system

Abstract

An object of the system according to the embodiment is to effectively appeal the approach to the SDGs without expertise.SOLUTION: A system includes a collection unit, an analysis unit, and a generation unit. The collection part collects business contents and basic information. The analysis part analyzes the information collected by the collection part and analyzes the activity contents of the enterprise. The generation part selects an activity target and generates a template on the basis of a result analyzed by the analysis part.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] With conventional technology, it was difficult for small and medium-sized enterprises and sole proprietors without specialized knowledge to effectively promote their efforts toward the SDGs.

[0005] The system according to the embodiment aims to effectively promote efforts toward the SDGs, even without specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects business details and basic information. The analysis unit analyzes the information collected by the collection unit and analyzes the company's activities. The generation unit selects activity goals and generates templates based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows people to effectively promote their efforts toward the SDGs, even if they do not have specialized knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 SDG promotion support system according to an embodiment of the present invention collects business details and basic information, analyzes them with a generation AI, and then selects activity goals and generates templates. The SDG promotion support system analyzes corporate activities using inputs of business details and basic information, information from blogs and e-mail newsletters, and information on strengths and areas of expertise via chat, and then comprehensively selects activity goals and outputs templates. For example, in the SDG promotion support system, a user inputs business details and basic information, such as the company's industry, the services and products it offers, and the number of employees. This information is then input into the generation AI. Next, in the SDG promotion support system, the generation AI imports information about the company's activities from blogs and e-mail newsletters. For example, information about environmental protection activities and social contribution activities is extracted from past articles and newsletters published by the company. Furthermore, in the SDG promotion support system, a user inputs information about the company's strengths and areas of expertise via chat, such as areas in which the company is particularly focused and points of differentiation from other companies. This information is also input into the generation AI. Next, the SDG Promotion Support System uses a generation AI to analyze the input information and analyze the company's activities. For example, it identifies which SDG goals the company is contributing to and organizes those activities. Next, the generation AI identifies the company's activity goals and generates a template. For example, it proposes SDG goals the company should work toward and a specific action plan for achieving them. Finally, the SDG Promotion Support System provides the template generated by the generation AI to the user. This allows small and medium-sized enterprises and sole proprietors to effectively promote their SDG efforts without specialized knowledge. This allows the SDG Promotion Support System to effectively analyze the company's activities and generate a template for externally promoting their SDG efforts. For example, if a company is engaged in environmental protection activities, it can organize its specific activities and results and easily create materials to promote them to external parties.Additionally, if a company is focusing on social contribution activities, it can create documents based on those activities that demonstrate its contribution to the SDGs.

[0029] The SDGs promotion support system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects business details and basic information. Examples of the business details and basic information include, but are not limited to, financial information, product information, and marketing information. The collection unit collects information entered by a user, such as the company's industry, the services and products it offers, and the number of employees. The collection unit can also import information about the company's activities from blogs, e-mail newsletters, and the like. For example, the collection unit extracts information about the company's environmental protection activities and social contribution activities from past articles and newsletters. The collection unit can also input information about a company's strengths and areas of expertise in chat format. For example, the collection unit inputs information about the company's areas of particular focus and points of differentiation from other companies. Some or all of the above-described processing by the collection unit may be performed using or without the generation AI. For example, the collection unit inputs information entered by a user into the generation AI, which then analyzes and collects the information. The analysis unit analyzes the information collected by the collection unit and analyzes the company's activities. The analysis may be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit may use the generation AI to identify which SDGs a company is contributing to and organize its activities. The analysis unit may also evaluate the company's contribution to the SDGs based on its activities. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit inputs information collected by the collection unit into the generation AI, which then analyzes the information and analyzes the company's activities. The generation unit generates a selection of activity goals and templates based on the results of the analysis by the analysis unit. The generation may be performed in the form of, for example, a report template, a plan template, or the like, but is not limited to these examples. For example, the generation unit may use the generation AI to propose SDGs that a company should address and specific action plans for achieving them. The generation unit may also provide the generated templates to users.Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the results of the analysis by the analysis unit into the generation AI, which then generates a template and provides it to the user. In this way, the SDGs appeal support system according to the embodiment can collect and analyze business details and basic information, and select activity goals and generate templates.

[0030] The collection unit can retrieve information about a company's activities from blogs or e-mail newsletters. Examples of blogs or e-mail newsletters include, but are not limited to, industry-specific blogs and official company e-mail newsletters. For example, the collection unit extracts information about environmental protection activities and social contribution activities from articles and newsletters previously published by the company. The collection unit can also retrieve information about a company's activities from blogs, e-mail newsletters, etc. using a generation AI. For example, the collection unit inputs the URL of a company's blog or e-mail newsletter into the generation AI, which then analyzes the content and extracts information. This makes it possible to retrieve information about a company's activities from blogs, e-mail newsletters, etc.

[0031] The collection unit can input information about a company's competitive advantages and areas of expertise in chat format. Examples of chat formats include, but are not limited to, text chat, voice chat, and video chat. The collection unit can input, for example, areas in which the company is particularly focused and points of differentiation from other companies. The collection unit can also input information about a company's strengths and areas of expertise in chat format using the generation AI. For example, the collection unit inputs questions about a company's strengths and areas of expertise to the generation AI, and the generation AI analyzes the answers to collect information. This allows information about a company's strengths and areas of expertise to be input in chat format.

[0032] The analysis unit can identify the extent to which a company contributes to a specific SDG goal and organize the details of its activities. SDG goals include, but are not limited to, Goal 1 (End poverty) and Goal 13 (Take concrete action to combat climate change). The analysis unit, for example, uses the generation AI to identify which SDG goals a company contributes to and organizes the details of its activities. The analysis unit can also evaluate the degree of contribution to SDG goals based on the details of the company's activities. For example, the analysis unit inputs the details of a company's activities into the generation AI, which analyzes the details and evaluates the degree of contribution to the SDG goals. This makes it possible to identify which SDG goals a company contributes to and organize the details of its activities.

[0033] The generation unit can propose SDG goals that the company should achieve and a detailed action plan for achieving them. The detailed action plan may include, but is not limited to, an implementation schedule, necessary resources, and evaluation criteria. For example, the generation unit may use a generation AI to propose SDG goals that the company should work toward and a specific action plan for achieving them. The generation unit can also provide the generated action plan to a user. For example, the generation unit inputs the company's activities into the generation AI, which then analyzes the content and proposes an action plan. This makes it possible to propose SDG goals that the company should work toward and a specific action plan for achieving them.

[0034] The generation unit can provide the generated template to the user. Examples of templates include, but are not limited to, report templates and plan templates. The generation unit, for example, uses a generation AI to generate a template based on the company's activities and provides it to the user. The generation unit can also customize the generated template. For example, the generation unit inputs the company's activities into the generation AI, which then analyzes the content to generate a template and provides it to the user. In this way, the generated template can be provided to the user.

[0035] The collection unit can analyze the company's past activity history and select an appropriate information collection method. Past activity history includes, but is not limited to, past project data, performance data, etc. For example, the collection unit allows the generation AI to prioritize collection of related information based on the company's history of past environmental protection activities. The collection unit can also analyze the content of email newsletters sent by the company in the past, allowing the generation AI to collect information on similar topics. The collection unit can also analyze the company's past blog posts, allowing the generation AI to collect new information related to those contents. This allows the company's past activity history to be analyzed and the optimal information collection method to be selected.

[0036] When collecting information, the collection unit can filter the information based on the company's current projects and areas of interest. Current projects and areas of interest include, but are not limited to, research and development projects, marketing, and the like. For example, the collection unit can prioritize collecting information related to projects currently underway by the company. The collection unit can also filter and collect information related to areas in which the company is interested (e.g., renewable energy). The collection unit can also select and collect highly relevant information based on the company's current activities. This makes it possible to filter information based on the company's current projects and areas of interest.

[0037] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit causes the generation AI to analyze the voice data and collect related information. Furthermore, when the user uses text input, the collection unit can also cause the generation AI to analyze the text data and collect related information. Furthermore, when the user uploads an image, the collection unit can also cause the generation AI to analyze the image data and collect related information. This makes it possible to select the optimal collection means depending on the user's input method.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the company. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit can prioritize collecting information about environmental protection activities in the area where the company is located. The collection unit can also collect information about local SDG-related events based on the geographical location of the company. The collection unit can also prioritize collecting information about laws, regulations, and guidelines related to the company's location. This allows highly relevant information to be prioritized by taking into account the geographical location information of the company.

[0039] When collecting information, the collection unit can analyze the company's social media activities and collect related information. Social media activities include, but are not limited to, for example, the content of posts, the number of followers, and the engagement rate. For example, the collection unit can analyze the content of the company's social media posts and collect related SDG activity information. The collection unit can also analyze the reactions of the company's followers and customers and collect information of high interest. The collection unit can also grasp trends on the company's social media and collect related information. This makes it possible to analyze the company's social media activities and collect related information.

[0040] When collecting information, the collection unit can adjust the collection method by reflecting the company's past feedback. Past feedback includes, but is not limited to, customer reviews, survey results, etc. For example, the collection unit adjusts the information collection method of the generation AI based on feedback provided by the company in the past. The collection unit can also customize the type and scope of information to be collected by reflecting the company's past feedback. The collection unit can also adjust the frequency and timing of information collection by the generation AI based on the company's feedback. In this way, the collection method can be customized by reflecting the company's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the company's activities. Examples of the priority of the activities include, but are not limited to, the impact on sales and the social impact. In the analysis unit, for example, the generation AI performs a detailed analysis of important company activities. In addition, the analysis unit can also have the generation AI adjust the depth of the analysis according to the importance of the company's activities. In addition, the analysis unit can have the generation AI determine the level of detail of the analysis based on the priority of the company's activities. This makes it possible to adjust the level of detail of the analysis based on the importance of the company's activities.

[0042] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the industry and size of the company. Examples of industry and size include, but are not limited to, manufacturing, services, large companies, and small and medium-sized enterprises. For example, the analysis unit applies an analysis algorithm for small and medium-sized enterprises, and the generation AI performs the analysis. The analysis unit can also apply an analysis algorithm for sole proprietors, and the generation AI performs the analysis. The analysis unit can also apply an analysis algorithm according to the company's industry, and the generation AI performs the analysis. This makes it possible to apply different analysis algorithms depending on the company's industry and size.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results. Past analysis results include, but are not limited to, past reports, databases, etc. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the company's past analysis results. The analysis unit can also refer to the company's past analysis data and have the generation AI optimize the analysis method. The analysis unit can also analyze the company's past analysis results and have the generation AI improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the company's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of the company's activity content. Submission timing includes, but is not limited to, for example, the end of a fiscal year or the end of a quarter. For example, the analysis unit prioritizes analysis of activity content for which the company's submission deadline is approaching. The analysis unit can also cause the generation AI to determine the order of analysis based on the timing of submission of the company's activity content. The analysis unit can also cause the generation AI to adjust the priority of analysis, taking into account the timing of submission of the company's activity content. This makes it possible to determine the priority of analysis based on the timing of submission of the company's activity content.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relationships between the company's activities. Examples of the relationships between the activities include, but are not limited to, dependencies between projects and common goals. In the analysis unit, for example, the generation AI determines the order of analysis based on the relationships between the company's activities. The analysis unit can also cause the generation AI to adjust the priority of analysis according to the relationships between the company's activities. The analysis unit can also cause the generation AI to optimize the order of analysis by taking into account the relationships between the company's activities. This makes it possible to adjust the order of analysis based on the relationships between the company's activities.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the company's level of expertise. Examples of knowledge levels include, but are not limited to, beginner, intermediate, and expert. For example, if the company's level of expertise is low, the analysis unit can cause the generation AI to provide analysis results in simple language. Alternatively, if the company's level of expertise is high, the analysis unit can cause the generation AI to provide detailed analysis results using technical terms. Additionally, the analysis unit can adjust the way the generation AI expresses the analysis results according to the company's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the company's level of expertise.

[0047] During generation, the generation unit can adjust the level of detail of the template based on the priority of the company's activities. Examples of the priority of the activities include, but are not limited to, the impact on sales and the social impact. For example, the generation unit causes the generation AI to provide a detailed template for important company activities. The generation unit can also cause the generation AI to adjust the level of detail of the template according to the importance of the company's activities. The generation unit can also cause the generation AI to determine the level of detail of the template based on the priority of the company's activities. This makes it possible to adjust the level of detail of the template based on the importance of the company's activities.

[0048] During generation, the generation unit can apply an appropriate template generation algorithm depending on the industry and size of the company. Examples of industry and size include, but are not limited to, manufacturing, service, large companies, and small and medium-sized enterprises. For example, the generation unit applies a template generation algorithm for small and medium-sized enterprises, and the generation AI generates a template. The generation unit can also apply a template generation algorithm for sole proprietors, and the generation AI can generate a template. The generation unit can also apply a template generation algorithm depending on the industry of the company, and the generation AI can generate a template. This makes it possible to apply different template generation algorithms depending on the industry and size of the company.

[0049] During generation, the generation unit can improve the accuracy of generation by referring to the company's past template generation results. Past template generation results include, but are not limited to, past reports, databases, etc. The generation unit, for example, causes the generation AI to improve the accuracy of generation based on the company's past template generation results. The generation unit can also refer to the company's past template data and cause the generation AI to optimize the generation method. The generation unit can also analyze the company's past template generation results and cause the generation AI to improve the accuracy of generation. In this way, the generation accuracy can be improved by referring to the company's past template generation results.

[0050] During generation, the generation unit can determine the priority of templates based on the timing of submission of the company's activity content. Examples of submission timing include, but are not limited to, the end of a fiscal year or the end of a quarter. For example, the generation unit can prioritize the reflection of activity content for which the company's submission deadline is approaching in the template. The generation unit can also cause the generation AI to determine the order of templates based on the timing of submission of the company's activity content. The generation unit can also cause the generation AI to adjust the priority of templates taking into account the timing of submission of the company's activity content. This makes it possible to determine the priority of templates based on the timing of submission of the company's activity content.

[0051] During generation, the generation unit can adjust the order of the templates based on the relationships between the company's activities. Examples of the relationships between the activities include, but are not limited to, dependencies between projects and common goals. In the generation unit, for example, the generation AI determines the order of the templates based on the relationships between the company's activities. The generation unit can also cause the generation AI to adjust the priority of the templates according to the relationships between the company's activities. The generation unit can also cause the generation AI to optimize the order of the templates, taking into account the relationships between the company's activities. This makes it possible to adjust the order of the templates based on the relationships between the company's activities.

[0052] The generation unit may adjust the use of technical terminology in the template according to the company's level of expertise during generation. Examples of knowledge levels include, but are not limited to, beginner, intermediate, and expert. For example, if the company's level of expertise is low, the generation unit may cause the generation AI to provide a template in simple language. Alternatively, if the company's level of expertise is high, the generation unit may cause the generation AI to provide a detailed template using technical terminology. The generation unit may also cause the generation AI to adjust the way the template is expressed according to the company's level of expertise. This allows the use of technical terminology in the template to be adjusted according to the company's level of expertise.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can analyze the company's social media activities and collect related information. Social media activities include, but are not limited to, for example, the content of posts, the number of followers, and engagement rates. For example, the collection unit can analyze the content of the company's social media posts and collect related SDG activity information. The collection unit can also analyze the reactions of the company's followers and customers and collect information of high interest. The collection unit can also grasp trends on the company's social media and collect related information. This makes it possible to analyze the company's social media activities and collect related information.

[0055] The analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results. Past analysis results include, but are not limited to, past reports, databases, etc. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the company's past analysis results. The analysis unit can also refer to the company's past analysis data and the generation AI can optimize the analysis method. The analysis unit can also analyze the company's past analysis results and the generation AI can improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the company's past analysis results.

[0056] The generation unit can improve the accuracy of generation by referring to the company's past template generation results. Past template generation results include, but are not limited to, past reports, databases, etc. The generation unit, for example, causes the generation AI to improve the accuracy of generation based on the company's past template generation results. The generation unit can also refer to the company's past template data and cause the generation AI to optimize the generation method. The generation unit can also analyze the company's past template generation results and cause the generation AI to improve the accuracy of generation. In this way, the generation accuracy can be improved by referring to the company's past template generation results.

[0057] The collection unit can prioritize collection of highly relevant information taking into account the geographical location information of the company. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the collection unit can prioritize collection of information on environmental protection activities in the area where the company is located. The collection unit can also collect information on local SDG-related events based on the geographical location of the company. The collection unit can also prioritize collection of information on laws, regulations, and guidelines related to the company's location. This allows for prioritized collection of highly relevant information taking into account the geographical location information of the company.

[0058] The collection unit can analyze the company's past activity history and select an appropriate information collection method. Past activity history includes, but is not limited to, past project data, performance data, etc. For example, the collection unit allows the generation AI to prioritize collection of related information based on the company's history of past environmental protection activities. The collection unit can also analyze the content of email newsletters sent by the company in the past, allowing the generation AI to collect information on similar topics. The collection unit can also analyze the company's past blog posts, allowing the generation AI to collect new information related to those contents. This allows the company's past activity history to be analyzed and the optimal information collection method to be selected.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects business details and basic information. Business details and basic information include financial information, product information, marketing information, etc. The collection unit collects information entered by the user, such as the company's industry, the services and products it offers, and the number of employees. It can also import information about the company's activities from blogs, e-mail newsletters, etc. For example, it can extract information about environmental protection activities and social contribution activities from articles and newsletters that the company has previously published. In addition, information about the company's strengths and areas of expertise can be entered in chat format. Some or all of the above-mentioned processing in the collection unit may be performed using or without the use of generation AI. Step 2: The analysis unit analyzes the information collected by the collection unit and analyzes the company's activities. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI can be used to identify which SDG goals the company is contributing to and organize its activities. It can also evaluate the company's contribution to the SDG goals based on its activities. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. Step 3: The generation unit selects activity goals and generates templates based on the results of the analysis by the analysis unit. The generation is performed in the form of a report template, a plan template, or the like. For example, the generation AI can be used to propose SDG goals that companies should work toward and specific action plans for achieving them. The generated templates can also be provided to users. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI.

[0061] (Example 2) The SDG promotion support system according to an embodiment of the present invention collects business details and basic information, analyzes them with a generation AI, and then selects activity goals and generates templates. The SDG promotion support system analyzes corporate activities using inputs of business details and basic information, information from blogs and e-mail newsletters, and information on strengths and areas of expertise via chat, and then comprehensively selects activity goals and outputs templates. For example, in the SDG promotion support system, a user inputs business details and basic information, such as the company's industry, the services and products it offers, and the number of employees. This information is then input into the generation AI. Next, in the SDG promotion support system, the generation AI imports information about the company's activities from blogs and e-mail newsletters. For example, information about environmental protection activities and social contribution activities is extracted from past articles and newsletters published by the company. Furthermore, in the SDG promotion support system, a user inputs information about the company's strengths and areas of expertise via chat, such as areas in which the company is particularly focused and points of differentiation from other companies. This information is also input into the generation AI. Next, the SDG Promotion Support System uses a generation AI to analyze the input information and analyze the company's activities. For example, it identifies which SDG goals the company is contributing to and organizes those activities. Next, the generation AI identifies the company's activity goals and generates a template. For example, it proposes SDG goals the company should work toward and a specific action plan for achieving them. Finally, the SDG Promotion Support System provides the template generated by the generation AI to the user. This allows small and medium-sized enterprises and sole proprietors to effectively promote their SDG efforts without specialized knowledge. This allows the SDG Promotion Support System to effectively analyze the company's activities and generate a template for externally promoting their SDG efforts. For example, if a company is engaged in environmental protection activities, it can organize its specific activities and results and easily create materials to promote them to external parties.Additionally, if a company is focusing on social contribution activities, it can create documents based on those activities that demonstrate its contribution to the SDGs.

[0062] The SDGs promotion support system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects business details and basic information. Examples of the business details and basic information include, but are not limited to, financial information, product information, and marketing information. The collection unit collects information entered by a user, such as the company's industry, the services and products it offers, and the number of employees. The collection unit can also import information about the company's activities from blogs, e-mail newsletters, and the like. For example, the collection unit extracts information about the company's environmental protection activities and social contribution activities from past articles and newsletters. The collection unit can also input information about a company's strengths and areas of expertise in chat format. For example, the collection unit inputs information about the company's areas of particular focus and points of differentiation from other companies. Some or all of the above-described processing by the collection unit may be performed using or without the generation AI. For example, the collection unit inputs information entered by a user into the generation AI, which then analyzes and collects the information. The analysis unit analyzes the information collected by the collection unit and analyzes the company's activities. The analysis may be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit may use the generation AI to identify which SDGs a company is contributing to and organize its activities. The analysis unit may also evaluate the company's contribution to the SDGs based on its activities. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit inputs information collected by the collection unit into the generation AI, which then analyzes the information and analyzes the company's activities. The generation unit generates a selection of activity goals and templates based on the results of the analysis by the analysis unit. The generation may be performed in the form of, for example, a report template, a plan template, or the like, but is not limited to these examples. For example, the generation unit may use the generation AI to propose SDGs that a company should address and specific action plans for achieving them. The generation unit may also provide the generated templates to users.Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the results of the analysis by the analysis unit into the generation AI, which then generates a template and provides it to the user. In this way, the SDGs appeal support system according to the embodiment can collect and analyze business details and basic information, and select activity goals and generate templates.

[0063] The collection unit can retrieve information about a company's activities from blogs or e-mail newsletters. Examples of blogs or e-mail newsletters include, but are not limited to, industry-specific blogs and official company e-mail newsletters. For example, the collection unit extracts information about environmental protection activities and social contribution activities from articles and newsletters previously published by the company. The collection unit can also retrieve information about a company's activities from blogs, e-mail newsletters, etc. using a generation AI. For example, the collection unit inputs the URL of a company's blog or e-mail newsletter into the generation AI, which then analyzes the content and extracts information. This makes it possible to retrieve information about a company's activities from blogs, e-mail newsletters, etc.

[0064] The collection unit can input information about a company's competitive advantages and areas of expertise in chat format. Examples of chat formats include, but are not limited to, text chat, voice chat, and video chat. The collection unit can input, for example, areas in which the company is particularly focused and points of differentiation from other companies. The collection unit can also input information about a company's strengths and areas of expertise in chat format using the generation AI. For example, the collection unit inputs questions about a company's strengths and areas of expertise to the generation AI, and the generation AI analyzes the answers to collect information. This allows information about a company's strengths and areas of expertise to be input in chat format.

[0065] The analysis unit can identify the extent to which a company contributes to a specific SDG goal and organize the details of its activities. SDG goals include, but are not limited to, Goal 1 (End poverty) and Goal 13 (Take concrete action to combat climate change). The analysis unit, for example, uses the generation AI to identify which SDG goals a company contributes to and organizes the details of its activities. The analysis unit can also evaluate the degree of contribution to SDG goals based on the details of the company's activities. For example, the analysis unit inputs the details of a company's activities into the generation AI, which analyzes the details and evaluates the degree of contribution to the SDG goals. This makes it possible to identify which SDG goals a company contributes to and organize the details of its activities.

[0066] The generation unit can propose SDG goals that the company should achieve and a detailed action plan for achieving them. The detailed action plan may include, but is not limited to, an implementation schedule, necessary resources, and evaluation criteria. For example, the generation unit may use a generation AI to propose SDG goals that the company should work toward and a specific action plan for achieving them. The generation unit can also provide the generated action plan to a user. For example, the generation unit inputs the company's activities into the generation AI, which then analyzes the content and proposes an action plan. This makes it possible to propose SDG goals that the company should work toward and a specific action plan for achieving them.

[0067] The generation unit can provide the generated template to the user. Examples of templates include, but are not limited to, report templates and plan templates. The generation unit, for example, uses a generation AI to generate a template based on the company's activities and provides it to the user. The generation unit can also customize the generated template. For example, the generation unit inputs the company's activities into the generation AI, which then analyzes the content to generate a template and provides it to the user. In this way, the generated template can be provided to the user.

[0068] The collection unit can estimate the user's emotional state and adjust the timing of information collection based on the estimated emotional state. Emotional states include, but are not limited to, emotion classifications such as joy, sadness, and anger. For example, if the user is feeling stressed, the collection unit can cause the generation AI to reduce the frequency of information collection and collect information when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can also adjust the timing of information collection so as not to interrupt the user's work. Furthermore, if the user is tired, the collection unit can cause the generation AI to temporarily stop information collection and resume it after the user has rested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the timing of information collection to be adjusted based on the user's emotions.

[0069] The collection unit can analyze the company's past activity history and select an appropriate information collection method. Past activity history includes, but is not limited to, past project data, performance data, etc. For example, the collection unit allows the generation AI to prioritize collection of related information based on the company's history of past environmental protection activities. The collection unit can also analyze the content of email newsletters sent by the company in the past, allowing the generation AI to collect information on similar topics. The collection unit can also analyze the company's past blog posts, allowing the generation AI to collect new information related to those contents. This allows the company's past activity history to be analyzed and the optimal information collection method to be selected.

[0070] When collecting information, the collection unit can filter the information based on the company's current projects and areas of interest. Current projects and areas of interest include, but are not limited to, research and development projects, marketing, and the like. For example, the collection unit can prioritize collecting information related to projects currently underway by the company. The collection unit can also filter and collect information related to areas in which the company is interested (e.g., renewable energy). The collection unit can also select and collect highly relevant information based on the company's current activities. This makes it possible to filter information based on the company's current projects and areas of interest.

[0071] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the collection unit causes the generation AI to analyze the voice data and collect related information. Furthermore, when the user uses text input, the collection unit can also cause the generation AI to analyze the text data and collect related information. Furthermore, when the user uploads an image, the collection unit can also cause the generation AI to analyze the image data and collect related information. This makes it possible to select the optimal collection means depending on the user's input method.

[0072] The collection unit can estimate the user's emotional state and determine the priority of information to be collected based on the estimated emotional state. Emotional states include, but are not limited to, emotion classifications such as joy, sadness, and anger. For example, when the user is feeling stressed, the collection unit causes the generation AI to prioritize collecting information of high importance. Furthermore, when the user is relaxed, the collection unit can also cause the generation AI to collect detailed information. Furthermore, when the user is in a hurry, the collection unit can also prioritize information that the generation AI can collect quickly. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the priority of information to be collected based on the user's emotions.

[0073] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the company. Geographical location information includes, but is not limited to, GPS data, address information, etc. For example, the collection unit can prioritize collecting information about environmental protection activities in the area where the company is located. The collection unit can also collect information about local SDG-related events based on the geographical location of the company. The collection unit can also prioritize collecting information about laws, regulations, and guidelines related to the company's location. This allows highly relevant information to be prioritized by taking into account the geographical location information of the company.

[0074] When collecting information, the collection unit can analyze the company's social media activities and collect related information. Social media activities include, but are not limited to, for example, the content of posts, the number of followers, and the engagement rate. For example, the collection unit can analyze the content of the company's social media posts and collect related SDG activity information. The collection unit can also analyze the reactions of the company's followers and customers and collect information of high interest. The collection unit can also grasp trends on the company's social media and collect related information. This makes it possible to analyze the company's social media activities and collect related information.

[0075] When collecting information, the collection unit can adjust the collection method by reflecting the company's past feedback. Past feedback includes, but is not limited to, customer reviews, survey results, etc. For example, the collection unit adjusts the information collection method of the generation AI based on feedback provided by the company in the past. The collection unit can also customize the type and scope of information to be collected by reflecting the company's past feedback. The collection unit can also adjust the frequency and timing of information collection by the generation AI based on the company's feedback. In this way, the collection method can be customized by reflecting the company's past feedback.

[0076] The analysis unit can estimate the user's emotional state and adjust the way the analysis is presented based on the estimated emotional state. Emotional states include, but are not limited to, emotion classifications such as joy, sadness, and anger. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result through the generation AI. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the way the analysis is presented to be adjusted based on the user's emotion.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the company's activities. Examples of the priority of the activities include, but are not limited to, the impact on sales and the social impact. In the analysis unit, for example, the generation AI performs a detailed analysis of important company activities. In addition, the analysis unit can also have the generation AI adjust the depth of the analysis according to the importance of the company's activities. In addition, the analysis unit can have the generation AI determine the level of detail of the analysis based on the priority of the company's activities. This makes it possible to adjust the level of detail of the analysis based on the importance of the company's activities.

[0078] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the industry and size of the company. Examples of industry and size include, but are not limited to, manufacturing, services, large companies, and small and medium-sized enterprises. For example, the analysis unit applies an analysis algorithm for small and medium-sized enterprises, and the generation AI performs the analysis. The analysis unit can also apply an analysis algorithm for sole proprietors, and the generation AI performs the analysis. The analysis unit can also apply an analysis algorithm according to the company's industry, and the generation AI performs the analysis. This makes it possible to apply different analysis algorithms depending on the company's industry and size.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results. Past analysis results include, but are not limited to, past reports, databases, etc. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the company's past analysis results. The analysis unit can also refer to the company's past analysis data and have the generation AI optimize the analysis method. The analysis unit can also analyze the company's past analysis results and have the generation AI improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the company's past analysis results.

[0080] The analysis unit can estimate the user's emotional state and adjust the length of the analysis based on the estimated emotional state. Emotional states include, but are not limited to, emotion classifications such as joy, sadness, and anger. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. The emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the length of the analysis to be adjusted based on the user's emotions.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the timing of submission of the company's activity content. Submission timing includes, but is not limited to, for example, the end of a fiscal year or the end of a quarter. For example, the analysis unit prioritizes analysis of activity content for which the company's submission deadline is approaching. The analysis unit can also cause the generation AI to determine the order of analysis based on the timing of submission of the company's activity content. The analysis unit can also cause the generation AI to adjust the priority of analysis, taking into account the timing of submission of the company's activity content. This makes it possible to determine the priority of analysis based on the timing of submission of the company's activity content.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relationships between the company's activities. Examples of the relationships between the activities include, but are not limited to, dependencies between projects and common goals. In the analysis unit, for example, the generation AI determines the order of analysis based on the relationships between the company's activities. The analysis unit can also cause the generation AI to adjust the priority of analysis according to the relationships between the company's activities. The analysis unit can also cause the generation AI to optimize the order of analysis by taking into account the relationships between the company's activities. This makes it possible to adjust the order of analysis based on the relationships between the company's activities.

[0083] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the company's level of expertise. Examples of knowledge levels include, but are not limited to, beginner, intermediate, and expert. For example, if the company's level of expertise is low, the analysis unit can cause the generation AI to provide analysis results in simple language. Alternatively, if the company's level of expertise is high, the analysis unit can cause the generation AI to provide detailed analysis results using technical terms. Additionally, the analysis unit can adjust the way the generation AI expresses the analysis results according to the company's level of expertise. This allows the use of technical terms in the analysis to be adjusted according to the company's level of expertise.

[0084] The generation unit can estimate the user's emotional state and adjust the expression method of the generated template based on the estimated emotional state. Emotional states include, but are not limited to, emotion categories such as joy, sadness, and anger. For example, if the user is nervous, the generation AI can provide a simple, highly visible template. Furthermore, if the user is relaxed, the generation AI can provide a detailed template. Furthermore, if the user is in a hurry, the generation AI can provide a template that focuses on the main points. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the expression method of the generated template to be adjusted based on the user's emotion.

[0085] During generation, the generation unit can adjust the level of detail of the template based on the priority of the company's activities. Examples of the priority of the activities include, but are not limited to, the impact on sales and the social impact. For example, the generation unit causes the generation AI to provide a detailed template for important company activities. The generation unit can also cause the generation AI to adjust the level of detail of the template according to the importance of the company's activities. The generation unit can also cause the generation AI to determine the level of detail of the template based on the priority of the company's activities. This makes it possible to adjust the level of detail of the template based on the importance of the company's activities.

[0086] During generation, the generation unit can apply an appropriate template generation algorithm depending on the industry and size of the company. Examples of industry and size include, but are not limited to, manufacturing, service, large companies, and small and medium-sized enterprises. For example, the generation unit applies a template generation algorithm for small and medium-sized enterprises, and the generation AI generates a template. The generation unit can also apply a template generation algorithm for sole proprietors, and the generation AI can generate a template. The generation unit can also apply a template generation algorithm depending on the industry of the company, and the generation AI can generate a template. This makes it possible to apply different template generation algorithms depending on the industry and size of the company.

[0087] During generation, the generation unit can improve the accuracy of generation by referring to the company's past template generation results. Past template generation results include, but are not limited to, past reports, databases, etc. The generation unit, for example, causes the generation AI to improve the accuracy of generation based on the company's past template generation results. The generation unit can also refer to the company's past template data and cause the generation AI to optimize the generation method. The generation unit can also analyze the company's past template generation results and cause the generation AI to improve the accuracy of generation. In this way, the generation accuracy can be improved by referring to the company's past template generation results.

[0088] The generation unit can estimate the user's emotional state and adjust the length of the generated template based on the estimated emotional state. Emotional states include, but are not limited to, emotion categories such as joy, sadness, and anger. For example, if the user is in a hurry, the generation AI can provide a short, concise template. Alternatively, if the user is relaxed, the generation AI can provide a detailed template. Alternatively, if the user is excited, the generation AI can provide a visually stimulating template. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the length of the generated template to be adjusted based on the user's emotion.

[0089] During generation, the generation unit can determine the priority of templates based on the timing of submission of the company's activity content. Examples of submission timing include, but are not limited to, the end of a fiscal year or the end of a quarter. For example, the generation unit can prioritize the reflection of activity content for which the company's submission deadline is approaching in the template. The generation unit can also cause the generation AI to determine the order of templates based on the timing of submission of the company's activity content. The generation unit can also cause the generation AI to adjust the priority of templates taking into account the timing of submission of the company's activity content. This makes it possible to determine the priority of templates based on the timing of submission of the company's activity content.

[0090] During generation, the generation unit can adjust the order of the templates based on the relationships between the company's activities. Examples of the relationships between the activities include, but are not limited to, dependencies between projects and common goals. In the generation unit, for example, the generation AI determines the order of the templates based on the relationships between the company's activities. The generation unit can also cause the generation AI to adjust the priority of the templates according to the relationships between the company's activities. The generation unit can also cause the generation AI to optimize the order of the templates, taking into account the relationships between the company's activities. This makes it possible to adjust the order of the templates based on the relationships between the company's activities.

[0091] The generation unit may adjust the use of technical terminology in the template according to the company's level of expertise during generation. Examples of knowledge levels include, but are not limited to, beginner, intermediate, and expert. For example, if the company's level of expertise is low, the generation unit may cause the generation AI to provide a template in simple language. Alternatively, if the company's level of expertise is high, the generation unit may cause the generation AI to provide a detailed template using technical terminology. The generation unit may also cause the generation AI to adjust the way the template is expressed according to the company's level of expertise. This allows the use of technical terminology in the template to be adjusted according to the company's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit estimates the user's emotional state using the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of information collection by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and analyzes the company's activities. The generation unit, realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, selects activity goals and generates templates based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit estimates the user's emotional state using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the timing of information collection by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information and analyzes the company's activities. The generation unit, realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, selects activity goals and generates templates based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit estimates the user's emotional state using the camera 42 and microphone 238 of the headset terminal 314, and adjusts the timing of information collection by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and analyzes the company's activities. The generation unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and generates activity goal recommendations and templates based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit estimates the user's emotional state using the camera 42 and microphone 238 of the robot 414, and adjusts the timing of information collection by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and the company's activities. The generation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and generates activity goal recommendations and templates based on the analysis results.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The collection unit can analyze the company's social media activities and collect related information. Social media activities include, but are not limited to, for example, the content of posts, the number of followers, and engagement rates. For example, the collection unit can analyze the content of the company's social media posts and collect related SDG activity information. The collection unit can also analyze the reactions of the company's followers and customers and collect information of high interest. The collection unit can also grasp trends on the company's social media and collect related information. This makes it possible to analyze the company's social media activities and collect related information.

[0094] The analysis unit can improve the accuracy of the analysis by referring to the company's past analysis results. Past analysis results include, but are not limited to, past reports, databases, etc. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the company's past analysis results. The analysis unit can also refer to the company's past analysis data and the generation AI can optimize the analysis method. The analysis unit can also analyze the company's past analysis results and the generation AI can improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to the company's past analysis results.

[0095] The generation unit can improve the accuracy of generation by referring to the company's past template generation results. Past template generation results include, but are not limited to, past reports, databases, etc. The generation unit, for example, causes the generation AI to improve the accuracy of generation based on the company's past template generation results. The generation unit can also refer to the company's past template data and cause the generation AI to optimize the generation method. The generation unit can also analyze the company's past template generation results and cause the generation AI to improve the accuracy of generation. In this way, the generation accuracy can be improved by referring to the company's past template generation results.

[0096] The collection unit can prioritize collection of highly relevant information taking into account the geographical location information of the company. Examples of geographical location information include, but are not limited to, GPS data and address information. For example, the collection unit can prioritize collection of information on environmental protection activities in the area where the company is located. The collection unit can also collect information on local SDG-related events based on the geographical location of the company. The collection unit can also prioritize collection of information on laws, regulations, and guidelines related to the company's location. This allows for prioritized collection of highly relevant information taking into account the geographical location information of the company.

[0097] The collection unit can analyze the company's past activity history and select an appropriate information collection method. Past activity history includes, but is not limited to, past project data, performance data, etc. For example, the collection unit allows the generation AI to prioritize collection of related information based on the company's history of past environmental protection activities. The collection unit can also analyze the content of email newsletters sent by the company in the past, allowing the generation AI to collect information on similar topics. The collection unit can also analyze the company's past blog posts, allowing the generation AI to collect new information related to those contents. This allows the company's past activity history to be analyzed and the optimal information collection method to be selected.

[0098] The collection unit can estimate the user's emotional state and adjust the timing of information collection based on the estimated emotional state. Emotional states include, but are not limited to, emotion classifications such as joy, sadness, and anger. For example, if the user is feeling stressed, the collection unit can cause the generation AI to reduce the frequency of information collection and collect information when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can also adjust the timing of information collection so as not to interrupt the user's work. Furthermore, if the user is tired, the collection unit can cause the generation AI to temporarily stop information collection and resume it after the user has rested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the timing of information collection to be adjusted based on the user's emotions.

[0099] The collection unit can estimate the user's emotional state and determine the priority of information to be collected based on the estimated emotional state. Emotional states include, but are not limited to, emotion classifications such as joy, sadness, and anger. For example, when the user is feeling stressed, the collection unit causes the generation AI to prioritize collecting information of high importance. Furthermore, when the user is relaxed, the collection unit can also cause the generation AI to collect detailed information. Furthermore, when the user is in a hurry, the collection unit can also prioritize information that the generation AI can collect quickly. The emotion estimation is realized using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the priority of information to be collected based on the user's emotions.

[0100] The analysis unit can estimate the user's emotional state and adjust the way the analysis is presented based on the estimated emotional state. Emotional states include, but are not limited to, emotion classifications such as joy, sadness, and anger. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result through the generation AI. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the way the analysis is presented to be adjusted based on the user's emotion.

[0101] The analysis unit can estimate the user's emotional state and adjust the length of the analysis based on the estimated emotional state. Emotional states include, but are not limited to, emotion classifications such as joy, sadness, and anger. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. The emotion estimation is achieved using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the length of the analysis to be adjusted based on the user's emotions.

[0102] The generation unit can estimate the user's emotional state and adjust the expression method of the generated template based on the estimated emotional state. Emotional states include, but are not limited to, emotion categories such as joy, sadness, and anger. For example, if the user is nervous, the generation AI can provide a simple, highly visible template. Furthermore, if the user is relaxed, the generation AI can provide a detailed template. Furthermore, if the user is in a hurry, the generation AI can provide a template that focuses on the main points. The emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the expression method of the generated template to be adjusted based on the user's emotion.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects business details and basic information. Business details and basic information include financial information, product information, marketing information, etc. The collection unit collects information entered by the user, such as the company's industry, the services and products it offers, and the number of employees. It can also import information about the company's activities from blogs, e-mail newsletters, etc. For example, it can extract information about environmental protection activities and social contribution activities from articles and newsletters that the company has previously published. In addition, information about the company's strengths and areas of expertise can be entered in chat format. Some or all of the above-mentioned processing in the collection unit may be performed using or without the use of generation AI. Step 2: The analysis unit analyzes the information collected by the collection unit and analyzes the company's activities. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI can be used to identify which SDG goals the company is contributing to and organize its activities. It can also evaluate the company's contribution to the SDG goals based on its activities. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. Step 3: The generation unit selects activity goals and generates templates based on the results of the analysis by the analysis unit. The generation is performed in the form of a report template, a plan template, or the like. For example, the generation AI can be used to propose SDG goals that companies should work toward and specific action plans for achieving them. The generated templates can also be provided to users. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0107] 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, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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 AI 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 AI 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. The collection department collects business details and basic information, an analysis unit that analyzes the information collected by the collection unit and analyzes the activities of the company; a generation unit that selects an activity goal and generates a template based on the results of the analysis by the analysis unit. A system characterized by:

2. The collecting unit Import information about your company's activities from your blog or newsletter 2. The system of claim 1.

3. The collecting unit Enter information about your company's competitive advantages and areas of expertise in a chat format 2. The system of claim 1.

4. The analysis unit Identify the extent to which companies contribute to specific SDGs and organize their activities 2. The system of claim 1.

5. The generation unit Propose the SDGs that companies should achieve and a detailed action plan to achieve them 2. The system of claim 1.

6. The generation unit Providing the generated template to the user 2. The system of claim 1.

7. The collecting unit Estimate the user's emotional state and adjust the timing of information collection based on the estimated emotional state.

2. The system of claim 1.

8. The collecting unit Analyze the company's past activity history and select the appropriate information collection method 2. The system of claim 1.

9. The collecting unit When gathering information, filter based on the company's current projects and areas of interest 2. The system of claim 1.

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