system

Abstract

The system according to this embodiment aims to simultaneously optimize environmental sustainability and operational efficiency in supply chain management. [Solution] The system according to the embodiment comprises a tracking unit, an optimization unit, an evaluation unit, an identification unit, and a maintenance unit. The tracking unit tracks the carbon footprint. The optimization unit optimizes logistics routes based on the data tracked by the tracking unit. The evaluation unit evaluates the sustainability of suppliers based on the routes optimized by the optimization unit. The identification unit identifies circular economy opportunities based on the suppliers evaluated by the evaluation unit. The maintenance unit performs predictive maintenance based on the opportunities identified by the identification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in the management of the supply chain, environmental sustainability and operational efficiency have not been fully optimized simultaneously, and there is room for improvement.

[0005] The system according to the embodiment aims to simultaneously optimize environmental sustainability and operational efficiency in the management of the supply chain.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a tracking unit, an optimization unit, an evaluation unit, an identification unit, and a maintenance unit. The tracking unit tracks the carbon footprint. The optimization unit optimizes logistics routes based on the data tracked by the tracking unit. The evaluation unit assesses the sustainability of suppliers based on the routes optimized by the optimization unit. The identification unit identifies circular economy opportunities based on suppliers evaluated by the evaluation unit. The maintenance unit performs predictive maintenance based on opportunities identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can simultaneously optimize environmental sustainability and operational efficiency in supply chain management. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Sustainable Supply Chain Optimizer, according to an embodiment of the present invention, is an advanced AI-driven platform that innovates supply chain management by prioritizing environmental sustainability and operational efficiency. This system integrates state-of-the-art machine learning algorithms, real-time data analysis, and predictive modeling to optimize industry-wide supply chains, significantly reducing the carbon footprint while improving overall performance and cost efficiency. For example, the Sustainable Supply Chain Optimizer tracks the carbon footprint of the entire supply chain in real time, uses AI to plan optimal logistics routes, assesses supplier sustainability, identifies circular economy opportunities, and optimizes equipment lifecycles. This enables immediate understanding of the environmental impact of the supply chain and the creation of sustainable supply chains. As a result, the Sustainable Supply Chain Optimizer can significantly reduce the carbon footprint of the supply chain and improve overall performance and cost efficiency.

[0029] The sustainable supply chain optimizer according to this embodiment comprises a tracking unit, an optimization unit, an evaluation unit, an identification unit, and a maintenance unit. The tracking unit tracks the carbon footprint of the entire supply chain. For example, the tracking unit tracks CO2 emissions at each stage of the supply chain in real time. The tracking unit can also identify the types of greenhouse gases and measure their respective emissions. Furthermore, the tracking unit can set the frequency of data collection and select the sensors and technologies to be used. For example, the tracking unit collects data in real time using IoT sensors and transmits it to the cloud. The optimization unit uses AI to optimize logistics routes based on the data tracked by the tracking unit. For example, the optimization unit plans the optimal route considering cost reduction, time savings, and energy efficiency. The optimization unit can also predict the optimal route based on historical data using machine learning algorithms. Furthermore, the optimization unit can adjust the route in real time using deep learning technology. For example, the optimization unit considers traffic congestion information and weather data in real time to propose the optimal route. The evaluation unit evaluates the sustainability of suppliers based on the route optimized by the optimization unit. The evaluation department ranks suppliers based on criteria such as environmental impact, social impact, and economic sustainability. It can also analyze sustainability reports and ensure compliance using natural language processing technology. Furthermore, the evaluation department can provide detailed assessments of suppliers' energy efficiency and waste management capabilities. For example, it collects supplier energy consumption data and evaluates efficiency. The identification department identifies circular economy opportunities based on suppliers evaluated by the evaluation department. For example, it finds opportunities for recycling, reuse, and waste reduction. It can also identify types of recyclable materials and reusable parts. Additionally, the identification department can identify energy recovery opportunities and propose recovery measures. For example, it proposes waste heat recovery and the use of renewable energy. The maintenance department performs predictive maintenance based on opportunities identified by the identification department.The maintenance department can, for example, predict equipment failures and optimize maintenance schedules. It can also identify ways to minimize equipment energy consumption and waste generation. Furthermore, it can identify ways to minimize equipment water and chemical usage. For example, the maintenance department can collect energy consumption data for each piece of equipment and propose ways to minimize these. As a result, the sustainable supply chain optimizer according to this embodiment can significantly reduce the carbon footprint of the supply chain and improve overall performance and cost efficiency.

[0030] The tracking unit tracks the carbon footprint of the entire supply chain. For example, it tracks CO2 emissions at each stage of the supply chain in real time. Specifically, it uses IoT sensors to measure CO2 emissions in each process such as manufacturing, transportation, storage, and distribution, and transmits the data to the cloud. This allows for a detailed understanding of the environmental impact of each process. The tracking unit can also identify types of greenhouse gases and measure their respective emissions. For example, it can identify greenhouse gases such as methane and nitrous oxide and track their emissions individually. Furthermore, the tracking unit can set the frequency of data collection and select the sensors and technologies to be used. For example, if real-time data collection is required, it can select sensors that collect data frequently, and if periodic reports are required, it can select sensors that collect data infrequently. In this way, the tracking unit can track the carbon footprint of the entire supply chain in detail and accurately, contributing to the reduction of environmental impact.

[0031] The optimization unit uses AI to optimize logistics routes based on data tracked by the tracking unit. For example, the optimization unit plans the optimal route considering cost reduction, time savings, and energy efficiency. Specifically, it uses AI algorithms to evaluate the cost, time, and energy consumption of each route and select the optimal route. The optimization unit can also use machine learning algorithms to predict the optimal route based on historical data. For example, it analyzes past traffic and weather data to predict future traffic conditions and weather, thereby planning the optimal route. Furthermore, the optimization unit can use deep learning technology to adjust routes in real time. For example, it considers traffic congestion information and weather data in real time to propose the optimal route. This allows the optimization unit to maximize the efficiency of logistics routes and improve the overall performance of the supply chain.

[0032] The evaluation department assesses the sustainability of suppliers based on routes optimized by the optimization department. The evaluation department ranks suppliers using criteria such as environmental impact, social impact, and economic sustainability. Specifically, it collects data such as CO2 emissions, energy consumption, and waste generation from each supplier and evaluates sustainability based on this data. The evaluation department can also use natural language processing technology to analyze sustainability reports and ensure compliance. For example, it analyzes sustainability reports submitted by suppliers to assess their compliance with environmental regulations and social responsibilities. Furthermore, the evaluation department can also evaluate suppliers' energy efficiency and waste management capabilities in detail. For example, it collects supplier energy consumption data and evaluates efficiency. This allows the evaluation department to assess supplier sustainability in detail and improve the sustainability of the entire supply chain.

[0033] The Identification Department identifies circular economy opportunities based on suppliers evaluated by the Evaluation Department. For example, the Identification Department finds opportunities for recycling, reuse, and waste reduction. Specifically, it analyzes waste data from each supplier to identify types of recyclable materials and reusable components. The Identification Department can also identify types of recyclable materials and reusable components by analyzing waste generated during the product manufacturing process. Furthermore, the Identification Department can identify energy recovery opportunities and propose recovery measures, such as waste heat recovery or the use of renewable energy. This allows the Identification Department to identify circular economy opportunities and improve sustainability throughout the entire supply chain.

[0034] The maintenance department performs predictive maintenance based on opportunities identified by specific departments. For example, the maintenance department predicts equipment failures and optimizes maintenance schedules. Specifically, it analyzes equipment sensor data to detect early signs of failure. The maintenance department can also identify ways to minimize equipment energy consumption and waste generation. For example, it collects energy consumption data for each piece of equipment and proposes methods to optimize energy efficiency. Furthermore, the maintenance department can identify ways to minimize equipment water and chemical usage. For example, it collects water usage data for each piece of equipment and proposes methods to minimize usage. This allows the maintenance department to maximize equipment efficiency and improve the sustainability of the entire supply chain.

[0035] The tracking unit can track the carbon footprint of the entire supply chain in real time. For example, the tracking unit can track CO2 emissions at each stage of the supply chain in real time. The tracking unit can collect data in real time using IoT sensors and transmit it to the cloud. The tracking unit can also identify the types of greenhouse gases and measure their respective emissions. The tracking unit can set the frequency of data collection and select the sensors and technologies to be used. This allows for immediate understanding of environmental impacts by tracking the carbon footprint of the entire supply chain in real time. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input data acquired by IoT sensors into a generating AI and have the generating AI perform real-time carbon footprint tracking.

[0036] The optimization unit can plan the optimal logistics route using AI. For example, the optimization unit plans the optimal route considering cost reduction, time savings, and energy efficiency. The optimization unit can also predict the optimal route based on past data using machine learning algorithms. The optimization unit can also adjust the route in real time using deep learning technology. The optimization unit can also propose the optimal route by considering traffic congestion information and weather data in real time. This allows for efficient optimization of logistics routes using AI. Some or all of the above-described processes in the optimization unit may be performed using AI, or not. For example, the optimization unit can input past data into a generating AI and have the generating AI predict the optimal route.

[0037] The evaluation department can assess and rank the sustainability of suppliers. For example, the evaluation department can rank suppliers based on criteria such as environmental impact, social impact, and economic sustainability. The evaluation department can also use natural language processing technology to analyze sustainability reports and ensure compliance. The evaluation department can also provide detailed assessments of suppliers' energy efficiency and waste management capabilities. The evaluation department can collect supplier energy consumption data and assess efficiency. This allows for the construction of a sustainable supply chain by assessing and ranking supplier sustainability. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input supplier sustainability reports into a generating AI and have the generating AI analyze the reports.

[0038] The specific unit can identify circular economy opportunities and find opportunities for recycling and waste reduction. For example, the specific unit can find opportunities for recycling, reuse, and waste reduction. The specific unit can also identify types of recyclable materials and reusable parts. The specific unit can also identify energy recovery opportunities and make recovery proposals. This promotes recycling and waste reduction by identifying circular economy opportunities. Some or all of the above processing in the specific unit may be performed using AI, for example, or not using AI. For example, the specific unit can input data on recyclable materials into a generating AI and have the generating AI make recycling proposals.

[0039] The maintenance department can optimize the equipment lifecycle and reduce resource consumption and waste. For example, the maintenance department can predict equipment failures and optimize maintenance schedules. The maintenance department can also identify ways to minimize the equipment's energy consumption and waste generation. The maintenance department can also identify ways to minimize the equipment's water usage and chemical usage. This reduces resource consumption and waste by optimizing the equipment lifecycle. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not. For example, the maintenance department can input equipment energy consumption data into a generating AI and have the generating AI perform the task of minimizing energy consumption.

[0040] The tracking unit can predict environmental impacts using generative AI. For example, the tracking unit can use generative AI to predict CO2 emissions and types of greenhouse gases at each stage of the supply chain. The tracking unit can improve the accuracy of environmental impact predictions using generative models and simulation techniques. Thus, using generative AI improves the accuracy of environmental impact predictions. Some or all of the above-described processes in the tracking unit may be performed using generative AI, or not. For example, the tracking unit can input supply chain data into generative AI and have the generative AI perform environmental impact predictions.

[0041] The evaluation unit can analyze sustainability reports using natural language processing to ensure compliance. For example, the evaluation unit can use natural language processing techniques to perform text analysis and sentiment analysis of sustainability reports. The evaluation unit can also use topic modeling to extract key themes and keywords from the reports. This improves the accuracy of the sustainability report analysis and ensures compliance by using natural language processing. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the sustainability report into a generating AI and have the generating AI perform the analysis of the report.

[0042] The tracking unit can record energy consumption in detail at each stage of the supply chain. For example, the tracking unit can record energy consumption at each supply chain stage in real time and store it in a database. The tracking unit can also detect anomalies in energy consumption and issue alerts. The tracking unit can also analyze historical energy consumption data and suggest improvements to efficiency. This makes it possible to suggest improvements to efficiency by recording energy consumption in detail. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input energy consumption data into a generating AI and have the generating AI perform the recording and analysis of energy consumption.

[0043] The tracking unit can record in detail the amount of waste generated at each stage of the supply chain. For example, the tracking unit can record the amount of waste generated at each supply chain stage in real time and store it in a database. The tracking unit can also detect anomalies in the amount of waste generated and issue alerts. The tracking unit can also analyze historical data on the amount of waste generated and make suggestions for reduction. This makes it possible to make reduction suggestions by recording the amount of waste generated in detail. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input waste generation data into a generation AI and have the generation AI perform the recording and analysis of the amount of waste generated.

[0044] The tracking unit can record water usage at each stage of the supply chain. For example, the tracking unit can record water usage at each supply chain stage in real time and store it in a database. The tracking unit can also detect anomalies in water usage and issue alerts. The tracking unit can also analyze historical water usage data and suggest improvements to efficiency. This makes it possible to suggest improvements to efficiency by recording water usage. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input water usage data into a generating AI and have the generating AI perform the recording and analysis of water usage.

[0045] The tracking unit can record the amount of chemicals used at each stage of the supply chain. For example, the tracking unit can record the amount of chemicals used at each supply chain stage in real time and store it in a database. The tracking unit can also detect anomalies in chemical usage and issue alerts. The tracking unit can also analyze historical data on chemical usage and make suggestions for reduction. This makes it possible to make reduction suggestions by recording chemical usage. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input chemical usage data into a generating AI and have the generating AI perform the recording and analysis of chemical usage.

[0046] The optimization unit can consider traffic congestion information in real time during optimization. For example, the optimization unit proposes the optimal route based on real-time traffic congestion information. The optimization unit can also propose detour routes to avoid congestion. The optimization unit can also analyze congestion information and propose the most efficient route. In this way, by considering traffic congestion information, it is possible to propose efficient logistics routes. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input traffic congestion information into a generating AI and have the generating AI perform real-time route optimization.

[0047] The optimization unit can consider weather data in real time during optimization. For example, the optimization unit proposes the optimal route based on real-time weather data. The optimization unit can also propose detour routes to avoid bad weather. The optimization unit can also analyze weather data and propose the most efficient route. In this way, by considering weather data, it is possible to propose efficient logistics routes. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input weather data into a generating AI and have the generating AI perform real-time route optimization.

[0048] The optimization unit can consider the safety of the logistics route during optimization. For example, the optimization unit can prioritize and propose safe routes. The optimization unit can also propose routes that avoid dangerous areas. The optimization unit can analyze highly safe routes and propose the optimal route. In this way, by considering safety, it can propose safe logistics routes. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input safety data into a generating AI and have the generating AI propose safe routes.

[0049] The optimization unit can consider the cost of logistics routes during optimization. For example, the optimization unit can prioritize and propose cost-effective routes. The optimization unit can also propose alternative routes to reduce costs. The optimization unit can also analyze cost data and propose the most efficient route. In this way, by considering costs, it can propose cost-effective logistics routes. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input cost data into a generating AI and have the generating AI propose cost-effective routes.

[0050] The evaluation unit can perform a detailed assessment of a supplier's energy efficiency during the evaluation process. For example, the evaluation unit can collect energy consumption data from suppliers and evaluate their efficiency. The evaluation unit can also prioritize the evaluation of suppliers with high energy efficiency. The evaluation unit can also make improvement suggestions to suppliers with low energy efficiency. This allows for suggestions for efficiency improvements by conducting a detailed assessment of energy efficiency. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input energy consumption data into a generating AI and have the generating AI perform the energy efficiency evaluation.

[0051] The evaluation unit can assess a supplier's waste management capabilities in detail during the evaluation process. For example, the evaluation unit can collect supplier waste management data and evaluate their capabilities. The evaluation unit can also prioritize the evaluation of suppliers with high waste management capabilities. The evaluation unit can also make improvement suggestions to suppliers with low waste management capabilities. This allows for improvement suggestions by conducting a detailed evaluation of waste management capabilities. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input waste management data into a generating AI and have the generating AI perform the evaluation of waste management capabilities.

[0052] The evaluation unit can assess the water usage efficiency of suppliers during the evaluation process. For example, the evaluation unit can collect water usage data from suppliers and evaluate their efficiency. The evaluation unit can also prioritize the evaluation of suppliers with high water usage efficiency. The evaluation unit can also make improvement suggestions to suppliers with low water usage efficiency. Thus, by evaluating water usage efficiency, it becomes possible to propose efficiency improvements. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input water usage data into a generating AI and have the generating AI perform the evaluation of water usage efficiency.

[0053] The evaluation unit can assess a supplier's chemical management capabilities during the evaluation process. For example, the evaluation unit can collect chemical usage data from suppliers and evaluate their management capabilities. The evaluation unit can also prioritize the evaluation of suppliers with high chemical management capabilities. The evaluation unit can also make improvement suggestions to suppliers with low chemical management capabilities. Thus, improvement suggestions become possible by evaluating chemical management capabilities. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input chemical usage data into a generating AI and have the generating AI perform the evaluation of chemical management capabilities.

[0054] The identification unit can identify in detail the types of recyclable materials at the time of identification. For example, the identification unit can analyze the materials used at each stage of the supply chain and identify those that are recyclable. The identification unit can also store the types of recyclable materials in a database and make recycling suggestions. The identification unit can also make suggestions to promote the use of recyclable materials. Thus, by identifying recyclable materials, recycling suggestions become possible. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input material data into a generating AI and have the generating AI perform the identification of recyclable materials.

[0055] The identification unit can, at the time of identification, specify in detail concrete methods for waste reduction. For example, the identification unit can analyze the amount of waste generated at each stage of the supply chain and propose reductions. The identification unit can also store specific methods for waste reduction in a database. The identification unit can also propose training programs for waste reduction. This makes it possible to propose reductions by identifying specific methods for waste reduction. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input waste data into a generating AI and have the generating AI execute methods for waste reduction.

[0056] The identification unit can identify the types of reusable parts at the time of identification. For example, the identification unit analyzes the parts used at each stage of the supply chain and identifies those that are reusable. The identification unit can also store the types of reusable parts in a database and make suggestions for reuse. The identification unit can also make suggestions to promote the use of reusable parts. Thus, by identifying reusable parts, reuse suggestions become possible. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input part data into a generating AI and have the generating AI perform the identification of reusable parts.

[0057] The identification unit can identify energy recovery opportunities at specific times. For example, the identification unit analyzes energy consumption at each stage of the supply chain and proposes energy recovery. The identification unit can also store specific methods for energy recovery in a database. The identification unit can also propose training programs for energy recovery. This makes it possible to propose recovery by identifying energy recovery opportunities. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input energy consumption data into a generating AI and have the generating AI execute energy recovery opportunities.

[0058] The maintenance department can identify ways to minimize the energy consumption of equipment during maintenance. For example, the maintenance department can collect energy consumption data for each piece of equipment and propose ways to minimize it. The maintenance department can also detect anomalies in energy consumption and propose improvements. The maintenance department can also analyze historical energy consumption data and propose ways to improve efficiency. This makes it possible to propose ways to improve efficiency by identifying ways to minimize energy consumption. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input energy consumption data into a generating AI and have the generating AI perform the task of minimizing energy consumption.

[0059] The maintenance department can identify ways to minimize the amount of waste generated by equipment during maintenance. For example, the maintenance department can collect waste generation data for each piece of equipment and propose ways to minimize it. The maintenance department can also detect anomalies in the amount of waste generated and propose improvements. The maintenance department can also analyze historical data on the amount of waste generated and propose reductions. This makes it possible to propose reductions by identifying ways to minimize the amount of waste generated. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input waste generation data into a generation AI and have the generation AI perform waste generation minimization.

[0060] The maintenance department can identify ways to minimize the water usage of equipment during maintenance. For example, the maintenance department can collect water usage data for each piece of equipment and propose ways to minimize it. The maintenance department can also detect abnormalities in water usage and propose improvements. The maintenance department can also analyze historical water usage data and propose ways to improve efficiency. This makes it possible to propose ways to improve efficiency by identifying ways to minimize water usage. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input water usage data into a generating AI and have the generating AI perform the task of minimizing water usage.

[0061] The maintenance department can identify ways to minimize the amount of chemicals used by equipment during maintenance. For example, the maintenance department can collect chemical usage data for each piece of equipment and propose ways to minimize it. The maintenance department can also detect anomalies in chemical usage and propose improvements. The maintenance department can also analyze historical data on chemical usage and propose reductions. This makes it possible to propose reductions by identifying ways to minimize chemical usage. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input chemical usage data into a generating AI and have the generating AI perform chemical usage minimization.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The Sustainable Supply Chain Optimizer can also include a forecasting unit. This unit can predict demand at each stage of the supply chain and optimize supply plans. For example, it can analyze historical sales data and market trends to predict fluctuations in demand. It can also consider external factors such as seasonality and promotional activities when forecasting demand. Furthermore, the forecasting unit can use AI to improve the accuracy of demand forecasts. This allows for a quicker response to demand fluctuations and prevents inventory shortages and surpluses.

[0064] The Sustainable Supply Chain Optimizer can also be equipped with a notification unit. This unit can detect anomalies occurring at each stage of the supply chain and notify relevant parties. For example, it can detect anomalies such as logistics delays or inventory shortages in real time and issue alerts. Furthermore, it can analyze the cause of the anomaly and propose countermeasures. In addition, the notification unit can use AI to improve the accuracy of anomaly detection. This allows for a rapid response when anomalies occur, ensuring the stability of the supply chain.

[0065] The Sustainable Supply Chain Optimizer can also include an Education Department. This department can provide sustainability education to stakeholders in the supply chain. For example, it can offer training programs on how to build sustainable supply chains and reduce environmental impact. It can also share best practices at each stage of the supply chain to raise awareness among stakeholders. Furthermore, the Education Department can use AI to evaluate the effectiveness of educational programs and suggest improvements. This can enhance stakeholders' understanding of and ability to implement sustainability practices.

[0066] The Sustainable Supply Chain Optimizer can also include a reporting function. This function can automatically generate and provide reports on the sustainability of the supply chain to stakeholders. For example, the reporting function can collect data such as the carbon footprint, energy consumption, and waste generation across the entire supply chain and create reports. It can also evaluate the progress of sustainability improvements and the degree of goal achievement, and reflect this in the reports. Furthermore, the reporting function can use AI to improve the accuracy and efficiency of the reports. This allows stakeholders to quickly and accurately understand information regarding the sustainability of the supply chain.

[0067] The Sustainable Supply Chain Optimizer can also include an Incentives Unit. This unit can provide incentives to stakeholders in the supply chain to promote sustainable behavior. For example, it can offer preferential treatment to sustainable suppliers and encourage highly sustainable actions. It can also reward and commend stakeholders who contribute to reducing environmental impact. Furthermore, the Incentives Unit can use AI to evaluate the effectiveness of incentives and design optimal incentive programs. This promotes sustainable behavior among stakeholders and improves the sustainability of the entire supply chain.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The tracking unit tracks the carbon footprint of the entire supply chain. For example, it tracks CO2 emissions at each stage of the supply chain in real time, identifies the types of greenhouse gases, and measures the emissions of each. It also sets the frequency of data collection and selects the sensors and technologies to be used. For example, it uses IoT sensors to collect data in real time and transmits it to the cloud. Step 2: The optimization unit uses AI to optimize logistics routes based on data tracked by the tracking unit. For example, it plans the optimal route considering cost reduction, time savings, and energy efficiency, and uses machine learning algorithms to predict the optimal route based on historical data. Furthermore, it uses deep learning technology to adjust the route in real time and proposes the optimal route by considering traffic congestion information and weather data in real time. Step 3: The evaluation department assesses the sustainability of suppliers based on the routes optimized by the optimization department. For example, it ranks suppliers using environmental impact, social impact, and economic sustainability as evaluation criteria, analyzes sustainability reports using natural language processing technology, and ensures compliance. Furthermore, it evaluates suppliers' energy efficiency and waste management capabilities in detail, and collects energy consumption data to assess efficiency. Step 4: The Identification Department identifies circular economy opportunities based on the suppliers evaluated by the Evaluation Department. For example, it identifies recycling, reuse, and waste reduction opportunities, and identifies types of recyclable materials and reusable parts. Furthermore, it identifies energy recovery opportunities and proposes waste heat recovery and the use of renewable energy. Step 5: The maintenance department performs predictive maintenance based on opportunities identified by specific departments. For example, it predicts equipment failures, optimizes maintenance schedules, and identifies ways to minimize equipment energy consumption and waste generation. Furthermore, it identifies ways to minimize equipment water and chemical usage, collects energy consumption data for each piece of equipment, and proposes ways to minimize these uses.

[0070] (Example of form 2) The Sustainable Supply Chain Optimizer, according to an embodiment of the present invention, is an advanced AI-driven platform that innovates supply chain management by prioritizing environmental sustainability and operational efficiency. This system integrates state-of-the-art machine learning algorithms, real-time data analysis, and predictive modeling to optimize industry-wide supply chains, significantly reducing the carbon footprint while improving overall performance and cost efficiency. For example, the Sustainable Supply Chain Optimizer tracks the carbon footprint of the entire supply chain in real time, uses AI to plan optimal logistics routes, assesses supplier sustainability, identifies circular economy opportunities, and optimizes equipment lifecycles. This enables immediate understanding of the environmental impact of the supply chain and the creation of sustainable supply chains. As a result, the Sustainable Supply Chain Optimizer can significantly reduce the carbon footprint of the supply chain and improve overall performance and cost efficiency.

[0071] The sustainable supply chain optimizer according to this embodiment comprises a tracking unit, an optimization unit, an evaluation unit, an identification unit, and a maintenance unit. The tracking unit tracks the carbon footprint of the entire supply chain. For example, the tracking unit tracks CO2 emissions at each stage of the supply chain in real time. The tracking unit can also identify the types of greenhouse gases and measure their respective emissions. Furthermore, the tracking unit can set the frequency of data collection and select the sensors and technologies to be used. For example, the tracking unit collects data in real time using IoT sensors and transmits it to the cloud. The optimization unit uses AI to optimize logistics routes based on the data tracked by the tracking unit. For example, the optimization unit plans the optimal route considering cost reduction, time savings, and energy efficiency. The optimization unit can also predict the optimal route based on historical data using machine learning algorithms. Furthermore, the optimization unit can adjust the route in real time using deep learning technology. For example, the optimization unit considers traffic congestion information and weather data in real time to propose the optimal route. The evaluation unit evaluates the sustainability of suppliers based on the route optimized by the optimization unit. The evaluation department ranks suppliers based on criteria such as environmental impact, social impact, and economic sustainability. It can also analyze sustainability reports and ensure compliance using natural language processing technology. Furthermore, the evaluation department can provide detailed assessments of suppliers' energy efficiency and waste management capabilities. For example, it collects supplier energy consumption data and evaluates efficiency. The identification department identifies circular economy opportunities based on suppliers evaluated by the evaluation department. For example, it finds opportunities for recycling, reuse, and waste reduction. It can also identify types of recyclable materials and reusable parts. Additionally, the identification department can identify energy recovery opportunities and propose recovery measures. For example, it proposes waste heat recovery and the use of renewable energy. The maintenance department performs predictive maintenance based on opportunities identified by the identification department.The maintenance department can, for example, predict equipment failures and optimize maintenance schedules. It can also identify ways to minimize equipment energy consumption and waste generation. Furthermore, it can identify ways to minimize equipment water and chemical usage. For example, the maintenance department can collect energy consumption data for each piece of equipment and propose ways to minimize these. As a result, the sustainable supply chain optimizer according to this embodiment can significantly reduce the carbon footprint of the supply chain and improve overall performance and cost efficiency.

[0072] The tracking unit tracks the carbon footprint of the entire supply chain. For example, it tracks CO2 emissions at each stage of the supply chain in real time. Specifically, it uses IoT sensors to measure CO2 emissions in each process such as manufacturing, transportation, storage, and distribution, and transmits the data to the cloud. This allows for a detailed understanding of the environmental impact of each process. The tracking unit can also identify types of greenhouse gases and measure their respective emissions. For example, it can identify greenhouse gases such as methane and nitrous oxide and track their emissions individually. Furthermore, the tracking unit can set the frequency of data collection and select the sensors and technologies to be used. For example, if real-time data collection is required, it can select sensors that collect data frequently, and if periodic reports are required, it can select sensors that collect data infrequently. In this way, the tracking unit can track the carbon footprint of the entire supply chain in detail and accurately, contributing to the reduction of environmental impact.

[0073] The optimization unit uses AI to optimize logistics routes based on data tracked by the tracking unit. For example, the optimization unit plans the optimal route considering cost reduction, time savings, and energy efficiency. Specifically, it uses AI algorithms to evaluate the cost, time, and energy consumption of each route and select the optimal route. The optimization unit can also use machine learning algorithms to predict the optimal route based on historical data. For example, it analyzes past traffic and weather data to predict future traffic conditions and weather, thereby planning the optimal route. Furthermore, the optimization unit can use deep learning technology to adjust routes in real time. For example, it considers traffic congestion information and weather data in real time to propose the optimal route. This allows the optimization unit to maximize the efficiency of logistics routes and improve the overall performance of the supply chain.

[0074] The evaluation department assesses the sustainability of suppliers based on routes optimized by the optimization department. The evaluation department ranks suppliers using criteria such as environmental impact, social impact, and economic sustainability. Specifically, it collects data such as CO2 emissions, energy consumption, and waste generation from each supplier and evaluates sustainability based on this data. The evaluation department can also use natural language processing technology to analyze sustainability reports and ensure compliance. For example, it analyzes sustainability reports submitted by suppliers to assess their compliance with environmental regulations and social responsibilities. Furthermore, the evaluation department can also evaluate suppliers' energy efficiency and waste management capabilities in detail. For example, it collects supplier energy consumption data and evaluates efficiency. This allows the evaluation department to assess supplier sustainability in detail and improve the sustainability of the entire supply chain.

[0075] The Identification Department identifies circular economy opportunities based on suppliers evaluated by the Evaluation Department. For example, the Identification Department finds opportunities for recycling, reuse, and waste reduction. Specifically, it analyzes waste data from each supplier to identify types of recyclable materials and reusable components. The Identification Department can also identify types of recyclable materials and reusable components by analyzing waste generated during the product manufacturing process. Furthermore, the Identification Department can identify energy recovery opportunities and propose recovery measures, such as waste heat recovery or the use of renewable energy. This allows the Identification Department to identify circular economy opportunities and improve sustainability throughout the entire supply chain.

[0076] The maintenance department performs predictive maintenance based on opportunities identified by specific departments. For example, the maintenance department predicts equipment failures and optimizes maintenance schedules. Specifically, it analyzes equipment sensor data to detect early signs of failure. The maintenance department can also identify ways to minimize equipment energy consumption and waste generation. For example, it collects energy consumption data for each piece of equipment and proposes methods to optimize energy efficiency. Furthermore, the maintenance department can identify ways to minimize equipment water and chemical usage. For example, it collects water usage data for each piece of equipment and proposes methods to minimize usage. This allows the maintenance department to maximize equipment efficiency and improve the sustainability of the entire supply chain.

[0077] The tracking unit can track the carbon footprint of the entire supply chain in real time. For example, the tracking unit can track CO2 emissions at each stage of the supply chain in real time. The tracking unit can collect data in real time using IoT sensors and transmit it to the cloud. The tracking unit can also identify the types of greenhouse gases and measure their respective emissions. The tracking unit can set the frequency of data collection and select the sensors and technologies to be used. This allows for immediate understanding of environmental impacts by tracking the carbon footprint of the entire supply chain in real time. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input data acquired by IoT sensors into a generating AI and have the generating AI perform real-time carbon footprint tracking.

[0078] The optimization unit can plan the optimal logistics route using AI. For example, the optimization unit plans the optimal route considering cost reduction, time savings, and energy efficiency. The optimization unit can also predict the optimal route based on past data using machine learning algorithms. The optimization unit can also adjust the route in real time using deep learning technology. The optimization unit can also propose the optimal route by considering traffic congestion information and weather data in real time. This allows for efficient optimization of logistics routes using AI. Some or all of the above-described processes in the optimization unit may be performed using AI, or not. For example, the optimization unit can input past data into a generating AI and have the generating AI predict the optimal route.

[0079] The evaluation department can assess and rank the sustainability of suppliers. For example, the evaluation department can rank suppliers based on criteria such as environmental impact, social impact, and economic sustainability. The evaluation department can also use natural language processing technology to analyze sustainability reports and ensure compliance. The evaluation department can also provide detailed assessments of suppliers' energy efficiency and waste management capabilities. The evaluation department can collect supplier energy consumption data and assess efficiency. This allows for the construction of a sustainable supply chain by assessing and ranking supplier sustainability. Some or all of the above processes in the evaluation department may be performed using AI, for example, or not. For example, the evaluation department can input supplier sustainability reports into a generating AI and have the generating AI analyze the reports.

[0080] The specific unit can identify circular economy opportunities and find opportunities for recycling and waste reduction. For example, the specific unit can find opportunities for recycling, reuse, and waste reduction. The specific unit can also identify types of recyclable materials and reusable parts. The specific unit can also identify energy recovery opportunities and make recovery proposals. This promotes recycling and waste reduction by identifying circular economy opportunities. Some or all of the above processing in the specific unit may be performed using AI, for example, or not using AI. For example, the specific unit can input data on recyclable materials into a generating AI and have the generating AI make recycling proposals.

[0081] The maintenance department can optimize the equipment lifecycle and reduce resource consumption and waste. For example, the maintenance department can predict equipment failures and optimize maintenance schedules. The maintenance department can also identify ways to minimize the equipment's energy consumption and waste generation. The maintenance department can also identify ways to minimize the equipment's water usage and chemical usage. This reduces resource consumption and waste by optimizing the equipment lifecycle. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not. For example, the maintenance department can input equipment energy consumption data into a generating AI and have the generating AI perform the task of minimizing energy consumption.

[0082] The tracking unit can predict environmental impacts using generative AI. For example, the tracking unit can use generative AI to predict CO2 emissions and types of greenhouse gases at each stage of the supply chain. The tracking unit can improve the accuracy of environmental impact predictions using generative models and simulation techniques. Thus, using generative AI improves the accuracy of environmental impact predictions. Some or all of the above-described processes in the tracking unit may be performed using generative AI, or not. For example, the tracking unit can input supply chain data into generative AI and have the generative AI perform environmental impact predictions.

[0083] The evaluation unit can analyze sustainability reports using natural language processing to ensure compliance. For example, the evaluation unit can use natural language processing techniques to perform text analysis and sentiment analysis of sustainability reports. The evaluation unit can also use topic modeling to extract key themes and keywords from the reports. This improves the accuracy of the sustainability report analysis and ensures compliance by using natural language processing. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the sustainability report into a generating AI and have the generating AI perform the analysis of the report.

[0084] The tracking unit can estimate the user's emotions and adjust the frequency of carbon footprint tracking based on the estimated user emotions. The tracking unit estimates the user's emotions, for example, using emotion recognition technology. If the user is stressed, the tracking unit can reduce the tracking frequency and display only important data. If the user is relaxed, the tracking unit can also display detailed tracking data more frequently. If the user is in a hurry, the tracking unit can minimize the tracking frequency and display only the essentials. This reduces the user's burden by adjusting the tracking frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the tracking frequency.

[0085] The tracking unit can record energy consumption in detail at each stage of the supply chain. For example, the tracking unit can record energy consumption at each supply chain stage in real time and store it in a database. The tracking unit can also detect anomalies in energy consumption and issue alerts. The tracking unit can also analyze historical energy consumption data and suggest improvements to efficiency. This makes it possible to suggest improvements to efficiency by recording energy consumption in detail. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input energy consumption data into a generating AI and have the generating AI perform the recording and analysis of energy consumption.

[0086] The tracking unit can record in detail the amount of waste generated at each stage of the supply chain. For example, the tracking unit can record the amount of waste generated at each supply chain stage in real time and store it in a database. The tracking unit can also detect anomalies in the amount of waste generated and issue alerts. The tracking unit can also analyze historical data on the amount of waste generated and make suggestions for reduction. This makes it possible to make reduction suggestions by recording the amount of waste generated in detail. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input waste generation data into a generation AI and have the generation AI perform the recording and analysis of the amount of waste generated.

[0087] The tracking unit can estimate the user's emotions and adjust how the tracking data is displayed based on the estimated emotions. For example, the tracking unit can estimate the user's emotions using emotion recognition technology. If the user is stressed, the tracking unit can display the data in a simple interface. If the user is relaxed, the tracking unit can also display detailed data graphically. If the user is in a hurry, the tracking unit can highlight only the essential points. This reduces the user's burden by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's emotion data into the generative AI and have the generative AI perform the emotion-based adjustment of the display method.

[0088] The tracking unit can record water usage at each stage of the supply chain. For example, the tracking unit can record water usage at each supply chain stage in real time and store it in a database. The tracking unit can also detect anomalies in water usage and issue alerts. The tracking unit can also analyze historical water usage data and suggest improvements to efficiency. This makes it possible to suggest improvements to efficiency by recording water usage. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input water usage data into a generating AI and have the generating AI perform the recording and analysis of water usage.

[0089] The tracking unit can record the amount of chemicals used at each stage of the supply chain. For example, the tracking unit can record the amount of chemicals used at each supply chain stage in real time and store it in a database. The tracking unit can also detect anomalies in chemical usage and issue alerts. The tracking unit can also analyze historical data on chemical usage and make suggestions for reduction. This makes it possible to make reduction suggestions by recording chemical usage. Some or all of the above processes in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input chemical usage data into a generating AI and have the generating AI perform the recording and analysis of chemical usage.

[0090] The optimization unit can estimate the user's emotions and adjust the optimization criteria for the logistics route based on the estimated user emotions. The optimization unit estimates the user's emotions, for example, using emotion recognition technology. If the user is feeling stressed, the optimization unit can prioritize the shortest route. If the user is relaxed, the optimization unit can also prioritize an environmentally friendly route. If the user is in a hurry, the optimization unit can also prioritize the fastest route. This reduces the burden on the user by adjusting the optimization criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the optimization criteria based on emotions.

[0091] The optimization unit can consider traffic congestion information in real time during optimization. For example, the optimization unit proposes the optimal route based on real-time traffic congestion information. The optimization unit can also propose detour routes to avoid congestion. The optimization unit can also analyze congestion information and propose the most efficient route. In this way, by considering traffic congestion information, it is possible to propose efficient logistics routes. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input traffic congestion information into a generating AI and have the generating AI perform real-time route optimization.

[0092] The optimization unit can consider weather data in real time during optimization. For example, the optimization unit proposes the optimal route based on real-time weather data. The optimization unit can also propose detour routes to avoid bad weather. The optimization unit can also analyze weather data and propose the most efficient route. In this way, by considering weather data, it is possible to propose efficient logistics routes. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input weather data into a generating AI and have the generating AI perform real-time route optimization.

[0093] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the estimated user emotions. For example, the optimization unit estimates the user's emotions using emotion recognition technology. If the user is stressed, the optimization unit can display the results with a simple interface. If the user is relaxed, the optimization unit can also display detailed data graphically. If the user is in a hurry, the optimization unit can highlight and display only the essential points. This reduces the burden on the user by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the display method based on emotions.

[0094] The optimization unit can consider the safety of the logistics route during optimization. For example, the optimization unit can prioritize and propose safe routes. The optimization unit can also propose routes that avoid dangerous areas. The optimization unit can analyze highly safe routes and propose the optimal route. In this way, by considering safety, it can propose safe logistics routes. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input safety data into a generating AI and have the generating AI propose safe routes.

[0095] The optimization unit can consider the cost of logistics routes during optimization. For example, the optimization unit can prioritize and propose cost-effective routes. The optimization unit can also propose alternative routes to reduce costs. The optimization unit can also analyze cost data and propose the most efficient route. In this way, by considering costs, it can propose cost-effective logistics routes. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input cost data into a generating AI and have the generating AI propose cost-effective routes.

[0096] The evaluation unit can estimate the user's emotions and adjust the supplier evaluation criteria based on the estimated user emotions. The evaluation unit can estimate the user's emotions, for example, using emotion recognition technology. If the user is stressed, the evaluation unit can use simple evaluation criteria. If the user is relaxed, the evaluation unit can also use detailed evaluation criteria. If the user is in a hurry, the evaluation unit can also use evaluation criteria that emphasize only the essentials. This reduces the burden on the user by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of evaluation criteria based on emotions.

[0097] The evaluation unit can perform a detailed assessment of a supplier's energy efficiency during the evaluation process. For example, the evaluation unit can collect energy consumption data from suppliers and evaluate their efficiency. The evaluation unit can also prioritize the evaluation of suppliers with high energy efficiency. The evaluation unit can also make improvement suggestions to suppliers with low energy efficiency. This allows for suggestions for efficiency improvements by conducting a detailed assessment of energy efficiency. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input energy consumption data into a generating AI and have the generating AI perform the energy efficiency evaluation.

[0098] The evaluation unit can assess a supplier's waste management capabilities in detail during the evaluation process. For example, the evaluation unit can collect supplier waste management data and evaluate their capabilities. The evaluation unit can also prioritize the evaluation of suppliers with high waste management capabilities. The evaluation unit can also make improvement suggestions to suppliers with low waste management capabilities. This allows for improvement suggestions by conducting a detailed evaluation of waste management capabilities. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input waste management data into a generating AI and have the generating AI perform the evaluation of waste management capabilities.

[0099] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. The evaluation unit can estimate the user's emotions, for example, using emotion recognition technology. If the user is stressed, the evaluation unit can display the results with a simple interface. If the user is relaxed, the evaluation unit can also display detailed data graphically. If the user is in a hurry, the evaluation unit can highlight only the essential points. This reduces the burden on the user by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0100] The evaluation unit can assess the water usage efficiency of suppliers during the evaluation process. For example, the evaluation unit can collect water usage data from suppliers and evaluate their efficiency. The evaluation unit can also prioritize the evaluation of suppliers with high water usage efficiency. The evaluation unit can also make improvement suggestions to suppliers with low water usage efficiency. Thus, by evaluating water usage efficiency, it becomes possible to propose efficiency improvements. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input water usage data into a generating AI and have the generating AI perform the evaluation of water usage efficiency.

[0101] The evaluation unit can assess a supplier's chemical management capabilities during the evaluation process. For example, the evaluation unit can collect chemical usage data from suppliers and evaluate their management capabilities. The evaluation unit can also prioritize the evaluation of suppliers with high chemical management capabilities. The evaluation unit can also make improvement suggestions to suppliers with low chemical management capabilities. Thus, improvement suggestions become possible by evaluating chemical management capabilities. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input chemical usage data into a generating AI and have the generating AI perform the evaluation of chemical management capabilities.

[0102] The identification unit can estimate the user's emotions and adjust the criteria for identifying opportunities in the circular economy based on the estimated user emotions. The identification unit estimates the user's emotions, for example, using emotion recognition technology. The identification unit can use simple criteria if the user is stressed. The identification unit can also use detailed criteria if the user is relaxed. The identification unit can also use criteria that emphasize only the essentials if the user is in a hurry. This reduces the burden on the user by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of criteria based on emotions.

[0103] The identification unit can identify in detail the types of recyclable materials at the time of identification. For example, the identification unit can analyze the materials used at each stage of the supply chain and identify those that are recyclable. The identification unit can also store the types of recyclable materials in a database and make recycling suggestions. The identification unit can also make suggestions to promote the use of recyclable materials. Thus, by identifying recyclable materials, recycling suggestions become possible. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input material data into a generating AI and have the generating AI perform the identification of recyclable materials.

[0104] The identification unit can, at the time of identification, specify in detail concrete methods for waste reduction. For example, the identification unit can analyze the amount of waste generated at each stage of the supply chain and propose reductions. The identification unit can also store specific methods for waste reduction in a database. The identification unit can also propose training programs for waste reduction. This makes it possible to propose reductions by identifying specific methods for waste reduction. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input waste data into a generating AI and have the generating AI execute methods for waste reduction.

[0105] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. The identification unit can estimate the user's emotions, for example, using emotion recognition technology. If the user is feeling stressed, the identification unit can display the results with a simple interface. If the user is relaxed, the identification unit can also display detailed data graphically. If the user is in a hurry, the identification unit can highlight and display only the essential points. This reduces the burden on the user by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0106] The identification unit can identify the types of reusable parts at the time of identification. For example, the identification unit analyzes the parts used at each stage of the supply chain and identifies those that are reusable. The identification unit can also store the types of reusable parts in a database and make suggestions for reuse. The identification unit can also make suggestions to promote the use of reusable parts. Thus, by identifying reusable parts, reuse suggestions become possible. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input part data into a generating AI and have the generating AI perform the identification of reusable parts.

[0107] The identification unit can identify energy recovery opportunities at specific times. For example, the identification unit analyzes energy consumption at each stage of the supply chain and proposes energy recovery. The identification unit can also store specific methods for energy recovery in a database. The identification unit can also propose training programs for energy recovery. This makes it possible to propose recovery by identifying energy recovery opportunities. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input energy consumption data into a generating AI and have the generating AI execute energy recovery opportunities.

[0108] The maintenance unit can estimate the user's emotions and adjust the predictive maintenance schedule based on the estimated emotions. For example, the maintenance unit might use emotion recognition technology to estimate the user's emotions. If the user is stressed, the maintenance unit can reduce the maintenance frequency and perform only essential maintenance. If the user is relaxed, the maintenance unit can also provide a detailed maintenance schedule. If the user is in a hurry, the maintenance unit can minimize the maintenance frequency and emphasize only the essentials. This reduces the user's burden by adjusting the schedule according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the maintenance unit may be performed using AI or not. For example, the maintenance unit can input user emotion data into a generative AI and have the generative AI perform emotion-based schedule adjustments.

[0109] The maintenance department can identify ways to minimize the energy consumption of equipment during maintenance. For example, the maintenance department can collect energy consumption data for each piece of equipment and propose ways to minimize it. The maintenance department can also detect anomalies in energy consumption and propose improvements. The maintenance department can also analyze historical energy consumption data and propose ways to improve efficiency. This makes it possible to propose ways to improve efficiency by identifying ways to minimize energy consumption. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input energy consumption data into a generating AI and have the generating AI perform the task of minimizing energy consumption.

[0110] The maintenance department can identify ways to minimize the amount of waste generated by equipment during maintenance. For example, the maintenance department can collect waste generation data for each piece of equipment and propose ways to minimize it. The maintenance department can also detect anomalies in the amount of waste generated and propose improvements. The maintenance department can also analyze historical data on the amount of waste generated and propose reductions. This makes it possible to propose reductions by identifying ways to minimize the amount of waste generated. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input waste generation data into a generation AI and have the generation AI perform waste generation minimization.

[0111] The maintenance unit can estimate the user's emotions and adjust the display method of maintenance results based on the estimated user emotions. For example, the maintenance unit can estimate the user's emotions using emotion recognition technology. If the user is stressed, the maintenance unit can display the results with a simple interface. If the user is relaxed, the maintenance unit can also display detailed data graphically. If the user is in a hurry, the maintenance unit can highlight only the essential points. This reduces the user's burden by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the maintenance unit may be performed using AI, for example, or without AI. For example, the maintenance unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the display method.

[0112] The maintenance department can identify ways to minimize the water usage of equipment during maintenance. For example, the maintenance department can collect water usage data for each piece of equipment and propose ways to minimize it. The maintenance department can also detect abnormalities in water usage and propose improvements. The maintenance department can also analyze historical water usage data and propose ways to improve efficiency. This makes it possible to propose ways to improve efficiency by identifying ways to minimize water usage. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input water usage data into a generating AI and have the generating AI perform the task of minimizing water usage.

[0113] The maintenance department can identify ways to minimize the amount of chemicals used by equipment during maintenance. For example, the maintenance department can collect chemical usage data for each piece of equipment and propose ways to minimize it. The maintenance department can also detect anomalies in chemical usage and propose improvements. The maintenance department can also analyze historical data on chemical usage and propose reductions. This makes it possible to propose reductions by identifying ways to minimize chemical usage. Some or all of the above processes in the maintenance department may be performed using AI, for example, or not using AI. For example, the maintenance department can input chemical usage data into a generating AI and have the generating AI perform chemical usage minimization.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The Sustainable Supply Chain Optimizer can also include a forecasting unit. This unit can predict demand at each stage of the supply chain and optimize supply plans. For example, it can analyze historical sales data and market trends to predict fluctuations in demand. It can also consider external factors such as seasonality and promotional activities when forecasting demand. Furthermore, the forecasting unit can use AI to improve the accuracy of demand forecasts. This allows for a quicker response to demand fluctuations and prevents inventory shortages and surpluses.

[0116] The Sustainable Supply Chain Optimizer can also be equipped with a notification unit. This unit can detect anomalies occurring at each stage of the supply chain and notify relevant parties. For example, it can detect anomalies such as logistics delays or inventory shortages in real time and issue alerts. Furthermore, it can analyze the cause of the anomaly and propose countermeasures. In addition, the notification unit can use AI to improve the accuracy of anomaly detection. This allows for a rapid response when anomalies occur, ensuring the stability of the supply chain.

[0117] The Sustainable Supply Chain Optimizer can also include an Education Department. This department can provide sustainability education to stakeholders in the supply chain. For example, it can offer training programs on how to build sustainable supply chains and reduce environmental impact. It can also share best practices at each stage of the supply chain to raise awareness among stakeholders. Furthermore, the Education Department can use AI to evaluate the effectiveness of educational programs and suggest improvements. This can enhance stakeholders' understanding of and ability to implement sustainability practices.

[0118] The Sustainable Supply Chain Optimizer can also include a reporting function. This function can automatically generate and provide reports on the sustainability of the supply chain to stakeholders. For example, the reporting function can collect data such as the carbon footprint, energy consumption, and waste generation across the entire supply chain and create reports. It can also evaluate the progress of sustainability improvements and the degree of goal achievement, and reflect this in the reports. Furthermore, the reporting function can use AI to improve the accuracy and efficiency of the reports. This allows stakeholders to quickly and accurately understand information regarding the sustainability of the supply chain.

[0119] The Sustainable Supply Chain Optimizer can also include an Incentives Unit. This unit can provide incentives to stakeholders in the supply chain to promote sustainable behavior. For example, it can offer preferential treatment to sustainable suppliers and encourage highly sustainable actions. It can also reward and commend stakeholders who contribute to reducing environmental impact. Furthermore, the Incentives Unit can use AI to evaluate the effectiveness of incentives and design optimal incentive programs. This promotes sustainable behavior among stakeholders and improves the sustainability of the entire supply chain.

[0120] The Sustainable Supply Chain Optimizer can further utilize emotion estimation capabilities to provide feedback on supply chain sustainability based on the user's emotions. For example, by using emotion estimation to predict the user's emotions, it can provide simple feedback if the user is stressed, detailed feedback if the user is relaxed, and concise feedback highlighting only the essentials if the user is in a hurry. This reduces user burden and promotes sustainable behavior by providing feedback tailored to the user's emotions.

[0121] The Sustainable Supply Chain Optimizer can further utilize emotion estimation capabilities to provide advice on supply chain sustainability based on the user's emotions. For example, by using emotion estimation to predict the user's emotions, it can offer simple advice if the user is stressed, provide more detailed advice if the user is relaxed, and offer concise advice emphasizing only the essentials if the user is in a hurry. This reduces the user's burden and promotes sustainable behavior by providing advice tailored to their emotions.

[0122] The Sustainable Supply Chain Optimizer can further utilize emotion estimation capabilities to help users set sustainability goals based on their emotions. For example, by using emotion estimation to predict a user's emotions, it can set realistic goals if the user is stressed, challenging goals if the user is relaxed, and short-term goals if the user is in a hurry. By supporting goal setting in line with the user's emotions, it can reduce user burden and promote sustainable behavior.

[0123] The Sustainable Supply Chain Optimizer can further utilize its emotion estimation function to provide training programs on supply chain sustainability based on the user's emotions. For example, by using the emotion estimation function to estimate the user's emotions, it can provide a simple training program if the user is stressed. If the user is relaxed, it can provide a more detailed training program. Furthermore, if the user is in a hurry, it can provide a training program that emphasizes only the essentials. By providing training programs tailored to the user's emotions, it can reduce the burden on the user and promote sustainable behavior.

[0124] The Sustainable Supply Chain Optimizer can further utilize emotion estimation capabilities to support supply chain sustainability communication based on user emotions. For example, by estimating a user's emotions, it can support simpler communication if the user is stressed, more detailed communication if the user is relaxed, and communication that emphasizes only the essentials if the user is in a hurry. By supporting communication tailored to the user's emotions, it can reduce user burden and promote sustainable behavior.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The tracking unit tracks the carbon footprint of the entire supply chain. For example, it tracks CO2 emissions at each stage of the supply chain in real time, identifies the types of greenhouse gases, and measures the emissions of each. It also sets the frequency of data collection and selects the sensors and technologies to be used. For example, it uses IoT sensors to collect data in real time and transmits it to the cloud. Step 2: The optimization unit uses AI to optimize logistics routes based on data tracked by the tracking unit. For example, it plans the optimal route considering cost reduction, time savings, and energy efficiency, and uses machine learning algorithms to predict the optimal route based on historical data. Furthermore, it uses deep learning technology to adjust the route in real time and proposes the optimal route by considering traffic congestion information and weather data in real time. Step 3: The evaluation department assesses the sustainability of suppliers based on the routes optimized by the optimization department. For example, it ranks suppliers using environmental impact, social impact, and economic sustainability as evaluation criteria, analyzes sustainability reports using natural language processing technology, and ensures compliance. Furthermore, it evaluates suppliers' energy efficiency and waste management capabilities in detail, and collects energy consumption data to assess efficiency. Step 4: The Identification Department identifies circular economy opportunities based on the suppliers evaluated by the Evaluation Department. For example, it identifies recycling, reuse, and waste reduction opportunities, and identifies types of recyclable materials and reusable parts. Furthermore, it identifies energy recovery opportunities and proposes waste heat recovery and the use of renewable energy. Step 5: The maintenance department performs predictive maintenance based on opportunities identified by specific departments. For example, it predicts equipment failures, optimizes maintenance schedules, and identifies ways to minimize equipment energy consumption and waste generation. Furthermore, it identifies ways to minimize equipment water and chemical usage, collects energy consumption data for each piece of equipment, and proposes ways to minimize these uses.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the tracking unit, optimization unit, evaluation unit, identification unit, and maintenance unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the tracking unit collects data in real time using the IoT sensors of the smart device 14 and transmits it to the cloud. The optimization unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes logistics routes using AI. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the sustainability of suppliers. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies opportunities for the circular economy. The maintenance unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs predictive maintenance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the tracking unit, optimization unit, evaluation unit, identification unit, and maintenance unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the tracking unit collects data in real time using the IoT sensors of the smart glasses 214 and transmits it to the cloud. The optimization unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes logistics routes using AI. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the sustainability of suppliers. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies opportunities for the circular economy. The maintenance unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs predictive maintenance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the tracking unit, optimization unit, evaluation unit, identification unit, and maintenance unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the tracking unit collects data in real time using the IoT sensor of the headset terminal 314 and transmits it to the cloud. The optimization unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes logistics routes using AI. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the sustainability of suppliers. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies opportunities for the circular economy. The maintenance unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs predictive maintenance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the tracking unit, optimization unit, evaluation unit, identification unit, and maintenance unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the tracking unit collects data in real time using the IoT sensors of the robot 414 and transmits it to the cloud. The optimization unit is implemented by the identification processing unit 290 of the data processing unit 12 and optimizes logistics routes using AI. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates the sustainability of suppliers. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies opportunities for the circular economy. The maintenance unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs predictive maintenance. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0180] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) A tracking unit that tracks the carbon footprint, An optimization unit optimizes the logistics route based on the data tracked by the aforementioned tracking unit, An evaluation unit that evaluates the sustainability of suppliers based on the route optimized by the optimization unit, A special unit identifies circular economy opportunities based on suppliers evaluated by the aforementioned evaluation unit, The system includes a maintenance unit that performs predictive maintenance based on the opportunities identified by the specified unit. A system characterized by the following features. (Note 2) The aforementioned tracking unit is Track the carbon footprint of the entire supply chain in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, Plan the optimal logistics route using AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Evaluate and rank the sustainability of suppliers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The specified part is, Identify circular economy opportunities and find opportunities for recycling and waste reduction. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned maintenance unit is Optimize the equipment lifecycle and reduce resource consumption and waste. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned tracking unit is Predicting environmental impacts using generative AI The system described in Appendix 1, characterized by the features described herein. (Note 8) The evaluation unit, Analyze sustainability reports using natural language processing to ensure compliance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned tracking unit is We estimate user sentiment and adjust the frequency of carbon footprint tracking based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned tracking unit is Detailed recording of energy consumption at each stage of the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned tracking unit is Detailed documentation of waste generation at each stage of the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned tracking unit is It estimates the user's emotions and adjusts how tracking data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned tracking unit is Record water usage at each stage of the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned tracking unit is Record the amount of chemicals used at each stage of the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 15) The optimization unit, The system estimates user sentiment and adjusts logistics route optimization criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The optimization unit, Traffic congestion information is taken into consideration in real time during optimization. The system described in Appendix 1, characterized by the features described herein. (Note 17) The optimization unit, During optimization, weather data is taken into consideration in real time. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, It estimates the user's emotions and adjusts how the optimization results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, When optimizing, consider the safety of the logistics route. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, When optimizing, consider the cost of logistics routes. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, We estimate user sentiment and adjust supplier evaluation criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation, the energy efficiency of the supplier will be assessed in detail. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation, we will thoroughly assess the supplier's waste management capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During the evaluation, the supplier's water usage efficiency will be assessed. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit described above, During the evaluation, assess the supplier's chemical management capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 27) The specified part is, We estimate user sentiment and adjust the criteria for identifying circular economy opportunities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The specified part is, At the time of identification, the types of recyclable materials should be identified in detail. The system described in Appendix 1, characterized by the features described herein. (Note 29) The specified part is, At the time of identification, specify in detail the concrete methods for reducing waste. The system described in Appendix 1, characterized by the features described herein. (Note 30) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The specified part is, Identify the types of reusable parts at specific times. The system described in Appendix 1, characterized by the features described herein. (Note 32) The specified part is, Identify opportunities for energy recovery at specific times. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned maintenance unit is It estimates user sentiment and adjusts predictive maintenance schedules based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned maintenance unit is Identify ways to minimize the energy consumption of equipment during maintenance. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned maintenance unit is Identify ways to minimize equipment waste generation during maintenance. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned maintenance unit is The system estimates the user's emotions and adjusts how maintenance results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned maintenance unit is Identify ways to minimize water usage during maintenance. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned maintenance unit is Identify ways to minimize the use of chemicals in equipment during maintenance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A tracking unit that tracks the carbon footprint, An optimization unit optimizes the logistics route based on the data tracked by the aforementioned tracking unit, An evaluation unit that evaluates the sustainability of suppliers based on the route optimized by the optimization unit, A special unit identifies circular economy opportunities based on suppliers evaluated by the aforementioned evaluation unit, The system includes a maintenance unit that performs predictive maintenance based on the opportunities identified by the specified unit. A system characterized by the following features.

2. The aforementioned tracking unit is Track the carbon footprint of the entire supply chain in real time. The system according to feature 1.

3. The optimization unit, Use AI to plan the optimal logistics route. The system according to feature 1.

4. The evaluation unit, Evaluate and rank the sustainability of suppliers. The system according to feature 1.

5. The specified part is, Identify circular economy opportunities and find opportunities for recycling and waste reduction. The system according to feature 1.

6. The aforementioned maintenance unit is Optimize the equipment lifecycle and reduce resource consumption and waste. The system according to feature 1.

7. The aforementioned tracking unit is Predicting environmental impacts using generative AI The system according to feature 1.

8. The evaluation unit, Analyze sustainability reports using natural language processing to ensure compliance. The system according to feature 1.

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