System

Abstract

A system according to an embodiment aims to analyze up-to-date research data related to environmental protection and suggest customized sustainability strategies to companies.SOLUTION: A system according to an embodiment includes a research data collector, a generation strategy analyzer, and a AI suggester. The research data collection unit collects the latest research data related to environmental protection. The generated-data analysis unit analyzes the AI collected by the research-data collection unit. The suggestion component suggests customized sustainability strategies based on the results analyzed by the generation AI analysis component.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technologies have had the challenge of not being able to effectively utilize the latest research data related to environmental protection to propose customized sustainability strategies to companies.

[0005] The system according to the embodiment aims to analyze the latest research data related to environmental protection and propose customized sustainability strategies to companies. [Means for solving the problem]

[0006] The system according to the embodiment includes a research data collection unit, a generative AI analysis unit, and a strategy proposal unit. The research data collection unit collects the latest research data related to environmental protection. The generative AI analysis unit analyzes the data collected by the research data collection unit. The strategy proposal unit proposes a customized sustainability strategy based on the results of the analysis by the generative AI analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the latest research data related to environmental protection and propose customized sustainability strategies to companies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The EcoBoost AI system, an embodiment of the present invention, is a B2B AI advisory system that helps companies build sustainable business models and promote environmentally friendly innovation. The system uses AI to analyze the latest research, green technology trends, and regulatory changes related to environmental protection, and proposes customized sustainability strategies to companies. This allows the EcoBoost AI system to support innovative approaches to promote business growth while positively impacting the ecosystem.

[0029] The EcoBoost AI system according to the embodiment includes a research data collection unit, a generative AI analysis unit, and a strategy proposal unit. The research data collection unit collects the latest research data related to environmental protection. For example, it collects climate change data, environmental pollution data, biodiversity data, etc. The research data collection unit can also collect data from online databases and academic papers. For example, the research data collection unit automatically collects the latest research papers on climate change and stores them in a database. The generative AI analysis unit analyzes the data collected by the research data collection unit. For example, the generative AI can extract useful information from the collected data using data mining techniques. The generative AI can also analyze trends and patterns in the data using statistical analysis techniques. The generative AI can also build a predictive model of the data using machine learning algorithms. For example, the generative AI can analyze climate change data and build a predictive model of future temperature rise. The strategy proposal unit proposes a customized sustainability strategy based on the results of the analysis by the generative AI analysis unit. For example, the strategy proposal unit proposes energy efficiency methods based on a company's specific needs and goals. The strategy proposal department can also propose specific waste reduction measures tailored to the company's industry and size. The strategy proposal department can also propose improvements based on the company's current sustainability status. For example, the strategy proposal department can analyze the company's energy consumption data and propose specific measures to improve energy efficiency. As a result, the EcoBoost AI system according to the embodiment can provide customized sustainability strategies to help companies build sustainable business models and promote environmentally friendly innovation. For example, by introducing energy-efficient technologies, companies can achieve both cost reduction and environmental protection. Furthermore, by implementing a sustainability strategy that complies with regulations, companies can achieve sustainable growth while avoiding legal risks.

[0030] The generative AI analysis unit can extract sustainability strategies specialized for specific regions and climatic conditions. For example, the generative AI analysis unit analyzes research data on the climatic conditions of a specific region and extracts the optimal sustainability strategy for that region. For example, it can propose an efficient water use strategy based on research data on water resource management in arid regions. The generative AI analysis unit can also analyze data on urban climatic conditions and propose energy efficiency strategies suitable for urban areas. For example, it can analyze energy consumption data for urban buildings and propose specific measures for energy efficiency. The generative AI analysis unit can also propose a renewable energy introduction strategy based on the climatic conditions of a specific region. For example, it can propose a wind power introduction strategy for regions suitable for wind power generation. This makes it possible to provide the optimal sustainability strategy according to the region and climatic conditions.

[0031] The generative AI analysis unit can compare data with past data to discover new trends and patterns and propose sustainability strategies based on them. For example, the generative AI analysis unit can analyze climate change data from the past 10 years to discover new trends and patterns. For example, it can propose specific measures to combat global warming based on the trend of rising temperatures. The generative AI analysis unit can also compare data with past data to discover new patterns of environmental pollution and propose pollution control measures based on them. For example, it can analyze past air pollution data to identify new pollution sources and propose countermeasures for them. The generative AI analysis unit can also compare data with past data to discover new changes in biodiversity and propose protection strategies based on them. For example, it can analyze past biodiversity data to identify new endangered species and propose protection strategies for them. This makes it possible to discover new trends and patterns by comparing data with past data and provide strategies based on them.

[0032] The generative AI analysis unit can apply the analyzed research data to different industries and fields to propose applicable sustainability strategies. For example, the generative AI analysis unit analyzes research data on environmental protection and applies the results to different industries. For example, research on water resource management in the agricultural sector can be applied to the manufacturing industry to propose efficient water use strategies. The generative AI analysis unit can also analyze research data on renewable energy and apply the results to different fields. For example, research on solar power generation can be applied to the housing construction sector to propose energy-efficient housing construction strategies. The generative AI analysis unit can also analyze research data on environmental pollution countermeasures and apply the results to different industries. For example, research on air pollution countermeasures can be applied to the transportation sector to propose a strategy for introducing low-emission vehicles. This makes it possible to provide strategies that can be applied to different industries and fields.

[0033] The generative AI analysis unit can generate a multilingual sustainability strategy, including research data in different languages. For example, the generative AI analysis unit analyzes research data on environmental protection in different languages ​​to generate a multilingual sustainability strategy. For example, research data in English, French, and Chinese may be integrated and analyzed. The generative AI analysis unit can also use a translation algorithm to automatically translate and analyze research data in different languages. For example, English research data may be translated into French and analyzed. The generative AI analysis unit can also use a language model to analyze research data in different languages ​​to generate a multilingual strategy. For example, research data in English and Chinese may be integrated and analyzed to propose a multilingual sustainability strategy. This makes it possible to provide a multilingual strategy.

[0034] The generative AI analysis unit can track the evolution of specific technologies from green technology trend data and make future technology predictions. The generative AI analysis unit can, for example, analyze green technology trend data from the past 10 years to track the evolution of specific technologies. For example, future technology predictions can be made based on the evolution of solar power generation technology. The generative AI analysis unit can also use patent analysis to track the evolution of specific technologies. For example, it can analyze patent data for renewable energy technologies to track the evolution of technology. The generative AI analysis unit can also use technology roadmaps to track the evolution of specific technologies and make future technology predictions. For example, it can make future technology predictions based on the evolution of energy efficiency technology. This makes it possible to make future technology predictions.

[0035] The generative AI analysis unit can apply the analyzed trend data to different regions and markets to propose region-specific sustainability strategies. For example, the generative AI analysis unit analyzes green technology trend data and applies the results to different regions. For example, it can propose an energy strategy suitable for desert regions based on trends in solar power generation. The generative AI analysis unit can also analyze data from different markets and propose region-specific strategies based on the results. For example, it can apply an energy efficiency strategy for the North American market to the Asian market to propose a region-specific strategy. The generative AI analysis unit can also propose renewable energy introduction strategies based on the climatic conditions of different regions. For example, it can propose a wind power introduction strategy for regions where wind power generation is suitable. This makes it possible to provide region-specific strategies.

[0036] The generative AI analysis unit can compare technological trends in different industries and propose the introduction of crossover technologies. For example, the generative AI analysis unit can analyze green technology trend data in different industries and propose the introduction of crossover technologies. For example, it can propose a strategy to apply agricultural technology to manufacturing. The generative AI analysis unit can also use cross-industry collaboration technology to compare technological trends in different industries and propose the introduction of crossover technologies. For example, it can propose a strategy to integrate technologies from different industries using hybrid technology. The generative AI analysis unit can also compare technological trends in different industries and propose the introduction of crossover technologies based on common technologies. For example, it can propose a strategy to apply renewable energy technology to different industries. This makes it possible to compare technological trends in different industries and propose the introduction of crossover technologies.

[0037] The generative AI analysis unit can simulate the impact of regulatory changes and propose optimal countermeasures for companies. For example, the generative AI analysis unit analyzes data on new environmental regulations and simulates their impact. For example, it evaluates the impact of new emission standards on a company's production process and proposes optimal countermeasures. The generative AI analysis unit can also simulate the impact of regulatory changes using scenario analysis. For example, it evaluates the impact of regulatory changes on a company based on different scenarios and proposes corresponding countermeasures. The generative AI analysis unit can also simulate the impact of regulatory changes using modeling technology and propose optimal countermeasures. For example, it evaluates the impact of new environmental laws and regulations on a company's costs and proposes corresponding countermeasures. This makes it possible to simulate the impact of regulatory changes and provide optimal countermeasures.

[0038] When analyzing regulatory data, the generative AI analysis unit can compare past regulatory changes and their impacts to predict future regulations. The generative AI analysis unit, for example, analyzes data on past environmental regulatory changes and evaluates their impacts. For example, it predicts future regulations based on the impact of past changes in emission standards on a company's production process. The generative AI analysis unit can also use historical data analysis to compare past regulatory changes and their impacts to predict future regulations. For example, it can analyze data on past changes in environmental laws and regulations, evaluate their impacts, and predict future regulations. The generative AI analysis unit can also use an impact assessment model to compare past regulatory changes and their impacts to predict future regulations. For example, it can evaluate the impact of past changes in emission standards and predict future regulations based on that data. This makes it possible to compare past regulatory changes and their impacts to predict future regulations.

[0039] The generative AI analysis unit can compare the analyzed regulatory data with regulations in different countries and regions and propose a global regulatory response strategy. For example, the generative AI analysis unit analyzes environmental regulatory data and compares the results with regulations in different countries and regions. For example, it compares environmental regulations in the European Union and the United States and proposes a global regulatory response strategy. The generative AI analysis unit can also analyze regulatory data in different countries and regions and propose a global strategy based on the results. For example, it compares environmental regulations in Asia and North America and proposes a global regulatory response strategy. The generative AI analysis unit can also propose common regulatory response measures based on regulatory data from different countries and regions. For example, it proposes common regulatory response measures based on environmental regulations in the EU and the United States. This makes it possible to provide a global regulatory response strategy.

[0040] When analyzing regulatory data, the generative AI analysis unit can compare regulations from different industries and propose common regulatory responses. For example, the generative AI analysis unit can analyze environmental regulatory data from different industries and propose common regulatory responses. For example, it can compare emission standards for manufacturing and agriculture and propose common responses. The generative AI analysis unit can also analyze regulatory data from different industries and propose common regulatory responses based on the results. For example, it can compare environmental regulations for the service industry and manufacturing industry and propose common responses. The generative AI analysis unit can also propose common regulatory responses based on regulatory data from different industries. For example, it can propose common responses based on environmental regulations for agriculture and manufacturing. This makes it possible to compare regulations from different industries and provide common regulatory responses.

[0041] When analyzing corporate data, the generative AI analysis unit also takes into account the company's financial data and market data, and can propose economically feasible sustainability strategies. For example, the generative AI analysis unit analyzes a company's financial data and proposes an economically feasible sustainability strategy. For example, it evaluates the costs and benefits of energy efficiency projects and proposes the optimal strategy. The generative AI analysis unit can also analyze market data and propose an economically feasible strategy. For example, it can propose the optimal energy efficiency strategy based on market share data. The generative AI analysis unit can also integrate and analyze a company's financial data and market data to propose an economically feasible strategy. For example, it can propose the optimal sustainability strategy based on sales data and market share data. This makes it possible to provide an economically feasible strategy.

[0042] When analyzing corporate data, the generative AI analysis unit can apply the results to companies of different industries and scales, proposing versatile sustainability strategies. For example, the generative AI analysis unit analyzes corporate data and applies the results to a different industry. For example, it can adapt an energy efficiency strategy for the manufacturing industry to the service industry and propose a versatile strategy. The generative AI analysis unit can also propose strategies that can be applied to companies of different scales. For example, it can apply a sustainability strategy for a large company to a small or medium-sized enterprise and propose a versatile strategy. The generative AI analysis unit can also analyze common data to propose strategies that can be applied to companies of different industries and scales. For example, it can analyze common data for the manufacturing and service industries and propose a versatile sustainability strategy. This makes it possible to provide a versatile strategy.

[0043] When analyzing corporate data, the generative AI analysis unit takes into account data from different regions and markets and can propose region-specific sustainability strategies. For example, the generative AI analysis unit analyzes corporate data and applies the results to a different region. For example, an energy efficiency strategy for the North American market can be applied to the Asian market to propose a region-specific strategy. The generative AI analysis unit can also analyze data from different markets and propose region-specific strategies based on the results. For example, a waste reduction strategy for the European market can be applied to the African market to propose a region-specific strategy. The generative AI analysis unit can also propose renewable energy introduction strategies based on the climatic conditions of different regions. For example, a wind power introduction strategy can be proposed for regions where wind power generation is suitable. This makes it possible to provide region-specific strategies.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The EcoBoost AI system can also be equipped with a real-time data collection unit. The real-time data collection unit uses sensors and IoT devices to collect environmental data from a company's site in real time. For example, it can collect factory exhaust gas data and energy consumption data in real time and provide it to the generative AI analysis unit. The real-time data collection unit can also collect external data such as weather data and traffic data. For example, it can analyze a factory's energy consumption patterns based on weather data and propose efficient energy usage strategies. This allows companies to monitor environmental data in real time and respond quickly.

[0046] The generative AI analysis unit can also be equipped with an anomaly detection function. The anomaly detection function analyzes collected data and detects abnormal patterns and trends. For example, if an abnormal increase is observed in a factory's exhaust gas data, the anomaly detection function can identify the cause and quickly propose countermeasures. In addition, if an abnormal fluctuation is observed in energy consumption data, the anomaly detection function can analyze the cause and propose efficient energy usage strategies. Furthermore, if an abnormal fluctuation is observed in climate data, the anomaly detection function can evaluate the impact and propose appropriate countermeasures. This allows companies to respond quickly to abnormal situations and minimize their impact on the environment.

[0047] The generative AI analysis unit can further be equipped with a scenario planning function. The scenario planning function predicts future environmental changes based on different scenarios and proposes sustainability strategies based on those predictions. For example, based on a climate change scenario, it predicts future temperature increases and precipitation fluctuations and proposes corresponding energy efficiency strategies. The scenario planning function can also propose corporate growth strategies based on different economic scenarios. For example, based on an economic growth scenario, it can propose a strategy to address increased energy demand. The scenario planning function can also evaluate a company's legal risks based on different regulatory scenarios and propose appropriate countermeasures. This allows companies to respond flexibly to future uncertainties.

[0048] The generative AI analysis unit can also compare technological trends in different industries and propose the introduction of crossover technologies. For example, it can propose a strategy to apply agricultural technology to manufacturing. It can also compare technological trends in different industries using cross-industry collaboration technology and propose the introduction of crossover technologies. For example, it can propose a strategy to integrate technologies from different industries using hybrid technology. It can also compare technological trends in different industries and propose the introduction of crossover technologies based on common technologies. For example, it can propose a strategy to apply renewable energy technology to a different industry. This makes it possible to compare technological trends in different industries and propose the introduction of crossover technologies.

[0049] The generative AI analysis unit can further analyze data from different regions and markets to propose region-specific sustainability strategies. For example, an energy efficiency strategy for the North American market can be applied to the Asian market to propose a region-specific strategy. It can also analyze data from different markets and propose region-specific strategies based on the results. For example, a waste reduction strategy for the European market can be applied to the African market to propose a region-specific strategy. It can also propose renewable energy introduction strategies based on the climatic conditions of different regions. For example, a wind power introduction strategy can be proposed for regions where wind power generation is suitable. This makes it possible to provide region-specific strategies.

[0050] The generative AI analysis unit can also generate a multilingual sustainability strategy that includes research data in different languages. For example, research data on environmental protection in different languages ​​can be analyzed to generate a multilingual sustainability strategy. For example, research data in English, French, and Chinese can be integrated and analyzed. It can also use a translation algorithm to automatically translate and analyze research data in different languages. For example, English research data can be translated into French and analyzed. It can also use a language model to analyze research data in different languages ​​to generate a multilingual strategy. For example, research data in English and Chinese can be integrated and analyzed to propose a multilingual sustainability strategy. This allows it to provide a multilingual strategy.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The research data collection unit collects the latest research data related to environmental protection. For example, it collects climate change data, environmental pollution data, biodiversity data, etc. The research data collection unit can also collect data from online databases and academic papers. For example, the research data collection unit automatically collects the latest research papers on climate change and stores them in a database. Step 2: The Generative AI Analysis Unit analyzes the data collected by the Research Data Collection Unit. For example, the Generative AI uses data mining techniques to extract useful information from the collected data. The Generative AI can also use statistical analysis techniques to analyze trends and patterns in the data. The Generative AI can also use machine learning algorithms to build predictive models of the data. For example, the Generative AI analyzes climate change data and builds a predictive model of future temperature rises. Step 3: The Strategy Proposal Department proposes a customized sustainability strategy based on the results of the analysis by the Generative AI Analysis Department. For example, the Strategy Proposal Department proposes energy efficiency methods based on the company's specific needs and goals. The Strategy Proposal Department can also propose specific measures for waste reduction based on the company's industry and size. The Strategy Proposal Department can also suggest areas for improvement based on the company's current sustainability status. For example, the Strategy Proposal Department can analyze the company's energy consumption data and propose specific measures for energy efficiency.

[0053] (Example 2) The EcoBoost AI system, an embodiment of the present invention, is a B2B AI advisory system that helps companies build sustainable business models and promote environmentally friendly innovation. The system uses AI to analyze the latest research, green technology trends, and regulatory changes related to environmental protection, and proposes customized sustainability strategies to companies. This allows the EcoBoost AI system to support innovative approaches to promote business growth while positively impacting the ecosystem.

[0054] The EcoBoost AI system according to the embodiment includes a research data collection unit, a generative AI analysis unit, and a strategy proposal unit. The research data collection unit collects the latest research data related to environmental protection. For example, it collects climate change data, environmental pollution data, biodiversity data, etc. The research data collection unit can also collect data from online databases and academic papers. For example, the research data collection unit automatically collects the latest research papers on climate change and stores them in a database. The generative AI analysis unit analyzes the data collected by the research data collection unit. For example, the generative AI can extract useful information from the collected data using data mining techniques. The generative AI can also analyze trends and patterns in the data using statistical analysis techniques. The generative AI can also build a predictive model of the data using machine learning algorithms. For example, the generative AI can analyze climate change data and build a predictive model of future temperature rise. The strategy proposal unit proposes a customized sustainability strategy based on the results of the analysis by the generative AI analysis unit. For example, the strategy proposal unit proposes energy efficiency methods based on a company's specific needs and goals. The strategy proposal department can also propose specific waste reduction measures tailored to the company's industry and size. The strategy proposal department can also propose improvements based on the company's current sustainability status. For example, the strategy proposal department can analyze the company's energy consumption data and propose specific measures to improve energy efficiency. As a result, the EcoBoost AI system according to the embodiment can provide customized sustainability strategies to help companies build sustainable business models and promote environmentally friendly innovation. For example, by introducing energy-efficient technologies, companies can achieve both cost reduction and environmental protection. Furthermore, by implementing a sustainability strategy that complies with regulations, companies can achieve sustainable growth while avoiding legal risks.

[0055] The generative AI analysis unit can extract sustainability strategies specialized for specific regions and climatic conditions. For example, the generative AI analysis unit analyzes research data on the climatic conditions of a specific region and extracts the optimal sustainability strategy for that region. For example, it can propose an efficient water use strategy based on research data on water resource management in arid regions. The generative AI analysis unit can also analyze data on urban climatic conditions and propose energy efficiency strategies suitable for urban areas. For example, it can analyze energy consumption data for urban buildings and propose specific measures for energy efficiency. The generative AI analysis unit can also propose a renewable energy introduction strategy based on the climatic conditions of a specific region. For example, it can propose a wind power introduction strategy for regions suitable for wind power generation. This makes it possible to provide the optimal sustainability strategy according to the region and climatic conditions.

[0056] The generative AI analysis unit can compare data with past data to discover new trends and patterns and propose sustainability strategies based on them. For example, the generative AI analysis unit can analyze climate change data from the past 10 years to discover new trends and patterns. For example, it can propose specific measures to combat global warming based on the trend of rising temperatures. The generative AI analysis unit can also compare data with past data to discover new patterns of environmental pollution and propose pollution control measures based on them. For example, it can analyze past air pollution data to identify new pollution sources and propose countermeasures for them. The generative AI analysis unit can also compare data with past data to discover new changes in biodiversity and propose protection strategies based on them. For example, it can analyze past biodiversity data to identify new endangered species and propose protection strategies for them. This makes it possible to discover new trends and patterns by comparing data with past data and provide strategies based on them.

[0057] The generative AI analysis unit can analyze the emotions of researchers and experts toward research data and prioritize research results with positive emotions in its strategies. For example, the generative AI analysis unit can use its emotion estimation function to identify research data for which researchers have positive emotions and propose sustainability strategies based on that data. For example, it can prioritize analysis of research with positive emotions toward success stories. The generative AI analysis unit can also analyze the emotions of researchers and experts and exclude research data with negative emotions. For example, it can exclude research with negative emotions toward failure stories. The generative AI analysis unit can also evaluate the reliability of research data based on the emotions of researchers and experts and prioritize analysis of highly reliable data. For example, it can propose a highly reliable sustainability strategy based on research data with positive emotions. This makes it possible to provide strategies that take the emotions of researchers and experts into account.

[0058] The generative AI analysis unit can apply the analyzed research data to different industries and fields to propose applicable sustainability strategies. For example, the generative AI analysis unit analyzes research data on environmental protection and applies the results to different industries. For example, research on water resource management in the agricultural sector can be applied to the manufacturing industry to propose efficient water use strategies. The generative AI analysis unit can also analyze research data on renewable energy and apply the results to different fields. For example, research on solar power generation can be applied to the housing construction sector to propose energy-efficient housing construction strategies. The generative AI analysis unit can also analyze research data on environmental pollution countermeasures and apply the results to different industries. For example, research on air pollution countermeasures can be applied to the transportation sector to propose a strategy for introducing low-emission vehicles. This makes it possible to provide strategies that can be applied to different industries and fields.

[0059] The generative AI analysis unit can generate a multilingual sustainability strategy, including research data in different languages. For example, the generative AI analysis unit analyzes research data on environmental protection in different languages ​​to generate a multilingual sustainability strategy. For example, research data in English, French, and Chinese may be integrated and analyzed. The generative AI analysis unit can also use a translation algorithm to automatically translate and analyze research data in different languages. For example, English research data may be translated into French and analyzed. The generative AI analysis unit can also use a language model to analyze research data in different languages ​​to generate a multilingual strategy. For example, research data in English and Chinese may be integrated and analyzed to propose a multilingual sustainability strategy. This makes it possible to provide a multilingual strategy.

[0060] The generative AI analysis unit can analyze general consumer sentiment toward research data and propose sustainability strategies based on consumer sentiment. For example, the generative AI analysis unit can use sentiment estimation to identify research data for which general consumers have positive sentiment and propose sustainability strategies based on that data. For example, it can prioritize analysis of research on renewable energy, which consumers support. The generative AI analysis unit can also analyze general consumer sentiment and exclude research data for which consumers have negative sentiment. For example, it can exclude research on environmental pollution control measures that consumers oppose. The generative AI analysis unit can also evaluate the reliability of research data based on general consumer sentiment and prioritize analysis of reliable data. For example, it can propose a reliable sustainability strategy based on research data for which consumers have positive sentiment. This makes it possible to provide strategies that take general consumer sentiment into account.

[0061] The generative AI analysis unit can track the evolution of specific technologies from green technology trend data and make future technology predictions. The generative AI analysis unit can, for example, analyze green technology trend data from the past 10 years to track the evolution of specific technologies. For example, future technology predictions can be made based on the evolution of solar power generation technology. The generative AI analysis unit can also use patent analysis to track the evolution of specific technologies. For example, it can analyze patent data for renewable energy technologies to track the evolution of technology. The generative AI analysis unit can also use technology roadmaps to track the evolution of specific technologies and make future technology predictions. For example, it can make future technology predictions based on the evolution of energy efficiency technology. This makes it possible to make future technology predictions.

[0062] The generative AI analysis unit can analyze industry experts' sentiment toward technology trends and prioritize technologies with positive sentiment in the strategy. For example, the generative AI analysis unit can use the sentiment estimation function to identify technology trends that industry experts have positive sentiment about and propose a strategy based on that technology. For example, it can prioritize analyzing renewable energy technologies that experts support. The generative AI analysis unit can also analyze industry experts' sentiment and exclude technology trends with negative sentiment. For example, it can exclude technology trends that experts oppose. The generative AI analysis unit can also evaluate the reliability of technology trends based on the sentiment of industry experts and prioritize analyzing reliable technologies. For example, it can propose a reliable sustainability strategy based on technology trends with positive sentiment. This makes it possible to provide a strategy that takes into account the sentiment of industry experts.

[0063] The generative AI analysis unit can apply the analyzed trend data to different regions and markets to propose region-specific sustainability strategies. For example, the generative AI analysis unit analyzes green technology trend data and applies the results to different regions. For example, it can propose an energy strategy suitable for desert regions based on trends in solar power generation. The generative AI analysis unit can also analyze data from different markets and propose region-specific strategies based on the results. For example, it can apply an energy efficiency strategy for the North American market to the Asian market to propose a region-specific strategy. The generative AI analysis unit can also propose renewable energy introduction strategies based on the climatic conditions of different regions. For example, it can propose a wind power introduction strategy for regions where wind power generation is suitable. This makes it possible to provide region-specific strategies.

[0064] The generative AI analysis unit can compare technological trends in different industries and propose the introduction of crossover technologies. For example, the generative AI analysis unit can analyze green technology trend data in different industries and propose the introduction of crossover technologies. For example, it can propose a strategy to apply agricultural technology to manufacturing. The generative AI analysis unit can also use cross-industry collaboration technology to compare technological trends in different industries and propose the introduction of crossover technologies. For example, it can propose a strategy to integrate technologies from different industries using hybrid technology. The generative AI analysis unit can also compare technological trends in different industries and propose the introduction of crossover technologies based on common technologies. For example, it can propose a strategy to apply renewable energy technology to different industries. This makes it possible to compare technological trends in different industries and propose the introduction of crossover technologies.

[0065] The generative AI analysis unit can analyze consumer sentiment toward technology trends and propose technology adoption strategies based on consumer sentiment. For example, the generative AI analysis unit can use a sentiment estimation function to identify technology trends for which consumers have positive sentiment and propose a strategy based on that technology. For example, it can prioritize analysis of renewable energy technologies that consumers support. The generative AI analysis unit can also analyze consumer sentiment and exclude technology trends for which consumers have negative sentiment. For example, it can exclude technology trends that consumers oppose. The generative AI analysis unit can also evaluate the reliability of technology trends based on consumer sentiment and prioritize analysis of highly reliable technologies. For example, it can propose a highly reliable technology adoption strategy based on technology trends for which consumers have positive sentiment. This makes it possible to provide a technology adoption strategy that takes consumer sentiment into consideration.

[0066] The generative AI analysis unit can simulate the impact of regulatory changes and propose optimal countermeasures for companies. For example, the generative AI analysis unit analyzes data on new environmental regulations and simulates their impact. For example, it evaluates the impact of new emission standards on a company's production process and proposes optimal countermeasures. The generative AI analysis unit can also simulate the impact of regulatory changes using scenario analysis. For example, it evaluates the impact of regulatory changes on a company based on different scenarios and proposes corresponding countermeasures. The generative AI analysis unit can also simulate the impact of regulatory changes using modeling technology and propose optimal countermeasures. For example, it evaluates the impact of new environmental laws and regulations on a company's costs and proposes corresponding countermeasures. This makes it possible to simulate the impact of regulatory changes and provide optimal countermeasures.

[0067] When analyzing regulatory data, the generative AI analysis unit can compare past regulatory changes and their impacts to predict future regulations. The generative AI analysis unit, for example, analyzes data on past environmental regulatory changes and evaluates their impacts. For example, it predicts future regulations based on the impact of past changes in emission standards on a company's production process. The generative AI analysis unit can also use historical data analysis to compare past regulatory changes and their impacts to predict future regulations. For example, it can analyze data on past changes in environmental laws and regulations, evaluate their impacts, and predict future regulations. The generative AI analysis unit can also use an impact assessment model to compare past regulatory changes and their impacts to predict future regulations. For example, it can evaluate the impact of past changes in emission standards and predict future regulations based on that data. This makes it possible to compare past regulatory changes and their impacts to predict future regulations.

[0068] The generative AI analysis unit can analyze industry participants' emotions regarding regulatory changes and prioritize proposing regulatory countermeasures that have positive emotions. For example, the generative AI analysis unit can use an emotion estimation function to identify regulatory countermeasures that industry participants have positive emotions about and propose strategies based on those countermeasures. For example, it can prioritize analysis of emission standard countermeasures that industry participants support. The generative AI analysis unit can also analyze industry participants' emotions and exclude regulatory countermeasures that have negative emotions about them. For example, it can exclude regulatory countermeasures that industry participants oppose. The generative AI analysis unit can also evaluate the reliability of regulatory countermeasures based on industry participants' emotions and prioritize analysis of reliable countermeasures. For example, it can propose reliable strategies based on regulatory countermeasures that have positive emotions about them. This makes it possible to provide regulatory countermeasures that take into account the emotions of industry participants.

[0069] The generative AI analysis unit can compare the analyzed regulatory data with regulations in different countries and regions and propose a global regulatory response strategy. For example, the generative AI analysis unit analyzes environmental regulatory data and compares the results with regulations in different countries and regions. For example, it compares environmental regulations in the European Union and the United States and proposes a global regulatory response strategy. The generative AI analysis unit can also analyze regulatory data in different countries and regions and propose a global strategy based on the results. For example, it compares environmental regulations in Asia and North America and proposes a global regulatory response strategy. The generative AI analysis unit can also propose common regulatory response measures based on regulatory data from different countries and regions. For example, it proposes common regulatory response measures based on environmental regulations in the EU and the United States. This makes it possible to provide a global regulatory response strategy.

[0070] When analyzing regulatory data, the generative AI analysis unit can compare regulations from different industries and propose common regulatory responses. For example, the generative AI analysis unit can analyze environmental regulatory data from different industries and propose common regulatory responses. For example, it can compare emission standards for manufacturing and agriculture and propose common responses. The generative AI analysis unit can also analyze regulatory data from different industries and propose common regulatory responses based on the results. For example, it can compare environmental regulations for the service industry and manufacturing industry and propose common responses. The generative AI analysis unit can also propose common regulatory responses based on regulatory data from different industries. For example, it can propose common responses based on environmental regulations for agriculture and manufacturing. This makes it possible to compare regulations from different industries and provide common regulatory responses.

[0071] The generative AI analysis unit can analyze general consumer sentiment toward regulatory changes and propose a regulatory response strategy based on consumer sentiment. For example, the generative AI analysis unit can use a sentiment estimation function to identify regulatory responses for which general consumers have positive sentiment and propose a strategy based on those responses. For example, it can prioritize analysis of emission standard responses that consumers support. The generative AI analysis unit can also analyze general consumer sentiment and exclude regulatory responses for which consumers have negative sentiment. For example, it can exclude regulatory responses that consumers oppose. The generative AI analysis unit can also evaluate the reliability of regulatory responses based on general consumer sentiment and prioritize analysis of reliable responses. For example, it can propose a reliable strategy based on regulatory responses for which consumers have positive sentiment. This makes it possible to provide a regulatory response strategy that takes general consumer sentiment into account.

[0072] When analyzing corporate data, the generative AI analysis unit also takes into account the company's financial data and market data, and can propose economically feasible sustainability strategies. For example, the generative AI analysis unit analyzes a company's financial data and proposes an economically feasible sustainability strategy. For example, it evaluates the costs and benefits of energy efficiency projects and proposes the optimal strategy. The generative AI analysis unit can also analyze market data and propose an economically feasible strategy. For example, it can propose the optimal energy efficiency strategy based on market share data. The generative AI analysis unit can also integrate and analyze a company's financial data and market data to propose an economically feasible strategy. For example, it can propose the optimal sustainability strategy based on sales data and market share data. This makes it possible to provide an economically feasible strategy.

[0073] The generative AI analysis unit can analyze the emotions of a company's employees and stakeholders, and prioritize proposing strategies that are associated with positive emotions. For example, the generative AI analysis unit can use its emotion estimation function to identify sustainability strategies that are associated with positive emotions among company employees and make proposals based on those strategies. For example, it can prioritize analysis of energy efficiency strategies that are supported by employees. The generative AI analysis unit can also analyze the emotions of stakeholders and propose strategies that are associated with positive emotions. For example, it can prioritize analysis of waste reduction strategies that are supported by stakeholders. The generative AI analysis unit can also evaluate the reliability of strategies based on the emotions of employees and stakeholders, and prioritize proposing highly reliable strategies. For example, it can propose highly reliable sustainability strategies based on strategies that are associated with positive emotions. This makes it possible to provide strategies that take the emotions of employees and stakeholders into consideration.

[0074] When analyzing corporate data, the generative AI analysis unit can apply the results to companies of different industries and scales, proposing versatile sustainability strategies. For example, the generative AI analysis unit analyzes corporate data and applies the results to a different industry. For example, it can adapt an energy efficiency strategy for the manufacturing industry to the service industry and propose a versatile strategy. The generative AI analysis unit can also propose strategies that can be applied to companies of different scales. For example, it can apply a sustainability strategy for a large company to a small or medium-sized enterprise and propose a versatile strategy. The generative AI analysis unit can also analyze common data to propose strategies that can be applied to companies of different industries and scales. For example, it can analyze common data for the manufacturing and service industries and propose a versatile sustainability strategy. This makes it possible to provide a versatile strategy.

[0075] When analyzing corporate data, the generative AI analysis unit takes into account data from different regions and markets and can propose region-specific sustainability strategies. For example, the generative AI analysis unit analyzes corporate data and applies the results to a different region. For example, an energy efficiency strategy for the North American market can be applied to the Asian market to propose a region-specific strategy. The generative AI analysis unit can also analyze data from different markets and propose region-specific strategies based on the results. For example, a waste reduction strategy for the European market can be applied to the African market to propose a region-specific strategy. The generative AI analysis unit can also propose renewable energy introduction strategies based on the climatic conditions of different regions. For example, a wind power introduction strategy can be proposed for regions where wind power generation is suitable. This makes it possible to provide region-specific strategies.

[0076] The generative AI analysis unit can analyze the emotions of a company's customers and partners and propose sustainability strategies based on their emotions. For example, the generative AI analysis unit can use an emotion estimation function to identify sustainability strategies that a company's customers have positive emotions about and make proposals based on those strategies. For example, it can prioritize analysis of energy efficiency strategies that customers support. The generative AI analysis unit can also analyze the emotions of partners and propose strategies that have positive emotions about them. For example, it can prioritize analysis of waste reduction strategies that partners support. The generative AI analysis unit can also evaluate the reliability of strategies based on the emotions of customers and partners and prioritize proposals of highly reliable strategies. For example, it can propose a highly reliable sustainability strategy based on a strategy that has positive emotions about it. This makes it possible to provide strategies that take the emotions of customers and partners into consideration.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The EcoBoost AI system can also be equipped with a real-time data collection unit. The real-time data collection unit uses sensors and IoT devices to collect environmental data from a company's site in real time. For example, it can collect factory exhaust gas data and energy consumption data in real time and provide it to the generative AI analysis unit. The real-time data collection unit can also collect external data such as weather data and traffic data. For example, it can analyze a factory's energy consumption patterns based on weather data and propose efficient energy usage strategies. This allows companies to monitor environmental data in real time and respond quickly.

[0079] The generative AI analysis unit can also be equipped with an anomaly detection function. The anomaly detection function analyzes collected data and detects abnormal patterns and trends. For example, if an abnormal increase is observed in a factory's exhaust gas data, the anomaly detection function can identify the cause and quickly propose countermeasures. In addition, if an abnormal fluctuation is observed in energy consumption data, the anomaly detection function can analyze the cause and propose efficient energy usage strategies. Furthermore, if an abnormal fluctuation is observed in climate data, the anomaly detection function can evaluate the impact and propose appropriate countermeasures. This allows companies to respond quickly to abnormal situations and minimize their impact on the environment.

[0080] The generative AI analysis unit can further be equipped with a scenario planning function. The scenario planning function predicts future environmental changes based on different scenarios and proposes sustainability strategies based on those predictions. For example, based on a climate change scenario, it predicts future temperature increases and precipitation fluctuations and proposes corresponding energy efficiency strategies. The scenario planning function can also propose corporate growth strategies based on different economic scenarios. For example, based on an economic growth scenario, it can propose a strategy to address increased energy demand. The scenario planning function can also evaluate a company's legal risks based on different regulatory scenarios and propose appropriate countermeasures. This allows companies to respond flexibly to future uncertainties.

[0081] The generative AI analysis unit can further use its emotion estimation function to analyze the emotions of a company's employees and propose sustainability strategies based on their emotions. For example, it can identify energy efficiency strategies that employees have positive emotions about and propose strategies based on those strategies. It can also analyze employee emotions and exclude strategies that employees have negative emotions about. For example, it can exclude waste reduction strategies that employees oppose. It can also evaluate the reliability of strategies based on employee emotions and prioritize proposals based on reliable strategies. For example, it can propose reliable sustainability strategies based on strategies that have positive emotions. This makes it possible to provide strategies that take employee emotions into consideration.

[0082] The generative AI analysis unit can further use its emotion estimation function to analyze the emotions of a company's customers and propose sustainability strategies based on those emotions. For example, it can identify energy efficiency strategies that customers have positive emotions about and propose strategies based on those strategies. It can also analyze customer emotions and exclude strategies that have negative emotions. For example, it can exclude waste reduction strategies that customers oppose. It can also evaluate the reliability of strategies based on customer emotions and prioritize proposals based on reliable strategies. For example, it can propose reliable sustainability strategies based on strategies that have positive emotions. This makes it possible to provide strategies that take customer emotions into consideration.

[0083] The generative AI analysis unit can further use its emotion estimation function to analyze the emotions of corporate partners and propose sustainability strategies based on their emotions. For example, it can identify energy efficiency strategies that partners have positive emotions about and propose strategies based on those strategies. It can also analyze partner emotions and exclude strategies that partners have negative emotions about. For example, it can exclude waste reduction strategies that partners oppose. It can also evaluate the reliability of strategies based on partner emotions and prioritize proposals based on reliable strategies. For example, it can propose reliable sustainability strategies based on strategies that have positive emotions. This makes it possible to provide strategies that take partner emotions into consideration.

[0084] The generative AI analysis unit can further use its emotion estimation function to analyze the emotions of a company's stakeholders and propose sustainability strategies based on their emotions. For example, it can identify energy efficiency strategies that stakeholders have positive emotions about and propose strategies based on those. It can also analyze stakeholder emotions and eliminate strategies that stakeholders have negative emotions about. For example, it can eliminate waste reduction strategies that stakeholders oppose. It can also evaluate the reliability of strategies based on stakeholder emotions and prioritize proposals based on reliable strategies. For example, it can propose reliable sustainability strategies based on strategies that have positive emotions. This makes it possible to provide strategies that take stakeholder emotions into consideration.

[0085] The generative AI analysis unit can also compare technological trends in different industries and propose the introduction of crossover technologies. For example, it can propose a strategy to apply agricultural technology to manufacturing. It can also compare technological trends in different industries using cross-industry collaboration technology and propose the introduction of crossover technologies. For example, it can propose a strategy to integrate technologies from different industries using hybrid technology. It can also compare technological trends in different industries and propose the introduction of crossover technologies based on common technologies. For example, it can propose a strategy to apply renewable energy technology to a different industry. This makes it possible to compare technological trends in different industries and propose the introduction of crossover technologies.

[0086] The generative AI analysis unit can further analyze data from different regions and markets to propose region-specific sustainability strategies. For example, an energy efficiency strategy for the North American market can be applied to the Asian market to propose a region-specific strategy. It can also analyze data from different markets and propose region-specific strategies based on the results. For example, a waste reduction strategy for the European market can be applied to the African market to propose a region-specific strategy. It can also propose renewable energy introduction strategies based on the climatic conditions of different regions. For example, a wind power introduction strategy can be proposed for regions where wind power generation is suitable. This makes it possible to provide region-specific strategies.

[0087] The generative AI analysis unit can also generate a multilingual sustainability strategy that includes research data in different languages. For example, research data on environmental protection in different languages ​​can be analyzed to generate a multilingual sustainability strategy. For example, research data in English, French, and Chinese can be integrated and analyzed. It can also use a translation algorithm to automatically translate and analyze research data in different languages. For example, English research data can be translated into French and analyzed. It can also use a language model to analyze research data in different languages ​​to generate a multilingual strategy. For example, research data in English and Chinese can be integrated and analyzed to propose a multilingual sustainability strategy. This allows it to provide a multilingual strategy.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The research data collection unit collects the latest research data related to environmental protection. For example, it collects climate change data, environmental pollution data, biodiversity data, etc. The research data collection unit can also collect data from online databases and academic papers. For example, the research data collection unit automatically collects the latest research papers on climate change and stores them in a database. Step 2: The Generative AI Analysis Unit analyzes the data collected by the Research Data Collection Unit. For example, the Generative AI uses data mining techniques to extract useful information from the collected data. The Generative AI can also use statistical analysis techniques to analyze trends and patterns in the data. The Generative AI can also use machine learning algorithms to build predictive models of the data. For example, the Generative AI analyzes climate change data and builds a predictive model of future temperature rises. Step 3: The Strategy Proposal Department proposes a customized sustainability strategy based on the results of the analysis by the Generative AI Analysis Department. For example, the Strategy Proposal Department proposes energy efficiency methods based on the company's specific needs and goals. The Strategy Proposal Department can also propose specific measures for waste reduction based on the company's industry and size. The Strategy Proposal Department can also suggest areas for improvement based on the company's current sustainability status. For example, the Strategy Proposal Department can analyze the company's energy consumption data and propose specific measures for energy efficiency.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0131] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] 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.

[0149] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A research data collection department that collects the latest research data related to environmental protection; a generation AI analysis unit that analyzes the data collected by the research data collection unit; and a strategy proposal unit that proposes a customized sustainability strategy based on the results of the analysis by the generation AI analysis unit. A system characterized by:

2. The generation AI analysis unit Analyze the sentiment of researchers and experts toward research data, and prioritize research results with positive sentiment in your strategy.

2. The system of claim 1.

3. The generation AI analysis unit Generate multilingual sustainability strategies by including research data in different languages 2. The system of claim 1.

4. The generation AI analysis unit Track the evolution of specific technologies using green technology trend data and make future technology predictions 2. The system of claim 1.

5. The generation AI analysis unit Simulating the impact of regulatory changes and proposing optimal responses for companies 2. The system of claim 1.

6. The generation AI analysis unit Analyze the sentiment of company employees and stakeholders and prioritize strategies that generate positive sentiment 2. The system of claim 1.

7. The generation AI analysis unit Analyze industry experts' sentiments toward technology trends and prioritize technologies with positive sentiments into your strategy.

2. The system of claim 1.

8. The generation AI analysis unit Analyze general consumer sentiment regarding regulatory changes and propose regulatory response strategies based on consumer sentiment 2. The system of claim 1.

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