system

Abstract

The system according to this embodiment aims to streamline the patent application process and reduce costs. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a research unit, and a reporting unit. The reception unit receives input for the patent content to be filed. The search unit searches the patent information platform based on the information entered by the reception unit. The research unit verifies the patent content, conducts a preliminary keyword search, and performs a full patent investigation based on the information obtained by the search unit. The reporting unit prepares a report for patent application and amendment based on the information obtained by the research unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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[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 the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can streamline the patent application process and reduce costs. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 search support system according to an embodiment of the present invention is an AI agent that replaces and complements the patent search work in patent applications. This patent search support system receives input from the patent applicant regarding the patent content they wish to file, and the AI ​​agent searches the patent information platform to comprehensively investigate related patents. The patent search support system verifies the patent content, conducts preliminary keyword searches, and performs a full patent subject search, creating a report for patent application and revision. This report can be revised through consultations with the AI ​​agent beforehand, shortening the time to patent application. For example, the patent search support system can automate specialized patent search writing and searches of the patent information platform. This allows patent applicants to streamline the patent search process and shorten the time to patent application. The patent search support system has the potential to reduce patent search costs by several billion yen annually. For example, the market rate for patent clearance searches is around 500,000 yen, and assuming a 30% reduction in personnel costs through the patent search support system, an annual cost reduction of 1.5 billion yen is possible. Furthermore, the patent search support system can be offered as a SaaS (Software as a Service) to other companies. This allows the patent search support system to streamline the patent application process and automate patent searches and reporting.

[0029] The patent search support system according to the embodiment comprises a reception unit, a search unit, a research unit, and a reporting unit. The reception unit receives input for the patent content to be filed. The reception unit provides, for example, an interface for the user to input the patent content in text format. The reception unit can also receive input for patent content using voice input. For example, the reception unit converts the user's voice into text using speech recognition technology. Furthermore, the reception unit can also receive input for patent content using image input. For example, the reception unit scans handwritten patent content and converts it into digital data. The search unit searches the patent information platform based on the information entered by the reception unit. The search unit retrieves relevant patent information by, for example, searching the Japan Patent Office database. The search unit can also retrieve patent information by searching commercial patent databases. For example, the search unit searches for patent information using patent classification codes. Furthermore, the search unit can also search for patent information using keyword searches. For example, the search unit extracts keywords related to the patent content and uses them to search for patent information. The research unit verifies the patent content, conducts a preliminary keyword search, and performs a main search of the patent subject based on the information obtained by the search unit. The research department, for example, reviews patent documents in detail to confirm the patent content. The research department can also conduct a preliminary keyword search using patent classification codes. For example, the research department extracts relevant keywords based on patent classification codes. Furthermore, the research department can conduct a main search of the patent subject matter. For example, the research department performs a technical evaluation of the patent documents. The reporting department prepares reports for patent applications and amendments based on the information obtained by the research department. For example, the reporting department prepares a patent application. The reporting department can also prepare a patent amendment proposal. For example, the reporting department proposes amendments to the patent content. Furthermore, the reporting department can prepare a summary of the research results. For example, the reporting department summarizes the results of the patent search and includes them in the report. Thus, the patent search support system according to the embodiment can streamline the patent application process and automate patent searches and reporting.

[0030] The reception unit inputs the patent details to be filed. For example, the reception unit provides an interface for users to input patent details in text format. Specifically, it provides a web interface with text boxes and forms for users to input patent details, making it easy for users to input patent details. The reception unit can also input patent details using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice into text. Speech recognition technology includes a function that uses natural language processing technology to accurately recognize the user's utterances and convert them into text as patent details. Furthermore, the reception unit can also input patent details using image input. For example, the reception unit scans handwritten patent details and converts them into digital data. Using image recognition technology, handwritten characters and drawings can be converted into digital data and incorporated as patent details. This allows the reception unit to enable users to input patent details in various ways, facilitating the patent application process. Additionally, the reception unit automatically saves the entered patent details for use in subsequent processing. For example, the entered patent details are stored in a database, making them accessible to the search and research units. This allows the reception department to streamline data entry in the initial stages of patent applications and improve the overall performance of the patent search support system.

[0031] The search unit searches the patent information platform based on the information entered by the reception unit. For example, the search unit searches the Japan Patent Office database to obtain relevant patent information. Specifically, it accesses the Japan Patent Office's public patent database and searches for patent documents related to the entered patent content. The search unit can also search commercial patent databases to obtain patent information. For example, commercial patent databases contain patent information from all over the world, and the search unit can use these databases to obtain a wide range of patent information. The search unit searches for patent information using patent classification codes. Patent classification codes are codes used to classify patent content, and the search unit can use them to efficiently search for relevant patent documents. Furthermore, the search unit can also search for patent information using keyword searches. For example, the search unit extracts keywords related to the patent content and uses them to search for patent information. Keyword searches can obtain more accurate search results by using keywords that reflect the specific technical elements and characteristics of the patent content. The search unit can combine these search methods to quickly obtain the most relevant patent information. Furthermore, the search unit automatically organizes the search results and provides them in a format that is easy for the research unit to use. For example, search results can be organized by patent abstract or classification code, allowing the research unit to efficiently review patent content. This streamlines the patent information search process and improves the overall performance of the patent search support system.

[0032] The Research Department conducts patent content verification, keyword preliminary research, and full-scale patent subject research based on information obtained by the Search Department. For example, the Research Department verifies patent content by thoroughly reviewing patent documents. Specifically, it scrutinizes the technical content and claims of patent documents and evaluates the degree of similarity between the patent content and existing patents. The Research Department can also conduct keyword preliminary research using patent classification codes. For example, the Research Department extracts relevant keywords based on patent classification codes and uses them to clarify the technical scope of the patent content. Furthermore, the Research Department can also conduct full-scale patent subject research. For example, the Research Department performs a technical evaluation of patent documents and determines whether the patent content has novelty and inventiveness. Based on these research results, the Research Department can propose whether or not to file a patent application and suggest necessary modifications. Furthermore, the Research Department can also perform automated analysis of patent documents using AI. For example, it can automatically analyze the content of patent documents using natural language processing technology and extract relevant technical elements and keywords. This significantly improves the efficiency of patent research for the Research Department. The Research Department provides these research results to the Reporting Department to support the creation of reports for patent applications and modifications. This will allow the research department to improve the accuracy and efficiency of patent searches and increase the success rate of patent applications.

[0033] The reporting department prepares reports for patent applications and amendments based on information obtained by the research department. For example, the reporting department prepares patent applications. Specifically, it includes the patent content, claims, and a detailed description of the invention, following the format of a patent application. The reporting department can also prepare patent amendment proposals. For example, it proposes amendments to the patent content and presents specific amendments to increase the success rate of the patent application. Furthermore, the reporting department can prepare summaries of research findings. For example, it summarizes the results of the patent search and includes them in a report, clarifying the feasibility of filing a patent application and identifying areas for amendment. The reporting department provides these reports to users, supporting the patent application process. Furthermore, the reporting department can utilize AI to automate the report creation process. For example, it can use natural language generation technology to automatically generate reports based on research findings. This significantly improves the efficiency of report creation. Additionally, the reporting department can collect user feedback and continuously improve the content and format of the reports. This streamlines the patent application process and improves the overall performance of the patent search support system.

[0034] The consultation department allows patent applicants to have consultations with the AI ​​agent in advance and make revisions. For example, the consultation department can conduct consultations between the patent applicant and the AI ​​agent via online meetings. Alternatively, the consultation department can conduct consultations through face-to-face meetings. For example, the consultation department can have the patent applicant and the AI ​​agent meet in person. Furthermore, the consultation department can set the agenda. For example, the consultation department can have the patent applicant and the AI ​​agent set the agenda for the consultation and then proceed with the consultation based on that agenda. This allows patent applicants to shorten the time it takes to file a patent application by having consultations with the AI ​​agent.

[0035] The writing unit can perform specialized writing for patent searches. For example, it can write the claims. It can also provide technical explanations, for example, describing the technical details of the patent. Furthermore, the writing unit can prepare patent applications, for example, writing each section of the application. This automates the specialized writing of patent searches, thereby improving the quality of patent applications.

[0036] The automated search unit can automate searches on the patent information platform. For example, it can automatically search for patent information using a search algorithm. The automated search unit can also set search criteria. For instance, it can set keywords and patent classification codes related to the patent content and perform searches based on them. Furthermore, the automated search unit can filter search results. For example, it can narrow down search results based on specific criteria. By automating searches on the patent information platform, the efficiency of patent research can be improved.

[0037] The reception unit can analyze the user's past patent application history and suggest the optimal input method. For example, the reception unit can automatically display patent content that the user has frequently entered in the past as a candidate. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can suggest input methods related to specific fields based on the user's past application history. In this way, by analyzing the past patent application history, the reception unit can suggest the optimal input method for the user. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past patent application history into a generating AI and have the generating AI suggest the optimal input method.

[0038] The reception unit can filter the input content based on the user's current projects and areas of interest when inputting patent information. For example, the reception unit prioritizes inputting patent information related to the user's current projects. The reception unit can also automatically filter relevant patent information based on the user's areas of interest. Furthermore, the reception unit can integrate with the user's project management tool to input relevant patent information. This allows for the input of highly relevant patent information by filtering the input content based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data obtained from the user's project management tool into a generating AI and have the generating AI perform the filtering of relevant patent information.

[0039] The reception unit can prioritize inputting highly relevant information when entering patent content, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize inputting patent content related to that region. The reception unit can also automatically filter relevant patent content based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can input relevant patent content based on their current location. This improves the accuracy of patent content by inputting highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform filtering of relevant patent content.

[0040] The reception unit can analyze the user's social media activity and input relevant information when inputting patent content. For example, the reception unit can automatically extract relevant patent content from the user's social media activity. The reception unit can also input patent content based on information shared by the user on social media. Furthermore, the reception unit can analyze the user's social media activity and suggest relevant patent content. This allows relevant information to be reflected in the patent content by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI extract relevant patent content.

[0041] The search unit can adjust the level of detail in search results based on the importance of the patent information during a search. For example, the search unit can prioritize displaying important patent information and provide detailed information. The search unit can also display less important patent information concisely. Furthermore, the search unit can adjust the display order of search results based on the importance of the patent information. This allows important information to be displayed preferentially by adjusting the level of detail in search results based on the importance of the patent information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the importance of the patent information into a generating AI and have the generating AI perform the adjustment of the level of detail in the search results.

[0042] The search unit can apply different search algorithms depending on the patent category during a search. For example, the search unit can apply a specialized search algorithm to medical-related patent information. It can also apply a technical search algorithm to IT-related patent information. Furthermore, it can apply a chemical search algorithm to chemical-related patent information. By applying a search algorithm according to the patent category, the search accuracy is improved. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent category information into a generating AI and have the generating AI execute the application of the search algorithm.

[0043] The search unit can determine the priority of search results based on the patent filing date during a search. For example, the search unit may prioritize displaying the most recent patent information. It can also postpone older patent information. Furthermore, the search unit can adjust the display order of search results based on the filing date. This allows for the prioritization of the latest information by determining the priority of search results based on the patent filing date. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent filing date information into a generating AI and have the generating AI determine the priority of search results.

[0044] The search unit can adjust the order of search results based on the relevance of the patents during a search. For example, the search unit can prioritize displaying highly relevant patent information. It can also postpone displaying less relevant patent information. Furthermore, the search unit can adjust the display order of search results based on relevance. This allows for the priority display of highly relevant information by adjusting the order of search results based on the relevance of the patents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent relevance information into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0045] The research unit can improve the accuracy of its research by considering the interrelationships of patents during the research process. For example, the research unit can improve the accuracy of its research based on relevant patent information. It can also analyze the interrelationships of patents and supplement the research results. Furthermore, the research unit can adjust the level of detail of the research based on the interrelationships of patents. This improves the accuracy of the research by considering the interrelationships of patents. Some or all of the above processes in the research unit may be performed using AI, for example, or not using AI. For example, the research unit can input information on the interrelationships of patents into a generating AI and have the generating AI perform the task of improving the accuracy of the research.

[0046] The research unit can conduct its research while considering the attribute information of the patent applicant. For example, the research unit can improve the accuracy of the research based on the applicant's area of ​​expertise. The research unit can also conduct its research by referring to the applicant's past patent application history. Furthermore, the research unit can adjust the level of detail of the research based on the applicant's attribute information. This improves the accuracy of the research by considering the attribute information of the patent applicant. Some or all of the above processes in the research unit may be performed using AI, for example, or not using AI. For example, the research unit can input the applicant's attribute information into a generating AI and have the generating AI perform the adjustment of the level of detail of the research.

[0047] The research unit can conduct its research while considering the geographical distribution of patents. For example, the research unit can prioritize the search for patent information related to a specific region. The research unit can also adjust the level of detail of the search based on the geographical distribution. Furthermore, the research unit can display the search results while considering the geographical distribution. This improves the accuracy of the search by considering the geographical distribution of patents. Some or all of the above processing in the research unit may be performed using AI, for example, or not using AI. For example, the research unit can input the geographical distribution information of patents into a generating AI and have the generating AI perform the adjustment of the level of detail of the search.

[0048] The research unit can improve the accuracy of its research by referring to relevant patent documents during the research process. For example, the research unit can improve the accuracy of its research based on relevant documents. The research unit can also supplement the research results by referring to relevant documents. Furthermore, the research unit can adjust the level of detail of the research based on relevant documents. This improves the accuracy of the research by referring to relevant patent documents. Some or all of the above processes in the research unit may be performed using AI, for example, or not using AI. For example, the research unit can input relevant document information into a generating AI and have the generating AI perform the adjustment of the level of detail of the research.

[0049] The reporting unit can adjust the level of detail in a report based on the importance of the patents when creating the report. For example, the reporting unit will prioritize including important patent information in the report. It can also briefly describe less important patent information. Furthermore, the reporting unit can adjust the level of detail in the report based on the importance of the patent information. This allows important information to be prioritized by adjusting the level of detail in the report based on the importance of the patents. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the importance of the patent information into a generating AI and have the generating AI perform the adjustment of the level of detail in the report.

[0050] The reporting unit can apply different reporting algorithms depending on the patent category when creating a report. For example, the reporting unit can apply a specialized reporting algorithm to medical-related patent information. It can also apply a technical reporting algorithm to IT-related patent information. Furthermore, it can apply a chemical reporting algorithm to chemical-related patent information. By applying a reporting algorithm according to the patent category, the accuracy of the report is improved. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input patent category information into a generating AI and have the generating AI execute the application of the reporting algorithm.

[0051] The reporting unit can determine the priority of reports based on the filing dates of patents when creating them. For example, the reporting unit can prioritize the inclusion of the most recent patent information in the report. It can also postpone the inclusion of older patent information. Furthermore, the reporting unit can adjust the order in which information is presented in the report based on the filing dates. This ensures that the most recent information is included first by prioritizing the report based on the patent filing dates. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input patent filing date information into a generating AI and have the generating AI determine the priority of the reports.

[0052] The reporting unit can adjust the order of patents in a report based on their relevance when creating the report. For example, the reporting unit can prioritize the inclusion of highly relevant patent information in the report. It can also postpone the inclusion of less relevant patent information. Furthermore, the reporting unit can adjust the order of the information presented in the report based on relevance. This allows for the prioritization of highly relevant information by adjusting the order of the report based on the relevance of patents. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input patent relevance information into a generating AI and have the generating AI perform the adjustment of the report order.

[0053] The meeting unit can select the optimal meeting method by referring to the user's past meeting history. For example, the meeting unit can select the optimal method based on the user's preferred methods in the past. It can also select an effective method from the user's past meeting history. Furthermore, the meeting unit can analyze the user's past meeting history to select the most efficient method. This allows the optimal method to be selected by referring to the user's past meeting history. Some or all of the above processing in the meeting unit may be performed using AI, for example, or without AI. For example, the meeting unit can input the user's past meeting history into a generating AI and have the generating AI select the optimal method.

[0054] The meeting system can select the optimal meeting method by considering the user's device information. For example, if the user is using a smartphone, the system can provide a meeting method adapted to the screen size. It can also provide a meeting method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the system can provide a concise and highly visible meeting method. This allows the system to select the optimal meeting method by considering the user's device information. Some or all of the above processing in the meeting system may be performed using AI, for example, or without AI. For example, the meeting system can input the user's device information into a generating AI and have the generating AI select the optimal meeting method.

[0055] The writing unit can adjust the level of detail in the writing based on the importance of the patents during the writing process. For example, the writing unit prioritizes writing important patent information. The writing unit can also write less important patent information concisely. Furthermore, the writing unit can adjust the level of detail in the writing based on the importance of the patent information. This allows important information to be included preferentially by adjusting the level of detail in the writing based on the importance of the patents. Some or all of the above processing in the writing unit may be performed using AI, for example, or without AI. For example, the writing unit can input the importance of the patent information into a generating AI and have the generating AI perform the adjustment of the level of detail in the writing.

[0056] The writing unit can determine the priority of writing based on the patent filing date during the writing process. For example, the writing unit prioritizes writing the most recent patent information. It can also postpone writing older patent information. Furthermore, the writing unit can adjust the order of writing based on the filing date. This allows for the prioritization of the most recent information by determining the writing priority based on the patent filing date. Some or all of the above processing in the writing unit may be performed using AI, for example, or not using AI. For example, the writing unit can input patent filing date information into a generating AI and have the generating AI perform the determination of writing priority.

[0057] The automated search unit can select the optimal search method by referring to the past search history of patents during automated searches. For example, the automated search unit can select the optimal search method based on search methods previously used by the user. The automated search unit can also select an effective search method from the user's past search history. Furthermore, the automated search unit can analyze the user's past search history and select the most efficient search method. In this way, the optimal search method can be selected by referring to past search history. Some or all of the above processes in the automated search unit may be performed using AI, for example, or without AI. For example, the automated search unit can input the user's past search history into a generating AI and have the generating AI select the optimal search method.

[0058] The automated search unit can select the optimal search method by considering the geographical location information of patents during automated searches. For example, the automated search unit can prioritize searching for patent information related to a specific region. The automated search unit can also select the optimal search method based on geographical location information. Furthermore, the automated search unit can display search results while considering geographical location information. This allows for the selection of the optimal search method by considering geographical location information. Some or all of the above-described processes in the automated search unit may be performed using AI, for example, or without AI. For example, the automated search unit can input the geographical location information of patents into a generating AI and have the generating AI select the optimal search method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze user input in real time and automatically suggest relevant patent information based on that input. For example, if a user enters keywords related to a specific technical field, the reception unit will immediately display patent information related to those keywords. The reception unit can also automatically assign relevant patent classification codes based on the user's input. Furthermore, the reception unit can display summaries of relevant patent documents based on the user's input. This allows users to quickly find relevant patent information based on their input.

[0061] The writing unit can analyze the user's past writing style when writing patent content and suggest the optimal writing style. For example, it can automatically apply specific expressions and writing styles that the user has used in the past. The writing unit can also extract and suggest effective expressions from the user's past writing history. Furthermore, the writing unit can automatically adjust the writing of the patent content based on the user's writing style. This improves the quality of patent content writing by taking the user's past writing style into consideration.

[0062] The reception unit can analyze user input and automatically suggest relevant patent information based on that input. For example, if a user enters keywords related to a specific technical field, the reception unit will immediately display patent information related to those keywords. The reception unit can also automatically assign relevant patent classification codes based on the user's input. Furthermore, the reception unit can display summaries of relevant patent documents based on the user's input. This allows users to quickly find relevant patent information based on their input.

[0063] The search unit can analyze the user's past search history and suggest the optimal search method. For example, it can automatically display frequently used search keywords as suggestions. The search unit can also extract and suggest effective search methods from the user's past search history. Furthermore, the search unit can prioritize displaying relevant patent information based on the user's search history. This improves search efficiency by considering the user's past search history.

[0064] The research department can improve the accuracy of its searches by considering the interrelationships between patents. For example, it can improve the accuracy of its searches based on related patent information. Furthermore, the research department can analyze the interrelationships between patents and supplement the search results. In addition, the research department can adjust the level of detail in the search based on the interrelationships between patents. This improves the accuracy of the search by considering the interrelationships between patents.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives the patent details to be filed. The reception desk provides an interface for the user to input the patent details in text format, for example. The reception desk can also accept input using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text. Furthermore, the reception desk can accept input using image input. For example, the reception desk scans handwritten patent details and converts them into digital data. Step 2: The search unit searches the patent information platform based on the information entered by the reception unit. The search unit can, for example, search the Japan Patent Office database to obtain relevant patent information. The search unit can also search commercial patent databases to obtain patent information. For example, the search unit can search for patent information using patent classification codes. Furthermore, the search unit can also search for patent information using keyword searches. For example, the search unit can extract keywords related to the patent content and use them to search for patent information. Step 3: The research department verifies the patent content, conducts a preliminary keyword search, and performs a full patent subject search based on the information obtained by the search department. For example, the research department verifies the patent content by reviewing patent documents in detail. The research department can also conduct a preliminary keyword search using patent classification codes. For example, the research department extracts relevant keywords based on patent classification codes. Furthermore, the research department can also perform a full patent subject search. For example, the research department conducts a technical evaluation of patent documents. Step 4: The reporting department prepares a report for patent application and amendment based on the information obtained by the research department. For example, the reporting department prepares a patent application. The reporting department can also prepare a patent amendment proposal. For example, the reporting department proposes amendments to the patent content. Furthermore, the reporting department can also prepare a summary of the research results. For example, the reporting department summarizes the results of the patent search and includes them in the report.

[0067] (Example of form 2) The patent search support system according to an embodiment of the present invention is an AI agent that replaces and complements the patent search work in patent applications. This patent search support system receives input from the patent applicant regarding the patent content they wish to file, and the AI ​​agent searches the patent information platform to comprehensively investigate related patents. The patent search support system verifies the patent content, conducts preliminary keyword searches, and performs a full patent subject search, creating a report for patent application and revision. This report can be revised through consultations with the AI ​​agent beforehand, shortening the time to patent application. For example, the patent search support system can automate specialized patent search writing and searches of the patent information platform. This allows patent applicants to streamline the patent search process and shorten the time to patent application. The patent search support system has the potential to reduce patent search costs by several billion yen annually. For example, the market rate for patent clearance searches is around 500,000 yen, and assuming a 30% reduction in personnel costs through the patent search support system, an annual cost reduction of 1.5 billion yen is possible. Furthermore, the patent search support system can be offered as a SaaS (Software as a Service) to other companies. This allows the patent search support system to streamline the patent application process and automate patent searches and reporting.

[0068] The patent search support system according to the embodiment comprises a reception unit, a search unit, a research unit, and a reporting unit. The reception unit receives input for the patent content to be filed. The reception unit provides, for example, an interface for the user to input the patent content in text format. The reception unit can also receive input for patent content using voice input. For example, the reception unit converts the user's voice into text using speech recognition technology. Furthermore, the reception unit can also receive input for patent content using image input. For example, the reception unit scans handwritten patent content and converts it into digital data. The search unit searches the patent information platform based on the information entered by the reception unit. The search unit retrieves relevant patent information by, for example, searching the Japan Patent Office database. The search unit can also retrieve patent information by searching commercial patent databases. For example, the search unit searches for patent information using patent classification codes. Furthermore, the search unit can also search for patent information using keyword searches. For example, the search unit extracts keywords related to the patent content and uses them to search for patent information. The research unit verifies the patent content, conducts a preliminary keyword search, and performs a main search of the patent subject based on the information obtained by the search unit. The research department, for example, reviews patent documents in detail to confirm the patent content. The research department can also conduct a preliminary keyword search using patent classification codes. For example, the research department extracts relevant keywords based on patent classification codes. Furthermore, the research department can conduct a main search of the patent subject matter. For example, the research department performs a technical evaluation of the patent documents. The reporting department prepares reports for patent applications and amendments based on the information obtained by the research department. For example, the reporting department prepares a patent application. The reporting department can also prepare a patent amendment proposal. For example, the reporting department proposes amendments to the patent content. Furthermore, the reporting department can prepare a summary of the research results. For example, the reporting department summarizes the results of the patent search and includes them in the report. Thus, the patent search support system according to the embodiment can streamline the patent application process and automate patent searches and reporting.

[0069] The reception unit inputs the patent details to be filed. For example, the reception unit provides an interface for users to input patent details in text format. Specifically, it provides a web interface with text boxes and forms for users to input patent details, making it easy for users to input patent details. The reception unit can also input patent details using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice into text. Speech recognition technology includes a function that uses natural language processing technology to accurately recognize the user's utterances and convert them into text as patent details. Furthermore, the reception unit can also input patent details using image input. For example, the reception unit scans handwritten patent details and converts them into digital data. Using image recognition technology, handwritten characters and drawings can be converted into digital data and incorporated as patent details. This allows the reception unit to enable users to input patent details in various ways, facilitating the patent application process. Additionally, the reception unit automatically saves the entered patent details for use in subsequent processing. For example, the entered patent details are stored in a database, making them accessible to the search and research units. This allows the reception department to streamline data entry in the initial stages of patent applications and improve the overall performance of the patent search support system.

[0070] The search unit searches the patent information platform based on the information entered by the reception unit. For example, the search unit searches the Japan Patent Office database to obtain relevant patent information. Specifically, it accesses the Japan Patent Office's public patent database and searches for patent documents related to the entered patent content. The search unit can also search commercial patent databases to obtain patent information. For example, commercial patent databases contain patent information from all over the world, and the search unit can use these databases to obtain a wide range of patent information. The search unit searches for patent information using patent classification codes. Patent classification codes are codes used to classify patent content, and the search unit can use them to efficiently search for relevant patent documents. Furthermore, the search unit can also search for patent information using keyword searches. For example, the search unit extracts keywords related to the patent content and uses them to search for patent information. Keyword searches can obtain more accurate search results by using keywords that reflect the specific technical elements and characteristics of the patent content. The search unit can combine these search methods to quickly obtain the most relevant patent information. Furthermore, the search unit automatically organizes the search results and provides them in a format that is easy for the research unit to use. For example, search results can be organized by patent abstract or classification code, allowing the research unit to efficiently review patent content. This streamlines the patent information search process and improves the overall performance of the patent search support system.

[0071] The Research Department conducts patent content verification, keyword preliminary research, and full-scale patent subject research based on information obtained by the Search Department. For example, the Research Department verifies patent content by thoroughly reviewing patent documents. Specifically, it scrutinizes the technical content and claims of patent documents and evaluates the degree of similarity between the patent content and existing patents. The Research Department can also conduct keyword preliminary research using patent classification codes. For example, the Research Department extracts relevant keywords based on patent classification codes and uses them to clarify the technical scope of the patent content. Furthermore, the Research Department can also conduct full-scale patent subject research. For example, the Research Department performs a technical evaluation of patent documents and determines whether the patent content has novelty and inventiveness. Based on these research results, the Research Department can propose whether or not to file a patent application and suggest necessary modifications. Furthermore, the Research Department can also perform automated analysis of patent documents using AI. For example, it can automatically analyze the content of patent documents using natural language processing technology and extract relevant technical elements and keywords. This significantly improves the efficiency of patent research for the Research Department. The Research Department provides these research results to the Reporting Department to support the creation of reports for patent applications and modifications. This will allow the research department to improve the accuracy and efficiency of patent searches and increase the success rate of patent applications.

[0072] The reporting department prepares reports for patent applications and amendments based on information obtained by the research department. For example, the reporting department prepares patent applications. Specifically, it includes the patent content, claims, and a detailed description of the invention, following the format of a patent application. The reporting department can also prepare patent amendment proposals. For example, it proposes amendments to the patent content and presents specific amendments to increase the success rate of the patent application. Furthermore, the reporting department can prepare summaries of research findings. For example, it summarizes the results of the patent search and includes them in a report, clarifying the feasibility of filing a patent application and identifying areas for amendment. The reporting department provides these reports to users, supporting the patent application process. Furthermore, the reporting department can utilize AI to automate the report creation process. For example, it can use natural language generation technology to automatically generate reports based on research findings. This significantly improves the efficiency of report creation. Additionally, the reporting department can collect user feedback and continuously improve the content and format of the reports. This streamlines the patent application process and improves the overall performance of the patent search support system.

[0073] The consultation department allows patent applicants to have consultations with the AI ​​agent in advance and make revisions. For example, the consultation department can conduct consultations between the patent applicant and the AI ​​agent via online meetings. Alternatively, the consultation department can conduct consultations through face-to-face meetings. For example, the consultation department can have the patent applicant and the AI ​​agent meet in person. Furthermore, the consultation department can set the agenda. For example, the consultation department can have the patent applicant and the AI ​​agent set the agenda for the consultation and then proceed with the consultation based on that agenda. This allows patent applicants to shorten the time it takes to file a patent application by having consultations with the AI ​​agent.

[0074] The writing unit can perform specialized writing for patent searches. For example, it can write the claims. It can also provide technical explanations, for example, describing the technical details of the patent. Furthermore, the writing unit can prepare patent applications, for example, writing each section of the application. This automates the specialized writing of patent searches, thereby improving the quality of patent applications.

[0075] The automated search unit can automate searches on the patent information platform. For example, it can automatically search for patent information using a search algorithm. The automated search unit can also set search criteria. For instance, it can set keywords and patent classification codes related to the patent content and perform searches based on them. Furthermore, the automated search unit can filter search results. For example, it can narrow down search results based on specific criteria. By automating searches on the patent information platform, the efficiency of patent research can be improved.

[0076] The reception system can estimate the user's emotions and adjust the input interface for patent content based on those emotions. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick patent content entry. This makes patent content entry easier by adjusting the input interface 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The reception unit can analyze the user's past patent application history and suggest the optimal input method. For example, the reception unit can automatically display patent content that the user has frequently entered in the past as a candidate. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can suggest input methods related to specific fields based on the user's past application history. In this way, by analyzing the past patent application history, the reception unit can suggest the optimal input method for the user. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past patent application history into a generating AI and have the generating AI suggest the optimal input method.

[0078] The reception unit can filter the input content based on the user's current projects and areas of interest when inputting patent information. For example, the reception unit prioritizes inputting patent information related to the user's current projects. The reception unit can also automatically filter relevant patent information based on the user's areas of interest. Furthermore, the reception unit can integrate with the user's project management tool to input relevant patent information. This allows for the input of highly relevant patent information by filtering the input content based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data obtained from the user's project management tool into a generating AI and have the generating AI perform the filtering of relevant patent information.

[0079] The reception desk can estimate the user's emotions and prioritize input content based on those emotions. For example, if the user is stressed, the reception desk will prioritize displaying important input content. It can also prioritize displaying detailed input content if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize displaying the most important input content. This allows users to prioritize important information by prioritizing input content according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception unit can prioritize inputting highly relevant information when entering patent content, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize inputting patent content related to that region. The reception unit can also automatically filter relevant patent content based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can input relevant patent content based on their current location. This improves the accuracy of patent content by inputting highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI perform filtering of relevant patent content.

[0081] The reception unit can analyze the user's social media activity and input relevant information when inputting patent content. For example, the reception unit can automatically extract relevant patent content from the user's social media activity. The reception unit can also input patent content based on information shared by the user on social media. Furthermore, the reception unit can analyze the user's social media activity and suggest relevant patent content. This allows relevant information to be reflected in the patent content by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI extract relevant patent content.

[0082] The search unit can estimate the user's emotions and adjust how search results are displayed based on those emotions. For example, if the user is stressed, the search unit can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display. By adjusting how search results are displayed according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The search unit can adjust the level of detail in search results based on the importance of the patent information during a search. For example, the search unit can prioritize displaying important patent information and provide detailed information. The search unit can also display less important patent information concisely. Furthermore, the search unit can adjust the display order of search results based on the importance of the patent information. This allows important information to be displayed preferentially by adjusting the level of detail in search results based on the importance of the patent information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the importance of the patent information into a generating AI and have the generating AI perform the adjustment of the level of detail in the search results.

[0084] The search unit can apply different search algorithms depending on the patent category during a search. For example, the search unit can apply a specialized search algorithm to medical-related patent information. It can also apply a technical search algorithm to IT-related patent information. Furthermore, it can apply a chemical search algorithm to chemical-related patent information. By applying a search algorithm according to the patent category, the search accuracy is improved. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent category information into a generating AI and have the generating AI execute the application of the search algorithm.

[0085] The search engine can estimate the user's emotions and adjust the length of search results based on that estimation. For example, if the user is stressed, the search engine can provide short, concise search results. If the user is relaxed, it can provide detailed search results. Furthermore, if the user is in a hurry, it can provide brief search results. By adjusting the length of search results according to the user's emotions, readability is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The search unit can determine the priority of search results based on the patent filing date during a search. For example, the search unit may prioritize displaying the most recent patent information. It can also postpone older patent information. Furthermore, the search unit can adjust the display order of search results based on the filing date. This allows for the prioritization of the latest information by determining the priority of search results based on the patent filing date. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent filing date information into a generating AI and have the generating AI determine the priority of search results.

[0087] The search unit can adjust the order of search results based on the relevance of the patents during a search. For example, the search unit can prioritize displaying highly relevant patent information. It can also postpone displaying less relevant patent information. Furthermore, the search unit can adjust the display order of search results based on relevance. This allows for the priority display of highly relevant information by adjusting the order of search results based on the relevance of the patents. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input patent relevance information into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0088] The research department can estimate the user's emotions and adjust the research criteria based on those estimated emotions. For example, if the user is nervous, the research department can provide simple and easy-to-understand research criteria. If the user is relaxed, the research department can also provide detailed research criteria. Furthermore, if the user is in a hurry, the research department can provide concise research criteria. This improves the accuracy of the research by adjusting the research criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The research unit can improve the accuracy of its research by considering the interrelationships of patents during the research process. For example, the research unit can improve the accuracy of its research based on relevant patent information. It can also analyze the interrelationships of patents and supplement the research results. Furthermore, the research unit can adjust the level of detail of the research based on the interrelationships of patents. This improves the accuracy of the research by considering the interrelationships of patents. Some or all of the above processes in the research unit may be performed using AI, for example, or not using AI. For example, the research unit can input information on the interrelationships of patents into a generating AI and have the generating AI perform the task of improving the accuracy of the research.

[0090] The research unit can conduct its research while considering the attribute information of the patent applicant. For example, the research unit can improve the accuracy of the research based on the applicant's area of ​​expertise. The research unit can also conduct its research by referring to the applicant's past patent application history. Furthermore, the research unit can adjust the level of detail of the research based on the applicant's attribute information. This improves the accuracy of the research by considering the attribute information of the patent applicant. Some or all of the above processes in the research unit may be performed using AI, for example, or not using AI. For example, the research unit can input the applicant's attribute information into a generating AI and have the generating AI perform the adjustment of the level of detail of the research.

[0091] The research unit can estimate the user's emotions and adjust the order in which the research results are displayed based on the estimated emotions. For example, if the user is stressed, the research unit can prioritize displaying important research results. It can also prioritize displaying detailed research results if the user is relaxed. Furthermore, if the user is in a hurry, the research unit can prioritize displaying concise research results. This improves readability by adjusting the display order of research results 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The research unit can conduct its research while considering the geographical distribution of patents. For example, the research unit can prioritize the search for patent information related to a specific region. The research unit can also adjust the level of detail of the search based on the geographical distribution. Furthermore, the research unit can display the search results while considering the geographical distribution. This improves the accuracy of the search by considering the geographical distribution of patents. Some or all of the above processing in the research unit may be performed using AI, for example, or not using AI. For example, the research unit can input the geographical distribution information of patents into a generating AI and have the generating AI perform the adjustment of the level of detail of the search.

[0093] The research unit can improve the accuracy of its research by referring to relevant patent documents during the research process. For example, the research unit can improve the accuracy of its research based on relevant documents. The research unit can also supplement the research results by referring to relevant documents. Furthermore, the research unit can adjust the level of detail of the research based on relevant documents. This improves the accuracy of the research by referring to relevant patent documents. Some or all of the above processes in the research unit may be performed using AI, for example, or not using AI. For example, the research unit can input relevant document information into a generating AI and have the generating AI perform the adjustment of the level of detail of the research.

[0094] The reporting function can estimate the user's emotions and adjust the presentation of the report based on those emotions. For example, if the user is stressed, the reporting function can provide a simple and easy-to-read report. If the user is relaxed, it can provide a report with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise report. This improves readability by adjusting the presentation of the report 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The reporting unit can adjust the level of detail in a report based on the importance of the patents when creating the report. For example, the reporting unit will prioritize including important patent information in the report. It can also briefly describe less important patent information. Furthermore, the reporting unit can adjust the level of detail in the report based on the importance of the patent information. This allows important information to be prioritized by adjusting the level of detail in the report based on the importance of the patents. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the importance of the patent information into a generating AI and have the generating AI perform the adjustment of the level of detail in the report.

[0096] The reporting unit can apply different reporting algorithms depending on the patent category when creating a report. For example, the reporting unit can apply a specialized reporting algorithm to medical-related patent information. It can also apply a technical reporting algorithm to IT-related patent information. Furthermore, it can apply a chemical reporting algorithm to chemical-related patent information. By applying a reporting algorithm according to the patent category, the accuracy of the report is improved. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input patent category information into a generating AI and have the generating AI execute the application of the reporting algorithm.

[0097] The reporting unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is stressed, the reporting unit will provide a short, concise report. If the user is relaxed, the reporting unit can provide a detailed report. Furthermore, if the user is in a hurry, the reporting unit can provide a brief report. This improves readability by adjusting the length of the report 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The reporting unit can determine the priority of reports based on the filing dates of patents when creating them. For example, the reporting unit can prioritize the inclusion of the most recent patent information in the report. It can also postpone the inclusion of older patent information. Furthermore, the reporting unit can adjust the order in which information is presented in the report based on the filing dates. This ensures that the most recent information is included first by prioritizing the report based on the patent filing dates. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input patent filing date information into a generating AI and have the generating AI determine the priority of the reports.

[0099] The reporting unit can adjust the order of patents in a report based on their relevance when creating the report. For example, the reporting unit can prioritize the inclusion of highly relevant patent information in the report. It can also postpone the inclusion of less relevant patent information. Furthermore, the reporting unit can adjust the order of the information presented in the report based on relevance. This allows for the prioritization of highly relevant information by adjusting the order of the report based on the relevance of patents. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input patent relevance information into a generating AI and have the generating AI perform the adjustment of the report order.

[0100] The meeting system can estimate the user's emotions and adjust the meeting's progress based on those emotions. For example, if the user is nervous, the system can provide a relaxing approach. If the user is relaxed, it can provide a more detailed approach. Furthermore, if the user is in a hurry, it can provide a more efficient approach. By adjusting the meeting's progress according to the user's emotions, the efficiency of the meeting is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The meeting unit can select the optimal meeting method by referring to the user's past meeting history. For example, the meeting unit can select the optimal method based on the user's preferred methods in the past. It can also select an effective method from the user's past meeting history. Furthermore, the meeting unit can analyze the user's past meeting history to select the most efficient method. This allows the optimal method to be selected by referring to the user's past meeting history. Some or all of the above processing in the meeting unit may be performed using AI, for example, or without AI. For example, the meeting unit can input the user's past meeting history into a generating AI and have the generating AI select the optimal method.

[0102] The meeting system can estimate the user's emotions and determine meeting priorities based on those emotions. For example, if the user is nervous, the system will prioritize important topics. If the user is relaxed, the system can also prioritize detailed topics. Furthermore, if the user is in a hurry, the system can prioritize the most important topics. This ensures that important topics are addressed by prioritizing meetings according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The meeting system can select the optimal meeting method by considering the user's device information. For example, if the user is using a smartphone, the system can provide a meeting method adapted to the screen size. It can also provide a meeting method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the system can provide a concise and highly visible meeting method. This allows the system to select the optimal meeting method by considering the user's device information. Some or all of the above processing in the meeting system may be performed using AI, for example, or without AI. For example, the meeting system can input the user's device information into a generating AI and have the generating AI select the optimal meeting method.

[0104] The writing unit can estimate the user's emotions and adjust the writing style based on those emotions. For example, if the user is tense, the writing unit can provide a simple and easily readable style. If the user is relaxed, it can also provide a style that includes detailed information. Furthermore, if the user is in a hurry, it can provide a style that gets straight to the point. By adjusting the writing style according to the user's emotions, readability is improved. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The writing unit can adjust the level of detail in the writing based on the importance of the patents during the writing process. For example, the writing unit prioritizes writing important patent information. The writing unit can also write less important patent information concisely. Furthermore, the writing unit can adjust the level of detail in the writing based on the importance of the patent information. This allows important information to be included preferentially by adjusting the level of detail in the writing based on the importance of the patents. Some or all of the above processing in the writing unit may be performed using AI, for example, or without AI. For example, the writing unit can input the importance of the patent information into a generating AI and have the generating AI perform the adjustment of the level of detail in the writing.

[0106] The writing unit can estimate the user's emotions and adjust the length of the writing based on the estimated emotions. For example, if the user is nervous, the writing unit can provide short, concise writing. It can also provide detailed writing if the user is relaxed. Furthermore, if the user is in a hurry, it can provide brief writing. This improves readability by adjusting the length of the writing 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The writing unit can determine the priority of writing based on the patent filing date during the writing process. For example, the writing unit prioritizes writing the most recent patent information. It can also postpone writing older patent information. Furthermore, the writing unit can adjust the order of writing based on the filing date. This allows for the prioritization of the most recent information by determining the writing priority based on the patent filing date. Some or all of the above processing in the writing unit may be performed using AI, for example, or not using AI. For example, the writing unit can input patent filing date information into a generating AI and have the generating AI perform the determination of writing priority.

[0108] The search automation unit can estimate the user's emotions and adjust the search automation method based on the estimated emotions. For example, if the user is nervous, the search automation unit can provide a simple and highly visual search automation method. It can also provide a more detailed search automation method if the user is relaxed. Furthermore, if the user is in a hurry, the search automation unit can provide a concise search automation method. This improves search efficiency by adjusting the search automation method 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The automated search unit can select the optimal search method by referring to the past search history of patents during automated searches. For example, the automated search unit can select the optimal search method based on search methods previously used by the user. The automated search unit can also select an effective search method from the user's past search history. Furthermore, the automated search unit can analyze the user's past search history and select the most efficient search method. In this way, the optimal search method can be selected by referring to past search history. Some or all of the above processes in the automated search unit may be performed using AI, for example, or without AI. For example, the automated search unit can input the user's past search history into a generating AI and have the generating AI select the optimal search method.

[0110] The search automation unit can estimate the user's emotions and determine the priority of search automation based on the estimated emotions. For example, if the user is stressed, the search automation unit will prioritize important search automation. It can also prioritize detailed search automation if the user is relaxed. Furthermore, if the user is in a hurry, the search automation unit can prioritize the most important search automation. This allows for prioritizing important searches by determining the priority of search automation 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The automated search unit can select the optimal search method by considering the geographical location information of patents during automated searches. For example, the automated search unit can prioritize searching for patent information related to a specific region. The automated search unit can also select the optimal search method based on geographical location information. Furthermore, the automated search unit can display search results while considering geographical location information. This allows for the selection of the optimal search method by considering geographical location information. Some or all of the above-described processes in the automated search unit may be performed using AI, for example, or without AI. For example, the automated search unit can input the geographical location information of patents into a generating AI and have the generating AI select the optimal search method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception unit can analyze user input in real time and automatically suggest relevant patent information based on that input. For example, if a user enters keywords related to a specific technical field, the reception unit will immediately display patent information related to those keywords. The reception unit can also automatically assign relevant patent classification codes based on the user's input. Furthermore, the reception unit can display summaries of relevant patent documents based on the user's input. This allows users to quickly find relevant patent information based on their input.

[0114] The meeting system can estimate the user's emotions and adjust the meeting's progress based on those estimates. For example, if the user is nervous, it can provide a more relaxing approach. If the user is relaxed, it can provide a more detailed approach. Furthermore, if the user is in a hurry, it can provide a more efficient approach. By adjusting the meeting's progress according to the user's emotions, the efficiency of the meeting is improved.

[0115] The writing unit can analyze the user's past writing style when writing patent content and suggest the optimal writing style. For example, it can automatically apply specific expressions and writing styles that the user has used in the past. The writing unit can also extract and suggest effective expressions from the user's past writing history. Furthermore, the writing unit can automatically adjust the writing of the patent content based on the user's writing style. This improves the quality of patent content writing by taking the user's past writing style into consideration.

[0116] The search automation unit can estimate the user's emotions and adjust the search automation method based on those emotions. For example, if the user is stressed, it can provide a simple and highly visual search automation method. If the user is relaxed, it can provide a more detailed search automation method. Furthermore, if the user is in a hurry, it can provide a concise search automation method. By adjusting the search automation method according to the user's emotions, the efficiency of searches is improved.

[0117] The reception unit can analyze user input and automatically suggest relevant patent information based on that input. For example, if a user enters keywords related to a specific technical field, the reception unit will immediately display patent information related to those keywords. The reception unit can also automatically assign relevant patent classification codes based on the user's input. Furthermore, the reception unit can display summaries of relevant patent documents based on the user's input. This allows users to quickly find relevant patent information based on their input.

[0118] The reception system can estimate the user's emotions and adjust the patent content input interface based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick patent content entry. In this way, the input interface is adjusted according to the user's emotions, making patent content entry easier.

[0119] The search unit can analyze the user's past search history and suggest the optimal search method. For example, it can automatically display frequently used search keywords as suggestions. The search unit can also extract and suggest effective search methods from the user's past search history. Furthermore, the search unit can prioritize displaying relevant patent information based on the user's search history. This improves search efficiency by considering the user's past search history.

[0120] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimation. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how search results are displayed according to the user's emotions, visibility is improved.

[0121] The research department can improve the accuracy of its searches by considering the interrelationships between patents. For example, it can improve the accuracy of its searches based on related patent information. Furthermore, the research department can analyze the interrelationships between patents and supplement the search results. In addition, the research department can adjust the level of detail in the search based on the interrelationships between patents. This improves the accuracy of the search by considering the interrelationships between patents.

[0122] The reporting system can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read report. If the user is relaxed, it can provide a report with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise report. By adjusting the report's presentation according to the user's emotions, readability is improved.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk receives the patent details to be filed. The reception desk provides an interface for the user to input the patent details in text format, for example. The reception desk can also accept input using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text. Furthermore, the reception desk can accept input using image input. For example, the reception desk scans handwritten patent details and converts them into digital data. Step 2: The search unit searches the patent information platform based on the information entered by the reception unit. The search unit can, for example, search the Japan Patent Office database to obtain relevant patent information. The search unit can also search commercial patent databases to obtain patent information. For example, the search unit can search for patent information using patent classification codes. Furthermore, the search unit can also search for patent information using keyword searches. For example, the search unit can extract keywords related to the patent content and use them to search for patent information. Step 3: The research department verifies the patent content, conducts a preliminary keyword search, and performs a full patent subject search based on the information obtained by the search department. For example, the research department verifies the patent content by reviewing patent documents in detail. The research department can also conduct a preliminary keyword search using patent classification codes. For example, the research department extracts relevant keywords based on patent classification codes. Furthermore, the research department can also perform a full patent subject search. For example, the research department conducts a technical evaluation of patent documents. Step 4: The reporting department prepares a report for patent application and amendment based on the information obtained by the research department. For example, the reporting department prepares a patent application. The reporting department can also prepare a patent amendment proposal. For example, the reporting department proposes amendments to the patent content. Furthermore, the reporting department can also prepare a summary of the research results. For example, the reporting department summarizes the results of the patent search and includes them in the report.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, search unit, investigation unit, and reporting unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input patent content using the touch panel 38A or microphone 38B of the smart device 14. The search unit searches the patent information platform using the identification processing unit 290 of the data processing unit 12. The investigation unit verifies patent content and conducts preliminary keyword research using the identification processing unit 290 of the data processing unit 12. The reporting unit creates a report for patent application and amendment using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, search unit, investigation unit, and reporting unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input patent content by voice using the microphone 238 of the smart glasses 214. The search unit searches the patent information platform using the identification processing unit 290 of the data processing unit 12. The investigation unit verifies patent content and conducts preliminary keyword research using the identification processing unit 290 of the data processing unit 12. The reporting unit creates a report for patent application and amendment using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, search unit, investigation unit, and reporting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input patent content by voice using the microphone 238 of the headset terminal 314. The search unit searches the patent information platform using the identification processing unit 290 of the data processing unit 12. The investigation unit verifies patent content and conducts preliminary keyword research using the identification processing unit 290 of the data processing unit 12. The reporting unit creates a report for patent application and amendment using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, search unit, investigation unit, and reporting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can input patent content by voice using the microphone 238 of the robot 414. The search unit searches the patent information platform using the identification processing unit 290 of the data processing unit 12. The investigation unit verifies patent content and conducts preliminary keyword research using the identification processing unit 290 of the data processing unit 12. The reporting unit creates a report for patent application and amendment using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A reception desk where you enter the details of the patent you wish to file, A search unit that searches the patent information platform based on the information entered by the reception unit, Based on the information obtained by the aforementioned search unit, the research unit conducts verification of the patent content, preliminary keyword research, and main research on the patent subject matter. The reporting department prepares reports for patent applications and amendments based on the information obtained by the aforementioned research department, Equipped with A system characterized by the following features. (Note 2) The system includes a consultation section where patent applicants can meet with an AI agent in advance and make revisions as needed. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have a writing department that specializes in writing patent search results. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a search automation unit that automates searches on the patent information platform. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for patent content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We analyze the user's past patent application history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering patent details, the input is filtered based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering patent information, the system prioritizes inputting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering patent details, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, During the search, adjust the level of detail in the search results based on the importance of the patent information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, When searching, different search algorithms are applied depending on the patent category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, It estimates the user's sentiment and adjusts the length of search results based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, When searching, search results are prioritized based on the patent filing date. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the order of search results is adjusted based on the relevance of the patents. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned investigation department, We estimate user sentiment and adjust the survey criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned investigation department, During the search, consider the interrelationships between patents to improve the accuracy of the search. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned investigation department, During the investigation, the attribute information of the patent applicant will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned investigation department, It estimates the user's sentiment and adjusts the order in which the survey results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned investigation department, During the investigation, the geographical distribution of patents should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned investigation department, During the investigation, refer to relevant patent literature to improve the accuracy of the investigation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned report section is, We estimate the user's emotions and adjust the way the report is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned report section is, When preparing the report, adjust the level of detail based on the importance of the patent. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned report section is, When creating a report, different report generation algorithms are applied depending on the patent category. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned report section is, The system estimates the user's sentiment and adjusts the length of the report based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned report section is, When preparing the report, prioritize the reports based on the patent filing date. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned report section is, When preparing the report, adjust the order of the reports based on the relevance of the patents. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned meeting section, It estimates the user's emotions and adjusts the meeting process based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned meeting section, During meetings, the system selects the optimal approach by referring to the user's past meeting history. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned meeting section, It estimates the user's emotions and determines the priority of meetings based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned meeting section, During meetings, the optimal approach is selected by considering the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned lighting unit is It estimates the user's emotions and adjusts the writing style based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned lighting unit is When writing, adjust the level of detail in the writing based on the importance of the patent. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned lighting unit is It estimates the user's emotions and adjusts the length of the writing based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned lighting unit is When writing, prioritize writing based on the patent filing date. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned search automation unit, It estimates the user's sentiment and adjusts the search automation method based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned search automation unit, When automating the search, the system selects the optimal search method by referring to the patent's past search history. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned search automation unit, It estimates user sentiment and prioritizes search automation based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned search automation unit, When automating searches, the optimal search method is selected by considering the geographical location information of the patents. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 where you enter the details of the patent you wish to file, A search unit that searches the patent information platform based on the information entered by the reception unit, Based on the information obtained by the aforementioned search unit, the research unit conducts verification of the patent content, preliminary keyword research, and main research on the patent subject matter. The reporting department prepares reports for patent applications and amendments based on the information obtained by the aforementioned research department, Equipped with A system characterized by the following features.

2. The system includes a consultation section where patent applicants can meet with an AI agent in advance and make revisions as needed. The system according to feature 1.

3. We have a writing department that specializes in writing patent search results. The system according to feature 1.

4. It includes a search automation unit that automates searches on the patent information platform. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface for patent content based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is We analyze the user's past patent application history and suggest the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When entering patent details, the input is filtered based on the user's current projects and areas of interest. The system according to feature 1.

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

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