System

Abstract

An object of the system according to the embodiment is to efficiently make a patent application without expertise.SOLUTION: A system includes a patent information learning part, an interaction part, an image determination part, a format generation part, and a cooperation part. The patent information learning unit learns patent information. The interaction unit performs interaction with a user. The image determination unit determines an image related to a design. A format generation part generates a format for patent application. The cooperation unit cooperates with the patent office.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback that the patent application process is complicated and difficult to carry out without specialized knowledge.

[0005] The system according to the embodiment aims to enable efficient patent application filing even without specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a patent information learning unit, a dialogue unit, an image determination unit, a format generation unit, and a collaboration unit. The patent information learning unit learns patent information. The dialogue unit dialogues with the user. The image determination unit determines images related to the design. The format generation unit generates a format for patent applications. The collaboration unit collaborates with patent offices. [Effects of the Invention]

[0007] The system according to the embodiment allows a patent application to be filed efficiently even without specialized knowledge. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] 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.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] 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.

[0020] 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 (for example, 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 indicating 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 indicating the user input.

[0021] 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. 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 process 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] 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 generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The patent content refinement system according to an embodiment of the present invention uses an LLM that has learned patent information published by the Japan Patent Office to interactively refine patent content. The system allows users to refine the details of the patent content using LINE as an interface. For design-related content, the system uses Gemini to evaluate images and output the necessary information. When the accuracy of the patent content increases and it is determined that the patent can be applied for, it can be output in a patent application format. Furthermore, if the user does not wish to apply for the patent themselves, the system can also request the application in cooperation with a patent office (for a fee). This allows the user to easily refine the patent content and efficiently progress through the process leading up to the application.

[0029] A patent content filling system according to an embodiment includes a patent information learning unit, a dialogue unit, an image determination unit, a format generation unit, and a linking unit. The patent information learning unit learns patent information. For example, the patent information learning unit collects patent documents published by the Japan Patent Office and learns them using a large-scale language model (LLM). The patent information learning unit can also analyze patent classification information and extract features of patent content. The patent information learning unit can also evaluate the novelty and inventive step of patents based on patent application information. The dialogue unit interacts with users. For example, the dialogue unit responds to questions from users using LINE as an interface. The dialogue unit can also generate questions to clarify patent content based on user input. The dialogue unit can also analyze users' dialogue history and learn their question patterns and preferences. The image determination unit determines images related to designs. For example, the image determination unit uses Gemini to analyze design images uploaded by users. The image determination unit can also extract design features based on the image analysis results. Furthermore, the image assessment unit can analyze 3D models and CAD data to provide more detailed design information. The format generation unit generates a format for a patent application. For example, the format generation unit automatically generates patent application documents when the accuracy of the patent content is high and it is determined that the application can be filed. The format generation unit can also analyze the patent examiner's past examination trends and perform optimization to increase the pass rate. Furthermore, the format generation unit can customize the application format to meet the requirements of patent offices in different countries and regions. The collaboration unit collaborates with patent offices. For example, the collaboration unit requests an application from a patent office if the user does not file the application themselves. The collaboration unit can also refer to the user's past invention history and recommend the most appropriate patent office. Furthermore, the collaboration unit can analyze the user's emotional state and contact a patent office at the optimal time. As a result, the patent content finalization system according to the embodiment allows users to easily finalize patent content and efficiently proceed through the process to filing.For example, users can use LINE to finalize the details of patents, and design patent details can be easily finalized through image analysis. Furthermore, patent application documents can be automatically generated, facilitating smooth collaboration with patent offices.

[0030] In addition to learning patent information, the patent information learning unit also learns related academic papers and technical blogs, allowing it to refine patent content based on a broader range of knowledge. For example, in addition to learning patent information, the patent information learning unit also learns related academic papers and technical blogs to refine patent content. For example, it references both patent information and academic papers to supplement technical details and background information. The patent information learning unit can also analyze the content of academic papers and technical blogs to evaluate the novelty and inventiveness of the patent content. Furthermore, the patent information learning unit can suggest improvements to patent content and additional technical features based on information from academic papers and technical blogs. This allows patent content to be refined based on a broader range of knowledge.

[0031] The patent information learning unit can refer to the user's past invention history and provide advice based on past successes and failures. The patent information learning unit, for example, can refer to the user's past invention history and provide advice based on past successes and failures. For example, the patent information learning unit can analyze the factors that led to success or failure in past patent applications and reflect these in the content of the current patent. The patent information learning unit can also suggest improvements to the patent content or additional technical features based on the user's invention history. Furthermore, the patent information learning unit can analyze the user's invention history and provide advice to increase the success rate of patent applications. This allows advice to be provided based on the user's past invention history.

[0032] In addition to learning patent information, the patent information learning unit can also learn the patent information of competitors, and refine the patent content while conducting competitive analysis. For example, in addition to learning patent information, the patent information learning unit can also learn the patent information of competitors, and refine the patent content while conducting competitive analysis. For example, the patent information learning unit can analyze the technical features and market trends of competitors' patents and reflect these in the patent content. The patent information learning unit can also evaluate the novelty and inventiveness of the patent content based on the competitors' patent information. Furthermore, the patent information learning unit can analyze the competitors' patent information and propose improvements to the patent content or additional technical features. This allows the patent content to be refined based on the competitors' patent information.

[0033] The patent information learning unit can customize how the patent content is filled in according to the user's field of expertise, appropriately reflecting technical terminology and technical details. For example, the patent information learning unit can customize how the patent content is filled in according to the user's field of expertise, appropriately reflecting technical terminology and technical details. For example, for a user in the medical field, medical terminology is used and technical details are explained in detail. The patent information learning unit can also provide technical background information according to the user's field of expertise. Furthermore, the patent information learning unit can suggest improvements to the patent content or additional technical features specific to the user's field of expertise. This allows the patent content to be filled in according to the user's field of expertise.

[0034] The dialogue unit can analyze the LINE dialogue history, learn the user's question patterns and preferences, and provide more personalized responses. For example, the dialogue unit can analyze the LINE dialogue history, learn the user's question patterns and preferences, and provide more personalized responses. For example, the dialogue unit can provide detailed information on topics that the user frequently asks about. The dialogue unit can also generate responses based on the user's dialogue history, tailored to the user's interests and concerns. Furthermore, the dialogue unit can analyze the user's question patterns and quickly provide the information the user requests. This allows for the provision of personalized responses based on the user's question patterns and preferences.

[0035] The dialogue unit can provide patent-related news and trend information in real time during the dialogue, thereby keeping the user's knowledge up to date. For example, the dialogue unit can provide patent-related news and trend information in real time during the dialogue, thereby keeping the user's knowledge up to date. For example, the dialogue unit can automatically display the latest patent application information and technology trends. The dialogue unit can also suggest improvements to patent content and additional technical features to the user based on patent-related news. Furthermore, the dialogue unit can analyze patent-related trend information and provide the user with advice to increase the success rate of patent applications. This keeps the user's knowledge up to date.

[0036] The dialogue unit is also compatible with messaging apps other than LINE (e.g., WhatsApp and WeChat), improving user convenience. The dialogue unit is also compatible with messaging apps other than LINE (e.g., WhatsApp and WeChat), improving user convenience. For example, it is possible to integrate multiple messaging apps and access the patent content from any app. The dialogue unit can also provide an interface tailored to the characteristics of each messaging app. Furthermore, the dialogue unit can analyze the user's messaging app usage history and recommend the most suitable app. This improves user convenience.

[0037] The dialogue unit can automatically search for patent documents and technical documents that the user wants to refer to during the dialogue and provide links to them. For example, the dialogue unit can automatically search for patent documents and technical documents that the user wants to refer to during the dialogue and provide links to them. For example, when a user asks a question about a specific technology, links to related patent documents can be displayed. The dialogue unit can also analyze the content of the user's question and recommend the most appropriate patent documents and technical documents. Furthermore, the dialogue unit can provide summaries of patent documents and technical documents to enable the user to quickly obtain information. This automatically provides the patent documents and technical documents that the user wants to refer to.

[0038] In addition to image analysis, the image judgment unit can also analyze 3D models and CAD data to provide more detailed design information. For example, the image judgment unit can analyze the shape and dimensions of the 3D model to extract design features. The image judgment unit can also provide technical details of the design based on the CAD data. Furthermore, the image judgment unit can suggest design improvements and patentable features based on the analysis results of the 3D model and CAD data. This allows the analysis of the 3D model and CAD data to provide more detailed design information.

[0039] The image judgment unit can automatically suggest design improvements and highly patentable features based on the image analysis results. The image judgment unit automatically suggests design improvements and highly patentable features based on the image analysis results, for example. For example, it suggests improvements to the shape or color of the design. The image judgment unit can also extract highly patentable features and provide advice to increase the success rate of patent applications. Furthermore, the image judgment unit can analyze the technical details of the design and specifically indicate highly patentable features. This allows design improvements and highly patentable features to be automatically suggested.

[0040] The image judgment unit can analyze audio and video data in addition to image analysis to extract design features from multimedia information. The image judgment unit can, for example, analyze audio and video data in addition to image analysis to extract design features from multimedia information. For example, it can analyze an explanatory video of a design and extract features. The image judgment unit can also provide technical details of the design based on audio data. Furthermore, the image judgment unit can suggest design improvements and patentable features based on the results of the analysis of audio and video data. In this way, design features are extracted by analyzing audio and video data.

[0041] The image judgment unit can perform market suitability and competitive analysis of the design based on the analysis results and provide feedback to the user. The image judgment unit can, for example, perform market suitability and competitive analysis of the design based on the analysis results and provide feedback to the user. For example, it can compare the design with market needs and competing products. The image judgment unit can also evaluate the market suitability of the design and provide advice to increase the success rate of the patent application. Furthermore, the image judgment unit can suggest improvements to the design or additional technical features based on the results of the competitive analysis. This provides feedback based on the market suitability and competitive analysis of the design.

[0042] In addition to automatically generating application formats, the format generation unit can analyze the patent examiner's past examination trends and perform optimization to increase the examination pass rate. In addition to automatically generating application formats, the format generation unit can, for example, analyze the patent examiner's past examination trends and perform optimization to increase the examination pass rate. For example, the format generation unit can incorporate the examiner's preferred expressions and structures. The format generation unit can also provide advice to increase the success rate of patent applications based on the patent examiner's examination trends. Furthermore, the format generation unit can optimize the content of patent application documents based on the analysis of examination trends. This allows the patent examiner's past examination trends to be analyzed and optimization to increase the examination pass rate.

[0043] The format generation unit can refer to the user's past application history when generating an application format and provide a consistent format. For example, the format generation unit can generate new application documents based on the format of past application documents. The format generation unit can also optimize the content of patent application documents based on the user's application history. Furthermore, the format generation unit can analyze the user's application history and provide advice to increase the success rate of patent applications. This provides a consistent format based on the user's past application history.

[0044] In addition to generating application formats, the format generation unit can also automatically generate related documents required for patent applications (e.g., inventor's certificates and technical documents). In addition to generating application formats, the format generation unit can also automatically generate related documents required for patent applications (e.g., inventor's certificates and technical documents). For example, an inventor's certificate can be automatically created and attached to the application documents. The format generation unit can also generate related documents to complement the contents of the patent application documents based on the technical documents. Furthermore, the format generation unit can analyze the contents of the related documents and provide advice to increase the success rate of the patent application. In this way, the related documents required for the patent application are automatically generated.

[0045] The collaboration unit can refer to the user's past invention history and recommend the most suitable patent office. The collaboration unit, for example, refers to the user's past invention history and recommends the most suitable patent office. For example, it can recommend patent offices with specialized knowledge based on past invention fields and success stories. The collaboration unit can also select a patent office that will increase the success rate of patent applications based on the user's invention history. Furthermore, the collaboration unit can analyze the user's invention history and provide advice for optimizing collaboration with patent offices. This allows the most suitable patent office to be recommended based on the user's past invention history.

[0046] The collaboration unit can optimize collaboration with patent offices by referencing the user's past application history. The collaboration unit, for example, can optimize collaboration with patent offices by referencing the user's past application history. For example, the collaboration unit can optimize collaboration with patent offices based on the format and content of past application documents. The collaboration unit can also provide advice for optimizing communication with patent offices based on the user's application history. Furthermore, the collaboration unit can analyze the user's application history and propose specific measures to ensure smooth collaboration with patent offices. This optimizes collaboration with patent offices based on the user's past application history.

[0047] The collaboration unit can optimize collaboration with patent offices by referencing the user's past application history. The collaboration unit, for example, can optimize collaboration with patent offices by referencing the user's past application history. For example, the collaboration unit can optimize collaboration with patent offices based on the format and content of past application documents. The collaboration unit can also provide advice for optimizing communication with patent offices based on the user's application history. Furthermore, the collaboration unit can analyze the user's application history and propose specific measures to ensure smooth collaboration with patent offices. This optimizes collaboration with patent offices based on the user's past application history.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When finalizing a user's patent content, the patent content finalization system can refer to the user's past invention history and provide advice based on past successes and failures. For example, it can analyze the factors that led to success or failure in past patent applications and reflect them in the current patent content. The patent information learning unit can also suggest improvements to the patent content or additional technical features based on the user's invention history. Furthermore, the patent information learning unit can analyze the user's invention history and provide advice to increase the success rate of patent applications. This allows advice to be provided based on the user's past invention history.

[0050] In addition to learning patent information, the patent content filling system can also learn the patent information of competitors, allowing it to fill in the patent content while conducting competitive analysis. For example, it analyzes the technical features and market trends of competitors' patents and reflects these in the patent content. The patent information learning unit can also evaluate the novelty and inventiveness of the patent content based on competitors' patent information. Furthermore, the patent information learning unit can analyze competitors' patent information and suggest improvements to the patent content or additional technical features. This allows the patent content to be filled in based on competitors' patent information.

[0051] The patent content filling system can customize the way patent content is filled according to the user's field of expertise, appropriately reflecting technical terminology and technical details. For example, for a user in the medical field, medical terminology is used and technical details are explained in detail. The patent information learning unit can also provide technical background information according to the user's field of expertise. Furthermore, the patent information learning unit can suggest improvements to the patent content or additional technical features specific to the user's field of expertise. This allows the patent content to be filled according to the user's field of expertise.

[0052] The patented content filling system analyzes LINE dialogue history through a dialogue unit, learns the user's question patterns and preferences, and can provide more personalized responses. For example, it can provide detailed information on topics that the user frequently asks about. The dialogue unit can also generate responses based on the user's dialogue history in accordance with the user's interests and concerns. Furthermore, the dialogue unit can analyze the user's question patterns and quickly provide the information the user seeks. This allows for personalized responses to be provided based on the user's question patterns and preferences.

[0053] The patent content filling system can provide patent-related news and trend information in real time during the dialogue through the dialogue unit, keeping the user's knowledge up to date. For example, the latest patent application information and technology trends can be automatically displayed. The dialogue unit can also suggest improvements to the patent content and additional technical features to the user based on patent-related news. Furthermore, the dialogue unit can analyze patent-related trend information and provide the user with advice on how to increase the success rate of patent applications. This keeps the user's knowledge up to date.

[0054] The patent content filling system can extract design features from multimedia information by analyzing audio and video data in addition to image analysis through the image judgment unit. For example, it can analyze explanatory videos of designs and extract features. The image judgment unit can also provide technical details of the design based on audio data. Furthermore, the image judgment unit can suggest design improvements and highly patentable features based on the results of the analysis of audio and video data. In this way, design features are extracted by analyzing audio and video data.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The patent information learning unit learns patent information. For example, it collects patent documents published by the Patent Office and uses a large-scale language model (LLM) to learn from them. It can also analyze patent classification information and extract features of patent content. It can also evaluate the novelty and inventive step of patents based on patent application information. Step 2: The dialogue unit engages in dialogue with the user. For example, it uses LINE as an interface to respond to questions from the user. It can also generate questions to flesh out the patent content based on the user's input. It can also analyze the user's dialogue history and learn the user's question patterns and preferences. Step 3: The image judgment unit judges images related to the design. For example, Gemini is used to analyze design images uploaded by users. Design features can also be extracted based on the image analysis results. Furthermore, 3D models and CAD data can be analyzed to provide more detailed design information. Step 4: The format generation unit generates a format for a patent application. For example, if the certainty of the patent content increases and it is determined that the application can be filed, the documents for the patent application are automatically generated. It is also possible to analyze the past examination trends of patent examiners and perform optimization to increase the pass rate. Furthermore, it is possible to customize the application format to meet the requirements of patent offices in different countries and regions. Step 5: The collaboration unit collaborates with patent offices. For example, if the user does not file the patent themselves, it will request the patent office to apply. It can also refer to the user's past invention history and recommend the most suitable patent office. It can also analyze the user's emotional state and contact the patent office at the optimal time.

[0057] (Example 2) The patent content refinement system according to an embodiment of the present invention uses an LLM that has learned patent information published by the Japan Patent Office to interactively refine patent content. The system allows users to refine the details of the patent content using LINE as an interface. For design-related content, the system uses Gemini to evaluate images and output the necessary information. When the accuracy of the patent content increases and it is determined that the patent can be applied for, it can be output in a patent application format. Furthermore, if the user does not wish to apply for the patent themselves, the system can also request the application in cooperation with a patent office (for a fee). This allows the user to easily refine the patent content and efficiently progress through the process leading up to the application.

[0058] A patent content filling system according to an embodiment includes a patent information learning unit, a dialogue unit, an image determination unit, a format generation unit, and a linking unit. The patent information learning unit learns patent information. For example, the patent information learning unit collects patent documents published by the Japan Patent Office and learns them using a large-scale language model (LLM). The patent information learning unit can also analyze patent classification information and extract features of patent content. The patent information learning unit can also evaluate the novelty and inventive step of patents based on patent application information. The dialogue unit interacts with users. For example, the dialogue unit responds to questions from users using LINE as an interface. The dialogue unit can also generate questions to clarify patent content based on user input. The dialogue unit can also analyze users' dialogue history and learn their question patterns and preferences. The image determination unit determines images related to designs. For example, the image determination unit uses Gemini to analyze design images uploaded by users. The image determination unit can also extract design features based on the image analysis results. Furthermore, the image assessment unit can analyze 3D models and CAD data to provide more detailed design information. The format generation unit generates a format for a patent application. For example, the format generation unit automatically generates patent application documents when the accuracy of the patent content is high and it is determined that the application can be filed. The format generation unit can also analyze the patent examiner's past examination trends and perform optimization to increase the pass rate. Furthermore, the format generation unit can customize the application format to meet the requirements of patent offices in different countries and regions. The collaboration unit collaborates with patent offices. For example, the collaboration unit requests an application from a patent office if the user does not file the application themselves. The collaboration unit can also refer to the user's past invention history and recommend the most appropriate patent office. Furthermore, the collaboration unit can analyze the user's emotional state and contact a patent office at the optimal time. As a result, the patent content finalization system according to the embodiment allows users to easily finalize patent content and efficiently proceed through the process to filing.For example, users can use LINE to finalize the details of patents, and design patent details can be easily finalized through image analysis. Furthermore, patent application documents can be automatically generated, facilitating smooth collaboration with patent offices.

[0059] In addition to learning patent information, the patent information learning unit also learns related academic papers and technical blogs, allowing it to refine patent content based on a broader range of knowledge. For example, in addition to learning patent information, the patent information learning unit also learns related academic papers and technical blogs to refine patent content. For example, it references both patent information and academic papers to supplement technical details and background information. The patent information learning unit can also analyze the content of academic papers and technical blogs to evaluate the novelty and inventiveness of the patent content. Furthermore, the patent information learning unit can suggest improvements to patent content and additional technical features based on information from academic papers and technical blogs. This allows patent content to be refined based on a broader range of knowledge.

[0060] The patent information learning unit can refer to the user's past invention history and provide advice based on past successes and failures. The patent information learning unit, for example, can refer to the user's past invention history and provide advice based on past successes and failures. For example, the patent information learning unit can analyze the factors that led to success or failure in past patent applications and reflect these in the content of the current patent. The patent information learning unit can also suggest improvements to the patent content or additional technical features based on the user's invention history. Furthermore, the patent information learning unit can analyze the user's invention history and provide advice to increase the success rate of patent applications. This allows advice to be provided based on the user's past invention history.

[0061] The patent information learning unit can use the emotion estimation function to analyze the user's emotional state and generate questions that the user is most interested in. For example, the patent information learning unit can use the emotion estimation function to analyze the user's emotional state in real time and generate questions that the user is most interested in. For example, if the user is excited, the patent information learning unit can ask questions about technical details. The patent information learning unit can also generate questions to promote the concretization of patent content based on the user's emotional state. Furthermore, the patent information learning unit can analyze the user's emotional state and provide questions that correspond to the user's interests and concerns. In this way, questions based on the user's emotional state are generated.

[0062] In addition to learning patent information, the patent information learning unit can also learn the patent information of competitors, and refine the patent content while conducting competitive analysis. For example, in addition to learning patent information, the patent information learning unit can also learn the patent information of competitors, and refine the patent content while conducting competitive analysis. For example, the patent information learning unit can analyze the technical features and market trends of competitors' patents and reflect these in the patent content. The patent information learning unit can also evaluate the novelty and inventiveness of the patent content based on the competitors' patent information. Furthermore, the patent information learning unit can analyze the competitors' patent information and propose improvements to the patent content or additional technical features. This allows the patent content to be refined based on the competitors' patent information.

[0063] The patent information learning unit can customize how the patent content is filled in according to the user's field of expertise, appropriately reflecting technical terminology and technical details. For example, the patent information learning unit can customize how the patent content is filled in according to the user's field of expertise, appropriately reflecting technical terminology and technical details. For example, for a user in the medical field, medical terminology is used and technical details are explained in detail. The patent information learning unit can also provide technical background information according to the user's field of expertise. Furthermore, the patent information learning unit can suggest improvements to the patent content or additional technical features specific to the user's field of expertise. This allows the patent content to be filled in according to the user's field of expertise.

[0064] The patent information learning unit can use the emotion estimation function to make relaxation suggestions to reduce the user's stress level when finalizing patent content. The patent information learning unit, for example, uses the emotion estimation function to analyze the user's stress level in real time when finalizing patent content and make relaxation suggestions. For example, if the user is feeling stressed, it provides advice on how to relax. The patent information learning unit can also suggest relaxation methods to reduce the user's stress level. Furthermore, the patent information learning unit can also provide specific measures to reduce stress based on the user's emotional state. This makes relaxation suggestions to reduce the user's stress level.

[0065] The dialogue unit can analyze the LINE dialogue history, learn the user's question patterns and preferences, and provide more personalized responses. For example, the dialogue unit can analyze the LINE dialogue history, learn the user's question patterns and preferences, and provide more personalized responses. For example, the dialogue unit can provide detailed information on topics that the user frequently asks about. The dialogue unit can also generate responses based on the user's dialogue history, tailored to the user's interests and concerns. Furthermore, the dialogue unit can analyze the user's question patterns and quickly provide the information the user requests. This allows for the provision of personalized responses based on the user's question patterns and preferences.

[0066] The dialogue unit can provide patent-related news and trend information in real time during the dialogue, thereby keeping the user's knowledge up to date. For example, the dialogue unit can provide patent-related news and trend information in real time during the dialogue, thereby keeping the user's knowledge up to date. For example, the dialogue unit can automatically display the latest patent application information and technology trends. The dialogue unit can also suggest improvements to patent content and additional technical features to the user based on patent-related news. Furthermore, the dialogue unit can analyze patent-related trend information and provide the user with advice to increase the success rate of patent applications. This keeps the user's knowledge up to date.

[0067] The dialogue unit is also compatible with messaging apps other than LINE (e.g., WhatsApp and WeChat), improving user convenience. The dialogue unit is also compatible with messaging apps other than LINE (e.g., WhatsApp and WeChat), improving user convenience. For example, it is possible to integrate multiple messaging apps and access the patent content from any app. The dialogue unit can also provide an interface tailored to the characteristics of each messaging app. Furthermore, the dialogue unit can analyze the user's messaging app usage history and recommend the most suitable app. This improves user convenience.

[0068] The dialogue unit can automatically search for patent documents and technical documents that the user wants to refer to during the dialogue and provide links to them. For example, the dialogue unit can automatically search for patent documents and technical documents that the user wants to refer to during the dialogue and provide links to them. For example, when a user asks a question about a specific technology, links to related patent documents can be displayed. The dialogue unit can also analyze the content of the user's question and recommend the most appropriate patent documents and technical documents. Furthermore, the dialogue unit can provide summaries of patent documents and technical documents to enable the user to quickly obtain information. This automatically provides the patent documents and technical documents that the user wants to refer to.

[0069] The dialogue unit can use the emotion estimation function to detect doubts and anxieties the user feels during a dialogue in real time and provide appropriate support. The dialogue unit can, for example, use the emotion estimation function to detect doubts and anxieties the user feels during a dialogue in real time and provide appropriate support. For example, if the user feels anxious, the dialogue unit can provide a detailed explanation or additional information. The dialogue unit can also generate advice for providing appropriate support based on the user's emotional state. Furthermore, the dialogue unit can suggest specific measures to resolve the user's doubts and anxieties. In this way, doubts and anxieties the user feels during a dialogue can be detected in real time and appropriate support can be provided.

[0070] In addition to image analysis, the image judgment unit can also analyze 3D models and CAD data to provide more detailed design information. For example, the image judgment unit can analyze the shape and dimensions of the 3D model to extract design features. The image judgment unit can also provide technical details of the design based on the CAD data. Furthermore, the image judgment unit can suggest design improvements and patentable features based on the analysis results of the 3D model and CAD data. This allows the analysis of the 3D model and CAD data to provide more detailed design information.

[0071] The image judgment unit can automatically suggest design improvements and highly patentable features based on the image analysis results. The image judgment unit automatically suggests design improvements and highly patentable features based on the image analysis results, for example. For example, it suggests improvements to the shape or color of the design. The image judgment unit can also extract highly patentable features and provide advice to increase the success rate of patent applications. Furthermore, the image judgment unit can analyze the technical details of the design and specifically indicate highly patentable features. This allows design improvements and highly patentable features to be automatically suggested.

[0072] The image judgment unit can use the emotion estimation function to analyze the emotion a user feels toward a design and provide positive feedback. For example, the image judgment unit can use the emotion estimation function to analyze the emotion a user feels toward a design in real time and provide positive feedback. For example, if the user is satisfied with the design, the image judgment unit can provide a compliment. The image judgment unit can also suggest improvements to the design or additional technical features based on the user's emotional state. Furthermore, the image judgment unit can analyze the user's emotional state and provide specific criteria for evaluating the design. This allows positive feedback to be provided based on the emotion a user feels toward the design.

[0073] The image judgment unit can analyze audio and video data in addition to image analysis to extract design features from multimedia information. The image judgment unit can, for example, analyze audio and video data in addition to image analysis to extract design features from multimedia information. For example, it can analyze an explanatory video of a design and extract features. The image judgment unit can also provide technical details of the design based on audio data. Furthermore, the image judgment unit can suggest design improvements and patentable features based on the results of the analysis of audio and video data. In this way, design features are extracted by analyzing audio and video data.

[0074] The image judgment unit can perform market suitability and competitive analysis of the design based on the analysis results and provide feedback to the user. The image judgment unit can, for example, perform market suitability and competitive analysis of the design based on the analysis results and provide feedback to the user. For example, it can compare the design with market needs and competing products. The image judgment unit can also evaluate the market suitability of the design and provide advice to increase the success rate of the patent application. Furthermore, the image judgment unit can suggest improvements to the design or additional technical features based on the results of the competitive analysis. This provides feedback based on the market suitability and competitive analysis of the design.

[0075] The image assessment unit can use the emotion estimation function to monitor the user's level of satisfaction with the design in real time and make suggestions for improvement. The image assessment unit can, for example, use the emotion estimation function to monitor the user's level of satisfaction with the design in real time and make suggestions for improvement. For example, if the user is not satisfied, the image assessment unit can suggest improvements to the design. The image assessment unit can also provide technical details of the design based on the user's emotional state. Furthermore, the image assessment unit can analyze the user's level of satisfaction and provide specific criteria for evaluating the design. This allows the user's level of satisfaction with the design to be monitored in real time and suggestions for improvement to be made.

[0076] In addition to automatically generating application formats, the format generation unit can analyze the patent examiner's past examination trends and perform optimization to increase the examination pass rate. In addition to automatically generating application formats, the format generation unit can, for example, analyze the patent examiner's past examination trends and perform optimization to increase the examination pass rate. For example, the format generation unit can incorporate the examiner's preferred expressions and structures. The format generation unit can also provide advice to increase the success rate of patent applications based on the patent examiner's examination trends. Furthermore, the format generation unit can optimize the content of patent application documents based on the analysis of examination trends. This allows the patent examiner's past examination trends to be analyzed and optimization to increase the examination pass rate.

[0077] The format generation unit can refer to the user's past application history when generating an application format and provide a consistent format. For example, the format generation unit can generate new application documents based on the format of past application documents. The format generation unit can also optimize the content of patent application documents based on the user's application history. Furthermore, the format generation unit can analyze the user's application history and provide advice to increase the success rate of patent applications. This provides a consistent format based on the user's past application history.

[0078] The format generation unit can use the emotion estimation function to provide advice to reduce the anxiety the user feels about the content of the application documents. For example, the format generation unit uses the emotion estimation function to analyze the anxiety the user feels about the content of the application documents in real time and provide advice. For example, if the user feels anxious, the format generation unit can suggest specific improvements. The format generation unit can also provide advice to optimize the content of the application documents based on the user's emotional state. Furthermore, the format generation unit can also suggest specific measures to reduce the user's anxiety. This reduces the anxiety the user feels about the content of the application documents.

[0079] In addition to generating application formats, the format generation unit can also automatically generate related documents required for patent applications (e.g., inventor's certificates and technical documents). In addition to generating application formats, the format generation unit can also automatically generate related documents required for patent applications (e.g., inventor's certificates and technical documents). For example, an inventor's certificate can be automatically created and attached to the application documents. The format generation unit can also generate related documents to complement the contents of the patent application documents based on the technical documents. Furthermore, the format generation unit can analyze the contents of the related documents and provide advice to increase the success rate of the patent application. In this way, the related documents required for the patent application are automatically generated.

[0080] The format generation unit can use the emotion estimation function to detect in real time any doubts or anxieties the user may have during the application process and provide appropriate support. The format generation unit can, for example, use the emotion estimation function to detect in real time any doubts or anxieties the user may have during the application process and provide appropriate support. For example, if the user is feeling anxious, the format generation unit can provide detailed explanations or additional information. The format generation unit can also provide advice for optimizing the application process based on the user's emotional state. Furthermore, the format generation unit can suggest specific measures to resolve the user's doubts and anxieties. In this way, any doubts or anxieties the user may have during the application process can be detected in real time and appropriate support can be provided.

[0081] The collaboration unit can refer to the user's past invention history and recommend the most suitable patent office. The collaboration unit, for example, refers to the user's past invention history and recommends the most suitable patent office. For example, it can recommend patent offices with specialized knowledge based on past invention fields and success stories. The collaboration unit can also select a patent office that will increase the success rate of patent applications based on the user's invention history. Furthermore, the collaboration unit can analyze the user's invention history and provide advice for optimizing collaboration with patent offices. This allows the most suitable patent office to be recommended based on the user's past invention history.

[0082] The collaboration unit can use the emotion estimation function to analyze the user's emotional state and contact the patent office at the optimal timing. The collaboration unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and contact the patent office at the optimal timing. For example, the collaboration unit can contact the patent office when the user is relaxed. The collaboration unit can also provide advice to optimize communication with the patent office based on the user's emotional state. Furthermore, the collaboration unit can analyze the user's emotional state and suggest specific measures to ensure smooth communication with the patent office. As a result, the patent office is contacted at the optimal timing based on the user's emotional state.

[0083] The collaboration unit can optimize collaboration with patent offices by referencing the user's past application history. The collaboration unit, for example, can optimize collaboration with patent offices by referencing the user's past application history. For example, the collaboration unit can optimize collaboration with patent offices based on the format and content of past application documents. The collaboration unit can also provide advice for optimizing communication with patent offices based on the user's application history. Furthermore, the collaboration unit can analyze the user's application history and propose specific measures to ensure smooth collaboration with patent offices. This optimizes collaboration with patent offices based on the user's past application history.

[0084] The collaboration unit can use the emotion estimation function to analyze the user's emotional state and optimize communication with the patent office. For example, the collaboration unit can use the emotion estimation function to analyze the user's emotional state in real time and optimize communication with the patent office. For example, the collaboration unit can contact the patent office when the user is relaxed. The collaboration unit can also provide advice for smooth communication with the patent office based on the user's emotional state. Furthermore, the collaboration unit can analyze the user's emotional state and suggest specific measures for optimizing communication with the patent office. This optimizes communication with the patent office based on the user's emotional state.

[0085] The collaboration unit can optimize collaboration with patent offices by referencing the user's past application history. The collaboration unit, for example, can optimize collaboration with patent offices by referencing the user's past application history. For example, the collaboration unit can optimize collaboration with patent offices based on the format and content of past application documents. The collaboration unit can also provide advice for optimizing communication with patent offices based on the user's application history. Furthermore, the collaboration unit can analyze the user's application history and propose specific measures to ensure smooth collaboration with patent offices. This optimizes collaboration with patent offices based on the user's past application history.

[0086] The collaboration unit can use the emotion estimation function to analyze the user's emotional state and optimize communication with the patent office. For example, the collaboration unit can use the emotion estimation function to analyze the user's emotional state in real time and optimize communication with the patent office. For example, the collaboration unit can contact the patent office when the user is relaxed. The collaboration unit can also provide advice for smooth communication with the patent office based on the user's emotional state. Furthermore, the collaboration unit can analyze the user's emotional state and suggest specific measures for optimizing communication with the patent office. This optimizes communication with the patent office based on the user's emotional state.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] When finalizing a user's patent content, the patent content finalization system can refer to the user's past invention history and provide advice based on past successes and failures. For example, it can analyze the factors that led to success or failure in past patent applications and reflect them in the current patent content. The patent information learning unit can also suggest improvements to the patent content or additional technical features based on the user's invention history. Furthermore, the patent information learning unit can analyze the user's invention history and provide advice to increase the success rate of patent applications. This allows advice to be provided based on the user's past invention history.

[0089] In addition to learning patent information, the patent content filling system can also learn the patent information of competitors, allowing it to fill in the patent content while conducting competitive analysis. For example, it analyzes the technical features and market trends of competitors' patents and reflects these in the patent content. The patent information learning unit can also evaluate the novelty and inventiveness of the patent content based on competitors' patent information. Furthermore, the patent information learning unit can analyze competitors' patent information and suggest improvements to the patent content or additional technical features. This allows the patent content to be filled in based on competitors' patent information.

[0090] The patent content filling system can customize the way patent content is filled according to the user's field of expertise, appropriately reflecting technical terminology and technical details. For example, for a user in the medical field, medical terminology is used and technical details are explained in detail. The patent information learning unit can also provide technical background information according to the user's field of expertise. Furthermore, the patent information learning unit can suggest improvements to the patent content or additional technical features specific to the user's field of expertise. This allows the patent content to be filled according to the user's field of expertise.

[0091] The patent content filling system can use its emotion estimation function to analyze the user's emotional state and generate questions that interest the user most. For example, if the user is excited, it will ask questions about technical details. The patent information learning unit can also generate questions to promote the concretization of patent content based on the user's emotional state. Furthermore, the patent information learning unit can analyze the user's emotional state and provide questions that correspond to the user's interests and concerns. This allows questions to be generated based on the user's emotional state.

[0092] The patent content filling system can use the emotion estimation function to make relaxation suggestions to reduce the user's stress level when filling in the patent content. For example, if the user is feeling stressed, it can provide advice on how to relax. The patent information learning unit can also suggest relaxation methods to reduce the user's stress level. Furthermore, the patent information learning unit can also provide specific measures to reduce stress based on the user's emotional state. This allows for relaxation suggestions to be made to reduce the user's stress level.

[0093] The patented content filling system analyzes LINE dialogue history through a dialogue unit, learns the user's question patterns and preferences, and can provide more personalized responses. For example, it can provide detailed information on topics that the user frequently asks about. The dialogue unit can also generate responses based on the user's dialogue history in accordance with the user's interests and concerns. Furthermore, the dialogue unit can analyze the user's question patterns and quickly provide the information the user seeks. This allows for personalized responses to be provided based on the user's question patterns and preferences.

[0094] The patent content filling system can provide patent-related news and trend information in real time during the dialogue through the dialogue unit, keeping the user's knowledge up to date. For example, the latest patent application information and technology trends can be automatically displayed. The dialogue unit can also suggest improvements to the patent content and additional technical features to the user based on patent-related news. Furthermore, the dialogue unit can analyze patent-related trend information and provide the user with advice on how to increase the success rate of patent applications. This keeps the user's knowledge up to date.

[0095] The patent content filling system uses an emotion estimation function to detect doubts and anxieties felt by the user during a conversation in real time and provide appropriate support. For example, if the user feels anxious, detailed explanations and additional information are provided. The dialogue unit can also generate advice to provide appropriate support based on the user's emotional state. Furthermore, the dialogue unit can suggest specific measures to resolve the user's doubts and anxieties. In this way, doubts and anxieties felt by the user during a conversation can be detected in real time and appropriate support can be provided.

[0096] The patent content filling system can use the emotion estimation function to analyze the user's emotions toward the design and provide positive feedback. For example, if the user is satisfied with the design, it can provide a compliment. The image evaluation unit can also suggest improvements to the design or additional technical features based on the user's emotional state. Furthermore, the image evaluation unit can analyze the user's emotional state and provide specific criteria for evaluating the design. This allows for positive feedback based on the user's emotions toward the design.

[0097] The patent content filling system can extract design features from multimedia information by analyzing audio and video data in addition to image analysis through the image judgment unit. For example, it can analyze explanatory videos of designs and extract features. The image judgment unit can also provide technical details of the design based on audio data. Furthermore, the image judgment unit can suggest design improvements and highly patentable features based on the results of the analysis of audio and video data. In this way, design features are extracted by analyzing audio and video data.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The patent information learning unit learns patent information. For example, it collects patent documents published by the Patent Office and uses a large-scale language model (LLM) to learn from them. It can also analyze patent classification information and extract features of patent content. It can also evaluate the novelty and inventive step of patents based on patent application information. Step 2: The dialogue unit engages in dialogue with the user. For example, it uses LINE as an interface to respond to questions from the user. It can also generate questions to flesh out the patent content based on the user's input. It can also analyze the user's dialogue history and learn the user's question patterns and preferences. Step 3: The image judgment unit judges images related to the design. For example, Gemini is used to analyze design images uploaded by users. Design features can also be extracted based on the image analysis results. Furthermore, 3D models and CAD data can be analyzed to provide more detailed design information. Step 4: The format generation unit generates a format for a patent application. For example, if the certainty of the patent content increases and it is determined that the application can be filed, the documents for the patent application are automatically generated. It is also possible to analyze the past examination trends of patent examiners and perform optimization to increase the pass rate. Furthermore, it is possible to customize the application format to meet the requirements of patent offices in different countries and regions. Step 5: The collaboration unit collaborates with patent offices. For example, if the user does not file the patent themselves, it will request the patent office to apply. It can also refer to the user's past invention history and recommend the most suitable patent office. It can also analyze the user's emotional state and contact the patent office at the optimal time.

[0100] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0101] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with 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 specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 WAN and / or a LAN.

[0106] 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.

[0107] 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 audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the 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.

[0111] 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.

[0112] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with 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 using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 WAN and / or a LAN.

[0121] 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.

[0122] 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 audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0126] 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.

[0127] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with 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 using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 WAN and / or a LAN.

[0136] 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] 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 audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. 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.

[0141] Fig. 8 shows an example of the 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.

[0142] 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.

[0143] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with 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 using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] 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.

[0157] 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 USB (Universal Serial Bus) 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.

[0158] Alternatively, 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 in response to a request from the data processing device 12.

[0159] 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.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0162] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 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. a patent information learning unit that learns patent information; a dialogue unit that dialogues with a user; an image determination unit that determines images related to a design; a format generation unit that generates a format for a patent application; and a collaboration department that collaborates with patent offices. A system characterized by:

2. The patent information learning unit In addition to studying patent information, students will also study related academic papers and technical blogs to refine patent content based on a broader range of knowledge. The system of claim 1 .

3. The dialogue unit Analyzes conversation history across messaging apps to learn user question patterns and preferences to provide more personalized responses The system of claim 1 .

4. The image determination unit In addition to image analysis, 3D models and CAD data are also analyzed to provide more detailed design information. The system of claim 1 .

5. The format generation unit In addition to automatically generating application formats, we analyze the past examination trends of patent examiners and optimize the process to increase the pass rate. The system of claim 1 .

6. The linking unit is Using emotion estimation function, analyze the user's emotional state and contact the patent office at the optimal time The system of claim 1 .

7. The patent information learning unit Using emotion estimation, we analyze the user's emotional state and generate questions that interest the user most. The system of claim 1 .

Images