System

Abstract

An object of a system according to an embodiment is to efficiently collect proposals published by municipalities all over the country and to match the proposals with products and solutions of companies.SOLUTION: A system according to an embodiment includes a proposal collection unit, an analysis unit, a matching unit, and a plan generation unit. The proposal collection unit collects proposals made public by municipalities all over the country. The analysis unit analyzes the proposals collected by the proposal collection unit. The matching unit matches the proposal analyzed by the analysis unit with a product or a solution of the company. The planning paper generation unit automatically generates a planning paper according to the proposal matched by the matching unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had the challenge of making it difficult to efficiently collect the large number of proposals published by local governments across the country and match them with corporate products and solutions.

[0005] The system according to the embodiment aims to efficiently collect proposals published by local governments across the country and match them with corporate products and solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a proposal collection unit, an analysis unit, a matching unit, and a proposal generation unit. The proposal collection unit collects proposals made public by local governments across the country. The analysis unit analyzes the proposals collected by the proposal collection unit. The matching unit matches the proposals analyzed by the analysis unit with company products and solutions. The proposal generation unit automatically generates proposals tailored to the proposals matched by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect proposals published by local governments across the country and match them with corporate products and solutions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The matching planner system according to an embodiment of the present invention is a system that collects proposals published by local governments across the country, automatically matches them with corporate products and solutions, and supports preparations for applications for appropriate projects. As a result, the matching planner system enables companies to quickly and efficiently prepare applications for proposals published by local governments across the country.

[0029] A matching planner system according to an embodiment includes a proposal collection unit, an analysis unit, a matching unit, and a proposal generation unit. The proposal collection unit collects proposals published by local governments across the country. For example, the proposal collection unit automatically collects proposals from the official websites of local governments. The proposal collection unit can also collect proposals from public databases of local governments. The proposal collection unit can also collect proposals from email distribution services of local governments. For example, the proposal collection unit automatically collects proposals from the official websites of local governments using web scraping technology. Proposals can be collected from the public databases of local governments using APIs. Proposals are collected from the email distribution services of local governments by analyzing the content of emails. The analysis unit analyzes the proposals collected by the proposal collection unit. For example, the analysis unit analyzes the content of the proposals using generation AI (e.g., text generation AI or multimodal generation AI). The analysis unit can also process bureaucratic formats and technical terms contained in proposals. The analysis unit can also extract proposal requirements and identify the optimal proposal for a company. For example, the generation AI analyzes the proposal's text data and extracts important keywords and phrases. The generation AI processes bureaucratic formats and technical terms based on data it has previously learned. The generation AI extracts proposal requirements using natural language processing technology. The matching unit matches the proposal analyzed by the analysis unit with the company's products and solutions. For example, the matching unit references the company's product database to identify products and solutions that meet the proposal requirements. The matching unit can also reference the company's past project history and success stories when matching. The matching unit can also take into account the company's detailed technical information and patent information when matching. For example, the matching unit analyzes the company's product database to identify products and solutions that meet the proposal requirements. The generation AI references a database of past project history and success stories.The generation AI analyzes a product database and takes into account detailed technical information and patent information. The proposal generation unit automatically generates a proposal tailored to the proposal matched by the matching unit. For example, the proposal generation unit uses the generation AI to analyze the proposal requirements and, based on those requirements, creates a proposal that emphasizes the features of the company's product or solution. The proposal generation unit can also create a proposal by referencing templates and formats of past successful proposals. The proposal generation unit can also create a proposal by taking into account the company's brand guidelines and design policies. For example, the generation AI analyzes the proposal requirements and, based on those requirements, creates a proposal that emphasizes the features of the company's product or solution. The generation AI references templates and formats of past successful proposals stored in a database. The generation AI considers the company's brand guidelines and design policies based on data it has previously learned. This allows the matching planner system according to the embodiment to quickly and efficiently prepare applications for proposals published by local governments across the country. For example, companies can maximize business opportunities by applying for proposals that best suit their products or solutions. Furthermore, by utilizing generative AI, proposal analysis and project planning can be automated, reducing the burden on personnel, allowing companies to apply for more proposals and improving their competitiveness.

[0030] The proposal collection unit can analyze not only public data from local governments but also related news articles and reports to provide more comprehensive information. For example, the generation AI automatically collects related news articles and reports in addition to public data from local governments and analyzes the background information of the proposal. For example, the generation AI collects the latest news and research reports on regional revitalization projects to complement the content of the proposal. The generation AI can also analyze the content of news articles and reports to extract information related to the requirements of the proposal. For example, the generation AI analyzes the text data of news articles and reports to extract important keywords and phrases. This allows for a deeper understanding of the background information of the proposal.

[0031] The analysis unit can refer to past examples of successful and unsuccessful proposals and prioritize the extraction of proposals with a high probability of success. For example, the generation AI creates a database of past examples of successful and unsuccessful proposals and references it when analyzing new proposals. For example, the generation AI can extract elements of successful regional development projects in the past and prioritize the extraction of similar proposals. The generation AI can also analyze past examples of failure and identify elements to avoid similar failures. For example, the generation AI can analyze the factors behind past failures and evaluate the risks of new proposals based on that analysis. This allows the generation AI to prioritize the extraction of proposals with a high probability of success.

[0032] The proposal collection unit can also collect information from the local government's social media accounts and blogs, and analyze unofficial information as well. In the proposal collection unit, for example, the generation AI collects information not only from the local government's official website, but also from social media accounts and blogs, and analyzes the background information of the proposal. For example, the generation AI collects the latest local trends and resident opinions from the local government's social media posts. The generation AI can also extract information related to the proposal from the local government's blog articles. For example, the generation AI analyzes the text data of social media posts and blog articles and extracts important keywords and phrases. This makes it possible to analyze the background information of the proposal, including unofficial information.

[0033] The analysis unit can compare similar proposals between different municipalities and identify common trends and needs. For example, the analysis unit compares proposals collected by the generation AI from different municipalities and identifies common trends and needs. For example, the generation AI extracts common points between regional development projects proposed by multiple municipalities at the same time. The generation AI can also analyze proposals from different municipalities and identify common needs. For example, the generation AI analyzes the text data of the proposals and extracts keywords and phrases to identify common trends and needs. This makes it possible to identify common trends and needs.

[0034] The matching unit can refer to a company's past project history and success stories to make the optimal match. For example, the generation AI creates a database of a company's past project history and references it when matching with a new proposal. For example, the generation AI extracts elements of past successful projects and prioritizes matching with similar proposals. The generation AI can also analyze a company's success stories to make the optimal match. For example, the generation AI analyzes the factors behind a company's success stories and matches with a new proposal based on that. This makes it possible to make the optimal match based on a company's past project history and success stories.

[0035] When matching companies' products and solutions, the matching department can discover new business opportunities by matching products from different industries and fields as well. For example, the matching department's generative AI analyzes product databases from different industries and fields to discover new business opportunities. For example, the generative AI might propose a proposal to apply medical technology to other industries. The generative AI can also develop new markets by matching products from different industries and fields. For example, the generative AI might propose a proposal to apply manufacturing technology to the service industry. This makes it possible to match products from different industries and fields as well and discover new business opportunities.

[0036] When analyzing a company's product database, the matching unit also takes into account the product's market rating and user reviews, allowing for more practical matching. In the matching unit, for example, the generation AI analyzes a company's product database and performs matching by taking into account the product's market rating and user reviews. For example, the generation AI preferentially matches highly rated products to proposals. The generation AI can also analyze user reviews and evaluate the product's practicality. For example, the generation AI analyzes online reviews and customer feedback to evaluate the product's practicality. This allows for more practical matching by taking into account the product's market rating and user reviews.

[0037] The proposal generation unit can create proposals with a high probability of success by referring to templates and formats of past successful proposals. In the proposal generation unit, for example, the generation AI automatically generates a new proposal by referring to templates and formats of past successful proposals. For example, the generation AI creates a new proposal based on the structure and expression of a successful proposal. The generation AI can also analyze past success cases and create a proposal based on that. For example, the generation AI analyzes the factors behind past success cases and creates a new proposal based on that. In this way, proposals with a high probability of success can be created by referring to templates and formats of past successful proposals.

[0038] The proposal generation unit can create a proposal that matches the company's image, taking into account the company's brand guidelines and design policies. In the proposal generation unit, for example, the generation AI refers to the company's brand guidelines and design policies and automatically generates a proposal that matches the company's image. For example, the generation AI creates a proposal that reflects the company's logo and color scheme. The generation AI can also adjust the layout and design of the proposal based on the company's design policies. For example, the generation AI analyzes the company's design policies and adjusts the layout and design of the proposal based on them. This allows the creation of a proposal that matches the company's image, taking into account the company's brand guidelines and design policies.

[0039] The proposal generation unit can automatically translate a proposal into different languages ​​when automatically generating it, creating a proposal that can be used for international proposals. For example, when the generation AI automatically generates a proposal, the proposal generation unit can automatically translate it into different languages, creating a proposal that can be used for international proposals. For example, the generation AI translates into multiple languages, such as English, French, and Chinese. The generation AI can also take technical terms and industry jargon into account to improve the accuracy of the translation. For example, the generation AI learns technical terms and industry jargon in advance and improves the accuracy of the translation based on that. This makes it possible to automatically translate a proposal into different languages ​​and create a proposal that can be used for international proposals.

[0040] When automatically generating a proposal, the proposal generation unit makes extensive use of visual elements and infographics, making it possible to create a visually appealing proposal. For example, when the generation AI automatically generates a proposal, the proposal generation unit makes extensive use of visual elements and infographics, making it possible to create a visually appealing proposal. For example, the generation AI visually displays data using graphs and charts. The generation AI can also organize information using infographics and present it in an easy-to-understand manner. For example, the generation AI learns how to visualize data and organize information, and creates visual elements and infographics based on that. This makes extensive use of visual elements and infographics, making it possible to create a visually appealing proposal.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The proposal collection unit collects information not only from the local government's official website and public databases, but also from the local government's social media accounts and blogs, allowing it to analyze unofficial information as well. For example, the generation AI can collect the latest local trends and resident opinions from the local government's social media posts. The generation AI can also extract information related to the proposal from the local government's blog posts. This allows it to analyze the background information of the proposal, including unofficial information.

[0043] The analysis unit can compare similar proposals between different municipalities and identify common trends and needs. For example, the generation AI compares proposals collected from different municipalities and identifies common trends and needs. The generation AI extracts common points between regional development projects proposed by multiple municipalities at the same time. The generation AI can also analyze proposals from different municipalities and identify common needs. This makes it possible to identify common trends and needs.

[0044] When matching companies' products and solutions, the matching department can discover new business opportunities by matching products from different industries and fields. For example, the generative AI analyzes product databases from different industries and fields to discover new business opportunities. The generative AI proposes proposals for applying medical technology to other industries. The generative AI can also develop new markets by matching products from different industries and fields. This makes it possible to discover new business opportunities by matching products from different industries and fields.

[0045] When analyzing a company's product database, the matching unit also takes into account the product's market rating and user reviews, enabling more practical matching. For example, the generation AI analyzes a company's product database and performs matching while taking into account the product's market rating and user reviews. The generation AI preferentially matches highly rated products to proposals. The generation AI can also analyze user reviews and evaluate the product's practicality. This allows for more practical matching by taking into account the product's market rating and user reviews.

[0046] When automatically generating a proposal, the proposal generation unit can automatically translate it into different languages, creating a proposal that can be used for international proposals. For example, when the generation AI automatically generates a proposal, it can automatically translate it into different languages, creating a proposal that can be used for international proposals. The generation AI translates into multiple languages, including English, French, and Chinese. The generation AI can also take technical terms and industry jargon into account to improve the accuracy of the translation. This makes it possible to automatically translate it into different languages, creating a proposal that can be used for international proposals.

[0047] When automatically generating a proposal, the proposal generation unit can make use of many visual elements and infographics to create a visually appealing proposal. For example, when the generation AI automatically generates a proposal, it can make use of many visual elements and infographics to create a visually appealing proposal. The generation AI visually displays data using graphs and charts. The generation AI can also use infographics to organize information and present it in an easy-to-understand manner. This makes it possible to make use of many visual elements and infographics to create a visually appealing proposal.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The proposal collection unit collects proposals made public by local governments across the country. For example, the proposal collection unit automatically collects proposals from the official websites of local governments. The proposal collection unit can also collect proposals from the public databases of local governments. It can also collect proposals from the email distribution services of local governments. Specifically, proposals are automatically collected from the official websites of local governments using web scraping technology, proposals are collected from the public databases of local governments using APIs, and proposals are collected by analyzing the content of emails. Step 2: The analysis unit analyzes the proposals collected by the proposal collection unit. For example, it uses generative AI (text generation AI or multimodal generation AI) to analyze the content of the proposals and process bureaucratic formats and technical terms. It then extracts proposal requirements and identifies the proposal that is best suited to the company. Specifically, the generative AI analyzes the proposal text data, extracts important keywords and phrases, processes bureaucratic formats and technical terms based on pre-trained data, and uses natural language processing technology to extract proposal requirements. Step 3: The matching unit matches the proposal analyzed by the analysis unit with the company's products and solutions. For example, it references the company's product database to identify products and solutions that meet the proposal's requirements. It can also reference the company's past project history and success stories to perform matching. It also takes into account the company's detailed technical information and patent information to perform matching. Specifically, it analyzes the company's product database to identify products and solutions that meet the proposal's requirements, references the past project history and success stories databased by the generation AI, and analyzes the product database to consider the detailed technical information and patent information. Step 4: The proposal generation unit automatically generates a proposal tailored to the proposal matched by the matching unit. For example, the generation AI can be used to analyze the proposal requirements and, based on that, create a proposal that emphasizes the features of the company's products or solutions. The proposal can also be created by referencing templates and formats of past successful proposals. Furthermore, the proposal is created taking into account the company's brand guidelines and design policies. Specifically, the generation AI analyzes the proposal requirements and, based on that, creates a proposal that emphasizes the features of the company's products or solutions, referring to templates and formats of past successful proposals that the generation AI has stored in a database, and taking into account the company's brand guidelines and design policies based on the data it has learned in advance.

[0050] (Example 2) The matching planner system according to an embodiment of the present invention is a system that collects proposals published by local governments across the country, automatically matches them with corporate products and solutions, and supports preparations for applications for appropriate projects. As a result, the matching planner system enables companies to quickly and efficiently prepare applications for proposals published by local governments across the country.

[0051] A matching planner system according to an embodiment includes a proposal collection unit, an analysis unit, a matching unit, and a proposal generation unit. The proposal collection unit collects proposals published by local governments across the country. For example, the proposal collection unit automatically collects proposals from the official websites of local governments. The proposal collection unit can also collect proposals from public databases of local governments. The proposal collection unit can also collect proposals from email distribution services of local governments. For example, the proposal collection unit automatically collects proposals from the official websites of local governments using web scraping technology. Proposals can be collected from the public databases of local governments using APIs. Proposals are collected from the email distribution services of local governments by analyzing the content of emails. The analysis unit analyzes the proposals collected by the proposal collection unit. For example, the analysis unit analyzes the content of the proposals using generation AI (e.g., text generation AI or multimodal generation AI). The analysis unit can also process bureaucratic formats and technical terms contained in proposals. The analysis unit can also extract proposal requirements and identify the optimal proposal for a company. For example, the generation AI analyzes the proposal's text data and extracts important keywords and phrases. The generation AI processes bureaucratic formats and technical terms based on data it has previously learned. The generation AI extracts proposal requirements using natural language processing technology. The matching unit matches the proposal analyzed by the analysis unit with the company's products and solutions. For example, the matching unit references the company's product database to identify products and solutions that meet the proposal requirements. The matching unit can also reference the company's past project history and success stories when matching. The matching unit can also take into account the company's detailed technical information and patent information when matching. For example, the matching unit analyzes the company's product database to identify products and solutions that meet the proposal requirements. The generation AI references a database of past project history and success stories.The generation AI analyzes a product database and takes into account detailed technical information and patent information. The proposal generation unit automatically generates a proposal tailored to the proposal matched by the matching unit. For example, the proposal generation unit uses the generation AI to analyze the proposal requirements and, based on those requirements, creates a proposal that emphasizes the features of the company's product or solution. The proposal generation unit can also create a proposal by referencing templates and formats of past successful proposals. The proposal generation unit can also create a proposal by taking into account the company's brand guidelines and design policies. For example, the generation AI analyzes the proposal requirements and, based on those requirements, creates a proposal that emphasizes the features of the company's product or solution. The generation AI references templates and formats of past successful proposals stored in a database. The generation AI considers the company's brand guidelines and design policies based on data it has previously learned. This allows the matching planner system according to the embodiment to quickly and efficiently prepare applications for proposals published by local governments across the country. For example, companies can maximize business opportunities by applying for proposals that best suit their products or solutions. Furthermore, by utilizing generative AI, proposal analysis and project planning can be automated, reducing the burden on personnel, allowing companies to apply for more proposals and improving their competitiveness.

[0052] The proposal collection unit can analyze not only public data from local governments but also related news articles and reports to provide more comprehensive information. For example, the generation AI automatically collects related news articles and reports in addition to public data from local governments and analyzes the background information of the proposal. For example, the generation AI collects the latest news and research reports on regional revitalization projects to complement the content of the proposal. The generation AI can also analyze the content of news articles and reports to extract information related to the requirements of the proposal. For example, the generation AI analyzes the text data of news articles and reports to extract important keywords and phrases. This allows for a deeper understanding of the background information of the proposal.

[0053] The analysis unit can refer to past examples of successful and unsuccessful proposals and prioritize the extraction of proposals with a high probability of success. For example, the generation AI creates a database of past examples of successful and unsuccessful proposals and references it when analyzing new proposals. For example, the generation AI can extract elements of successful regional development projects in the past and prioritize the extraction of similar proposals. The generation AI can also analyze past examples of failure and identify elements to avoid similar failures. For example, the generation AI can analyze the factors behind past failures and evaluate the risks of new proposals based on that analysis. This allows the generation AI to prioritize the extraction of proposals with a high probability of success.

[0054] The analysis unit uses the emotion estimation function to estimate the local government's intentions and expectations regarding the proposal content, and can then propose the optimal proposal to the company based on that. In the analysis unit, for example, the generation AI analyzes the proposal content and uses the emotion estimation function to estimate the local government's intentions and expectations. For example, the generation AI extracts the points that the local government prioritizes from the proposal context and proposes the optimal proposal to the company based on that. The generation AI can also estimate the results that the local government expects from the proposal content and then propose the optimal proposal to the company based on that. For example, the generation AI analyzes the proposal's text data and calculates an emotion score to estimate the local government's intentions and expectations. This makes it possible to propose the optimal proposal based on the local government's intentions and expectations.

[0055] The proposal collection unit can also collect information from the local government's social media accounts and blogs, and analyze unofficial information as well. In the proposal collection unit, for example, the generation AI collects information not only from the local government's official website, but also from social media accounts and blogs, and analyzes the background information of the proposal. For example, the generation AI collects the latest local trends and resident opinions from the local government's social media posts. The generation AI can also extract information related to the proposal from the local government's blog articles. For example, the generation AI analyzes the text data of social media posts and blog articles and extracts important keywords and phrases. This makes it possible to analyze the background information of the proposal, including unofficial information.

[0056] The analysis unit can compare similar proposals between different municipalities and identify common trends and needs. For example, the analysis unit compares proposals collected by the generation AI from different municipalities and identifies common trends and needs. For example, the generation AI extracts common points between regional development projects proposed by multiple municipalities at the same time. The generation AI can also analyze proposals from different municipalities and identify common needs. For example, the generation AI analyzes the text data of the proposals and extracts keywords and phrases to identify common trends and needs. This makes it possible to identify common trends and needs.

[0057] The analysis unit uses the emotion estimation function to analyze the local government's emotions toward the proposal content, and can preferentially extract proposals with positive emotions. In the analysis unit, for example, the generation AI analyzes the proposal content and analyzes the local government's emotions using the emotion estimation function. For example, the generation AI extracts the local government's positive emotions from the proposal context and proposes the optimal proposal to the company based on that. The generation AI can also eliminate the local government's negative emotions from the proposal content and preferentially extract proposals with positive emotions. For example, the generation AI analyzes the proposal's text data and calculates an emotion score to estimate the local government's emotions. This allows proposals with positive emotions to be preferentially extracted.

[0058] The matching unit can refer to a company's past project history and success stories to make the optimal match. For example, the generation AI creates a database of a company's past project history and references it when matching with a new proposal. For example, the generation AI extracts elements of past successful projects and prioritizes matching with similar proposals. The generation AI can also analyze a company's success stories to make the optimal match. For example, the generation AI analyzes the factors behind a company's success stories and matches with a new proposal based on that. This makes it possible to make the optimal match based on a company's past project history and success stories.

[0059] The matching unit uses an emotion estimation function to analyze the emotions of company personnel and prioritizes proposing proposals that the personnel will be most interested in. For example, the generation AI in the matching unit analyzes the emotions of company personnel and prioritizes proposing proposals that the personnel will be most interested in. For example, the generation AI calculates an emotion score based on the personnel's past preference data and proposes proposals based on that. The generation AI can also monitor the personnel's emotions in real time and propose proposals at the optimal timing. For example, the generation AI analyzes the personnel's facial expressions and voice and presents proposals at the timing that they will be interested. This allows the proposals that the personnel will be most interested in to be proposed to the personnel with priority.

[0060] When matching companies' products and solutions, the matching department can discover new business opportunities by matching products from different industries and fields as well. For example, the matching department's generative AI analyzes product databases from different industries and fields to discover new business opportunities. For example, the generative AI might propose a proposal to apply medical technology to other industries. The generative AI can also develop new markets by matching products from different industries and fields. For example, the generative AI might propose a proposal to apply manufacturing technology to the service industry. This makes it possible to match products from different industries and fields as well and discover new business opportunities.

[0061] When analyzing a company's product database, the matching unit also takes into account the product's market rating and user reviews, allowing for more practical matching. In the matching unit, for example, the generation AI analyzes a company's product database and performs matching by taking into account the product's market rating and user reviews. For example, the generation AI preferentially matches highly rated products to proposals. The generation AI can also analyze user reviews and evaluate the product's practicality. For example, the generation AI analyzes online reviews and customer feedback to evaluate the product's practicality. This allows for more practical matching by taking into account the product's market rating and user reviews.

[0062] The matching unit uses an emotion estimation function to monitor the emotions of company personnel in real time and propose proposals at the optimal timing. In the matching unit, for example, the generation AI monitors the emotions of company personnel in real time and proposes proposals at the optimal timing. For example, the generation AI analyzes the personnel's facial expressions and voice and presents a proposal when it is time for them to become interested. The generation AI can also propose proposals at the optimal timing, taking into account the personnel's work schedule and the progress of the project. For example, the generation AI analyzes the personnel's work schedule and presents a proposal according to the progress of the project. This makes it possible to monitor the emotions of company personnel in real time and propose proposals at the optimal timing.

[0063] The proposal generation unit can create proposals with a high probability of success by referring to templates and formats of past successful proposals. In the proposal generation unit, for example, the generation AI automatically generates a new proposal by referring to templates and formats of past successful proposals. For example, the generation AI creates a new proposal based on the structure and expression of a successful proposal. The generation AI can also analyze past success cases and create a proposal based on that. For example, the generation AI analyzes the factors behind past success cases and creates a new proposal based on that. In this way, proposals with a high probability of success can be created by referring to templates and formats of past successful proposals.

[0064] The proposal generation unit can create a proposal that matches the company's image, taking into account the company's brand guidelines and design policies. In the proposal generation unit, for example, the generation AI refers to the company's brand guidelines and design policies and automatically generates a proposal that matches the company's image. For example, the generation AI creates a proposal that reflects the company's logo and color scheme. The generation AI can also adjust the layout and design of the proposal based on the company's design policies. For example, the generation AI analyzes the company's design policies and adjusts the layout and design of the proposal based on them. This allows the creation of a proposal that matches the company's image, taking into account the company's brand guidelines and design policies.

[0065] The proposal generation unit can use the emotion estimation function to estimate the local government's emotions regarding the contents of the proposal and emphasize content that elicits positive emotions. In the proposal generation unit, for example, the generation AI analyzes the contents of the proposal and uses the emotion estimation function to estimate the local government's emotions. For example, the generation AI creates a proposal that emphasizes expressions and elements that elicit positive emotions. The generation AI can also analyze the local government's emotions and adjust the content to eliminate negative emotions. For example, the generation AI analyzes the text data of the proposal and calculates an emotion score to estimate the local government's emotions. This makes it possible to create a proposal that emphasizes content that elicits positive emotions from the local government.

[0066] The proposal generation unit can automatically translate a proposal into different languages ​​when automatically generating it, creating a proposal that can be used for international proposals. For example, when the generation AI automatically generates a proposal, the proposal generation unit can automatically translate it into different languages, creating a proposal that can be used for international proposals. For example, the generation AI translates into multiple languages, such as English, French, and Chinese. The generation AI can also take technical terms and industry jargon into account to improve the accuracy of the translation. For example, the generation AI learns technical terms and industry jargon in advance and improves the accuracy of the translation based on that. This makes it possible to automatically translate a proposal into different languages ​​and create a proposal that can be used for international proposals.

[0067] When automatically generating a proposal, the proposal generation unit makes extensive use of visual elements and infographics, making it possible to create a visually appealing proposal. For example, when the generation AI automatically generates a proposal, the proposal generation unit makes extensive use of visual elements and infographics, making it possible to create a visually appealing proposal. For example, the generation AI visually displays data using graphs and charts. The generation AI can also organize information using infographics and present it in an easy-to-understand manner. For example, the generation AI learns how to visualize data and organize information, and creates visual elements and infographics based on that. This makes extensive use of visual elements and infographics, making it possible to create a visually appealing proposal.

[0068] The proposal generation unit uses an emotion estimation function to analyze the emotions of company personnel regarding the contents of the proposal, and can emphasize the content that will be most convincing to the personnel. In the proposal generation unit, for example, the generation AI analyzes the contents of the proposal and uses the emotion estimation function to analyze the emotions of company personnel. For example, the generation AI creates a proposal that emphasizes the content that will be most convincing to the personnel. The generation AI can also analyze the emotions of the personnel and adjust content that is difficult to convince. For example, the generation AI analyzes the text data of the proposal and calculates an emotion score to estimate the emotions of the personnel. This makes it possible to create a proposal that emphasizes the content that will be most convincing to the personnel.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The proposal collection unit collects information not only from the local government's official website and public databases, but also from the local government's social media accounts and blogs, allowing it to analyze unofficial information as well. For example, the generation AI can collect the latest local trends and resident opinions from the local government's social media posts. The generation AI can also extract information related to the proposal from the local government's blog posts. This allows it to analyze the background information of the proposal, including unofficial information.

[0071] The analysis unit can compare similar proposals between different municipalities and identify common trends and needs. For example, the generation AI compares proposals collected from different municipalities and identifies common trends and needs. The generation AI extracts common points between regional development projects proposed by multiple municipalities at the same time. The generation AI can also analyze proposals from different municipalities and identify common needs. This makes it possible to identify common trends and needs.

[0072] The analysis unit uses the emotion estimation function to infer the local government's intentions and expectations regarding the proposal content, and can then propose the optimal proposal to the company based on that. For example, the generation AI analyzes the proposal content and uses the emotion estimation function to infer the local government's intentions and expectations. The generation AI extracts the points that the local government values ​​from the proposal context and uses that to propose the optimal proposal to the company. The generation AI can also infer the results that the local government expects from the proposal content and use that to propose the optimal proposal to the company. This makes it possible to propose the optimal proposal based on the local government's intentions and expectations.

[0073] When matching companies' products and solutions, the matching department can discover new business opportunities by matching products from different industries and fields. For example, the generative AI analyzes product databases from different industries and fields to discover new business opportunities. The generative AI proposes proposals for applying medical technology to other industries. The generative AI can also develop new markets by matching products from different industries and fields. This makes it possible to discover new business opportunities by matching products from different industries and fields.

[0074] The matching unit uses an emotion estimation function to analyze the emotions of company personnel and prioritize proposals that will interest them most. For example, the generation AI analyzes the emotions of company personnel and prioritizes proposals that will interest them most. The generation AI calculates an emotion score based on the personnel's past preference data and proposes proposals based on that. The generation AI can also monitor the personnel's emotions in real time and propose proposals at the optimal timing. This allows the generation AI to prioritize proposals that will interest the company personnel most.

[0075] When analyzing a company's product database, the matching unit also takes into account the product's market rating and user reviews, enabling more practical matching. For example, the generation AI analyzes a company's product database and performs matching while taking into account the product's market rating and user reviews. The generation AI preferentially matches highly rated products to proposals. The generation AI can also analyze user reviews and evaluate the product's practicality. This allows for more practical matching by taking into account the product's market rating and user reviews.

[0076] The proposal generation unit can use the emotion estimation function to estimate the local government's emotions regarding the proposal content and emphasize content that elicits positive emotions. For example, the generation AI analyzes the content of the proposal and uses the emotion estimation function to estimate the local government's emotions. The generation AI creates a proposal that emphasizes expressions and elements that elicit positive emotions. The generation AI can also analyze the local government's emotions and adjust content to eliminate negative emotions. This makes it possible to create a proposal that emphasizes content that elicits positive emotions from the local government.

[0077] When automatically generating a proposal, the proposal generation unit can automatically translate it into different languages, creating a proposal that can be used for international proposals. For example, when the generation AI automatically generates a proposal, it can automatically translate it into different languages, creating a proposal that can be used for international proposals. The generation AI translates into multiple languages, including English, French, and Chinese. The generation AI can also take technical terms and industry jargon into account to improve the accuracy of the translation. This makes it possible to automatically translate it into different languages, creating a proposal that can be used for international proposals.

[0078] The proposal generation unit uses the emotion estimation function to analyze the emotions of company personnel regarding the contents of the proposal, and can emphasize the content that will be most convincing to the personnel. For example, the generation AI analyzes the contents of the proposal and uses the emotion estimation function to analyze the emotions of the company personnel. The generation AI creates a proposal that emphasizes the content that will be most convincing to the personnel. The generation AI can also analyze the emotions of the personnel and adjust the content that is difficult to convince. This makes it possible to create a proposal that emphasizes the content that will be most convincing to the personnel.

[0079] When automatically generating a proposal, the proposal generation unit can make use of many visual elements and infographics to create a visually appealing proposal. For example, when the generation AI automatically generates a proposal, it can make use of many visual elements and infographics to create a visually appealing proposal. The generation AI visually displays data using graphs and charts. The generation AI can also use infographics to organize information and present it in an easy-to-understand manner. This makes it possible to make use of many visual elements and infographics to create a visually appealing proposal.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The proposal collection unit collects proposals made public by local governments across the country. For example, the proposal collection unit automatically collects proposals from the official websites of local governments. The proposal collection unit can also collect proposals from the public databases of local governments. It can also collect proposals from the email distribution services of local governments. Specifically, proposals are automatically collected from the official websites of local governments using web scraping technology, proposals are collected from the public databases of local governments using APIs, and proposals are collected by analyzing the content of emails. Step 2: The analysis unit analyzes the proposals collected by the proposal collection unit. For example, it uses generative AI (text generation AI or multimodal generation AI) to analyze the content of the proposals and process bureaucratic formats and technical terms. It then extracts proposal requirements and identifies the proposal that is best suited to the company. Specifically, the generative AI analyzes the proposal text data, extracts important keywords and phrases, processes bureaucratic formats and technical terms based on pre-trained data, and uses natural language processing technology to extract proposal requirements. Step 3: The matching unit matches the proposal analyzed by the analysis unit with the company's products and solutions. For example, it references the company's product database to identify products and solutions that meet the proposal's requirements. It can also reference the company's past project history and success stories to perform matching. It also takes into account the company's detailed technical information and patent information to perform matching. Specifically, it analyzes the company's product database to identify products and solutions that meet the proposal's requirements, references the past project history and success stories databased by the generation AI, and analyzes the product database to consider the detailed technical information and patent information. Step 4: The proposal generation unit automatically generates a proposal tailored to the proposal matched by the matching unit. For example, the generation AI can be used to analyze the proposal requirements and, based on that, create a proposal that emphasizes the features of the company's products or solutions. The proposal can also be created by referencing templates and formats of past successful proposals. Furthermore, the proposal is created taking into account the company's brand guidelines and design policies. Specifically, the generation AI analyzes the proposal requirements and, based on that, creates a proposal that emphasizes the features of the company's products or solutions, referring to templates and formats of past successful proposals that the generation AI has stored in a database, and taking into account the company's brand guidelines and design policies based on the data it has learned in advance.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A proposal collection department that collects proposals published by local governments across the country; an analysis unit that analyzes the proposals collected by the proposal collection unit; a matching unit that matches the proposal analyzed by the analysis unit with products and solutions from companies; a proposal generation unit that automatically generates a proposal that matches the proposal matched by the matching unit. A system characterized by:

2. The proposal collection unit Analyze not only publicly available data from local governments but also related news articles and reports to provide more comprehensive information 2. The system of claim 1.

3. The proposal collection unit We also collect information from local government social media accounts and blogs, and analyze unofficial information.

2. The system of claim 1.

4. The matching unit We refer to companies' past project history and success stories to find the best match 2. The system of claim 1.

5. The matching unit When matching companies' products and solutions, we also match products from different industries and fields to discover new business opportunities.

2. The system of claim 1.

6. The proposal creation unit Refer to templates and formats of past successful proposals to create proposals with a high probability of success 2. The system of claim 1.

7. The proposal creation unit Estimate the local government's feelings about the contents of the proposal and emphasize the content that elicits positive feelings 2. The system of claim 1.

8. The analysis unit Estimate the local government's intentions and expectations regarding the proposal content, and then propose the most suitable proposal to the company based on that.

2. The system of claim 1.

Images