system

Abstract

The system according to the embodiment aims to efficiently and automatically generate corporate sustainability reports. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to corporate sustainability. The analysis unit analyzes the data collected by the collection unit and determines the structure of the report. The generation unit generates the content of each section based on the structure determined by the analysis unit. The provision unit integrates the content of each section generated by the generation unit and provides a final report.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to efficiently create corporate sustainability reports, and the process has been time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to efficiently and automatically generate corporate sustainability reports. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to corporate sustainability. The analysis unit analyzes the data collected by the collection unit and determines the structure of the report. The generation unit generates the content of each section based on the structure determined by the analysis unit. The provision unit integrates the content of each section generated by the generation unit and provides a final report. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently and automatically generate a corporate sustainability report. [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) An automatic sustainability report creation system according to an embodiment of the present invention is a system that automatically creates a sustainability report using a generation AI. This system collects data related to a company's sustainability, analyzes it using the generation AI, determines the report structure, generates the content of each section, and creates a final report. For example, data related to a company's sustainability includes information on environmental protection activities, social contribution activities, and governance. Data is collected, including details of environmental protection projects implemented by the company, the results of social contribution activities, and areas for governance improvement. The generation AI then analyzes the collected data and determines the report structure. For example, the content of each section is determined, such as a section on environmental protection activities, a section on social contribution activities, and a section on governance. The generation AI generates the content of each section based on the analysis results. For example, the section on environmental protection activities describes the details and results of projects implemented by the company. The section on social contribution activities describes the specific details and results of the company's activities. The section on governance describes the company's governance improvements and initiatives. Finally, the generation AI integrates the content of each section to create a final sustainability report. This report will comprehensively show a company's sustainability efforts and will be accurate and consistent. This will streamline the creation of sustainability reports and provide accurate and consistent reports. This will enable the automatic sustainability report creation system to efficiently and automatically create corporate sustainability reports.

[0029] An automatic sustainability report creation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to a company's sustainability. Examples of data related to a company's sustainability include, but are not limited to, information on environmental protection activities, social contribution activities, and governance. The collection unit collects data such as details of environmental protection projects implemented by the company, the results of social contribution activities, and areas for governance improvement. The collection unit can also collect data from the company's internal database or publicly available reports. The analysis unit analyzes the data collected by the collection unit and determines the report structure. The analysis unit analyzes the data using methods such as statistical analysis, text analysis, and data mining. The analysis unit uses a generation AI to determine the report's components, section types, order, and content details based on the data analysis results. The generation unit generates the content of each section based on the structure determined by the analysis unit. The generation unit generates the content of each section using methods such as template-based generation and algorithm-based generation. The generation unit uses a generation AI to generate content for a section on environmental protection activities, a section on social contribution activities, and a section on governance. The provision unit integrates the content of each section generated by the generation unit and provides a final sustainability report. The provision unit integrates the content of each section using, for example, a data merging method or a method for processing duplicate data. The provision unit can provide the final report in PDF format, web page format, or the like. As a result, the sustainability report automatic creation system according to the embodiment can efficiently and automatically create a corporate sustainability report.

[0030] The collection unit can collect multiple data related to environmental protection activities, social contribution activities, and governance. For example, the collection unit can collect details and results of environmental protection projects implemented by a company. For example, the collection unit can collect data on the usage status of renewable energy and the results of waste reduction activities. The collection unit can also collect specific content and results of social contribution activities implemented by a company. For example, the collection unit can collect data on details of volunteer activities and donation activities. Furthermore, the collection unit can collect data related to corporate governance. For example, the collection unit can collect data on details of a company's internal control and compliance activities. This enables the collection unit to collect comprehensive data related to sustainability. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can collect data using an AI model that automatically collects data from a company's internal database.

[0031] The analysis unit can analyze the collected data and determine the report structure. The analysis unit can analyze the data using, for example, statistical analysis. For example, the analysis unit can analyze the statistical trends of the collected data and determine the components of the report. The analysis unit can also analyze the data using text analysis. For example, the analysis unit can analyze the collected text data and extract important keywords and phrases to determine the report structure. The analysis unit can also analyze the data using data mining. For example, the analysis unit can discover patterns and trends from the collected data and determine the report structure based on the patterns and trends. This enables the analysis unit to create a report based on data analysis. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which can analyze the data and determine the report structure.

[0032] The generation unit can generate content for multiple sections, including a section on environmental protection activities, a section on social contribution activities, and a section on governance, based on the analysis results. The generation unit can generate content for each section using, for example, template-based generation. For example, the generation unit can generate content for the environmental protection activities section based on a pre-prepared template. The generation unit can also generate content for each section using algorithmic generation. For example, the generation unit can automatically generate content for each section based on the analysis results using generation AI. For example, in the section on environmental protection activities, the generation unit can describe details and results of projects implemented by the company. For example, the generation unit can specifically describe the utilization status of renewable energy and the results of waste reduction activities. Furthermore, in the section on social contribution activities, the generation unit can describe specific details and results of activities implemented by the company. For example, the generation unit can specifically describe details of volunteer activities and donation activities. Furthermore, in the section on governance, the generation unit can describe improvements and initiatives for the company's governance. For example, the generation unit can specifically describe details of the company's internal control and compliance activities. This allows the generation unit to automatically generate the content of each section, thereby improving the consistency and accuracy of the report. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the analysis results into the generation AI, which then generates the content of each section.

[0033] The providing unit may integrate the content of each generated section and provide a final sustainability report. The providing unit may integrate the content of each section using, for example, a data merging method. For example, the providing unit may merge the data to consolidate the content of each generated section into a single report. The providing unit may also integrate the content of each section using a duplicate data processing method. For example, the providing unit may remove duplicate data to create a consistent report. The providing unit may provide the final report in PDF format, webpage format, or the like. For example, the providing unit may save the generated report in PDF format and upload it to the company's website. The providing unit may also provide the generated report in webpage format so that users can view it online. This allows the providing unit to automatically provide the final report, thereby streamlining the creation process. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the content of each generated section into an AI model, which may then merge the data and process the duplicate data.

[0034] The collection unit can analyze a company's past sustainability reports and select the optimal data collection method. For example, the collection unit can identify frequently used data sources from past reports and prioritize collection. For example, the collection unit can analyze past sustainability reports and identify which data sources were most frequently used. The collection unit can also analyze the structure of past reports, list necessary data items, and collect them. For example, the collection unit can analyze the section structure of past reports and list the necessary data items for each section. Furthermore, the collection unit can select and implement an effective data collection method based on an evaluation of past reports. For example, the collection unit can analyze the evaluation results of past reports, identify which data collection method was most effective, and implement that method. Thus, the collection unit can select an effective data collection method by analyzing past reports. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past report data into AI, which then selects the optimal data collection method.

[0035] When collecting data, the collection unit may filter the data based on the company's current projects and areas of interest. For example, the collection unit may prioritize collecting data related to the company's ongoing projects. For example, the collection unit may prioritize collecting data related to environmental protection projects currently being implemented by the company. The collection unit may also filter and collect highly relevant data based on the company's areas of interest. For example, the collection unit may filter and collect data related to specific environmental issues that the company is interested in. Furthermore, the collection unit may select and collect data that is in line with the company's strategic goals. For example, the collection unit may select and collect data related to the company's long-term strategic goals. This enables the collection unit to collect data that is appropriate for the company's current situation. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data related to the company's current projects and areas of interest into AI, and the AI ​​may filter and collect highly relevant data.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. The collection unit, for example, prioritizes collecting environmental data related to the company's location. For example, the collection unit can prioritize collecting data related to environmental protection activities in the company's location. The collection unit can also prioritize collecting data on social contribution activities in the company's area of ​​activity. For example, the collection unit can prioritize collecting data on volunteer activities and donation activities in the company's area of ​​activity. Furthermore, the collection unit can prioritize collecting data related to governance based on the company's geographical location. For example, the collection unit can prioritize collecting data related to governance improvements and initiatives in the company's area of ​​activity. This enables the collection unit to collect data based on the company's geographical location. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0037] During data collection, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit can collect data related to environmental protection activities from the company's social media posts. For example, the collection unit can analyze the content of the company's social media posts and collect data related to environmental protection activities. The collection unit can also analyze reports of the company's social media contribution activities and collect data. For example, the collection unit can analyze reports of the company's volunteer activities and donation activities on social media and collect related data. Furthermore, the collection unit can also collect information related to the company's social media governance. For example, the collection unit can collect information related to improvements and efforts made by the company in social media governance. This enables the collection unit to collect data based on the company's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's social media data into AI, which then collects related data.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit can perform a detailed analysis of important data related to a company's environmental protection activities and reflect the results in a report. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit can simplify and analyze data of low importance related to a company's social contribution activities. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit can analyze data of medium importance related to corporate governance with a moderate level of detail. This enables the analysis unit to perform analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an environmental impact assessment algorithm to data related to environmental protection activities. For example, the analysis unit can analyze data related to a company's environmental protection activities using an environmental impact assessment algorithm and reflect the results in a report. The analysis unit can also apply a social impact assessment algorithm to data related to social contribution activities. For example, the analysis unit can analyze data related to a company's social contribution activities using a social impact assessment algorithm and reflect the results in a report. Furthermore, the analysis unit can apply a governance assessment algorithm to data related to governance. For example, the analysis unit can analyze data related to a company's governance using a governance assessment algorithm and reflect the results in a report. This enables the analysis unit to perform appropriate analysis depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the data category into a generation AI, which can then apply an appropriate analysis algorithm.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit can prioritize analyzing data related to the company's latest environmental protection activities and reflect the results in the report. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit can analyze data related to the company's past environmental protection activities while prioritizing the most recent data. Furthermore, the analysis unit can determine an appropriate analysis order based on the time when the data was collected. For example, the analysis unit can determine an appropriate analysis order based on the time when the data related to the company's environmental protection activities was collected. This enables the analysis unit to prioritize the most recent information by determining the priority of analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can determine the priority of analysis.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit can prioritize analysis of highly relevant data among data related to a company's environmental protection activities and reflect the results in a report. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can postpone analysis of less relevant data among data related to a company's social contribution activities. Furthermore, the analysis unit can determine an appropriate analysis order based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data among data related to a company's governance and postpone analysis of less relevant data. This enables efficient analysis by adjusting the analysis order based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can then adjust the analysis order.

[0042] The generation unit can adjust the level of detail of the generated report based on the importance of the section during generation. For example, the generation unit generates detailed content for a section with high importance. For example, the generation unit can generate a detailed section about an important section related to a company's environmental protection activities and reflect that content in the report. The generation unit can also generate simplified content for a section with low importance. For example, the generation unit can generate a simplified section of low importance among sections related to a company's social contribution activities. Furthermore, the generation unit can generate content with a moderate level of detail for a section with medium importance. For example, the generation unit can generate a moderate level of detail among sections related to corporate governance. This allows the generation unit to provide an appropriate report by generating detailed content according to the importance of the section. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the section into the generation AI, which can adjust the level of detail of the generated report.

[0043] The generation unit can apply different generation algorithms depending on the section category during generation. For example, the generation unit can apply an environmental impact assessment algorithm to a section related to environmental protection activities. For example, the generation unit can generate a section related to a company's environmental protection activities using an environmental impact assessment algorithm and reflect the content of the section in the report. The generation unit can also apply a social impact assessment algorithm to a section related to social contribution activities. For example, the generation unit can generate a section related to a company's social contribution activities using a social impact assessment algorithm and reflect the content of the section in the report. The generation unit can also apply a governance evaluation algorithm to a section related to governance. For example, the generation unit can generate a section related to a company's governance using a governance evaluation algorithm and reflect the content of the section in the report. This allows the generation unit to provide a highly accurate report by applying an appropriate generation algorithm depending on the section category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the section category into the generation AI, which can apply an appropriate generation algorithm.

[0044] The generation unit can determine the generation priority based on the time when the sections were collected during generation. For example, the generation unit can prioritize generating sections based on the most recent data. For example, the generation unit can prioritize generating sections based on data regarding the company's latest environmental protection activities and reflect the content of the sections in the report. The generation unit can also generate sections that emphasize the most recent data while referring to past data. For example, the generation unit can generate sections that emphasize the most recent data while referring to data regarding the company's past environmental protection activities. Furthermore, the generation unit can determine an appropriate generation order based on the time when the data was collected. For example, the generation unit can determine an appropriate generation order based on the time when the data regarding the company's environmental protection activities was collected. As a result, the generation unit can provide a report that emphasizes the most recent information by determining the generation priority based on the time when the sections were collected. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the time when the sections were collected into the generation AI, and the generation AI can determine the generation priority.

[0045] The generation unit can adjust the order of generation based on the relevance of the sections during generation. For example, the generation unit prioritizes generating highly relevant sections. For example, the generation unit can prioritize generating highly relevant sections among sections related to the company's environmental protection activities and reflect the contents of those sections in the report. The generation unit can also postpone generating less relevant sections. For example, the generation unit can postpone generating less relevant sections among sections related to the company's social contribution activities. Furthermore, the generation unit can determine an appropriate generation order based on the relevance of the sections. For example, the generation unit can prioritize generating highly relevant sections among sections related to the company's governance and postpone generating less relevant sections. This enables efficient report generation by adjusting the generation order based on the relevance of the sections. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the sections into the generation AI, which can then adjust the generation order.

[0046] When providing a report, the providing unit can select an optimal reporting method by referring to the user's past report viewing history. The providing unit, for example, can prioritize displaying sections that the user has frequently viewed in the past. For example, the providing unit can analyze the user's past report viewing history and prioritize displaying sections that have been frequently viewed. The providing unit can also suggest an optimal report format based on the user's past viewing history. For example, the providing unit can analyze the user's past report viewing history and suggest an optimal report format. Furthermore, the providing unit can highlight and display highly relevant sections based on the user's past viewing history. For example, the providing unit can analyze the user's past report viewing history and highlight and display highly relevant sections. This enables the providing unit to provide an optimal report based on the user's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past report viewing history into AI, which can select an optimal reporting method.

[0047] The providing unit can customize the display content of the report based on the user's current areas of interest when providing the report. The providing unit, for example, can prioritize displaying sections related to the user's current areas of interest. For example, the providing unit can analyze the user's current areas of interest and prioritize displaying related sections. The providing unit can also highlight highly relevant data based on the user's areas of interest. For example, the providing unit can analyze the user's areas of interest and highlight highly relevant data. Furthermore, the providing unit can customize the layout of the report according to the user's areas of interest. For example, the providing unit can analyze the user's areas of interest and customize the layout of the report. This enables the providing unit to customize the report based on the user's areas of interest. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's areas of interest into AI, which can customize the display content of the report.

[0048] The providing unit can select the optimal providing method by taking the user's geographical location information into consideration when providing the data. The providing unit, for example, prioritizes displaying data related to the user's location. For example, the providing unit can prioritize displaying environmental data related to the user's location. The providing unit can also highlight data on social contribution activities in the user's area of ​​activity. For example, the providing unit can highlight data on volunteer activities and donation activities in the user's area of ​​activity. Furthermore, the providing unit can provide highly relevant data based on the user's geographical location. For example, the providing unit can prioritize displaying governance data related to the user's location. This enables the providing unit to provide optimal reports based on the user's geographical location. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can select the optimal providing method.

[0049] When providing the report, the providing unit can analyze the user's social media activity and suggest a method for providing the report. The providing unit, for example, can prioritize displaying relevant sections based on the user's social media interests. For example, the providing unit can analyze the user's social media activity and prioritize displaying sections related to the interests. The providing unit can also suggest an optimal report format based on the user's social media activity. For example, the providing unit can analyze the user's social media posts and suggest an optimal report format. Furthermore, the providing unit can customize the display content of the report based on the user's social media feedback. For example, the providing unit can analyze the user's social media feedback and customize the display content of the report. This enables the providing unit to provide an optimal report based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into AI, which can then suggest an optimal report method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When collecting data on a company's sustainability, the collection unit can adjust the data collection method taking into account the company's industry characteristics. For example, for manufacturing companies, the collection unit can focus on collecting data on environmental impact. For service companies, the collection unit can focus on collecting data on social contribution activities. For financial companies, the collection unit can focus on collecting data on governance. This enables the collection unit to collect data according to the company's industry characteristics. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the company's industry characteristics into AI, which can select the optimal data collection method.

[0052] When analyzing data, the analysis unit can adjust the level of detail of the analysis based on the company's sustainability goals. For example, a detailed analysis can be performed on the company's environmental goals to evaluate the degree of achievement. Furthermore, a detailed analysis of specific activities and their results can be performed on the company's social contribution goals. Furthermore, a detailed analysis of internal control and compliance activities can be performed on the company's governance goals. This enables the analysis unit to perform analysis according to the company's sustainability goals. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the company's sustainability goals into the generation AI, which can adjust the level of detail of the analysis.

[0053] The generation unit can adjust the content of sections of the report to be generated taking into account the user's past feedback. For example, more detailed content can be generated for a section for which the user previously requested detailed information. Concise content can also be generated for a section for which the user previously requested concise information. Furthermore, if the user previously expressed interest in a particular topic, the report can be generated with information related to that topic emphasized. This enables the generation unit to generate a report based on the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback into the generation AI, which can then adjust the content.

[0054] When collecting data on a company's sustainability, the collection unit can prioritize collecting highly relevant data by taking into account the company's geographical location information. For example, it can prioritize collecting environmental data related to the company's location. It can also prioritize collecting data on social contribution activities in the company's area of ​​activity. Furthermore, it can prioritize collecting data on governance based on the company's geographical location. This enables the collection unit to collect data based on the company's geographical location. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0055] The generation unit can adjust the content of sections of the report to be generated taking into account the user's past feedback. For example, more detailed content can be generated for a section for which the user previously requested detailed information. Concise content can also be generated for a section for which the user previously requested concise information. Furthermore, if the user previously expressed interest in a particular topic, the report can be generated with information related to that topic emphasized. This enables the generation unit to generate a report based on the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback into the generation AI, which can then adjust the content.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection department collects data on the company's sustainability. Data on a company's sustainability includes information on environmental protection activities, social contribution activities, and governance. The collection department collects data such as details of environmental protection projects implemented by the company, the results of social contribution activities, and areas for improvement in governance. Data can also be collected from the company's internal databases and publicly available reports. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the report structure. The analysis unit analyzes the data using methods such as statistical analysis, text analysis, and data mining, and uses generation AI to determine the report's components, section types, order, and content details. Step 3: The generator generates content for each section based on the structure determined by the analyzer. The generator generates content for the environmental protection section, social contribution section, and governance section using methods such as template-based generation or algorithmic generation. Step 4: The provider integrates the content of each section generated by the generator and provides the final sustainability report. The provider integrates the content of each section using a data merging method, a method for handling duplicate data, etc., and provides the final report in PDF format, web page format, etc.

[0058] (Example 2) An automatic sustainability report creation system according to an embodiment of the present invention is a system that automatically creates a sustainability report using a generation AI. This system collects data related to a company's sustainability, analyzes it using the generation AI, determines the report structure, generates the content of each section, and creates a final report. For example, data related to a company's sustainability includes information on environmental protection activities, social contribution activities, and governance. Data is collected, including details of environmental protection projects implemented by the company, the results of social contribution activities, and areas for governance improvement. The generation AI then analyzes the collected data and determines the report structure. For example, the content of each section is determined, such as a section on environmental protection activities, a section on social contribution activities, and a section on governance. The generation AI generates the content of each section based on the analysis results. For example, the section on environmental protection activities describes the details and results of projects implemented by the company. The section on social contribution activities describes the specific details and results of the company's activities. The section on governance describes the company's governance improvements and initiatives. Finally, the generation AI integrates the content of each section to create a final sustainability report. This report will comprehensively show a company's sustainability efforts and will be accurate and consistent. This will streamline the creation of sustainability reports and provide accurate and consistent reports. This will enable the automatic sustainability report creation system to efficiently and automatically create corporate sustainability reports.

[0059] An automatic sustainability report creation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to a company's sustainability. Examples of data related to a company's sustainability include, but are not limited to, information on environmental protection activities, social contribution activities, and governance. The collection unit collects data such as details of environmental protection projects implemented by the company, the results of social contribution activities, and areas for governance improvement. The collection unit can also collect data from the company's internal database or publicly available reports. The analysis unit analyzes the data collected by the collection unit and determines the report structure. The analysis unit analyzes the data using methods such as statistical analysis, text analysis, and data mining. The analysis unit uses a generation AI to determine the report's components, section types, order, and content details based on the data analysis results. The generation unit generates the content of each section based on the structure determined by the analysis unit. The generation unit generates the content of each section using methods such as template-based generation and algorithm-based generation. The generation unit uses a generation AI to generate content for a section on environmental protection activities, a section on social contribution activities, and a section on governance. The provision unit integrates the content of each section generated by the generation unit and provides a final sustainability report. The provision unit integrates the content of each section using, for example, a data merging method or a method for processing duplicate data. The provision unit can provide the final report in PDF format, web page format, or the like. As a result, the sustainability report automatic creation system according to the embodiment can efficiently and automatically create a corporate sustainability report.

[0060] The collection unit can collect multiple data related to environmental protection activities, social contribution activities, and governance. For example, the collection unit can collect details and results of environmental protection projects implemented by a company. For example, the collection unit can collect data on the usage status of renewable energy and the results of waste reduction activities. The collection unit can also collect specific content and results of social contribution activities implemented by a company. For example, the collection unit can collect data on details of volunteer activities and donation activities. Furthermore, the collection unit can collect data related to corporate governance. For example, the collection unit can collect data on details of a company's internal control and compliance activities. This enables the collection unit to collect comprehensive data related to sustainability. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can collect data using an AI model that automatically collects data from a company's internal database.

[0061] The analysis unit can analyze the collected data and determine the report structure. The analysis unit can analyze the data using, for example, statistical analysis. For example, the analysis unit can analyze the statistical trends of the collected data and determine the components of the report. The analysis unit can also analyze the data using text analysis. For example, the analysis unit can analyze the collected text data and extract important keywords and phrases to determine the report structure. The analysis unit can also analyze the data using data mining. For example, the analysis unit can discover patterns and trends from the collected data and determine the report structure based on the patterns and trends. This enables the analysis unit to create a report based on data analysis. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which can analyze the data and determine the report structure.

[0062] The generation unit can generate content for multiple sections, including a section on environmental protection activities, a section on social contribution activities, and a section on governance, based on the analysis results. The generation unit can generate content for each section using, for example, template-based generation. For example, the generation unit can generate content for the environmental protection activities section based on a pre-prepared template. The generation unit can also generate content for each section using algorithmic generation. For example, the generation unit can automatically generate content for each section based on the analysis results using generation AI. For example, in the section on environmental protection activities, the generation unit can describe details and results of projects implemented by the company. For example, the generation unit can specifically describe the utilization status of renewable energy and the results of waste reduction activities. Furthermore, in the section on social contribution activities, the generation unit can describe specific details and results of activities implemented by the company. For example, the generation unit can specifically describe details of volunteer activities and donation activities. Furthermore, in the section on governance, the generation unit can describe improvements and initiatives for the company's governance. For example, the generation unit can specifically describe details of the company's internal control and compliance activities. This allows the generation unit to automatically generate the content of each section, thereby improving the consistency and accuracy of the report. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the analysis results into the generation AI, which then generates the content of each section.

[0063] The providing unit may integrate the content of each generated section and provide a final sustainability report. The providing unit may integrate the content of each section using, for example, a data merging method. For example, the providing unit may merge the data to consolidate the content of each generated section into a single report. The providing unit may also integrate the content of each section using a duplicate data processing method. For example, the providing unit may remove duplicate data to create a consistent report. The providing unit may provide the final report in PDF format, webpage format, or the like. For example, the providing unit may save the generated report in PDF format and upload it to the company's website. The providing unit may also provide the generated report in webpage format so that users can view it online. This allows the providing unit to automatically provide the final report, thereby streamlining the creation process. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the content of each generated section into an AI model, which may then merge the data and process the duplicate data.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. For example, the collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. For example, the collection unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can speed up the timing of data collection to collect necessary data in a short period of time. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the collection unit to adjust the timing of data collection according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI, and the generation AI may adjust the timing of data collection.

[0065] The collection unit can analyze a company's past sustainability reports and select the optimal data collection method. For example, the collection unit can identify frequently used data sources from past reports and prioritize collection. For example, the collection unit can analyze past sustainability reports and identify which data sources were most frequently used. The collection unit can also analyze the structure of past reports, list necessary data items, and collect them. For example, the collection unit can analyze the section structure of past reports and list the necessary data items for each section. Furthermore, the collection unit can select and implement an effective data collection method based on an evaluation of past reports. For example, the collection unit can analyze the evaluation results of past reports, identify which data collection method was most effective, and implement that method. Thus, the collection unit can select an effective data collection method by analyzing past reports. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past report data into AI, which then selects the optimal data collection method.

[0066] When collecting data, the collection unit may filter the data based on the company's current projects and areas of interest. For example, the collection unit may prioritize collecting data related to the company's ongoing projects. For example, the collection unit may prioritize collecting data related to environmental protection projects currently being implemented by the company. The collection unit may also filter and collect highly relevant data based on the company's areas of interest. For example, the collection unit may filter and collect data related to specific environmental issues that the company is interested in. Furthermore, the collection unit may select and collect data that is in line with the company's strategic goals. For example, the collection unit may select and collect data related to the company's long-term strategic goals. This enables the collection unit to collect data that is appropriate for the company's current situation. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input data related to the company's current projects and areas of interest into AI, and the AI ​​may filter and collect highly relevant data.

[0067] The collection unit can estimate the user's emotions and prioritize multiple data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. For example, the collection unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and prioritize collecting data of high importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting detailed data. For example, the collection unit can record the user's voice, estimate the user's emotions using voice analysis technology, and prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and prioritize collecting data that can be collected quickly. This allows the collection unit to prioritize data according to the user's emotions, enabling efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI, and the generation AI may determine the priority of the data.

[0068] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. The collection unit, for example, prioritizes collecting environmental data related to the company's location. For example, the collection unit can prioritize collecting data related to environmental protection activities in the company's location. The collection unit can also prioritize collecting data on social contribution activities in the company's area of ​​activity. For example, the collection unit can prioritize collecting data on volunteer activities and donation activities in the company's area of ​​activity. Furthermore, the collection unit can prioritize collecting data related to governance based on the company's geographical location. For example, the collection unit can prioritize collecting data related to governance improvements and initiatives in the company's area of ​​activity. This enables the collection unit to collect data based on the company's geographical location. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0069] During data collection, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit can collect data related to environmental protection activities from the company's social media posts. For example, the collection unit can analyze the content of the company's social media posts and collect data related to environmental protection activities. The collection unit can also analyze reports of the company's social media contribution activities and collect data. For example, the collection unit can analyze reports of the company's volunteer activities and donation activities on social media and collect related data. Furthermore, the collection unit can also collect information related to the company's social media governance. For example, the collection unit can collect information related to improvements and efforts made by the company in social media governance. This enables the collection unit to collect data based on the company's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's social media data into AI, which then collects related data.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. For example, the analysis unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide a summary analysis result. This enables the analysis unit to provide an analysis result that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input user emotion data into the generation AI, which may then adjust the method of expression of the analysis.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit can perform a detailed analysis of important data related to a company's environmental protection activities and reflect the results in a report. The analysis unit can also perform a simplified analysis on data of low importance. For example, the analysis unit can simplify and analyze data of low importance related to a company's social contribution activities. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit can analyze data of medium importance related to corporate governance with a moderate level of detail. This enables the analysis unit to perform analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an environmental impact assessment algorithm to data related to environmental protection activities. For example, the analysis unit can analyze data related to a company's environmental protection activities using an environmental impact assessment algorithm and reflect the results in a report. The analysis unit can also apply a social impact assessment algorithm to data related to social contribution activities. For example, the analysis unit can analyze data related to a company's social contribution activities using a social impact assessment algorithm and reflect the results in a report. Furthermore, the analysis unit can apply a governance assessment algorithm to data related to governance. For example, the analysis unit can analyze data related to a company's governance using a governance assessment algorithm and reflect the results in a report. This enables the analysis unit to perform appropriate analysis depending on the data category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the data category into a generation AI, which can then apply an appropriate analysis algorithm.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, the analysis unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a short, concise analysis. The analysis unit can also provide a detailed analysis if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed analysis. Furthermore, the analysis unit can provide a visually stimulating analysis if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide a visually stimulating analysis. This allows the analysis unit to provide appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI, and the generation AI may adjust the length of the analysis.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit can prioritize analyzing data related to the company's latest environmental protection activities and reflect the results in the report. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit can analyze data related to the company's past environmental protection activities while prioritizing the most recent data. Furthermore, the analysis unit can determine an appropriate analysis order based on the time when the data was collected. For example, the analysis unit can determine an appropriate analysis order based on the time when the data related to the company's environmental protection activities was collected. This enables the analysis unit to prioritize the most recent information by determining the priority of analysis based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can determine the priority of analysis.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit can prioritize analysis of highly relevant data among data related to a company's environmental protection activities and reflect the results in a report. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can postpone analysis of less relevant data among data related to a company's social contribution activities. Furthermore, the analysis unit can determine an appropriate analysis order based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data among data related to a company's governance and postpone analysis of less relevant data. This enables efficient analysis by adjusting the analysis order based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can then adjust the analysis order.

[0076] The generation unit can estimate the user's emotions and adjust the presentation of the generated sections based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a section that progresses at a leisurely pace. For example, the generation unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and generate a section that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a section that emphasizes the shortest route. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and generate a section that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a section that adds visually stimulating effects. For example, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and generate a section that adds visually stimulating effects. This allows the generation unit to provide an appropriate report by adjusting the presentation of the section according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI, and the generation AI may adjust the expression method of the section.

[0077] The generation unit can adjust the level of detail of the generated report based on the importance of the section during generation. For example, the generation unit generates detailed content for a section with high importance. For example, the generation unit can generate a detailed section about an important section related to a company's environmental protection activities and reflect that content in the report. The generation unit can also generate simplified content for a section with low importance. For example, the generation unit can generate a simplified section of low importance among sections related to a company's social contribution activities. Furthermore, the generation unit can generate content with a moderate level of detail for a section with medium importance. For example, the generation unit can generate a moderate level of detail among sections related to corporate governance. This allows the generation unit to provide an appropriate report by generating detailed content according to the importance of the section. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the section into the generation AI, which can adjust the level of detail of the generated report.

[0078] The generation unit can apply different generation algorithms depending on the section category during generation. For example, the generation unit can apply an environmental impact assessment algorithm to a section related to environmental protection activities. For example, the generation unit can generate a section related to a company's environmental protection activities using an environmental impact assessment algorithm and reflect the content of the section in the report. The generation unit can also apply a social impact assessment algorithm to a section related to social contribution activities. For example, the generation unit can generate a section related to a company's social contribution activities using a social impact assessment algorithm and reflect the content of the section in the report. The generation unit can also apply a governance evaluation algorithm to a section related to governance. For example, the generation unit can generate a section related to a company's governance using a governance evaluation algorithm and reflect the content of the section in the report. This allows the generation unit to provide a highly accurate report by applying an appropriate generation algorithm depending on the section category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the section category into the generation AI, which can apply an appropriate generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the length of the generated section based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point section. For example, the generation unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and generate a short, to-the-point section. Furthermore, if the user is relaxed, the generation unit can generate a longer section with detailed explanations. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and generate a longer section with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a section with visually stimulating effects. For example, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and generate a section with visually stimulating effects. This allows the generation unit to provide an appropriate report by adjusting the length of the section according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI, and the generation AI may adjust the length of the section.

[0080] The generation unit can determine the generation priority based on the time when the sections were collected during generation. For example, the generation unit can prioritize generating sections based on the most recent data. For example, the generation unit can prioritize generating sections based on data regarding the company's latest environmental protection activities and reflect the content of the sections in the report. The generation unit can also generate sections that emphasize the most recent data while referring to past data. For example, the generation unit can generate sections that emphasize the most recent data while referring to data regarding the company's past environmental protection activities. Furthermore, the generation unit can determine an appropriate generation order based on the time when the data was collected. For example, the generation unit can determine an appropriate generation order based on the time when the data regarding the company's environmental protection activities was collected. As a result, the generation unit can provide a report that emphasizes the most recent information by determining the generation priority based on the time when the sections were collected. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the time when the sections were collected into the generation AI, and the generation AI can determine the generation priority.

[0081] The generation unit can adjust the order of generation based on the relevance of the sections during generation. For example, the generation unit prioritizes generating highly relevant sections. For example, the generation unit can prioritize generating highly relevant sections among sections related to the company's environmental protection activities and reflect the contents of those sections in the report. The generation unit can also postpone generating less relevant sections. For example, the generation unit can postpone generating less relevant sections among sections related to the company's social contribution activities. Furthermore, the generation unit can determine an appropriate generation order based on the relevance of the sections. For example, the generation unit can prioritize generating highly relevant sections among sections related to the company's governance and postpone generating less relevant sections. This enables efficient report generation by adjusting the generation order based on the relevance of the sections. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the sections into the generation AI, which can then adjust the generation order.

[0082] The providing unit can estimate the user's emotions and adjust the report presentation method based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible report. For example, the providing unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a simple, highly visible report. The providing unit can also provide a detailed report if the user is relaxed. For example, the providing unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed report. Furthermore, if the user is in a hurry, the providing unit can provide a report that is concise. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide a report that is concise. This allows the providing unit to provide an appropriate report by adjusting the report presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI, and the generation AI may adjust the way the report is provided.

[0083] When providing a report, the providing unit can select an optimal reporting method by referring to the user's past report viewing history. The providing unit, for example, can prioritize displaying sections that the user has frequently viewed in the past. For example, the providing unit can analyze the user's past report viewing history and prioritize displaying sections that have been frequently viewed. The providing unit can also suggest an optimal report format based on the user's past viewing history. For example, the providing unit can analyze the user's past report viewing history and suggest an optimal report format. Furthermore, the providing unit can highlight and display highly relevant sections based on the user's past viewing history. For example, the providing unit can analyze the user's past report viewing history and highlight and display highly relevant sections. This enables the providing unit to provide an optimal report based on the user's past viewing history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past report viewing history into AI, which can select an optimal reporting method.

[0084] The providing unit can customize the display content of the report based on the user's current areas of interest when providing the report. The providing unit, for example, can prioritize displaying sections related to the user's current areas of interest. For example, the providing unit can analyze the user's current areas of interest and prioritize displaying related sections. The providing unit can also highlight highly relevant data based on the user's areas of interest. For example, the providing unit can analyze the user's areas of interest and highlight highly relevant data. Furthermore, the providing unit can customize the layout of the report according to the user's areas of interest. For example, the providing unit can analyze the user's areas of interest and customize the layout of the report. This enables the providing unit to customize the report based on the user's areas of interest. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's areas of interest into AI, which can customize the display content of the report.

[0085] The providing unit can estimate the user's emotions and adjust the order in which reports are presented based on the estimated user emotions. For example, if the user is nervous, the providing unit can display important sections first. For example, the providing unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and display important sections first. Furthermore, if the user is relaxed, the providing unit can display detailed sections first. For example, the providing unit can record the user's voice, estimate the user's emotions using voice analysis technology, and display detailed sections first. Furthermore, if the user is in a hurry, the providing unit can display key sections first. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and display key sections first. This allows the providing unit to provide appropriate reports by adjusting the order in which reports are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI, and the generation AI may adjust the order in which reports are provided.

[0086] The providing unit can select the optimal providing method by taking the user's geographical location information into consideration when providing the data. The providing unit, for example, prioritizes displaying data related to the user's location. For example, the providing unit can prioritize displaying environmental data related to the user's location. The providing unit can also highlight data on social contribution activities in the user's area of ​​activity. For example, the providing unit can highlight data on volunteer activities and donation activities in the user's area of ​​activity. Furthermore, the providing unit can provide highly relevant data based on the user's geographical location. For example, the providing unit can prioritize displaying governance data related to the user's location. This enables the providing unit to provide optimal reports based on the user's geographical location. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can select the optimal providing method.

[0087] When providing the report, the providing unit can analyze the user's social media activity and suggest a method for providing the report. The providing unit, for example, can prioritize displaying relevant sections based on the user's social media interests. For example, the providing unit can analyze the user's social media activity and prioritize displaying sections related to the interests. The providing unit can also suggest an optimal report format based on the user's social media activity. For example, the providing unit can analyze the content of the user's social media posts and suggest an optimal report format. Furthermore, the providing unit can customize the display content of the report based on the user's social media feedback. For example, the providing unit can analyze the user's social media feedback and customize the display content of the report. This enables the providing unit to provide an optimal report based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into AI, which can then suggest an optimal report method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 collects data related to corporate sustainability using the camera 42 and microphone 38B of the smart device 14 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the structure of the report. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates the content of each section based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart device 14, integrates the content of each generated section and provides a final sustainability report. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 collects data related to corporate sustainability using the camera 42 and microphone 238 of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the structure of the report. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates the content of each section based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, integrates the generated content of each section and provides a final sustainability report. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data related to corporate sustainability using the camera 42 and microphone 238 of the headset-type terminal 314 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the structure of the report. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates the content of each section based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the headset-type terminal 314, integrates the generated content of each section and provides a final sustainability report. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 collects data related to corporate sustainability using the camera 42 and microphone 238 of the robot 414 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the structure of the report. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates the content of each section based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the robot 414, integrates the generated content of each section and provides a final sustainability report.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis of important data and provide results quickly. Alternatively, if the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive results. Furthermore, if the user is in a hurry, the analysis unit can perform a concise analysis that focuses on the main points. This allows the analysis unit to adjust the analysis priority according to the user's emotions and provide efficient and appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the analysis priority.

[0090] The providing unit can estimate the user's emotions and adjust the report presentation format based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible report. Alternatively, if the user is relaxed, the providing unit can provide a detailed report. Furthermore, if the user is in a hurry, the providing unit can provide a report that focuses on the main points. This allows the providing unit to adjust the report presentation format according to the user's emotions to provide an appropriate report. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into the generation AI, which then adjusts the report presentation format.

[0091] When collecting data on a company's sustainability, the collection unit can adjust the data collection method taking into account the company's industry characteristics. For example, for manufacturing companies, the collection unit can focus on collecting data on environmental impact. For service companies, the collection unit can focus on collecting data on social contribution activities. For financial companies, the collection unit can focus on collecting data on governance. This enables the collection unit to collect data according to the company's industry characteristics. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the company's industry characteristics into AI, which can select the optimal data collection method.

[0092] When analyzing data, the analysis unit can adjust the level of detail of the analysis based on the company's sustainability goals. For example, a detailed analysis can be performed on the company's environmental goals to evaluate the degree of achievement. Furthermore, a detailed analysis of specific activities and their results can be performed on the company's social contribution goals. Furthermore, a detailed analysis of internal control and compliance activities can be performed on the company's governance goals. This enables the analysis unit to perform analysis according to the company's sustainability goals. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the company's sustainability goals into the generation AI, which can adjust the level of detail of the analysis.

[0093] The generation unit can adjust the content of sections of the report to be generated taking into account the user's past feedback. For example, more detailed content can be generated for a section for which the user previously requested detailed information. Concise content can also be generated for a section for which the user previously requested concise information. Furthermore, if the user previously expressed interest in a particular topic, the report can be generated with information related to that topic emphasized. This enables the generation unit to generate a report based on the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback into the generation AI, which can then adjust the content.

[0094] The providing unit can estimate the user's emotions and adjust the order in which reports are presented based on the estimated user's emotions. For example, if the user is nervous, important sections can be displayed first. Furthermore, if the user is relaxed, detailed sections can be displayed first. Furthermore, if the user is in a hurry, sections that highlight the main points can be displayed first. This allows the providing unit to adjust the order in which reports are presented according to the user's emotions, thereby providing appropriate reports. The estimation of emotions is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, which then adjusts the order in which reports are presented.

[0095] When collecting data on a company's sustainability, the collection unit can prioritize collecting highly relevant data by taking into account the company's geographical location information. For example, it can prioritize collecting environmental data related to the company's location. It can also prioritize collecting data on social contribution activities in the company's area of ​​activity. Furthermore, it can prioritize collecting data on governance based on the company's geographical location. This enables the collection unit to collect data based on the company's geographical location. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's geographical location information into AI, which can then prioritize collecting highly relevant data.

[0096] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. This enables the analysis unit to provide analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the presentation method of the analysis.

[0097] The generation unit can adjust the content of sections of the report to be generated taking into account the user's past feedback. For example, more detailed content can be generated for a section for which the user previously requested detailed information. Concise content can also be generated for a section for which the user previously requested concise information. Furthermore, if the user previously expressed interest in a particular topic, the report can be generated with information related to that topic emphasized. This enables the generation unit to generate a report based on the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past feedback into the generation AI, which can then adjust the content.

[0098] The providing unit can estimate the user's emotions and adjust the order in which reports are presented based on the estimated user's emotions. For example, if the user is nervous, important sections can be displayed first. Furthermore, if the user is relaxed, detailed sections can be displayed first. Furthermore, if the user is in a hurry, sections that highlight the main points can be displayed first. This allows the providing unit to adjust the order in which reports are presented according to the user's emotions, thereby providing appropriate reports. The estimation of emotions is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, which then adjusts the order in which reports are presented.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection department collects data on the company's sustainability. Data on a company's sustainability includes information on environmental protection activities, social contribution activities, and governance. The collection department collects data such as details of environmental protection projects implemented by the company, the results of social contribution activities, and areas for improvement in governance. Data can also be collected from the company's internal databases and publicly available reports. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the report structure. The analysis unit analyzes the data using methods such as statistical analysis, text analysis, and data mining, and uses generation AI to determine the report's components, section types, order, and content details. Step 3: The generator generates content for each section based on the structure determined by the analyzer. The generator generates content for the environmental protection section, social contribution section, and governance section using methods such as template-based generation or algorithmic generation. Step 4: The provider integrates the content of each section generated by the generator and provides the final sustainability report. The provider integrates the content of each section using a data merging method, a method for handling duplicate data, etc., and provides the final report in PDF format, web page format, etc.

[0101] 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.

[0102] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects data on corporate sustainability; an analysis unit that analyzes the data collected by the collection unit and determines a report configuration; a generation unit that generates the content of each section based on the structure determined by the analysis unit; a providing unit that integrates the contents of each section generated by the generating unit and provides a final report; A system characterized by:

2. The collecting unit Collect multiple data points related to environmental protection activities, social contribution activities, and governance The system of claim 1 .

3. The analysis unit Analyze the collected data and determine the report structure The system of claim 1 .

4. The generation unit Generate content for multiple sections based on the analysis, including a section on environmental protection, a section on social contribution, and a section on governance. The system of claim 1 .

5. The providing unit The content of each generated section is integrated to provide the final sustainability report. The system of claim 1 .

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions The system of claim 1 .

7. The collecting unit Analyze a company's past sustainability reports and select the most appropriate data collection method The system of claim 1 .

8. The collecting unit As data is collected, filtering is performed based on the company's current projects and areas of interest. The system of claim 1 .

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