system

Abstract

The system according to this embodiment aims to lower the hurdles for filing patent applications. [Solution] The system according to the embodiment comprises a reception unit, a judgment unit, a creation unit, and a provision unit. The reception unit receives the user's idea. The judgment unit determines the patentability based on the idea received by the reception unit. The creation unit creates patent application documents if the judgment unit determines that the idea is patentable. The provision unit provides the patent application documents created by the creation unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of patent application is highly specialized and costly, so the hurdle for patent application is high.

[0005] The system according to the embodiment aims to lower the hurdle for patent application.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a determination unit, a creation unit, and a provision unit. The reception unit receives the user's idea. The determination unit determines the patentability based on the idea received by the reception unit. The creation unit creates patent application documents when it is determined by the determination unit that patent application is possible. The provision unit provides the patent application documents created by the creation unit. [Effects of the Invention]

[0007] The system according to this embodiment can lower the hurdle for filing a patent application. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The patent application support system according to an embodiment of the present invention is a system that simplifies the patent application process and reduces costs by using a generating AI that has learned from publicly available patent information. When a user communicates their idea to the generating AI, the generating AI, having learned from publicly available patent information, analyzes the user's idea and determines whether it is patentable. If the patent application support system determines that it is patentable, it creates the necessary documents for the application. The generating AI automatically collects the information necessary for the patent application and creates the patent application documents. This system simplifies the patent application process and reduces costs. Traditionally, it was expensive to hire a patent attorney, but by utilizing the generating AI, the hurdles to patent application are lowered, making it easy for anyone to obtain a patent. Because the generating AI constantly learns from publicly available patent information, it makes decisions based on the latest patent information. This improves the success rate of patent applications, and users can apply for patents with confidence. This system is extremely beneficial for individuals and companies that wish to apply for patents, and by simplifying the patent application process, new ideas and technologies are more likely to be patented, promoting innovation. By utilizing generative AI, the patent application process is accelerated, allowing for the completion of patent applications in a shorter timeframe. This enables users to quickly obtain patents and expand their businesses. As a result, the patent application support system can efficiently receive user ideas, assess patentability, and create and provide patent application documents.

[0029] The patent application support system according to the embodiment comprises a reception unit, a judgment unit, a creation unit, and a provision unit. The reception unit receives ideas from users. User ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. For example, users can submit ideas through an input form. The reception unit can also accept voice input and image input. For example, a user can explain an idea by voice, and the system can convert it into text and accept it. Alternatively, a user can submit a handwritten idea as an image, and the system can analyze and accept it. The judgment unit uses a generation AI to determine the patentability of the ideas received by the reception unit. The determination of patentability is based on criteria such as novelty, inventive step, and industrial applicability, but is not limited to these criteria. For example, the judgment unit can use the generation AI to learn from published patent information and compare the user's idea with existing patent information to determine patentability. The judgment unit can also use the generation AI to automatically update the patentability criteria and make decisions based on the latest patent information. Furthermore, the Judgment Unit can provide feedback to the user regarding the patentability judgment results of the Generating AI and offer advice to help the user revise their idea. The Creation Unit creates the patent application documents if the Judgment Unit determines that the idea is patentable. The Creation Unit uses the Generating AI to automatically collect the information necessary for the patent application and create the application documents. For example, the Creation Unit can automatically generate documents such as claims, specifications, and drawings based on the user's idea. The Creation Unit can also use the Generating AI to check the format and content of the patent application documents and make revisions as needed. In addition, the Creation Unit can use the Generating AI to optimize the patent application document creation process and create documents efficiently. The Delivery Unit provides the patent application documents created by the Creation Unit to the user. The Delivery Unit can provide the documents by methods such as email, cloud storage, or physical document delivery. For example, the Delivery Unit can send the created patent application documents to the user in PDF format. The Delivery Unit can also upload the documents to cloud storage so that the user can download them. Furthermore, the Delivery Unit can also mail physical documents.This enables the patent application support system to efficiently receive user ideas, assess their patentability, and create and provide patent application documents.

[0030] The reception desk accepts user ideas. User ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. The reception desk allows users to submit ideas through input forms, for example. It can also accept voice and image input. For example, a user can explain their idea verbally, and the system will convert it to text for submission. Alternatively, a user can submit a handwritten idea as an image, which will then be analyzed and submitted. Specifically, the reception desk provides an interface accessible to users through a website or mobile application, making it easy for users to input ideas. In the case of voice input, speech recognition technology is used to convert the user's speech into text, and natural language processing technology is used to analyze the content of the idea. In the case of image input, image recognition technology is used to analyze handwritten characters and diagrams and convert them into text data. This allows users to submit ideas in a variety of ways, and the reception desk can process this data efficiently. Furthermore, the reception desk has a function to automatically classify the content of the ideas submitted by users and allocate them to the relevant categories. For example, ideas can be organized based on categories such as business ideas, technical ideas, and creative ideas, streamlining subsequent processing. The reception department also manages a database to save the content of ideas submitted by users, making them available for later reference. This allows users to review and revise ideas they have previously submitted.

[0031] The judgment unit uses a generative AI to determine patentability based on the ideas received by the reception unit. Patentability is determined based on criteria such as novelty, inventive step, and industrial applicability, but is not limited to these examples. For example, the judgment unit can have the generative AI learn from publicly available patent information and compare the user's idea with existing patent information to determine patentability. The judgment unit can also have the generative AI automatically update the patentability criteria and make judgments based on the latest patent information. Furthermore, the judgment unit can have the generative AI provide feedback to the user regarding the patentability judgment results and offer advice for the user to revise their idea. Specifically, the generative AI evaluates novelty by searching for relevant patent documents in a patent database and comparing them with the user's idea. For evaluating inventive step, the generative AI analyzes technological advancements and market trends to determine the degree of inventiveness of the user's idea compared to existing technology. Regarding industrial applicability, the generative AI evaluates market demand and the practicality of the technology to determine whether the user's idea is actually usable in industry. This allows the judgment unit to determine with high accuracy whether the user's idea is suitable for patent application. Furthermore, the judgment unit provides an interface for the generating AI to provide easily understandable feedback to the user regarding the patentability assessment results. For example, it displays the patentability evaluation results for the user's submitted idea in graphs and charts, and suggests specific areas for modification and improvement. The generating AI also provides information and examples that can be used as reference when the user modifies their idea, supporting the user in creating better ideas. In this way, the judgment unit can provide support to the user in optimizing their idea for patent application.

[0032] The creation unit prepares patent application documents if the judgment unit determines that a patent application is possible. The creation unit uses a generation AI to automatically collect the necessary information for the patent application and prepare the application documents. For example, the creation unit automatically generates documents such as claims, specifications, and drawings based on the user's idea. The creation unit can also have the generation AI check the format and content of the patent application documents and make corrections as needed. Furthermore, the creation unit can have the generation AI optimize the patent application document preparation process, enabling efficient document creation. Specifically, the generation AI analyzes the user's idea and extracts keywords and phrases to clearly define the claims. For the specification, the generation AI automatically generates technical details and examples to concretely explain the user's idea. For the drawings, the generation AI generates appropriate diagrams based on the user's idea and attaches them to the patent application documents. This reduces the user's burden and allows the creation of patent application documents quickly and accurately. Additionally, the creation unit has the generation AI check the format and content of the patent application documents to ensure they meet the requirements of the patent office. For example, it checks whether the claims are properly written, whether the specification is sufficiently detailed, and whether the drawings are accurately drawn. If necessary, the generating AI automatically makes corrections to improve the quality of the patent application documents. Furthermore, the creation unit uses algorithms to optimize the patent application document creation process and efficiently produce documents. This allows the creation unit to support users in filing patent applications quickly.

[0033] The provisioning department provides users with patent application documents prepared by the creation department. The provisioning department can provide documents via methods such as email, cloud storage, or physical mail. For example, the provisioning department can send the prepared patent application documents to the user in PDF format. Alternatively, the provisioning department can upload the documents to cloud storage for user download. Furthermore, the provisioning department can also mail physical documents. Specifically, the provisioning department provides an interface that allows users to select their preferred method of delivery, improving user convenience. For email delivery, the provisioning department attaches the prepared patent application documents in PDF format and sends them to the user. For cloud storage delivery, the provisioning department uploads the prepared patent application documents to cloud storage and provides a link for user access. For physical mailing, the provisioning department prints the prepared patent application documents and mails them to the address specified by the user. This allows the provisioning department to offer users diverse methods for receiving patent application documents, enabling flexible responses to user needs. Furthermore, the provisioning department continues to support users even after providing the patent application documents. For example, if a user has questions or requests for revisions regarding patent application documents, the service department will respond promptly and provide the necessary support. The service department will also manage the history of patent application document provision, allowing users to review previously provided documents. This enables the service department to support users in smoothly navigating the patent application process and improve the overall reliability and usability of the patent application support system.

[0034] The patent application support system includes a learning unit that learns from publicly available patent information. The learning unit learns from publicly available patent information using a generating AI. Publicly available patent information includes, but is not limited to, patent publications, patent application publications, and patent examination information. For example, the learning unit collects patent publications and the generating AI analyzes and learns their contents. The learning unit can also collect patent application publication information and the generating AI analyzes and learns its contents. Furthermore, the learning unit can collect patent examination information and the generating AI analyzes and learns its contents. As a result, the learning unit improves the accuracy of its patentability determination by learning from publicly available patent information. Some or all of the above processing in the learning unit is performed using the generating AI. For example, the learning unit inputs patent publications into the generating AI, and the generating AI analyzes and learns their contents. As a result, the learning unit improves the accuracy of its patentability determination by learning from publicly available patent information.

[0035] The patent application support system includes a management unit that manages the progress of the patent application. The management unit manages the progress of the patent application. Progress management includes, but is not limited to, tracking the progress of each step of the patent application and notifying the user. For example, the management unit can display the progress of each step of the patent application on a dashboard so that the user can see the progress at a glance. The management unit can also notify the user of the progress of the patent application by email. Furthermore, the management unit can update the progress of the patent application in real time so that the user always has the latest information. In this way, by the management unit managing the progress of the patent application, the user can easily understand the status of the patent application. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit inputs the progress of the patent application into AI, and the AI ​​manages the progress. In this way, by the management unit managing the progress of the patent application, the user can easily understand the status of the patent application.

[0036] The judgment unit determines patentability based on publicly available patent information. The judgment unit uses a generating AI to determine patentability based on publicly available patent information. Publicly available patent information includes, but is not limited to, patent gazettes, published patent applications, and patent examination information. For example, the judgment unit inputs a patent gazette into the generating AI, which analyzes its contents to determine patentability. The judgment unit can also input published patent application information into the generating AI, which analyzes its contents to determine patentability. Furthermore, the judgment unit can input patent examination information into the generating AI, which analyzes its contents to determine patentability. This improves the accuracy of the judgment unit's determination by basing its determination on publicly available patent information. Some or all of the above-described processes in the judgment unit are performed using a generating AI. For example, the judgment unit inputs a patent gazette into the generating AI, which analyzes its contents to determine patentability. This improves the accuracy of the judgment unit's determination by basing its determination on publicly available patent information.

[0037] The creation unit automatically collects the information necessary for a patent application and creates the patent application documents. The creation unit uses a generative AI to automatically collect the information necessary for a patent application and create the patent application documents. Methods for collecting information include, but are not limited to, web scraping and API integration. For example, the creation unit can collect the information necessary for a patent application using web scraping. The creation unit can also collect the information necessary for a patent application using API integration. Furthermore, the creation unit can have a generative AI automatically collect the information necessary for a patent application and create the patent application documents. This improves the efficiency of patent applications by allowing the creation unit to automatically collect the information necessary for a patent application and create the patent application documents. Some or all of the above processes in the creation unit are performed using a generative AI. For example, the creation unit inputs the information necessary for a patent application into the generative AI, and the generative AI creates the patent application documents based on that information. This improves the efficiency of patent applications by allowing the creation unit to automatically collect the information necessary for a patent application and create the patent application documents.

[0038] The service provider provides the user with the prepared patent application documents. The service provider can provide the documents by methods such as email, cloud storage, or physical mail. For example, the service provider can send the prepared patent application documents to the user in PDF format. The service provider can also upload the documents to cloud storage so that the user can download them. Furthermore, the service provider can also mail physical documents. This allows the user to quickly file a patent application by providing the user with the prepared patent application documents. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the prepared patent application documents into an AI, which can then analyze the contents and provide them to the user. This allows the user to quickly file a patent application by providing the user with the prepared patent application documents.

[0039] The reception desk can analyze a user's past idea submission history and select the optimal submission method. For example, the reception desk can analyze the trends of ideas previously submitted by the user and propose the most suitable submission method. The reception desk can also prioritize suggesting submission methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can suggest the most suitable submission method for a specific time period based on the user's past submission history. In this way, the reception desk can select the most suitable submission method by analyzing the user's past idea submission history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past idea submission history into an AI, which then analyzes the data and selects the most suitable submission method. In this way, the reception desk can select the most suitable submission method by analyzing the user's past idea submission history.

[0040] The reception unit can filter ideas based on the user's current projects and areas of interest when receiving them. For example, the reception unit can prioritize receiving ideas related to the user's current projects. The reception unit can also filter and receive relevant ideas based on the user's areas of interest. Furthermore, the reception unit can receive the most suitable ideas depending on the progress of the user's projects. This allows for the priority of receiving highly relevant ideas by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's project information into an AI, which then analyzes that information and filters relevant ideas. This allows the reception unit to filter ideas based on the user's current projects and areas of interest.

[0041] The reception system can prioritize receiving highly relevant ideas by considering the user's geographical location when receiving ideas. For example, if the user is in a specific region, the reception system will prioritize receiving ideas related to that region. The reception system can also filter and receive highly relevant ideas based on the user's geographical location. Furthermore, if the user is on the move, the reception system can prioritize receiving ideas related to their current location. In this way, by considering the user's geographical location, highly relevant ideas can be prioritized. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the user's geographical location into an AI, which then analyzes that information and filters out relevant ideas. In this way, the reception system can prioritize receiving highly relevant ideas by considering the user's geographical location.

[0042] The reception department can analyze the user's social media activity when receiving ideas and accept relevant ideas. For example, the reception department can prioritize accepting ideas related to the user's areas of interest based on their social media activity. The reception department can also accept relevant ideas based on information shared by the user on social media. Furthermore, the reception department can analyze the user's social media activity and accept the most suitable ideas. This allows the reception department to prioritize accepting relevant ideas by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's social media activity data into an AI, which then analyzes the data and filters out relevant ideas. This allows the reception department to prioritize accepting relevant ideas by analyzing the user's social media activity.

[0043] The decision unit can optimize its decision algorithm by referring to past patent application data when determining patentability. The decision unit uses a generating AI to optimize the decision algorithm by referring to past patent application data. Past patent application data includes, but is not limited to, patent publications, patent application publications, and patent examination information. For example, the decision unit inputs past patent application data into the generating AI, and the generating AI analyzes that data to optimize the decision algorithm. The decision unit can also improve the accuracy of its patentability determination by referring to past patent application data. Furthermore, the decision unit can analyze past patent application data to improve the patentability determination algorithm. As a result, the decision unit improves the accuracy of its patentability determination by referring to past patent application data. Some or all of the above processing in the decision unit is performed using the generating AI. For example, the decision unit inputs past patent application data into the generating AI, and the generating AI analyzes that data to optimize the decision algorithm. As a result, the decision unit improves the accuracy of its patentability determination by referring to past patent application data.

[0044] The judgment unit can apply different judgment algorithms to each category of idea when determining patentability. The judgment unit uses generative AI to apply different judgment algorithms to each category of idea. Idea categories include, but are not limited to, technical fields, applications, and target markets. The judgment unit customizes the patentability judgment algorithm for each technical field, for example. The judgment unit can also adjust the criteria for patentability determination according to the application. Furthermore, the judgment unit can apply the optimal patentability judgment algorithm for each target market. As a result, the judgment unit improves the accuracy of patentability determination by applying different judgment algorithms to each category of idea. Some or all of the above processing in the judgment unit is performed using generative AI. For example, the judgment unit inputs idea category information into the generative AI, which analyzes the information and applies the optimal judgment algorithm. As a result, the judgment unit improves the accuracy of patentability determination by applying different judgment algorithms to each category of idea.

[0045] The judgment unit can determine the priority of patentability decisions based on the submission timing of ideas. The judgment unit uses generative AI to determine the priority of decisions based on the submission timing of ideas. The submission timing includes, but is not limited to, the submission date, submission time, and submission order. For example, the judgment unit may prioritize ideas submitted earlier. It can also postpone the evaluation of ideas submitted later. Furthermore, the judgment unit can determine the priority of patentability decisions based on the submission timing. This enables the judgment unit to make efficient patentability decisions by determining the priority of decisions based on the submission timing of ideas. Some or all of the above processing in the judgment unit is performed using generative AI. For example, the judgment unit inputs idea submission timing information into the generative AI, and the generative AI analyzes that information to determine the priority of decisions. This enables the judgment unit to make efficient patentability decisions by determining the priority of decisions based on the submission timing of ideas.

[0046] The judgment unit can improve the accuracy of its judgment by referring to relevant literature related to the idea when determining patentability. The judgment unit uses a generative AI to improve the accuracy of its judgment by referring to relevant literature related to the idea. Relevant literature includes, but is not limited to, academic papers, patent documents, and technical reports. For example, the judgment unit inputs relevant literature into the generative AI, and the generative AI analyzes its contents to improve the accuracy of the judgment. The judgment unit can also optimize its patentability judgment algorithm based on the relevant literature. Furthermore, the judgment unit can analyze the relevant literature to improve the accuracy of its patentability judgment. As a result, the judgment unit improves the accuracy of its patentability judgment by referring to relevant literature related to the idea. Some or all of the above processing in the judgment unit is performed using a generative AI. For example, the judgment unit inputs relevant literature related to the idea into the generative AI, and the generative AI analyzes its contents to improve the accuracy of the judgment. As a result, the judgment unit improves the accuracy of its patentability judgment by referring to relevant literature related to the idea.

[0047] The creation unit can optimize its creation algorithm by referring to past patent application documents when creating patent application documents. The creation unit uses a generating AI to optimize the creation algorithm by referring to past patent application documents. Past patent application documents include, but are not limited to, patent gazettes, patent application publications, and patent examination information. For example, the creation unit inputs past patent application documents into the generating AI, which analyzes their contents and optimizes the creation algorithm. The creation unit can also improve the accuracy of patent application document creation by referring to past patent application documents. Furthermore, the creation unit can analyze past patent application documents and improve the patent application document creation algorithm. As a result, the creation unit improves the accuracy of patent application document creation by referring to past patent application documents. Some or all of the above processes in the creation unit are performed using the generating AI. For example, the creation unit inputs past patent application documents into the generating AI, which analyzes their contents and optimizes the creation algorithm. As a result, the creation unit improves the accuracy of patent application document creation by referring to past patent application documents.

[0048] The creation unit can apply different creation algorithms to each idea category when creating patent application documents. The creation unit uses generative AI to apply different creation algorithms to each idea category. Idea categories include, but are not limited to, technical fields, applications, and target markets. The creation unit can, for example, customize the patent application document creation algorithm for each technical field. The creation unit can also adjust the criteria for creating patent application documents according to the application. Furthermore, the creation unit can apply the optimal patent application document creation algorithm for each target market. As a result, the creation unit improves the accuracy of patent application document creation by applying different creation algorithms to each idea category. Some or all of the above processes in the creation unit are performed using generative AI. For example, the creation unit inputs idea category information into the generative AI, which analyzes the information and applies the optimal creation algorithm. As a result, the creation unit improves the accuracy of patent application document creation by applying different creation algorithms to each idea category.

[0049] The drafting unit can determine the priority of drafting patent application documents based on the submission timing of ideas. The drafting unit uses generative AI to determine the priority of drafting based on the submission timing of ideas. Submission timing includes, but is not limited to, the submission date, submission time, and submission order. For example, the drafting unit can prioritize drafting ideas that were submitted earlier. It can also postpone drafting ideas that were submitted later. Furthermore, the drafting unit can determine the priority of drafting patent application documents based on the submission timing. This allows the drafting unit to efficiently draft documents by determining the priority of drafting based on the submission timing of ideas. Some or all of the above processing in the drafting unit is performed using generative AI. For example, the drafting unit inputs idea submission timing information into the generative AI, which analyzes that information to determine the priority of drafting. This allows the drafting unit to efficiently draft documents by determining the priority of drafting based on the submission timing of ideas.

[0050] The drafting unit can improve the accuracy of its patent application document creation by referring to relevant literature related to the idea. The drafting unit uses a generation AI to improve the accuracy of its drafting by referring to relevant literature related to the idea. Relevant literature includes, but is not limited to, academic papers, patent documents, and technical reports. For example, the drafting unit inputs relevant literature into the generation AI, which analyzes its contents to improve the accuracy of its drafting. The drafting unit can also optimize its patent application document creation algorithm based on the relevant literature. Furthermore, the drafting unit can analyze the relevant literature to improve the accuracy of its patent application document creation. As a result, the drafting unit improves the accuracy of its patent application document creation by referring to relevant literature related to the idea. Some or all of the above processes in the drafting unit are performed using a generation AI. For example, the drafting unit inputs relevant literature related to the idea into the generation AI, which analyzes its contents to improve the accuracy of its drafting. As a result, the drafting unit improves the accuracy of its patent application document creation by referring to relevant literature related to the idea.

[0051] The delivery unit can select the optimal delivery method when providing patent application documents by referring to the user's past operation history. For example, the delivery unit optimizes the delivery method of patent application documents based on the user's past operation history. The delivery unit can also prioritize suggesting delivery methods that the user has used in the past (email, download link, etc.). Furthermore, the delivery unit can suggest the optimal delivery method for a specific time period based on the user's past operation history. This allows the delivery unit to select the optimal delivery method by referring to the user's past operation history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit inputs the user's past operation history into AI, and the AI ​​analyzes that data to select the optimal delivery method. This allows the delivery unit to select the optimal delivery method by referring to the user's past operation history.

[0052] The provisioning unit can select the optimal provisioning method when providing patent application documents, taking into account the user's device information. For example, if the user is using a smartphone, the provisioning unit can select a mobile-friendly provisioning method. Furthermore, if the user is using a tablet, the provisioning unit can select a provisioning method optimized for larger screens. In addition, if the user is using a desktop computer, the provisioning unit can select a high-resolution provisioning method. This allows the provisioning unit to select the optimal provisioning method by considering the user's device information. Some or all of the above processing in the provisioning unit may be performed using AI, or not. For example, the provisioning unit can input the user's device information into an AI, which then analyzes the information to select the optimal provisioning method. This allows the provisioning unit to select the optimal provisioning method by considering the user's device information.

[0053] The provisioning unit can select the optimal provisioning method when providing patent application documents, taking into account the user's geographical location information. For example, if the user is in a specific region, the provisioning unit can select a provisioning method related to that region. The provisioning unit can also filter and select the optimal provisioning method based on the user's geographical location information. Furthermore, if the user is on the move, the provisioning unit can select a provisioning method related to their current location. In this way, the optimal provisioning method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the user's geographical location information into AI, and the AI ​​can analyze that information to select the optimal provisioning method. In this way, the provisioning unit can select the optimal provisioning method by taking into account the user's geographical location information.

[0054] The service provider can analyze the user's social media activity and adjust the service provision method when providing patent application documents. For example, the service provider can select a service provision method related to the user's areas of interest based on the user's social media activity. The service provider can also select the optimal service provision method based on information shared by the user on social media. Furthermore, the service provider can analyze the user's social media activity and select the optimal service provision method. This allows the service provider to select the optimal service provision method by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can analyze that data to select the optimal service provision method. This allows the service provider to select the optimal service provision method by analyzing the user's social media activity.

[0055] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. The learning unit uses a generative AI to optimize the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, patent publications, patent application publications, and patent examination information. For example, the learning unit inputs past learning data into the generative AI, which analyzes the data to optimize the learning algorithm. The learning unit can also improve the accuracy of learning by referring to past learning data. Furthermore, the learning unit can analyze past learning data and improve the learning algorithm. As a result, the learning unit improves the accuracy of learning by referring to past learning data. Some or all of the above processes in the learning unit are performed using a generative AI. For example, the learning unit inputs past learning data into the generative AI, which analyzes the data to optimize the learning algorithm. As a result, the learning unit improves the accuracy of learning by referring to past learning data.

[0056] The learning unit can apply different learning algorithms to each patent category during the learning process. The learning unit uses generative AI to apply different learning algorithms to each patent category. Patent categories include, but are not limited to, technical fields, applications, and target markets. The learning unit can, for example, customize the learning algorithm for each technical field. The learning unit can also adjust the learning criteria according to the application. Furthermore, the learning unit can apply the optimal learning algorithm for each target market. This improves the accuracy of learning by applying different learning algorithms to each patent category. Some or all of the above processes in the learning unit are performed using generative AI. For example, the learning unit inputs patent category information into the generative AI, which analyzes the information and applies the optimal learning algorithm. This improves the accuracy of learning by applying different learning algorithms to each patent category.

[0057] The learning unit can weight the training data based on the patent filing date during training. The learning unit uses a generative AI to weight the training data based on the patent filing date. The filing date includes, but is not limited to, the filing date, filing time, and filing order. For example, the learning unit prioritizes learning patent data that was filed earlier. The learning unit can also postpone learning patent data that was filed later. Furthermore, the learning unit can weight the training data based on the filing date. This enables efficient training by weighting the training data based on the patent filing date. Some or all of the above processing in the learning unit is performed using a generative AI. For example, the learning unit inputs patent filing date information into the generative AI, which analyzes that information and weights the training data. This enables efficient training by weighting the training data based on the patent filing date.

[0058] The learning unit can improve the accuracy of its learning by referring to relevant patent documents during the learning process. The learning unit uses a generative AI to improve the accuracy of its learning by referring to relevant patent documents. Relevant documents include, but are not limited to, academic papers, patent documents, and technical reports. For example, the learning unit inputs relevant documents into the generative AI, which then analyzes their content to improve the accuracy of its learning. The learning unit can also optimize its learning algorithm based on the relevant documents. Furthermore, the learning unit can analyze the relevant documents to improve the accuracy of its learning. As a result, the learning unit improves the accuracy of its learning by referring to relevant patent documents. Some or all of the above processes in the learning unit are performed using a generative AI. For example, the learning unit inputs relevant patent documents into the generative AI, which then analyzes their content to improve the accuracy of its learning. As a result, the learning unit improves the accuracy of its learning by referring to relevant patent documents.

[0059] The management department can select the optimal management method when managing the progress of a patent application by referring to the user's past application history. The management department uses a generating AI to select the optimal management method by referring to the user's past application history. Past application history includes, but is not limited to, the patent application date, application content, and examination results. For example, the management department inputs the user's past application history into the generating AI, which analyzes the data and selects the optimal management method. The management department can also prioritize suggesting management methods that the user has used in the past (email notifications, dashboard displays, etc.). Furthermore, the management department can suggest the optimal management method for a specific time period based on the user's past application history. This allows the management department to select the optimal progress management method by referring to the user's past application history. Some or all of the above processing in the management department is performed using a generating AI. For example, the management department inputs the user's past application history into the generating AI, which analyzes the data and selects the optimal management method. This allows the management department to select the optimal progress management method by referring to the user's past application history.

[0060] The management department can select the optimal management method when managing the progress of a patent application, taking into account the user's device information. For example, if the user is using a smartphone, the management department can select a mobile-friendly management method. If the user is using a tablet, the management department can also select a management method optimized for larger screens. Furthermore, if the user is using a desktop computer, the management department can select a high-resolution management method. This allows the management department to select the optimal progress management method by considering the user's device information. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the user's device information into the AI, which then analyzes the information to select the optimal management method. This allows the management department to select the optimal management method by considering the user's device information.

[0061] The management department can select the optimal management method when managing the progress of a patent application, taking into account the user's geographical location information. For example, if the user is in a specific region, the management department can select a management method related to that region. The management department can also filter and select the optimal management method based on the user's geographical location information. Furthermore, if the user is on the move, the management department can select a management method related to the user's current location. In this way, the management department can select the optimal progress management method by taking into account the user's geographical location information. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the user's geographical location information into the AI, and the AI ​​can analyze that information to select the optimal management method. In this way, the management department can select the optimal management method by taking into account the user's geographical location information.

[0062] The management department can analyze users' social media activity and adjust management methods when managing the progress of patent applications. For example, the management department can select management methods related to areas of interest based on the user's social media activity. The management department can also select the optimal management method based on information shared by the user on social media. Furthermore, the management department can analyze users' social media activity and select the optimal management method. This allows the management department to select the optimal management method by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user social media activity data into AI, and the AI ​​can analyze that data to select the optimal management method. This allows the management department to select the optimal management method by analyzing users' social media activity.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception department can optimize the idea submission process by referring to the user's past patent application history when receiving user ideas. For example, it can analyze trends in ideas previously submitted by the user and suggest the most suitable submission method. It can also prioritize suggesting submission methods the user has used in the past (voice, text, etc.). Furthermore, it can suggest the most suitable submission method for a specific time period based on the user's past submission history. In this way, the optimal submission method can be selected by analyzing the user's past patent application history.

[0065] The reception system can prioritize receiving highly relevant ideas by considering the user's geographical location. For example, if a user is in a specific region, it will prioritize receiving ideas related to that region. It can also filter and receive relevant ideas based on the user's geographical location. Furthermore, if a user is on the move, it can prioritize receiving ideas related to their current location. In this way, by considering the user's geographical location, it can prioritize receiving highly relevant ideas.

[0066] The judgment unit can apply different judgment algorithms to each category of idea when determining patentability. For example, the patentability judgment algorithm can be customized for each technical field. Furthermore, the criteria for determining patentability can be adjusted according to the application. In addition, the optimal patentability judgment algorithm can be applied to each target market. This improves the accuracy of patentability judgments by applying different judgment algorithms to each category of idea.

[0067] The creation unit can optimize its creation algorithm by referring to past patent application documents when creating patent application documents. For example, past patent application documents can be input into the generation AI, which then analyzes their content to optimize the creation algorithm. It can also improve the accuracy of patent application document creation by referring to past patent application documents. Furthermore, it can analyze past patent application documents to improve the patent application document creation algorithm. As a result, the accuracy of patent application document creation is improved by referring to past patent application documents.

[0068] The provisioning department can select the optimal provisioning method when providing patent application documents, taking into account the user's device information. For example, if the user is using a smartphone, a mobile-friendly provisioning method can be selected. If the user is using a tablet, a provisioning method optimized for a large screen can be selected. Furthermore, if the user is using a desktop computer, a high-resolution provisioning method can be selected. In this way, the optimal provisioning method can be selected by considering the user's device information.

[0069] The management department can select the optimal management method when managing the progress of patent applications by referring to the user's past application history. For example, the user's past application history can be input into a generating AI, which then analyzes the data to select the optimal management method. It can also prioritize suggesting management methods that the user has used in the past (email notifications, dashboard displays, etc.). Furthermore, it can suggest the optimal management method for a specific time period based on the user's past application history. In this way, the optimal progress management method can be selected by referring to the user's past application history.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk receives user ideas. User ideas include business ideas, technical ideas, and creative ideas. Users can submit ideas through input forms, and the reception desk also accepts voice and image input. For example, a user can explain their idea verbally, and the system will convert it to text and accept it. Alternatively, a user can submit a handwritten idea as an image, which will then be analyzed and accepted. Step 2: The judgment unit uses a generating AI to determine patentability based on the idea received by the reception unit. Patentability is determined based on criteria such as novelty, inventive step, and industrial applicability. The judgment unit uses the generating AI to learn from publicly available patent information and compares the user's idea with existing patent information to determine patentability. The generating AI can also automatically update the patentability criteria and make judgments based on the latest patent information. Furthermore, the judgment unit can provide feedback to the user regarding the patentability judgment result from the generating AI and offer advice for the user to revise their idea. Step 3: The creation unit prepares the patent application documents if the judgment unit determines that a patent application is possible. The creation unit uses generation AI to automatically collect the information necessary for the patent application and prepare the patent application documents. For example, it automatically generates documents such as claims, specifications, and drawings based on the user's idea. The generation AI can also check the format and content of the patent application documents and make corrections as needed. Furthermore, the generation AI can optimize the patent application document creation process and create documents efficiently. Step 4: The providing department provides the user with the patent application documents prepared by the creation department. The providing department can provide the documents via email, cloud storage, or physical mail. For example, the prepared patent application documents can be sent to the user in PDF format. Alternatively, the documents can be uploaded to cloud storage for the user to download. Furthermore, physical documents can be mailed.

[0072] (Example of form 2) The patent application support system according to an embodiment of the present invention is a system that simplifies the patent application process and reduces costs by using a generating AI that has learned from publicly available patent information. When a user communicates their idea to the generating AI, the generating AI, having learned from publicly available patent information, analyzes the user's idea and determines whether it is patentable. If the patent application support system determines that it is patentable, it creates the necessary documents for the application. The generating AI automatically collects the information necessary for the patent application and creates the patent application documents. This system simplifies the patent application process and reduces costs. Traditionally, it was expensive to hire a patent attorney, but by utilizing the generating AI, the hurdles to patent application are lowered, making it easy for anyone to obtain a patent. Because the generating AI constantly learns from publicly available patent information, it makes decisions based on the latest patent information. This improves the success rate of patent applications, and users can apply for patents with confidence. This system is extremely beneficial for individuals and companies that wish to apply for patents, and by simplifying the patent application process, new ideas and technologies are more likely to be patented, promoting innovation. By utilizing generative AI, the patent application process is accelerated, allowing for the completion of patent applications in a shorter timeframe. This enables users to quickly obtain patents and expand their businesses. As a result, the patent application support system can efficiently receive user ideas, assess patentability, and create and provide patent application documents.

[0073] The patent application support system according to the embodiment comprises a reception unit, a judgment unit, a creation unit, and a provision unit. The reception unit receives ideas from users. User ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. For example, users can submit ideas through an input form. The reception unit can also accept voice input and image input. For example, a user can explain an idea by voice, and the system can convert it into text and accept it. Alternatively, a user can submit a handwritten idea as an image, and the system can analyze and accept it. The judgment unit uses a generation AI to determine the patentability of the ideas received by the reception unit. The determination of patentability is based on criteria such as novelty, inventive step, and industrial applicability, but is not limited to these criteria. For example, the judgment unit can use the generation AI to learn from published patent information and compare the user's idea with existing patent information to determine patentability. The judgment unit can also use the generation AI to automatically update the patentability criteria and make decisions based on the latest patent information. Furthermore, the Judgment Unit can provide feedback to the user regarding the patentability judgment results of the Generating AI and offer advice to help the user revise their idea. The Creation Unit creates the patent application documents if the Judgment Unit determines that the idea is patentable. The Creation Unit uses the Generating AI to automatically collect the information necessary for the patent application and create the application documents. For example, the Creation Unit can automatically generate documents such as claims, specifications, and drawings based on the user's idea. The Creation Unit can also use the Generating AI to check the format and content of the patent application documents and make revisions as needed. In addition, the Creation Unit can use the Generating AI to optimize the patent application document creation process and create documents efficiently. The Delivery Unit provides the patent application documents created by the Creation Unit to the user. The Delivery Unit can provide the documents by methods such as email, cloud storage, or physical document delivery. For example, the Delivery Unit can send the created patent application documents to the user in PDF format. The Delivery Unit can also upload the documents to cloud storage so that the user can download them. Furthermore, the Delivery Unit can also mail physical documents.This enables the patent application support system to efficiently receive user ideas, assess their patentability, and create and provide patent application documents.

[0074] The reception desk accepts user ideas. User ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. The reception desk allows users to submit ideas through input forms, for example. It can also accept voice and image input. For example, a user can explain their idea verbally, and the system will convert it to text for submission. Alternatively, a user can submit a handwritten idea as an image, which will then be analyzed and submitted. Specifically, the reception desk provides an interface accessible to users through a website or mobile application, making it easy for users to input ideas. In the case of voice input, speech recognition technology is used to convert the user's speech into text, and natural language processing technology is used to analyze the content of the idea. In the case of image input, image recognition technology is used to analyze handwritten characters and diagrams and convert them into text data. This allows users to submit ideas in a variety of ways, and the reception desk can process this data efficiently. Furthermore, the reception desk has a function to automatically classify the content of the ideas submitted by users and allocate them to the relevant categories. For example, ideas can be organized based on categories such as business ideas, technical ideas, and creative ideas, streamlining subsequent processing. The reception department also manages a database to save the content of ideas submitted by users, making them available for later reference. This allows users to review and revise ideas they have previously submitted.

[0075] The judgment unit uses a generative AI to determine patentability based on the ideas received by the reception unit. Patentability is determined based on criteria such as novelty, inventive step, and industrial applicability, but is not limited to these examples. For example, the judgment unit can have the generative AI learn from publicly available patent information and compare the user's idea with existing patent information to determine patentability. The judgment unit can also have the generative AI automatically update the patentability criteria and make judgments based on the latest patent information. Furthermore, the judgment unit can have the generative AI provide feedback to the user regarding the patentability judgment results and offer advice for the user to revise their idea. Specifically, the generative AI evaluates novelty by searching for relevant patent documents in a patent database and comparing them with the user's idea. For evaluating inventive step, the generative AI analyzes technological advancements and market trends to determine the degree of inventiveness of the user's idea compared to existing technology. Regarding industrial applicability, the generative AI evaluates market demand and the practicality of the technology to determine whether the user's idea is actually usable in industry. This allows the judgment unit to determine with high accuracy whether the user's idea is suitable for patent application. Furthermore, the judgment unit provides an interface for the generating AI to provide easily understandable feedback to the user regarding the patentability assessment results. For example, it displays the patentability evaluation results for the user's submitted idea in graphs and charts, and suggests specific areas for modification and improvement. The generating AI also provides information and examples that can be used as reference when the user modifies their idea, supporting the user in creating better ideas. In this way, the judgment unit can provide support to the user in optimizing their idea for patent application.

[0076] The creation unit prepares patent application documents if the judgment unit determines that a patent application is possible. The creation unit uses a generation AI to automatically collect the necessary information for the patent application and prepare the application documents. For example, the creation unit automatically generates documents such as claims, specifications, and drawings based on the user's idea. The creation unit can also have the generation AI check the format and content of the patent application documents and make corrections as needed. Furthermore, the creation unit can have the generation AI optimize the patent application document preparation process, enabling efficient document creation. Specifically, the generation AI analyzes the user's idea and extracts keywords and phrases to clearly define the claims. For the specification, the generation AI automatically generates technical details and examples to concretely explain the user's idea. For the drawings, the generation AI generates appropriate diagrams based on the user's idea and attaches them to the patent application documents. This reduces the user's burden and allows the creation of patent application documents quickly and accurately. Additionally, the creation unit has the generation AI check the format and content of the patent application documents to ensure they meet the requirements of the patent office. For example, it checks whether the claims are properly written, whether the specification is sufficiently detailed, and whether the drawings are accurately drawn. If necessary, the generating AI automatically makes corrections to improve the quality of the patent application documents. Furthermore, the creation unit uses algorithms to optimize the patent application document creation process and efficiently produce documents. This allows the creation unit to support users in filing patent applications quickly.

[0077] The provisioning department provides users with patent application documents prepared by the creation department. The provisioning department can provide documents via methods such as email, cloud storage, or physical mail. For example, the provisioning department can send the prepared patent application documents to the user in PDF format. Alternatively, the provisioning department can upload the documents to cloud storage for user download. Furthermore, the provisioning department can also mail physical documents. Specifically, the provisioning department provides an interface that allows users to select their preferred method of delivery, improving user convenience. For email delivery, the provisioning department attaches the prepared patent application documents in PDF format and sends them to the user. For cloud storage delivery, the provisioning department uploads the prepared patent application documents to cloud storage and provides a link for user access. For physical mailing, the provisioning department prints the prepared patent application documents and mails them to the address specified by the user. This allows the provisioning department to offer users diverse methods for receiving patent application documents, enabling flexible responses to user needs. Furthermore, the provisioning department continues to support users even after providing the patent application documents. For example, if a user has questions or requests for revisions regarding patent application documents, the service department will respond promptly and provide the necessary support. The service department will also manage the history of patent application document provision, allowing users to review previously provided documents. This enables the service department to support users in smoothly navigating the patent application process and improve the overall reliability and usability of the patent application support system.

[0078] The patent application support system includes a learning unit that learns from publicly available patent information. The learning unit learns from publicly available patent information using a generating AI. Publicly available patent information includes, but is not limited to, patent publications, patent application publications, and patent examination information. For example, the learning unit collects patent publications and the generating AI analyzes and learns their contents. The learning unit can also collect patent application publication information and the generating AI analyzes and learns its contents. Furthermore, the learning unit can collect patent examination information and the generating AI analyzes and learns its contents. As a result, the learning unit improves the accuracy of its patentability determination by learning from publicly available patent information. Some or all of the above processing in the learning unit is performed using the generating AI. For example, the learning unit inputs patent publications into the generating AI, and the generating AI analyzes and learns their contents. As a result, the learning unit improves the accuracy of its patentability determination by learning from publicly available patent information.

[0079] The patent application support system includes a management unit that manages the progress of the patent application. The management unit manages the progress of the patent application. Progress management includes, but is not limited to, tracking the progress of each step of the patent application and notifying the user. For example, the management unit can display the progress of each step of the patent application on a dashboard so that the user can see the progress at a glance. The management unit can also notify the user of the progress of the patent application by email. Furthermore, the management unit can update the progress of the patent application in real time so that the user always has the latest information. In this way, by the management unit managing the progress of the patent application, the user can easily understand the status of the patent application. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit inputs the progress of the patent application into AI, and the AI ​​manages the progress. In this way, by the management unit managing the progress of the patent application, the user can easily understand the status of the patent application.

[0080] The judgment unit determines patentability based on publicly available patent information. The judgment unit uses a generating AI to determine patentability based on publicly available patent information. Publicly available patent information includes, but is not limited to, patent gazettes, published patent applications, and patent examination information. For example, the judgment unit inputs a patent gazette into the generating AI, which analyzes its contents to determine patentability. The judgment unit can also input published patent application information into the generating AI, which analyzes its contents to determine patentability. Furthermore, the judgment unit can input patent examination information into the generating AI, which analyzes its contents to determine patentability. This improves the accuracy of the judgment unit's determination by basing its determination on publicly available patent information. Some or all of the above-described processes in the judgment unit are performed using a generating AI. For example, the judgment unit inputs a patent gazette into the generating AI, which analyzes its contents to determine patentability. This improves the accuracy of the judgment unit's determination by basing its determination on publicly available patent information.

[0081] The creation unit automatically collects the information necessary for a patent application and creates the patent application documents. The creation unit uses a generative AI to automatically collect the information necessary for a patent application and create the patent application documents. Methods for collecting information include, but are not limited to, web scraping and API integration. For example, the creation unit can collect the information necessary for a patent application using web scraping. The creation unit can also collect the information necessary for a patent application using API integration. Furthermore, the creation unit can have a generative AI automatically collect the information necessary for a patent application and create the patent application documents. This improves the efficiency of patent applications by allowing the creation unit to automatically collect the information necessary for a patent application and create the patent application documents. Some or all of the above processes in the creation unit are performed using a generative AI. For example, the creation unit inputs the information necessary for a patent application into the generative AI, and the generative AI creates the patent application documents based on that information. This improves the efficiency of patent applications by allowing the creation unit to automatically collect the information necessary for a patent application and create the patent application documents.

[0082] The service provider provides the user with the prepared patent application documents. The service provider can provide the documents by methods such as email, cloud storage, or physical mail. For example, the service provider can send the prepared patent application documents to the user in PDF format. The service provider can also upload the documents to cloud storage so that the user can download them. Furthermore, the service provider can also mail physical documents. This allows the user to quickly file a patent application by providing the user with the prepared patent application documents. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the prepared patent application documents into an AI, which can then analyze the contents and provide them to the user. This allows the user to quickly file a patent application by providing the user with the prepared patent application documents.

[0083] The reception unit can estimate the user's emotions and adjust the timing of idea submission based on the estimated emotions. For example, if the user is feeling stressed, the reception unit will accept ideas at a time when the user is relaxed. It can also accept ideas when the user is focused. Furthermore, if the user is tired, the reception unit can accept ideas after a break. By adjusting the timing of idea submission according to the user's emotions, ideas can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs user emotion data into a generative AI, which analyzes the data to estimate the emotion. This allows the reception unit to adjust the timing of idea submission based on the user's emotions.

[0084] The reception desk can analyze a user's past idea submission history and select the optimal submission method. For example, the reception desk can analyze the trends of ideas previously submitted by the user and propose the most suitable submission method. The reception desk can also prioritize suggesting submission methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can suggest the most suitable submission method for a specific time period based on the user's past submission history. In this way, the reception desk can select the most suitable submission method by analyzing the user's past idea submission history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past idea submission history into an AI, which then analyzes the data and selects the most suitable submission method. In this way, the reception desk can select the most suitable submission method by analyzing the user's past idea submission history.

[0085] The reception unit can filter ideas based on the user's current projects and areas of interest when receiving them. For example, the reception unit can prioritize receiving ideas related to the user's current projects. The reception unit can also filter and receive relevant ideas based on the user's areas of interest. Furthermore, the reception unit can receive the most suitable ideas depending on the progress of the user's projects. This allows for the priority of receiving highly relevant ideas by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's project information into an AI, which then analyzes that information and filters relevant ideas. This allows the reception unit to filter ideas based on the user's current projects and areas of interest.

[0086] The reception unit can estimate the user's emotions and prioritize ideas to receive based on those emotions. For example, if the user is excited, the reception unit will prioritize that idea. Conversely, if the user is relaxed, the reception unit may prioritize other ideas. Furthermore, if the user is stressed, the reception unit can adjust the priority of ideas. This allows for the prioritization of more appropriate ideas based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs user emotion data into a generative AI, which analyzes the data to estimate the emotion. This allows the reception unit to prioritize ideas based on the user's emotions.

[0087] The reception system can prioritize receiving highly relevant ideas by considering the user's geographical location when receiving ideas. For example, if the user is in a specific region, the reception system will prioritize receiving ideas related to that region. The reception system can also filter and receive highly relevant ideas based on the user's geographical location. Furthermore, if the user is on the move, the reception system can prioritize receiving ideas related to their current location. In this way, by considering the user's geographical location, highly relevant ideas can be prioritized. Some or all of the above processing in the reception system may be performed using AI or not. For example, the reception system can input the user's geographical location into an AI, which then analyzes that information and filters out relevant ideas. In this way, the reception system can prioritize receiving highly relevant ideas by considering the user's geographical location.

[0088] The reception department can analyze the user's social media activity when receiving ideas and accept relevant ideas. For example, the reception department can prioritize accepting ideas related to the user's areas of interest based on their social media activity. The reception department can also accept relevant ideas based on information shared by the user on social media. Furthermore, the reception department can analyze the user's social media activity and accept the most suitable ideas. This allows the reception department to prioritize accepting relevant ideas by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's social media activity data into an AI, which then analyzes the data and filters out relevant ideas. This allows the reception department to prioritize accepting relevant ideas by analyzing the user's social media activity.

[0089] The decision unit can estimate the user's emotions and adjust the patentability criteria based on the estimated user emotions. For example, if the user is excited, the decision unit may tighten the patentability criteria. Conversely, if the user is relaxed, the decision unit may loosen the patentability criteria. Furthermore, if the user is stressed, the decision unit may adjust the patentability criteria. This allows for more appropriate judgments by adjusting the patentability criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using or without a generative AI. For example, the decision unit inputs user emotion data into a generative AI, and the generative AI analyzes the data to estimate emotions. This allows the decision unit to adjust the patentability criteria based on the user's emotions.

[0090] The decision unit can optimize its decision algorithm by referring to past patent application data when determining patentability. The decision unit uses a generating AI to optimize the decision algorithm by referring to past patent application data. Past patent application data includes, but is not limited to, patent publications, patent application publications, and patent examination information. For example, the decision unit inputs past patent application data into the generating AI, and the generating AI analyzes that data to optimize the decision algorithm. The decision unit can also improve the accuracy of its patentability determination by referring to past patent application data. Furthermore, the decision unit can analyze past patent application data to improve the patentability determination algorithm. As a result, the decision unit improves the accuracy of its patentability determination by referring to past patent application data. Some or all of the above processing in the decision unit is performed using the generating AI. For example, the decision unit inputs past patent application data into the generating AI, and the generating AI analyzes that data to optimize the decision algorithm. As a result, the decision unit improves the accuracy of its patentability determination by referring to past patent application data.

[0091] The judgment unit can apply different judgment algorithms to each category of idea when determining patentability. The judgment unit uses generative AI to apply different judgment algorithms to each category of idea. Idea categories include, but are not limited to, technical fields, applications, and target markets. The judgment unit customizes the patentability judgment algorithm for each technical field, for example. The judgment unit can also adjust the criteria for patentability determination according to the application. Furthermore, the judgment unit can apply the optimal patentability judgment algorithm for each target market. As a result, the judgment unit improves the accuracy of patentability determination by applying different judgment algorithms to each category of idea. Some or all of the above processing in the judgment unit is performed using generative AI. For example, the judgment unit inputs idea category information into the generative AI, which analyzes the information and applies the optimal judgment algorithm. As a result, the judgment unit improves the accuracy of patentability determination by applying different judgment algorithms to each category of idea.

[0092] The decision unit can estimate the user's emotions and adjust the order in which patentability assessment results are displayed based on the estimated user emotions. For example, if the user is excited, the decision unit may prioritize displaying ideas with high patentability. It may also prioritize displaying ideas with low patentability if the user is relaxed. Furthermore, if the user is stressed, the decision unit may adjust the display of patentability assessment results. This allows for more appropriate information to be provided by adjusting the order in which patentability assessment results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the decision unit may be performed using or without a generative AI. For example, the decision unit inputs user emotion data into a generative AI, which analyzes the data to estimate emotions. This allows the decision unit to adjust the order in which patentability assessment results are displayed based on the user's emotions.

[0093] The judgment unit can determine the priority of patentability decisions based on the submission timing of ideas. The judgment unit uses generative AI to determine the priority of decisions based on the submission timing of ideas. The submission timing includes, but is not limited to, the submission date, submission time, and submission order. For example, the judgment unit may prioritize ideas submitted earlier. It can also postpone the evaluation of ideas submitted later. Furthermore, the judgment unit can determine the priority of patentability decisions based on the submission timing. This enables the judgment unit to make efficient patentability decisions by determining the priority of decisions based on the submission timing of ideas. Some or all of the above processing in the judgment unit is performed using generative AI. For example, the judgment unit inputs idea submission timing information into the generative AI, and the generative AI analyzes that information to determine the priority of decisions. This enables the judgment unit to make efficient patentability decisions by determining the priority of decisions based on the submission timing of ideas.

[0094] The judgment unit can improve the accuracy of its judgment by referring to relevant literature related to the idea when determining patentability. The judgment unit uses a generative AI to improve the accuracy of its judgment by referring to relevant literature related to the idea. Relevant literature includes, but is not limited to, academic papers, patent documents, and technical reports. For example, the judgment unit inputs relevant literature into the generative AI, and the generative AI analyzes its contents to improve the accuracy of the judgment. The judgment unit can also optimize its patentability judgment algorithm based on the relevant literature. Furthermore, the judgment unit can analyze the relevant literature to improve the accuracy of its patentability judgment. As a result, the judgment unit improves the accuracy of its patentability judgment by referring to relevant literature related to the idea. Some or all of the above processing in the judgment unit is performed using a generative AI. For example, the judgment unit inputs relevant literature related to the idea into the generative AI, and the generative AI analyzes its contents to improve the accuracy of the judgment. As a result, the judgment unit improves the accuracy of its patentability judgment by referring to relevant literature related to the idea.

[0095] The drafting unit can estimate the user's emotions and adjust the method of drafting the patent application document based on the estimated user emotions. For example, if the user is relaxed, the drafting unit can draft a detailed patent application document. If the user is in a hurry, the drafting unit can draft a concise patent application document. Furthermore, if the user is stressed, the drafting unit can adjust the method of drafting the patent application document. This allows for more appropriate document creation by adjusting the method of drafting the patent application document according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the drafting unit may be performed using or without generative AI. For example, the drafting unit inputs user emotion data into the generative AI, and the generative AI analyzes the data to estimate the emotions. This allows the drafting unit to adjust the method of drafting the patent application document based on the user's emotions.

[0096] The creation unit can optimize its creation algorithm by referring to past patent application documents when creating patent application documents. The creation unit uses a generating AI to optimize the creation algorithm by referring to past patent application documents. Past patent application documents include, but are not limited to, patent gazettes, patent application publications, and patent examination information. For example, the creation unit inputs past patent application documents into the generating AI, which analyzes their contents and optimizes the creation algorithm. The creation unit can also improve the accuracy of patent application document creation by referring to past patent application documents. Furthermore, the creation unit can analyze past patent application documents and improve the patent application document creation algorithm. As a result, the creation unit improves the accuracy of patent application document creation by referring to past patent application documents. Some or all of the above processes in the creation unit are performed using the generating AI. For example, the creation unit inputs past patent application documents into the generating AI, which analyzes their contents and optimizes the creation algorithm. As a result, the creation unit improves the accuracy of patent application document creation by referring to past patent application documents.

[0097] The creation unit can apply different creation algorithms to each idea category when creating patent application documents. The creation unit uses generative AI to apply different creation algorithms to each idea category. Idea categories include, but are not limited to, technical fields, applications, and target markets. The creation unit can, for example, customize the patent application document creation algorithm for each technical field. The creation unit can also adjust the criteria for creating patent application documents according to the application. Furthermore, the creation unit can apply the optimal patent application document creation algorithm for each target market. As a result, the creation unit improves the accuracy of patent application document creation by applying different creation algorithms to each idea category. Some or all of the above processes in the creation unit are performed using generative AI. For example, the creation unit inputs idea category information into the generative AI, which analyzes the information and applies the optimal creation algorithm. As a result, the creation unit improves the accuracy of patent application document creation by applying different creation algorithms to each idea category.

[0098] The creation unit can estimate the user's emotions and adjust the order in which patent application documents are created based on the estimated emotions. For example, if the user is relaxed, the creation unit can prioritize creating detailed patent application documents. If the user is in a hurry, the creation unit can also prioritize creating concise patent application documents. Furthermore, if the user is stressed, the creation unit can adjust the order in which patent application documents are created. This allows for more appropriate document creation by adjusting the order in which patent application documents are created according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using generative AI or not. For example, the creation unit inputs user emotion data into a generative AI, and the generative AI analyzes the data to estimate the emotions. This allows the creation unit to adjust the order in which patent application documents are created based on the user's emotions.

[0099] The drafting unit can determine the priority of drafting patent application documents based on the submission timing of ideas. The drafting unit uses generative AI to determine the priority of drafting based on the submission timing of ideas. Submission timing includes, but is not limited to, the submission date, submission time, and submission order. For example, the drafting unit can prioritize drafting ideas that were submitted earlier. It can also postpone drafting ideas that were submitted later. Furthermore, the drafting unit can determine the priority of drafting patent application documents based on the submission timing. This allows the drafting unit to efficiently draft documents by determining the priority of drafting based on the submission timing of ideas. Some or all of the above processing in the drafting unit is performed using generative AI. For example, the drafting unit inputs idea submission timing information into the generative AI, which analyzes that information to determine the priority of drafting. This allows the drafting unit to efficiently draft documents by determining the priority of drafting based on the submission timing of ideas.

[0100] The drafting unit can improve the accuracy of its patent application document creation by referring to relevant literature related to the idea. The drafting unit uses a generation AI to improve the accuracy of its drafting by referring to relevant literature related to the idea. Relevant literature includes, but is not limited to, academic papers, patent documents, and technical reports. For example, the drafting unit inputs relevant literature into the generation AI, which analyzes its contents to improve the accuracy of its drafting. The drafting unit can also optimize its patent application document creation algorithm based on the relevant literature. Furthermore, the drafting unit can analyze the relevant literature to improve the accuracy of its patent application document creation. As a result, the drafting unit improves the accuracy of its patent application document creation by referring to relevant literature related to the idea. Some or all of the above processes in the drafting unit are performed using a generation AI. For example, the drafting unit inputs relevant literature related to the idea into the generation AI, which analyzes its contents to improve the accuracy of its drafting. As a result, the drafting unit improves the accuracy of its patent application document creation by referring to relevant literature related to the idea.

[0101] The service provider can estimate the user's emotions and adjust the method of providing patent application documents based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed patent application documents. If the user is in a hurry, the service provider can also provide concise patent application documents. Furthermore, if the user is stressed, the service provider can adjust the method of providing patent application documents. This allows for more appropriate document provision by adjusting the method of providing patent application documents according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider inputs user emotion data into a generative AI, and the generative AI analyzes the data to estimate the emotions. This allows the service provider to adjust the method of providing patent application documents based on the user's emotions.

[0102] The delivery unit can select the optimal delivery method when providing patent application documents by referring to the user's past operation history. For example, the delivery unit optimizes the delivery method of patent application documents based on the user's past operation history. The delivery unit can also prioritize suggesting delivery methods that the user has used in the past (email, download link, etc.). Furthermore, the delivery unit can suggest the optimal delivery method for a specific time period based on the user's past operation history. This allows the delivery unit to select the optimal delivery method by referring to the user's past operation history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit inputs the user's past operation history into AI, and the AI ​​analyzes that data to select the optimal delivery method. This allows the delivery unit to select the optimal delivery method by referring to the user's past operation history.

[0103] The provisioning unit can select the optimal provisioning method when providing patent application documents, taking into account the user's device information. For example, if the user is using a smartphone, the provisioning unit can select a mobile-friendly provisioning method. Furthermore, if the user is using a tablet, the provisioning unit can select a provisioning method optimized for larger screens. In addition, if the user is using a desktop computer, the provisioning unit can select a high-resolution provisioning method. This allows the provisioning unit to select the optimal provisioning method by considering the user's device information. Some or all of the above processing in the provisioning unit may be performed using AI, or not. For example, the provisioning unit can input the user's device information into an AI, which then analyzes the information to select the optimal provisioning method. This allows the provisioning unit to select the optimal provisioning method by considering the user's device information.

[0104] The service provider can estimate the user's emotions and adjust the order in which patent application documents are provided based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize providing detailed patent application documents. If the user is in a hurry, the service provider may also prioritize providing concise patent application documents. Furthermore, if the user is stressed, the service provider may adjust the order in which patent application documents are provided. This allows for more appropriate document provision by adjusting the order of provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without generative AI. For example, the service provider inputs user emotion data into a generative AI, which analyzes the data to estimate the emotions. This allows the service provider to adjust the order in which patent application documents are provided based on the user's emotions.

[0105] The provisioning unit can select the optimal provisioning method when providing patent application documents, taking into account the user's geographical location information. For example, if the user is in a specific region, the provisioning unit can select a provisioning method related to that region. The provisioning unit can also filter and select the optimal provisioning method based on the user's geographical location information. Furthermore, if the user is on the move, the provisioning unit can select a provisioning method related to their current location. In this way, the optimal provisioning method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the user's geographical location information into AI, and the AI ​​can analyze that information to select the optimal provisioning method. In this way, the provisioning unit can select the optimal provisioning method by taking into account the user's geographical location information.

[0106] The service provider can analyze the user's social media activity and adjust the service provision method when providing patent application documents. For example, the service provider can select a service provision method related to the user's areas of interest based on the user's social media activity. The service provider can also select the optimal service provision method based on information shared by the user on social media. Furthermore, the service provider can analyze the user's social media activity and select the optimal service provision method. This allows the service provider to select the optimal service provision method by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can analyze that data to select the optimal service provision method. This allows the service provider to select the optimal service provision method by analyzing the user's social media activity.

[0107] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. If the user is in a hurry, the learning unit can also select concise training data. Furthermore, if the user is stressed, the learning unit can adjust the selection of training data. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using generative AI or not. For example, the learning unit inputs user emotion data into a generative AI, and the generative AI analyzes that data to estimate emotions. This allows the learning unit to select training data based on the user's emotions.

[0108] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. The learning unit uses a generative AI to optimize the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, patent publications, patent application publications, and patent examination information. For example, the learning unit inputs past learning data into the generative AI, which analyzes the data to optimize the learning algorithm. The learning unit can also improve the accuracy of learning by referring to past learning data. Furthermore, the learning unit can analyze past learning data and improve the learning algorithm. As a result, the learning unit improves the accuracy of learning by referring to past learning data. Some or all of the above processes in the learning unit are performed using a generative AI. For example, the learning unit inputs past learning data into the generative AI, which analyzes the data to optimize the learning algorithm. As a result, the learning unit improves the accuracy of learning by referring to past learning data.

[0109] The learning unit can apply different learning algorithms to each patent category during the learning process. The learning unit uses generative AI to apply different learning algorithms to each patent category. Patent categories include, but are not limited to, technical fields, applications, and target markets. The learning unit can, for example, customize the learning algorithm for each technical field. The learning unit can also adjust the learning criteria according to the application. Furthermore, the learning unit can apply the optimal learning algorithm for each target market. This improves the accuracy of learning by applying different learning algorithms to each patent category. Some or all of the above processes in the learning unit are performed using generative AI. For example, the learning unit inputs patent category information into the generative AI, which analyzes the information and applies the optimal learning algorithm. This improves the accuracy of learning by applying different learning algorithms to each patent category.

[0110] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency if the user is relaxed. It can also decrease the learning frequency if the user is in a hurry. Furthermore, it can adjust the learning frequency if the user is stressed. By adjusting the learning frequency according to the user's emotions, more appropriate learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using or without a generative AI. For example, the learning unit inputs user emotion data into a generative AI, and the generative AI analyzes that data to estimate emotions. This allows the learning unit to adjust the learning frequency based on the user's emotions.

[0111] The learning unit can weight the training data based on the patent filing date during training. The learning unit uses a generative AI to weight the training data based on the patent filing date. The filing date includes, but is not limited to, the filing date, filing time, and filing order. For example, the learning unit prioritizes learning patent data that was filed earlier. The learning unit can also postpone learning patent data that was filed later. Furthermore, the learning unit can weight the training data based on the filing date. This enables efficient training by weighting the training data based on the patent filing date. Some or all of the above processing in the learning unit is performed using a generative AI. For example, the learning unit inputs patent filing date information into the generative AI, which analyzes that information and weights the training data. This enables efficient training by weighting the training data based on the patent filing date.

[0112] The learning unit can improve the accuracy of its learning by referring to relevant patent documents during the learning process. The learning unit uses a generative AI to improve the accuracy of its learning by referring to relevant patent documents. Relevant documents include, but are not limited to, academic papers, patent documents, and technical reports. For example, the learning unit inputs relevant documents into the generative AI, which then analyzes their content to improve the accuracy of its learning. The learning unit can also optimize its learning algorithm based on the relevant documents. Furthermore, the learning unit can analyze the relevant documents to improve the accuracy of its learning. As a result, the learning unit improves the accuracy of its learning by referring to relevant patent documents. Some or all of the above processes in the learning unit are performed using a generative AI. For example, the learning unit inputs relevant patent documents into the generative AI, which then analyzes their content to improve the accuracy of its learning. As a result, the learning unit improves the accuracy of its learning by referring to relevant patent documents.

[0113] The management unit can estimate the user's emotions and adjust the patent application progress management method based on the estimated user emotions. For example, if the user is relaxed, the management unit can provide a detailed progress management method. If the user is in a hurry, the management unit can also provide a concise progress management method. Furthermore, if the user is stressed, the management unit can adjust the progress management method. This allows for more appropriate progress management by adjusting the patent application progress management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using generative AI or not. For example, the management unit inputs user emotion data into a generative AI, and the generative AI analyzes the data to estimate the emotion. This allows the management unit to adjust the patent application progress management method based on the user's emotions.

[0114] The management department can select the optimal management method when managing the progress of a patent application by referring to the user's past application history. The management department uses a generating AI to select the optimal management method by referring to the user's past application history. Past application history includes, but is not limited to, the patent application date, application content, and examination results. For example, the management department inputs the user's past application history into the generating AI, which analyzes the data and selects the optimal management method. The management department can also prioritize suggesting management methods that the user has used in the past (email notifications, dashboard displays, etc.). Furthermore, the management department can suggest the optimal management method for a specific time period based on the user's past application history. This allows the management department to select the optimal progress management method by referring to the user's past application history. Some or all of the above processing in the management department is performed using a generating AI. For example, the management department inputs the user's past application history into the generating AI, which analyzes the data and selects the optimal management method. This allows the management department to select the optimal progress management method by referring to the user's past application history.

[0115] The management department can select the optimal management method when managing the progress of a patent application, taking into account the user's device information. For example, if the user is using a smartphone, the management department can select a mobile-friendly management method. If the user is using a tablet, the management department can also select a management method optimized for larger screens. Furthermore, if the user is using a desktop computer, the management department can select a high-resolution management method. This allows the management department to select the optimal progress management method by considering the user's device information. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can input the user's device information into the AI, which then analyzes the information to select the optimal management method. This allows the management department to select the optimal management method by considering the user's device information.

[0116] The management department can estimate the user's emotions and determine the priority of patent application progress management based on the estimated emotions. For example, if the user is relaxed, the management department can prioritize detailed progress management. If the user is in a hurry, the management department can also prioritize concise progress management. Furthermore, if the user is stressed, the management department can adjust the priority of progress management. This allows for more appropriate progress management by determining the priority of patent application progress management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using generative AI or not. For example, the management department inputs user emotion data into a generative AI, and the generative AI analyzes the data to estimate the emotions. This allows the management department to determine the priority of patent application progress management based on the user's emotions.

[0117] The management department can select the optimal management method when managing the progress of a patent application, taking into account the user's geographical location information. For example, if the user is in a specific region, the management department can select a management method related to that region. The management department can also filter and select the optimal management method based on the user's geographical location information. Furthermore, if the user is on the move, the management department can select a management method related to the user's current location. In this way, the management department can select the optimal progress management method by taking into account the user's geographical location information. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the user's geographical location information into the AI, and the AI ​​can analyze that information to select the optimal management method. In this way, the management department can select the optimal management method by taking into account the user's geographical location information.

[0118] The management department can analyze users' social media activity and adjust management methods when managing the progress of patent applications. For example, the management department can select management methods related to areas of interest based on the user's social media activity. The management department can also select the optimal management method based on information shared by the user on social media. Furthermore, the management department can analyze users' social media activity and select the optimal management method. This allows the management department to select the optimal management method by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user social media activity data into AI, and the AI ​​can analyze that data to select the optimal management method. This allows the management department to select the optimal management method by analyzing users' social media activity.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The reception department can optimize the idea submission process by referring to the user's past patent application history when receiving user ideas. For example, it can analyze trends in ideas previously submitted by the user and suggest the most suitable submission method. It can also prioritize suggesting submission methods the user has used in the past (voice, text, etc.). Furthermore, it can suggest the most suitable submission method for a specific time period based on the user's past submission history. In this way, the optimal submission method can be selected by analyzing the user's past patent application history.

[0121] The judgment unit can estimate the user's emotions when determining patentability and adjust the patentability criteria based on those emotions. For example, if the user is excited, the patentability criteria can be made stricter. Conversely, if the user is relaxed, the criteria can be made more lenient. Furthermore, if the user is stressed, the criteria can be adjusted. By adjusting the patentability criteria according to the user's emotions, a more appropriate judgment becomes possible.

[0122] The creation unit can estimate the user's emotions when creating patent application documents and adjust the creation method based on those emotions. For example, if the user is relaxed, it can create a detailed patent application document. If the user is in a hurry, it can create a concise patent application document. Furthermore, if the user is stressed, it can adjust the creation method. By adjusting the creation method according to the user's emotions, it becomes possible to create more appropriate documents.

[0123] The delivery unit can estimate the user's emotions when providing patent application documents to a user and adjust the method of delivery based on the estimated emotions. For example, if the user is relaxed, detailed patent application documents can be provided. If the user is in a hurry, concise patent application documents can be provided. Furthermore, if the user is stressed, the method of delivery can be adjusted. By adjusting the method of delivery of patent application documents according to the user's emotions, more appropriate document delivery becomes possible.

[0124] The management department can estimate the user's emotions when managing the progress of a patent application and adjust the progress management method based on those emotions. For example, if the user is relaxed, a detailed progress management method can be provided. If the user is in a hurry, a concise progress management method can be provided. Furthermore, if the user is stressed, the progress management method can be adjusted accordingly. This allows for more appropriate progress management by adjusting the progress management method according to the user's emotions.

[0125] The reception system can prioritize receiving highly relevant ideas by considering the user's geographical location. For example, if a user is in a specific region, it will prioritize receiving ideas related to that region. It can also filter and receive relevant ideas based on the user's geographical location. Furthermore, if a user is on the move, it can prioritize receiving ideas related to their current location. In this way, by considering the user's geographical location, it can prioritize receiving highly relevant ideas.

[0126] The judgment unit can apply different judgment algorithms to each category of idea when determining patentability. For example, the patentability judgment algorithm can be customized for each technical field. Furthermore, the criteria for determining patentability can be adjusted according to the application. In addition, the optimal patentability judgment algorithm can be applied to each target market. This improves the accuracy of patentability judgments by applying different judgment algorithms to each category of idea.

[0127] The creation unit can optimize its creation algorithm by referring to past patent application documents when creating patent application documents. For example, past patent application documents can be input into the generation AI, which then analyzes their content to optimize the creation algorithm. It can also improve the accuracy of patent application document creation by referring to past patent application documents. Furthermore, it can analyze past patent application documents to improve the patent application document creation algorithm. As a result, the accuracy of patent application document creation is improved by referring to past patent application documents.

[0128] The provisioning department can select the optimal provisioning method when providing patent application documents, taking into account the user's device information. For example, if the user is using a smartphone, a mobile-friendly provisioning method can be selected. If the user is using a tablet, a provisioning method optimized for a large screen can be selected. Furthermore, if the user is using a desktop computer, a high-resolution provisioning method can be selected. In this way, the optimal provisioning method can be selected by considering the user's device information.

[0129] The management department can select the optimal management method when managing the progress of patent applications by referring to the user's past application history. For example, the user's past application history can be input into a generating AI, which then analyzes the data to select the optimal management method. It can also prioritize suggesting management methods that the user has used in the past (email notifications, dashboard displays, etc.). Furthermore, it can suggest the optimal management method for a specific time period based on the user's past application history. In this way, the optimal progress management method can be selected by referring to the user's past application history.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The reception desk receives user ideas. User ideas include business ideas, technical ideas, and creative ideas. Users can submit ideas through input forms, and the reception desk also accepts voice and image input. For example, a user can explain their idea verbally, and the system will convert it to text and accept it. Alternatively, a user can submit a handwritten idea as an image, which will then be analyzed and accepted. Step 2: The judgment unit uses a generating AI to determine patentability based on the idea received by the reception unit. Patentability is determined based on criteria such as novelty, inventive step, and industrial applicability. The judgment unit uses the generating AI to learn from publicly available patent information and compares the user's idea with existing patent information to determine patentability. The generating AI can also automatically update the patentability criteria and make judgments based on the latest patent information. Furthermore, the judgment unit can provide feedback to the user regarding the patentability judgment result from the generating AI and offer advice for the user to revise their idea. Step 3: The creation unit prepares the patent application documents if the judgment unit determines that a patent application is possible. The creation unit uses generation AI to automatically collect the information necessary for the patent application and prepare the patent application documents. For example, it automatically generates documents such as claims, specifications, and drawings based on the user's idea. The generation AI can also check the format and content of the patent application documents and make corrections as needed. Furthermore, the generation AI can optimize the patent application document creation process and create documents efficiently. Step 4: The providing department provides the user with the patent application documents prepared by the creation department. The providing department can provide the documents via email, cloud storage, or physical mail. For example, the prepared patent application documents can be sent to the user in PDF format. Alternatively, the documents can be uploaded to cloud storage for the user to download. Furthermore, physical documents can be mailed.

[0132] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0133] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0134] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0135] Each of the multiple elements described above, including the reception unit, judgment unit, creation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's idea. The judgment unit is implemented by the specific processing unit 290 of the data processing device 12 and determines patentability using generated AI. The creation unit is implemented by the specific processing unit 290 of the data processing device 12 and creates the patent application documents. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the created patent application documents to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] As shown in Figure 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, judgment unit, creation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's idea. The judgment unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and determines patentability using generated AI. The creation unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and creates the patent application document. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the created patent application document to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0156] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the reception unit, judgment unit, creation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's idea. The judgment unit is implemented by the specific processing unit 290 of the data processing device 12 and determines patentability using generated AI. The creation unit is implemented by the specific processing unit 290 of the data processing device 12 and creates the patent application document. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the created patent application document to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] As shown in Figure 7, the 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.

[0170] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0172] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0174] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0175] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0176] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0177] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the reception unit, judgment unit, creation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's idea. The judgment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and determines patentability using generated AI. The creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates the patent application documents. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the created patent application documents to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0185] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0187] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0188] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0189] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0193] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0194] 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.

[0195] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0196] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0197] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0198] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0200] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0201] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0202] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0203] (Note 1) A reception desk for receiving user ideas, A determination unit that determines patentability based on the idea received by the aforementioned reception unit, If the aforementioned determination unit determines that a patent application is possible, the creation unit prepares the patent application documents, The system includes a providing unit that provides patent application documents prepared by the preparation unit. A system characterized by the following features. (Note 2) It includes a learning unit that learns from publicly available patent information. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a management department for managing the progress of patent applications. The system described in Appendix 1, characterized by the features described herein. (Note 4) The unit that makes the determination said, Patentability is determined based on publicly available patent information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Automatically collects the information necessary for patent applications and creates patent application documents. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the created patent application documents to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the timing of idea submission based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past idea submission history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving ideas, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the ideas to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving ideas, we prioritize accepting highly relevant ideas by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When accepting ideas, we analyze users' social media activity and accept relevant ideas. The system described in Appendix 1, characterized by the features described herein. (Note 13) The unit that makes the determination said, We estimate user sentiment and adjust patentability criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The unit that makes the determination said, When determining patentability, the decision algorithm is optimized by referring to past patent application data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The unit that makes the determination said, When determining patentability, different decision algorithms are applied to each category of idea. The system described in Appendix 1, characterized by the features described herein. (Note 16) The unit that makes the determination said, The system estimates the user's emotions and adjusts the order in which the patentability assessment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The unit that makes the determination said, When determining patentability, priority is given to the decision based on when the idea was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The unit that makes the determination said, When determining patentability, referencing relevant literature on the idea improves the accuracy of the decision. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, We estimate user sentiment and adjust the method of preparing patent application documents based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When preparing patent application documents, the preparation algorithm is optimized by referring to past patent application documents. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, When preparing patent application documents, different creation algorithms are applied for each category of idea. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, It estimates user sentiment and adjusts the order in which patent application documents are prepared based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When preparing patent application documents, prioritize the preparation based on when the idea was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, When preparing patent application documents, referencing relevant literature on the idea improves the accuracy of the document. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust the method of providing patent application documents based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing patent application documents, the system selects the optimal method of provision by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing patent application documents, the optimal method of delivery is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates user sentiment and adjusts the order in which patent application documents are provided based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing patent application documents, the optimal method of delivery is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing patent application documents, we analyze the user's social media activity and adjust the method of provision accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned learning unit, During training, different learning algorithms are applied for each patent category. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned learning unit, During training, the training data is weighted based on the patent filing date. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned learning unit, During the learning process, refer to relevant patent literature to improve the accuracy of the learning. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned management department, We estimate user sentiment and adjust the patent application progress management method based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned management department, When managing the progress of a patent application, the system selects the optimal management method by referring to the user's past application history. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned management department, When managing the progress of a patent application, the optimal management method is selected by considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned management department, The system estimates user sentiment and determines the priority of patent application progress management based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned management department, When managing the progress of a patent application, the optimal management method is selected by considering the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned management department, When managing the progress of patent applications, we analyze users' social media activity and adjust management methods accordingly. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk for receiving user ideas, A determination unit that determines patentability based on the idea received by the aforementioned reception unit, If the aforementioned determination unit determines that a patent application is possible, the creation unit prepares the patent application documents, The system includes a providing unit that provides patent application documents prepared by the preparation unit. A system characterized by the following features.

2. It includes a learning unit that learns from publicly available patent information. The system according to feature 1.

3. It includes a management department for managing the progress of patent applications. The system according to feature 1.

4. The unit that makes the determination said, Patentability is determined based on publicly available patent information. The system according to feature 1.

5. The aforementioned creation unit, Automatically collects the information necessary for patent applications and creates patent application documents. The system according to feature 1.

6. The aforementioned supply unit is, Provide the user with the created patent application documents. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the timing of idea submission based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past idea submission history to select the most suitable submission method. The system according to feature 1.

9. The aforementioned reception unit is When receiving ideas, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes the ideas to accept based on those estimated emotions. The system according to feature 1.

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