated in real time. This makes it possible to increase the success rate of patent applications by reflecting the latest trends and regulatory information.

[0156] The generation unit can automatically generate multiple variations using the generation AI based on the information input by the inventor and select the optimal one. The generation unit generates variations of, for example, different technical approaches or writing styles and selects the optimal one. The generation unit can also present multiple candidates to select the variation that best suits the inventor's intention. The generation unit can also set criteria for comparing the generated variations and selecting the optimal one. In this way, it is possible to create a patent application manuscript that best suits the inventor's intention by automatically generating multiple variations and selecting the optimal one.

[0157] The generation unit can automatically translate the manuscript created by the generation AI into different languages ​​to support multilingual patent applications. The generation unit supports international patent applications by automatically translating into, for example, English, Chinese, Spanish, etc. The generation unit can also check the quality of the translated manuscript and correct it as necessary. The generation unit can also perform translations that take into account the patent requirements of each country in order to support patent applications in different languages. This makes it possible to support global patent applications by automatically translating into different languages.

[0158] The process flow of each embodiment will be described below.

[0159] Step 1: The information acquisition unit acquires information indicating the inventor's idea. For example, it acquires text information, drawings, and audio data of oral explanations provided by the inventor. The information acquisition unit also monitors the inventor's emotional state in real time and selects a method of acquiring ideas according to the emotions. For example, it measures the emotional state using emotion analysis software or a device with an emotion sensor, and acquires ideas when positive emotions are high. Furthermore, the information acquisition unit uses an emotion estimation function to identify the environment and time of day when the inventor is most creative, and acquires ideas under those conditions. For example, it analyzes the inventor's emotion data and identifies the time of day and environment when creativity is high.

[0160] Step 2: The search section searches existing patents using keywords and classification codes related to the inventor's idea, giving priority to searching existing patents that are highly relevant.

[0161] Step 3: The abstract section extracts and summarizes the key points of the searched existing patents. It generates an abstract and displays it with the important points highlighted.

[0162] Step 4: The analysis unit analyzes the similarity and technical relevance between the inventor's idea and existing patents. It analyzes the relationship between the inventor's idea and existing patents and generates improvement suggestions.

[0163] Step 5: The suggestion unit presents the analysis results and improvement suggestions to a user (e.g., the inventor). The suggestion content is revised based on the user's feedback. The information acquisition unit acquires information indicating the inventor's idea revised by the suggestion unit based on the user's feedback.

[0164] Step 6: The generation unit inputs the inventor's idea acquired by the information acquisition unit in step 1 or step 5 into the generation AI, and creates a patent application manuscript. For example, the patent application manuscript is created using natural language generation technology or a machine learning model.

[0165] Step 7: The drawing creation unit automatically generates drawings for patent application documents using deep learning technology and generative adversarial networks (GANs). It performs integrated analysis of text and images to automatically generate drawings.

[0166] Step 8: The presentation unit presents the patent application document including the patent application manuscript created by the generation unit and the drawings created by the drawing creation unit to the user. For example, the presentation is performed by digital display, printing, sending by email, or the like. The presentation unit also uses an emotion estimation function to present the manuscript in a format that elicits the most positive response from the user. For example, the presentation is performed by devising color usage, layout, and fonts.

[0167] Step 9: The correction unit accepts and corrects the corrections made by the user to the patent application document presented by the presentation unit. For example, the corrections made by the user are reflected in the corrections made by input, handwritten, or voice instructions.

[0168] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58.

[0170] In the above embodiment, an example was given in which the specific processing is mainly performed by the data processing device 12, but as mentioned above, the technology disclosed herein is not limited to this, and some or all of the specific processing may be performed by the smart device 14.

[0171] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0172] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0174] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0178] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0181] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user.

[0183] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0184] The system according to the second embodiment performs the same processing as the system according to the first embodiment. In the system according to the second embodiment, the smart glasses 214 have the same functions as the smart device 14 according to the first embodiment and can perform the same processing as the smart device 14 according to the first embodiment, but is not limited to such an example.

[0185] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0186] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0187] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0188] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0192] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0193] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0194] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0195] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0196] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user.

[0197] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0198] The system according to the third embodiment performs the same processing as the system according to the first embodiment. In the system according to the third embodiment, the headset type terminal 314 has the same functions as the smart device 14 according to the first embodiment and can perform the same processing as the smart device 14 according to the first embodiment, but is not limited to such an example.

[0199] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0200] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0201] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0202] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0203] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0205] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0206] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0207] Fig. 8 shows an example of main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0208] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0209] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0210] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0211] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a ChatGPT server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, or a home appliance) owned by a user.

[0212] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0213] The system according to the fourth embodiment performs the same process as the system according to the first embodiment. In the system according to the third embodiment, the robot 414 has the same function as the smart device 14 according to the first embodiment and can perform the same process as the smart device 14 according to the first embodiment, but is not limited to such an example.

[0214] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0215] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0216] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0217] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0218] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0219] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0220] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0221] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0222] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0223] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0224] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0225] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing the specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0226] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0227] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0228] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0229] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

[0230] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or standard was specifically and individually indicated to be incorporated by reference.

[0231] The following is further disclosed regarding the above embodiment. (Appendix 1) an information acquisition unit that acquires information indicating an idea of ​​an inventor; a generation unit that inputs the inventor's idea acquired by the information acquisition unit into a generation AI and creates a patent application manuscript; a presentation unit that presents the patent application manuscript created by the generation unit to a user; a correction unit that accepts and corrects corrections from a user to the patent application manuscript presented by the presentation unit. A system characterized by: (Appendix 2) a search unit for searching for related existing patents based on the inventor's idea; A summary unit that summarizes the existing patents searched by the search unit and presents the summary to a user. 2. The system of claim 1, (Appendix 3) An analysis unit that analyzes the relationship between the inventor's idea and existing patents using a generation AI; A suggestion unit that presents the results of the analysis by the analysis unit and suggestions for improvement to a user. 2. The system of claim 1, (Appendix 4) Equipped with a drawing creation section that automatically creates drawings for patent application documents using image generation AI or multimodal AI 2. The system of claim 1, (Appendix 5) The information acquisition unit Monitor the emotional state of the inventor in real time and select a method of acquiring ideas according to the emotional state. 2. The system of claim 1, (Appendix 6) The information acquisition unit Refer to the inventor's past idea history and analyze patterns to optimize the acquisition of new ideas 2. The system of claim 1, (Appendix 7) The information acquisition unit The inventor's oral explanation is converted into text using voice recognition technology, and the main points are automatically extracted. 2. The system of claim 1, (Appendix 8) The information acquisition unit When the inventor acquires an idea, they hold real-time discussions with experts in other fields to incorporate perspectives from different fields. 2. The system of claim 1, (Appendix 9) The information acquisition unit Providing an interactive visual tool for the inventor to draw out ideas and visually materialize the ideas 2. The system of claim 1, (Appendix 10) The information acquisition unit Using emotion estimation function, the environment and time period in which the inventor is most creative are identified, and ideas are obtained under those conditions. 2. The system of claim 1, (Appendix 11) The generation unit is Have the generative AI write the application in an appropriate tone and style based on the inventor's emotional state 2. The system of claim 1, (Appendix 12) The generation unit is The generative AI learns from past success stories to create manuscripts with a high probability of success. 2. The system of claim 1, (Appendix 13) The generation unit is The generative AI will be updated with the latest trends and regulatory information for patent applications in real time. 2. The system of claim 1, (Appendix 14) The generation unit is Based on the information entered by the inventor, the generation AI automatically generates multiple variations and selects the optimal one. 2. The system of claim 1, (Appendix 15) The generation unit is The manuscript created by the generative AI is automatically translated into different languages ​​to support patent applications in multiple languages. 2. The system of claim 1, (Appendix 16) The generation unit is Using sentiment estimation, feedback is collected to select the application manuscript that the inventor is most satisfied with. 2. The system of claim 1, (Appendix 17) The presentation unit is Analyze users' emotional reactions to the presented manuscript and generate improvement suggestions to elicit positive reactions 2. The system of claim 1, (Appendix 18) The presentation unit is Reflects user feedback on proposed content in real time and provides immediate correction suggestions 2. The system of claim 1, (Appendix 19) The presentation unit is At the same time as presenting the manuscript, related existing patents or reference materials are also displayed to facilitate comparison and review. 2. The system of claim 1, (Appendix 20) The presentation unit is When users review a manuscript, an interactive comment function is provided, enabling collaborative editing. 2. The system of claim 1, (Appendix 21) The presentation unit is Along with presenting the manuscript, it also displays it in visual notes or mind map format to aid visual understanding 2. The system of claim 1, (Appendix 22) The presentation unit is Using emotion estimation, the system presents the manuscript in a format that elicits the most positive response from the user. 2. The system of claim 1, (Appendix 23) The correction unit is Analyzes user modifications and automatically understands and reflects the intent and background of the modifications. 2. The system of claim 1, (Appendix 24) The correction unit is Track revision history and learn revision patterns to improve future proposals 2. The system of claim 1, (Appendix 25) The correction unit is Perform sentiment analysis on the revision and generate suggestions to improve areas with negative sentiment 2. The system of claim 1, (Appendix 26) The correction unit is Integrates corrections from multiple users in real time and presents optimal correction suggestions 2. The system of claim 1, (Appendix 27) The correction unit is Share the revisions with experts in different fields and get cross-feedback. 2. The system of claim 1, (Appendix 28) The correction unit is Using emotion estimation, we automatically generate revision suggestions that will satisfy the user the most. 2. The system of claim 1, (Appendix 29) The search unit is Prioritizing the search for the most relevant existing patents based on the inventor's emotional state 3. The system of claim 2, (Appendix 30) The search unit is Filtering search results for existing patents based on the inventor's field of expertise and past research work 3. The system of claim 2, (Appendix 31) The search unit is Summarizing search results highlights key points and makes them intuitively understandable to the inventor 3. The system of claim 2, (Appendix 32) The search unit is Automatically translate search results into different languages ​​and evaluate their global relevance 3. The system of claim 2, (Appendix 33) The search unit is Display search results for existing patents in visual notes or mind maps for easy visual understanding 3. The system of claim 2, (Appendix 34) The search unit is Using emotion estimation, the system prioritizes existing patents that the inventor finds most interesting. 3. The system of claim 2, (Appendix 35) The summary portion comprises: Analyze users' emotional reactions to summaries and generate improvement suggestions to elicit positive reactions 3. The system of claim 2, (Appendix 36) The summary portion comprises: Refer to relevant industry news or trending information to improve your summary 3. The system of claim 2, (Appendix 37) The summary portion comprises: Visualize the summary results and generate graphs or charts for users to understand intuitively 3. The system of claim 2, (Appendix 38) The summary portion comprises: Automatically translate abstracts into different languages ​​and get feedback from an international perspective 3. The system of claim 2, (Appendix 39) The summary portion comprises: Convert your summaries into visual notes or mind maps for easy visual understanding 3. The system of claim 2, (Appendix 40) The summary portion comprises: Using emotion estimation, we collect users' emotional reactions to the summary text and use them to improve the accuracy of the summary. 3. The system of claim 2, (Appendix 41) The analysis unit is Taking into account the emotional state of the inventor, provide emotion-dependent analysis results 4. The system of claim 3. (Appendix 42) The analysis unit is Based on the analysis results, highlight the most advantageous points of the inventor's idea 4. The system of claim 3. (Appendix 43) The analysis unit is Visualize the analysis results and generate interactive graphs or charts for users to understand intuitively 4. The system of claim 3. (Appendix 44) The analysis unit is Share analysis results with experts in different fields to obtain cross-feedback 4. The system of claim 3. (Appendix 45) The analysis unit is Automatically translate analysis results into different languages ​​and conduct multilingual evaluations 4. The system of claim 3. (Appendix 46) The analysis unit is Using emotion estimation function, the analysis results that are of most interest to the user are presented preferentially. 4. The system of claim 3. (Appendix 47) The suggestion unit, Analyze users' emotional reactions to the suggestions and generate improvement suggestions to elicit positive responses 4. The system of claim 3. (Appendix 48) The suggestion unit, Optimize your offers based on past success stories 4. The system of claim 3. (Appendix 49) The suggestion unit, Generate interactive graphs or charts to visualize the proposal and help users understand it intuitively 4. The system of claim 3. (Appendix 50) The suggestion unit, Share the proposals with experts in different fields and get cross-feedback. 4. The system of claim 3. (Appendix 51) The suggestion unit, Automatically translate proposals into different languages ​​and conduct multilingual evaluations 4. The system of claim 3. (Appendix 52) The suggestion unit, Using emotion estimation, we automatically generate suggestions that elicit the most positive responses from users. 4. The system of claim 3. (Appendix 53) The drawing creation unit includes: When creating a drawing, the emotional state of the inventor is reflected in real time, and a design is generated according to the emotion. 5. The system of claim 4. (Appendix 54) The drawing creation unit includes: Get real-time feedback on your drawings and make immediate revisions 5. The system of claim 4. (Appendix 55) The drawing creation unit includes: Visualize the drawing creation process to enable users to intuitively understand 5. The system of claim 4. (Appendix 56) The drawing creation unit includes: Automatically generate drawings in different styles and formats and choose the most suitable one 5. The system of claim 4. (Appendix 57) The drawing creation unit includes: Share drawings with experts from different fields to get cross-feedback 5. The system of claim 4. (Appendix 58) The drawing creation unit includes: Using emotion estimation function, we automatically generate drawings that will satisfy users the most. 5. The system of claim 4. [Explanation of symbols]

[0232] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information acquisition unit that acquires information indicating an idea of ​​an inventor; A generation unit that inputs the inventor's idea acquired by the information acquisition unit into a generation AI to create a patent application manuscript; a presentation unit that presents the patent application manuscript created by the generation unit to a user; a correction unit that accepts and corrects corrections from a user to the patent application manuscript presented by the presentation unit. A system characterized by:

2. a search unit for searching for related existing patents based on the inventor's idea; A summary unit that summarizes the existing patents searched by the search unit and presents the summary to a user.

2. The system of claim 1 .

3. An analysis unit that analyzes the relationship between the inventor's idea and existing patents using a generation AI; A suggestion unit that presents the results of the analysis by the analysis unit and suggestions for improvement to a user.

2. The system of claim 1 .

4. Equipped with a drawing creation section that automatically creates drawings for patent application documents using image generation AI or multimodal AI 2. The system of claim 1 .

5. The information acquisition unit Monitor the emotional state of the inventor in real time and select a method of acquiring ideas according to the emotional state.

2. The system of claim 1 .

6. The information acquisition unit Using emotion estimation function, the environment and time period in which the inventor is most creative are identified, and ideas are obtained under those conditions.

2. The system of claim 1 .

7. The presentation unit is Using emotion estimation, the system presents the manuscript in a format that elicits the most positive response from the user.

2. The system of claim 1 .

8. 1. A computer-implemented method comprising: An information acquisition step of acquiring information indicating an idea of ​​an inventor; A generation step of inputting the inventor's idea acquired by the information acquisition step into a generation AI to create a patent application manuscript; a presentation step of presenting the patent application manuscript generated by the generation step to a user; A correction step of accepting and correcting corrections from a user to the patent application manuscript presented by the presentation step.

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