system

Abstract

The system according to this embodiment aims to evaluate the environmental impact of a company and to propose improvements for sustainable business operations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects corporate CSR reports and image data. The analysis unit analyzes the data collected by the collection unit and evaluates the environmental impact of the company. The proposal unit makes improvement proposals for sustainable corporate management based on the evaluation results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it cannot be said that the environmental impact of enterprises has been accurately evaluated and improvement proposals for sustainable enterprise operations have been fully made, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the environmental impact of enterprises and make improvement proposals for sustainable enterprise operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects corporate social responsibility (CSR) reports and image data. The analysis unit analyzes the data collected by the data collection unit and evaluates the environmental impact of the company. The proposal unit makes improvement proposals for sustainable corporate management based on the evaluation results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the environmental impact of a company and make suggestions for improvements for sustainable business operations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The environmental impact analysis system according to an embodiment of the present invention is a system that analyzes environmental impacts from a company's CSR reports and images of data centers, offices, factories, etc., and makes improvement proposals for sustainable business operations based on the state of the company's business processes and supply chain. This environmental impact analysis system collects images of a company's CSR reports and data centers, offices, factories, etc., and evaluates the company's environmental impact by analyzing them with a generating AI. Next, the generating AI evaluates the state of the company's business processes and supply chain based on the collected data. Finally, the generating AI makes specific improvement proposals for sustainable business operations based on the evaluation results. For example, the environmental impact analysis system collects images of a company's CSR reports and data centers, offices, factories, etc. These data are analyzed by the generating AI, and the company's environmental impact is evaluated. Next, the generating AI evaluates the state of the company's business processes and supply chain based on the collected data. For example, the generating AI evaluates the efficiency of the company's business processes and the optimization of the supply chain. Finally, the generating AI makes specific improvement proposals for sustainable business operations based on the evaluation results. For example, the generating AI makes improvement proposals such as improving energy efficiency, reducing waste, and optimizing the supply chain. This system is useful for companies that are environmentally conscious but lack concrete improvement measures, or for companies that cannot allocate human resources. It is also effective for medium to large-scale manufacturing, retail, and IT companies. Through this, the environmental impact analysis system can support sustainable business operations by collecting, analyzing, and proposing improvements based on companies' CSR reports and image data.

[0029] The environmental impact analysis system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects corporate social responsibility (CSR) reports and image data. For example, the collection unit collects corporate CSR reports and images of data centers, offices, factories, etc. For example, the collection unit collects corporate CSR reports in digital format. The collection unit can also collect images of data centers. Furthermore, the collection unit can also collect images of offices and factories. For example, the collection unit collects corporate CSR reports in PDF format. The collection unit can also collect images of data centers in JPEG format. The collection unit can also collect images of offices and factories in PNG format. The analysis unit analyzes the data collected by the collection unit and evaluates the environmental impact of the company. For example, the analysis unit evaluates the state of the company's business processes and supply chain based on the collected data. For example, the analysis unit analyzes corporate CSR reports using natural language processing technology. Furthermore, the analysis unit can also analyze images of data centers, offices, and factories using image recognition technology. For example, the analysis unit analyzes the contents of corporate CSR reports using natural language processing technology. The analysis unit can also analyze images of data centers using image recognition technology. The analysis unit can also analyze images of offices and factories. The proposal unit makes improvement suggestions for sustainable business operations based on the evaluation results obtained by the analysis unit. For example, the proposal unit makes improvement suggestions such as improving energy efficiency, reducing waste, and optimizing the supply chain based on the evaluation results. For example, the proposal unit may propose measures to improve energy efficiency. The proposal unit may also propose measures to reduce waste. Furthermore, the proposal unit may also propose measures to optimize the supply chain. For example, the proposal unit may propose the introduction of renewable energy as a measure to improve energy efficiency. The proposal unit may also propose the promotion of recycling as a measure to reduce waste. The proposal unit may also propose the improvement of logistics efficiency as a measure to optimize the supply chain. In this way, the environmental impact analysis system according to the embodiment can support sustainable business operations by collecting, analyzing, and making improvement suggestions for companies' CSR reports and image data.

[0030] The data collection unit collects corporate social responsibility (CSR) reports and image data. Specifically, it collects corporate CSR reports in digital format and images of data centers, offices, factories, etc. For example, corporate CSR reports are collected in PDF format, and images of data centers are collected in JPEG format. Images of offices and factories are often collected in PNG format. The data collection unit can utilize web scraping technology and APIs to efficiently collect this data. By using web scraping technology, CSR reports can be automatically obtained from corporate websites and stored in a database. Furthermore, by using APIs, it is possible to obtain data directly from the company's internal systems. In addition, the data collection unit can utilize devices such as drones and surveillance cameras for image data collection. By using drones, wide-area image data can be efficiently collected, and by using surveillance cameras, detailed image data of specific locations can be collected. As a result, the data collection unit can collect a wide range of data from diverse data sources and provide foundational data for comprehensively evaluating a company's environmental impact.

[0031] The analysis unit analyzes data collected by the data collection unit to assess the environmental impact of companies. Specifically, it uses natural language processing technology to analyze companies' CSR reports and image recognition technology to analyze images of data centers, offices, and factories. By using natural language processing technology, environmental descriptions can be extracted from companies' CSR reports and structured into text data. For example, information such as a company's energy consumption, waste generation, and renewable energy utilization can be extracted and stored in a database. By using image recognition technology, environmental information can be extracted from images of data centers, offices, and factories. For example, the operation status of cooling systems can be analyzed from images of data centers to evaluate energy efficiency. In addition, the amount and type of waste can be identified from images of offices and factories to evaluate the recycling status. Furthermore, the analysis unit can use AI to integrate the collected data and evaluate the state of companies' business processes and supply chains. Based on historical data and statistical information, the AI ​​can predict a company's environmental impact and assess future risks. As a result, the analysis unit can comprehensively assess a company's environmental impact and provide foundational data for sustainable business operations.

[0032] The Proposal Department will propose improvements for sustainable business operations based on the evaluation results obtained by the Analysis Department. Specifically, it will propose improvements such as improving energy efficiency, reducing waste, and optimizing the supply chain. For example, as a measure to improve energy efficiency, it can propose the introduction of renewable energy. By introducing renewable energy, the company's energy consumption can be reduced, and its environmental impact can be mitigated. As a measure to reduce waste, it can propose the promotion of recycling. By promoting recycling, the amount of waste can be reduced, and the effective use of resources can be promoted. Furthermore, as a measure to optimize the supply chain, it can propose the efficiency of logistics. By improving logistics efficiency, transportation costs can be reduced, and the environmental impact can be mitigated. The Proposal Department can provide these improvement proposals to companies as concrete action plans and support their implementation. For example, as a measure to improve energy efficiency, it can propose the introduction of solar power generation systems and energy management systems. As a measure to reduce waste, it can propose the introduction of recycling programs and thorough separate collection of waste. As a measure to optimize the supply chain, it can propose a review of transportation routes and the introduction of joint delivery. In this way, the Proposal Department can support the sustainable operation of companies and contribute to reducing their environmental impact.

[0033] The data collection unit can collect corporate CSR reports and images of data centers, offices, factories, etc. For example, the data collection unit can collect corporate CSR reports in digital format. The data collection unit can also collect images of data centers. The data collection unit can also collect images of offices and factories. For example, the data collection unit can collect corporate CSR reports in PDF format. The data collection unit can also collect images of data centers in JPEG format. The data collection unit can also collect images of offices and factories in PNG format. By collecting corporate CSR reports and images, data necessary for environmental impact assessment can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input corporate CSR reports and image data into a generating AI and have the generating AI perform the data collection.

[0034] The analysis unit can evaluate the state of a company's business processes and supply chain based on the collected data. For example, the analysis unit can evaluate the efficiency of a company's business processes based on the collected data. The analysis unit can also evaluate the state of the supply chain. For example, the analysis unit can perform statistical analysis of data to evaluate the efficiency of a company's business processes. The analysis unit can also perform data analysis to evaluate the state of the supply chain. By evaluating the state of a company's business processes and supply chain, it is possible to identify areas for improvement for sustainable business operations. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the evaluation of the state of business processes and supply chains.

[0035] The proposal department can make specific improvement proposals for sustainable business operations based on the evaluation results. For example, the proposal department can propose measures to improve energy efficiency based on the evaluation results. The proposal department can also propose measures to reduce waste. The proposal department can also propose measures to optimize the supply chain. For example, the proposal department can propose the introduction of renewable energy as a measure to improve energy efficiency. The proposal department can also propose the promotion of recycling as a measure to reduce waste. The proposal department can also propose the improvement of logistics efficiency as a measure to optimize the supply chain. In this way, by making specific improvement proposals based on the evaluation results, it is possible to support the sustainable operation of companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the evaluation results into a generating AI and have the generating AI generate improvement proposals.

[0036] The analysis unit can analyze data using natural language processing and image recognition technologies. For example, the analysis unit can use natural language processing to analyze a company's CSR report. The analysis unit can also use image recognition technologies to analyze images of data centers, offices, and factories. For example, the analysis unit can use natural language processing to analyze the contents of a company's CSR report. The analysis unit can also use image recognition technologies to analyze images of data centers. The analysis unit can also analyze images of offices and factories. This improves the accuracy of data analysis by using natural language processing and image recognition technologies. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a company's CSR report or image data into a generating AI and have the generating AI perform data analysis using natural language processing and image recognition technologies.

[0037] The proposal department can make improvement suggestions such as improving energy efficiency, reducing waste, and optimizing the supply chain. For example, the proposal department can propose measures to improve energy efficiency. The proposal department can also propose measures to reduce waste. The proposal department can also propose measures to optimize the supply chain. For example, the proposal department can propose the introduction of renewable energy as a measure to improve energy efficiency. The proposal department can also propose the promotion of recycling as a measure to reduce waste. The proposal department can also propose the improvement of logistics efficiency as a measure to optimize the supply chain. In this way, by making concrete improvement suggestions such as improving energy efficiency, reducing waste, and optimizing the supply chain, it is possible to support the sustainable operation of companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input evaluation results into a generating AI and have the generating AI generate improvement suggestions.

[0038] The data collection unit can analyze a company's past CSR reports and image data to select the optimal data collection method. For example, the data collection unit can analyze the contents of past CSR reports and prioritize the collection of important data. Based on the results of the image data analysis, the data collection unit can also collect data indicating specific environmental impacts. The data collection unit can also evaluate the effectiveness of past data collection methods and select the optimal method. This enables efficient data collection by selecting the optimal method through the analysis of past data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past CSR reports and image data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the company's current business operations and environmental objectives during data collection. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the company's current business operations. The data collection unit can also filter data that indicates specific environmental impacts based on the company's environmental objectives. The data collection unit can also select and collect necessary data according to the company's business processes. This allows for the collection of highly relevant data by filtering data based on the company's business operations and environmental objectives. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's business operations and environmental objectives into a generating AI and have the generating AI perform the data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can prioritize the collection of data indicating regional environmental impacts based on the company's location. The data collection unit can also collect relevant environmental data by considering the geographical location information of companies. The data collection unit can also select and collect data indicating specific environmental impacts according to the geographical conditions of companies. This allows for the priority collection of data related to regional environmental impacts by considering the geographical location information of companies. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of companies into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media posts and collect data related to environmental impact. The data collection unit can also collect data on specific environmental issues from a company's social media activities. The data collection unit can also select and collect important data based on the company's social media responses. This allows for the collection of data related to environmental impact by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a company's social media activities into a generating AI and have the generating AI collect the relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific analysis algorithm to environmental data. The analysis unit can also apply a different analysis algorithm to business process data. The analysis unit can also apply the optimal analysis algorithm to supply chain data. By applying the optimal analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize analyzing data with an approaching submission deadline. It can also postpone analyzing data with ample time before the submission deadline. The analysis unit can also adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The proposal department can adjust the level of detail of a proposal based on its importance. For example, it can provide detailed proposals for high-importance improvement suggestions, and simplified proposals for low-importance suggestions. The proposal department can also adjust the depth of the proposal according to its importance. This allows for more efficient proposals by adjusting the level of detail according to the importance of the improvement suggestion. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the improvement suggestion into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the improvement proposal when making a proposal. For example, the proposal unit can apply a specific proposal algorithm to an energy efficiency improvement proposal. The proposal unit can also apply a different proposal algorithm to a waste reduction improvement proposal. The proposal unit can also apply the optimal proposal algorithm to a supply chain optimization proposal. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of the improvement proposal. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the improvement proposal into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0048] The proposal department can determine the priority of improvement proposals based on the submission timing at the time of proposal submission. For example, the proposal department may prioritize improvement proposals with approaching submission deadlines. The proposal department may also postpone improvement proposals with ample time for submission. The proposal department can also adjust the proposal schedule based on the submission timing. This allows for more efficient proposals by prioritizing proposals based on the submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of improvement proposals into a generating AI and have the generating AI determine the priority of the proposals.

[0049] The proposal unit can adjust the order of improvement suggestions based on their relevance when submitting them. For example, the proposal unit may prioritize providing highly relevant improvement suggestions. The proposal unit may also postpone less relevant improvement suggestions. The proposal unit can also adjust the order of suggestions based on their relevance. This allows for more efficient proposals by adjusting the order of suggestions based on their relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of improvement suggestions into a generation AI and have the generation AI perform the adjustment of the order of suggestions.

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

[0051] The data collection unit can evaluate the reliability of data in real time when collecting corporate CSR reports and image data. For example, the collection unit can verify the source and creation date of the data and prioritize the collection of highly reliable data. The collection unit can also determine the priority of data to collect based on its reliability. Furthermore, the collection unit can cross-check multiple data sources to evaluate data reliability. This improves the accuracy of analysis results by collecting highly reliable data.

[0052] The analysis unit can detect patterns of data variation and identify outliers when analyzing collected data. For example, it can detect abnormal values ​​by comparing them with past data and identify their causes. The analysis unit can also evaluate the frequency and scope of impact of outliers. Furthermore, it can analyze data correlations to identify the causes of outliers. By detecting patterns of data variation and identifying outliers, it becomes possible to clearly identify areas for improvement in a company's business processes and supply chains.

[0053] The proposal department can assess the feasibility of improvement proposals when making them based on evaluation results. For example, the proposal department can evaluate the resources and costs required to implement a proposal and prioritize feasible proposals. The proposal department can also assess the risks associated with implementing a proposal. Furthermore, the proposal department can predict the effects of implementing a proposal and select the most effective proposal. In this way, by evaluating the feasibility of proposals, it is possible to provide improvement proposals that are easy for companies to implement.

[0054] The data collection unit can collect data while taking into account seasonal variations in a company's business processes. For example, the collection unit can understand seasonal variations in business processes and collect data at the appropriate time. The collection unit can also focus on collecting data during specific periods according to seasonal variations. Furthermore, the collection unit can adjust the data collection schedule to minimize the impact of seasonal variations. By collecting data while considering seasonal variations in a company's business processes, more accurate data can be obtained.

[0055] The analysis unit can evaluate the quality of collected data and exclude low-quality data when analyzing it. For example, the analysis unit can detect missing or outlier values ​​and exclude low-quality data. The analysis unit can also verify the consistency and accuracy of the data in order to evaluate its quality. Furthermore, the analysis unit can perform data cleaning to improve data quality. By evaluating data quality and excluding low-quality data, the accuracy of the analysis results can be improved.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects corporate CSR reports and image data. For example, it collects corporate CSR reports in digital format (PDF), data center images in JPEG format, and office and factory images in PNG format. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the company's environmental impact. For example, it uses natural language processing technology to analyze the company's CSR report and image recognition technology to analyze images of data centers, offices, and factories. Step 3: The proposal department makes improvement proposals for sustainable business operations based on the evaluation results obtained by the analysis department. For example, they might propose the introduction of renewable energy as a measure to improve energy efficiency, promote recycling as a measure to reduce waste, and improve the efficiency of logistics as a measure to optimize the supply chain.

[0058] (Example of form 2) The environmental impact analysis system according to an embodiment of the present invention is a system that analyzes environmental impacts from a company's CSR reports and images of data centers, offices, factories, etc., and makes improvement proposals for sustainable business operations based on the state of the company's business processes and supply chain. This environmental impact analysis system collects images of a company's CSR reports and data centers, offices, factories, etc., and evaluates the company's environmental impact by analyzing them with a generating AI. Next, the generating AI evaluates the state of the company's business processes and supply chain based on the collected data. Finally, the generating AI makes specific improvement proposals for sustainable business operations based on the evaluation results. For example, the environmental impact analysis system collects images of a company's CSR reports and data centers, offices, factories, etc. These data are analyzed by the generating AI, and the company's environmental impact is evaluated. Next, the generating AI evaluates the state of the company's business processes and supply chain based on the collected data. For example, the generating AI evaluates the efficiency of the company's business processes and the optimization of the supply chain. Finally, the generating AI makes specific improvement proposals for sustainable business operations based on the evaluation results. For example, the generating AI makes improvement proposals such as improving energy efficiency, reducing waste, and optimizing the supply chain. This system is useful for companies that are environmentally conscious but lack concrete improvement measures, or for companies that cannot allocate human resources. It is also effective for medium to large-scale manufacturing, retail, and IT companies. Through this, the environmental impact analysis system can support sustainable business operations by collecting, analyzing, and proposing improvements based on companies' CSR reports and image data.

[0059] The environmental impact analysis system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects corporate social responsibility (CSR) reports and image data. For example, the collection unit collects corporate CSR reports and images of data centers, offices, factories, etc. For example, the collection unit collects corporate CSR reports in digital format. The collection unit can also collect images of data centers. Furthermore, the collection unit can also collect images of offices and factories. For example, the collection unit collects corporate CSR reports in PDF format. The collection unit can also collect images of data centers in JPEG format. The collection unit can also collect images of offices and factories in PNG format. The analysis unit analyzes the data collected by the collection unit and evaluates the environmental impact of the company. For example, the analysis unit evaluates the state of the company's business processes and supply chain based on the collected data. For example, the analysis unit analyzes corporate CSR reports using natural language processing technology. Furthermore, the analysis unit can also analyze images of data centers, offices, and factories using image recognition technology. For example, the analysis unit analyzes the contents of corporate CSR reports using natural language processing technology. The analysis unit can also analyze images of data centers using image recognition technology. The analysis unit can also analyze images of offices and factories. The proposal unit makes improvement suggestions for sustainable business operations based on the evaluation results obtained by the analysis unit. For example, the proposal unit makes improvement suggestions such as improving energy efficiency, reducing waste, and optimizing the supply chain based on the evaluation results. For example, the proposal unit may propose measures to improve energy efficiency. The proposal unit may also propose measures to reduce waste. Furthermore, the proposal unit may also propose measures to optimize the supply chain. For example, the proposal unit may propose the introduction of renewable energy as a measure to improve energy efficiency. The proposal unit may also propose the promotion of recycling as a measure to reduce waste. The proposal unit may also propose the improvement of logistics efficiency as a measure to optimize the supply chain. In this way, the environmental impact analysis system according to the embodiment can support sustainable business operations by collecting, analyzing, and making improvement suggestions for companies' CSR reports and image data.

[0060] The data collection unit collects corporate social responsibility (CSR) reports and image data. Specifically, it collects corporate CSR reports in digital format and images of data centers, offices, factories, etc. For example, corporate CSR reports are collected in PDF format, and images of data centers are collected in JPEG format. Images of offices and factories are often collected in PNG format. The data collection unit can utilize web scraping technology and APIs to efficiently collect this data. By using web scraping technology, CSR reports can be automatically obtained from corporate websites and stored in a database. Furthermore, by using APIs, it is possible to obtain data directly from the company's internal systems. In addition, the data collection unit can utilize devices such as drones and surveillance cameras for image data collection. By using drones, wide-area image data can be efficiently collected, and by using surveillance cameras, detailed image data of specific locations can be collected. As a result, the data collection unit can collect a wide range of data from diverse data sources and provide foundational data for comprehensively evaluating a company's environmental impact.

[0061] The analysis unit analyzes data collected by the data collection unit to assess the environmental impact of companies. Specifically, it uses natural language processing technology to analyze companies' CSR reports and image recognition technology to analyze images of data centers, offices, and factories. By using natural language processing technology, environmental descriptions can be extracted from companies' CSR reports and structured into text data. For example, information such as a company's energy consumption, waste generation, and renewable energy utilization can be extracted and stored in a database. By using image recognition technology, environmental information can be extracted from images of data centers, offices, and factories. For example, the operation status of cooling systems can be analyzed from images of data centers to evaluate energy efficiency. In addition, the amount and type of waste can be identified from images of offices and factories to evaluate the recycling status. Furthermore, the analysis unit can use AI to integrate the collected data and evaluate the state of companies' business processes and supply chains. Based on historical data and statistical information, the AI ​​can predict a company's environmental impact and assess future risks. As a result, the analysis unit can comprehensively assess a company's environmental impact and provide foundational data for sustainable business operations.

[0062] The Proposal Department will propose improvements for sustainable business operations based on the evaluation results obtained by the Analysis Department. Specifically, it will propose improvements such as improving energy efficiency, reducing waste, and optimizing the supply chain. For example, as a measure to improve energy efficiency, it can propose the introduction of renewable energy. By introducing renewable energy, the company's energy consumption can be reduced, and its environmental impact can be mitigated. As a measure to reduce waste, it can propose the promotion of recycling. By promoting recycling, the amount of waste can be reduced, and the effective use of resources can be promoted. Furthermore, as a measure to optimize the supply chain, it can propose the efficiency of logistics. By improving logistics efficiency, transportation costs can be reduced, and the environmental impact can be mitigated. The Proposal Department can provide these improvement proposals to companies as concrete action plans and support their implementation. For example, as a measure to improve energy efficiency, it can propose the introduction of solar power generation systems and energy management systems. As a measure to reduce waste, it can propose the introduction of recycling programs and thorough separate collection of waste. As a measure to optimize the supply chain, it can propose a review of transportation routes and the introduction of joint delivery. In this way, the Proposal Department can support the sustainable operation of companies and contribute to reducing their environmental impact.

[0063] The data collection unit can collect corporate CSR reports and images of data centers, offices, factories, etc. For example, the data collection unit can collect corporate CSR reports in digital format. The data collection unit can also collect images of data centers. The data collection unit can also collect images of offices and factories. For example, the data collection unit can collect corporate CSR reports in PDF format. The data collection unit can also collect images of data centers in JPEG format. The data collection unit can also collect images of offices and factories in PNG format. By collecting corporate CSR reports and images, data necessary for environmental impact assessment can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input corporate CSR reports and image data into a generating AI and have the generating AI perform the data collection.

[0064] The analysis unit can evaluate the state of a company's business processes and supply chain based on the collected data. For example, the analysis unit can evaluate the efficiency of a company's business processes based on the collected data. The analysis unit can also evaluate the state of the supply chain. For example, the analysis unit can perform statistical analysis of data to evaluate the efficiency of a company's business processes. The analysis unit can also perform data analysis to evaluate the state of the supply chain. By evaluating the state of a company's business processes and supply chain, it is possible to identify areas for improvement for sustainable business operations. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the evaluation of the state of business processes and supply chains.

[0065] The proposal department can make specific improvement proposals for sustainable business operations based on the evaluation results. For example, the proposal department can propose measures to improve energy efficiency based on the evaluation results. The proposal department can also propose measures to reduce waste. The proposal department can also propose measures to optimize the supply chain. For example, the proposal department can propose the introduction of renewable energy as a measure to improve energy efficiency. The proposal department can also propose the promotion of recycling as a measure to reduce waste. The proposal department can also propose the improvement of logistics efficiency as a measure to optimize the supply chain. In this way, by making specific improvement proposals based on the evaluation results, it is possible to support the sustainable operation of companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the evaluation results into a generating AI and have the generating AI generate improvement proposals.

[0066] The analysis unit can analyze data using natural language processing and image recognition technologies. For example, the analysis unit can use natural language processing to analyze a company's CSR report. The analysis unit can also use image recognition technologies to analyze images of data centers, offices, and factories. For example, the analysis unit can use natural language processing to analyze the contents of a company's CSR report. The analysis unit can also use image recognition technologies to analyze images of data centers. The analysis unit can also analyze images of offices and factories. This improves the accuracy of data analysis by using natural language processing and image recognition technologies. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a company's CSR report or image data into a generating AI and have the generating AI perform data analysis using natural language processing and image recognition technologies.

[0067] The proposal department can make improvement suggestions such as improving energy efficiency, reducing waste, and optimizing the supply chain. For example, the proposal department can propose measures to improve energy efficiency. The proposal department can also propose measures to reduce waste. The proposal department can also propose measures to optimize the supply chain. For example, the proposal department can propose the introduction of renewable energy as a measure to improve energy efficiency. The proposal department can also propose the promotion of recycling as a measure to reduce waste. The proposal department can also propose the improvement of logistics efficiency as a measure to optimize the supply chain. In this way, by making concrete improvement suggestions such as improving energy efficiency, reducing waste, and optimizing the supply chain, it is possible to support the sustainable operation of companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input evaluation results into a generating AI and have the generating AI generate improvement suggestions.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can also increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0069] The data collection unit can analyze a company's past CSR reports and image data to select the optimal data collection method. For example, the data collection unit can analyze the contents of past CSR reports and prioritize the collection of important data. Based on the results of the image data analysis, the data collection unit can also collect data indicating specific environmental impacts. The data collection unit can also evaluate the effectiveness of past data collection methods and select the optimal method. This enables efficient data collection by selecting the optimal method through the analysis of past data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past CSR reports and image data into a generating AI and have the generating AI select the optimal data collection method.

[0070] The data collection unit can filter data based on the company's current business operations and environmental objectives during data collection. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the company's current business operations. The data collection unit can also filter data that indicates specific environmental impacts based on the company's environmental objectives. The data collection unit can also select and collect necessary data according to the company's business processes. This allows for the collection of highly relevant data by filtering data based on the company's business operations and environmental objectives. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's business operations and environmental objectives into a generating AI and have the generating AI perform the data filtering.

[0071] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can also collect detailed data. If the user is in a hurry, the data collection unit can also prioritize data that can be collected quickly. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of companies during data collection. For example, the data collection unit can prioritize the collection of data indicating regional environmental impacts based on the company's location. The data collection unit can also collect relevant environmental data by considering the geographical location information of companies. The data collection unit can also select and collect data indicating specific environmental impacts according to the geographical conditions of companies. This allows for the priority collection of data related to regional environmental impacts by considering the geographical location information of companies. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of companies into a generating AI and have the generating AI perform the collection of highly relevant data.

[0073] The data collection unit can analyze a company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze a company's social media posts and collect data related to environmental impact. The data collection unit can also collect data on specific environmental issues from a company's social media activities. The data collection unit can also select and collect important data based on the company's social media responses. This allows for the collection of data related to environmental impact by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a company's social media activities into a generating AI and have the generating AI collect the relevant data.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific analysis algorithm to environmental data. The analysis unit can also apply a different analysis algorithm to business process data. The analysis unit can also apply the optimal analysis algorithm to supply chain data. By applying the optimal analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize analyzing data with an approaching submission deadline. It can also postpone analyzing data with ample time before the submission deadline. The analysis unit can also adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0080] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The proposal department can adjust the level of detail of a proposal based on its importance. For example, it can provide detailed proposals for high-importance improvement suggestions, and simplified proposals for low-importance suggestions. The proposal department can also adjust the depth of the proposal according to its importance. This allows for more efficient proposals by adjusting the level of detail according to the importance of the improvement suggestion. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the importance of the improvement suggestion into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0082] The proposal unit can apply different proposal algorithms depending on the category of the improvement proposal when making a proposal. For example, the proposal unit can apply a specific proposal algorithm to an energy efficiency improvement proposal. The proposal unit can also apply a different proposal algorithm to a waste reduction improvement proposal. The proposal unit can also apply the optimal proposal algorithm to a supply chain optimization proposal. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of the improvement proposal. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the improvement proposal into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0083] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The proposal department can determine the priority of improvement proposals based on the submission timing at the time of proposal submission. For example, the proposal department may prioritize improvement proposals with approaching submission deadlines. The proposal department may also postpone improvement proposals with ample time for submission. The proposal department can also adjust the proposal schedule based on the submission timing. This allows for more efficient proposals by prioritizing proposals based on the submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of improvement proposals into a generating AI and have the generating AI determine the priority of the proposals.

[0085] The proposal unit can adjust the order of improvement suggestions based on their relevance when submitting them. For example, the proposal unit may prioritize providing highly relevant improvement suggestions. The proposal unit may also postpone less relevant improvement suggestions. The proposal unit can also adjust the order of suggestions based on their relevance. This allows for more efficient proposals by adjusting the order of suggestions based on their relevance. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of improvement suggestions into a generation AI and have the generation AI perform the adjustment of the order of suggestions.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The data collection unit can evaluate the reliability of data in real time when collecting corporate CSR reports and image data. For example, the collection unit can verify the source and creation date of the data and prioritize the collection of highly reliable data. The collection unit can also determine the priority of data to collect based on its reliability. Furthermore, the collection unit can cross-check multiple data sources to evaluate data reliability. This improves the accuracy of analysis results by collecting highly reliable data.

[0088] The analysis unit can detect patterns of data variation and identify outliers when analyzing collected data. For example, it can detect abnormal values ​​by comparing them with past data and identify their causes. The analysis unit can also evaluate the frequency and scope of impact of outliers. Furthermore, it can analyze data correlations to identify the causes of outliers. By detecting patterns of data variation and identifying outliers, it becomes possible to clearly identify areas for improvement in a company's business processes and supply chains.

[0089] The proposal department can assess the feasibility of improvement proposals when making them based on evaluation results. For example, the proposal department can evaluate the resources and costs required to implement a proposal and prioritize feasible proposals. The proposal department can also assess the risks associated with implementing a proposal. Furthermore, the proposal department can predict the effects of implementing a proposal and select the most effective proposal. In this way, by evaluating the feasibility of proposals, it is possible to provide improvement proposals that are easy for companies to implement.

[0090] The data collection unit can collect data while taking into account seasonal variations in a company's business processes. For example, the collection unit can understand seasonal variations in business processes and collect data at the appropriate time. The collection unit can also focus on collecting data during specific periods according to seasonal variations. Furthermore, the collection unit can adjust the data collection schedule to minimize the impact of seasonal variations. By collecting data while considering seasonal variations in a company's business processes, more accurate data can be obtained.

[0091] The analysis unit can evaluate the quality of collected data and exclude low-quality data when analyzing it. For example, the analysis unit can detect missing or outlier values ​​and exclude low-quality data. The analysis unit can also verify the consistency and accuracy of the data in order to evaluate its quality. Furthermore, the analysis unit can perform data cleaning to improve data quality. By evaluating data quality and excluding low-quality data, the accuracy of the analysis results can be improved.

[0092] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data in a way that does not burden the user. If the user is relaxed, the data collection unit can also collect detailed data. If the user is in a hurry, the data collection unit can choose a method to collect data quickly. In this way, the burden on the user can be reduced by adjusting the data collection method according to the user's emotions.

[0093] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. By adjusting the presentation method of the analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0094] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide easy-to-implement suggestions. If the user is relaxed, the suggestion function can also provide more detailed suggestions. If the user is in a hurry, the suggestion function can also provide suggestions that can be implemented quickly. In this way, by adjusting the content of suggestions according to the user's emotions, it is possible to provide suggestions that are easy for the user to implement.

[0095] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on those emotions. For example, if the user is stressed, the unit reduces the frequency of data collection to lessen the user's burden. If the user is relaxed, the unit can increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the unit can adjust the timing of data collection to quickly collect the necessary data. In this way, by adjusting the frequency of data collection according to the user's emotions, the user's burden can be reduced.

[0096] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function will prioritize providing high-priority suggestions. If the user is relaxed, the suggestion function may also provide more detailed suggestions. If the user is in a hurry, the suggestion function may also prioritize suggestions that can be implemented quickly. This allows for the prioritization of important suggestions based on the user's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The collection unit collects corporate CSR reports and image data. For example, it collects corporate CSR reports in digital format (PDF), data center images in JPEG format, and office and factory images in PNG format. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the company's environmental impact. For example, it uses natural language processing technology to analyze the company's CSR report and image recognition technology to analyze images of data centers, offices, and factories. Step 3: The proposal department makes improvement proposals for sustainable business operations based on the evaluation results obtained by the analysis department. For example, they might propose the introduction of renewable energy as a measure to improve energy efficiency, promote recycling as a measure to reduce waste, and improve the efficiency of logistics as a measure to optimize the supply chain.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects corporate CSR reports and image data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate the company's environmental impact. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and makes improvement proposals for sustainable business operations based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects corporate CSR reports and image data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate the company's environmental impact. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and makes improvement suggestions for sustainable business operations based on the evaluation results. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects corporate CSR reports and image data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to evaluate the company's environmental impact. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and makes improvement proposals for sustainable business operations based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects corporate CSR reports and image data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to evaluate the company's environmental impact. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which makes improvement proposals for sustainable business operations based on the evaluation results. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The collection department collects corporate CSR reports and image data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the environmental impact of the company, The system includes a proposal unit that makes suggestions for improvements for sustainable business operations based on the evaluation results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect images of companies' CSR reports, data centers, offices, factories, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we evaluate the state of a company's business processes and supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the evaluation results, we will make specific improvement proposals for sustainable business operations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze data using natural language processing and image recognition technologies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose improvements such as increasing energy efficiency, reducing waste, and optimizing the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze past corporate social responsibility (CSR) reports and image data from companies to select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the company's current operational status and environmental goals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze the company's social media activities and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting a proposal, adjust the level of detail based on the importance of the improvement suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the category of the improvement suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, the priority of the proposals will be determined based on when the improvement suggestions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, adjust the order of the suggestions based on their relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects corporate CSR reports and image data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the environmental impact of the company, The system includes a proposal unit that makes suggestions for improvements for sustainable business operations based on the evaluation results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect images of corporate social responsibility (CSR) reports, data centers, offices, factories, etc. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we evaluate the state of a company's business processes and supply chain. The system according to feature 1.

4. The aforementioned proposal section is, Based on the evaluation results, we will make specific improvement proposals for sustainable business operations. The system according to feature 1.

5. The aforementioned analysis unit, Analyze data using natural language processing and image recognition technologies. The system according to feature 1.

6. The aforementioned proposal section is, We propose improvements such as increasing energy efficiency, reducing waste, and optimizing the supply chain. The system according to feature 1.

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

8. The aforementioned collection unit is We analyze past corporate social responsibility (CSR) reports and image data from companies to select the most suitable data collection method. The system according to feature 1.

Images