System

Abstract

A system according to an embodiment aims to minimize the carbon footprint of the entire food supply chain.SOLUTION: A system includes a collection unit, an analysis unit, and an execution unit. The aggregator analyzes in detail the carbon footprint in the supply chain from the food producer to the consumer. The analysis unit analyzes the data collected by the collection unit and proposes an optimal method for minimizing the amount of carbon emissions in each step. The execution unit executes the optimization method proposed by the analysis unit and monitors carbon emissions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately analyze and optimize the carbon footprint of the entire food supply chain in detail, and there is room for improvement.

[0005] The system according to the embodiment aims to minimize the carbon footprint throughout the food supply chain. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an execution unit. The collection unit performs a detailed analysis of the carbon footprint in the supply chain from food producers to consumers. The analysis unit analyzes the data collected by the collection unit and proposes an optimal method for minimizing carbon emissions at each step. The execution unit executes the optimization method proposed by the analysis unit and monitors carbon emissions. [Effects of the Invention]

[0007] Systems according to embodiments can minimize the carbon footprint throughout the food supply chain. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A carbon-neutral food supply network system according to an embodiment of the present invention performs a detailed analysis of the carbon footprint of the entire supply chain from food producers to consumers, proposes optimal methods for minimizing carbon emissions at each step, and monitors actual carbon emissions. For example, the carbon-neutral food supply network system collects data using IoT sensors and existing databases, analyzes energy consumption and carbon emissions at each step using AI, and proposes the selection of energy-efficient transportation methods and the promotion of renewable energy use. Furthermore, the proposed optimization method is implemented and actual carbon emissions are monitored. This results in a carbon-neutral food supply network. This carbon-neutral food supply network system can reduce carbon emissions throughout the entire food supply network and achieve carbon neutrality. For example, introducing energy-efficient transportation methods reduces carbon emissions during transportation. Furthermore, using renewable energy reduces energy consumption. This results in reduced carbon emissions throughout the entire food supply network and achieves carbon neutrality.

[0029] A carbon-neutral food supply network system according to an embodiment includes a collection unit, an analysis unit, and an execution unit. The collection unit performs a detailed analysis of the carbon footprint of the supply chain from food producers to consumers. The collection unit, for example, uses IoT sensors to measure energy consumption and carbon emissions at each step. The collection unit can also collect data using an existing database. For example, the collection unit uses IoT sensors and an existing database to obtain a detailed understanding of the carbon footprint at each step, such as production at farms, processing at processing plants, transportation, storage in warehouses, and sales at stores. The analysis unit analyzes the data collected by the collection unit and proposes optimal methods for minimizing carbon emissions at each step. For example, the analysis unit uses AI to analyze the energy consumption and carbon emissions at each step. For example, the analysis unit proposes the selection of energy-efficient transportation methods and the promotion of renewable energy use. The execution unit executes the optimization method proposed by the analysis unit and monitors actual carbon emissions. For example, the execution unit introduces energy-efficient transportation methods and utilizes renewable energy. The execution unit also monitors actual carbon emissions. For example, the execution unit reduces actual carbon emissions by introducing energy-efficient transportation methods and using renewable energy. As a result, the carbon-neutral food supply network system according to the embodiment can reduce carbon emissions throughout the food supply network and achieve carbon neutrality.

[0030] The collection unit can collect data using IoT sensors or an existing database. For example, the collection unit measures energy consumption and carbon emissions at each step using IoT sensors. For example, the collection unit collects data using temperature sensors, humidity sensors, position sensors, etc. The collection unit can also collect data using an existing database. For example, the collection unit collects data using government statistical data or internal company data. This improves the accuracy of data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from IoT sensors into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can propose the selection of an energy-efficient means of transportation or the promotion of renewable energy use. The analysis unit, for example, proposes the selection of an energy-efficient means of transportation. For example, the analysis unit proposes transportation means such as electric vehicles, hybrid vehicles, and fuel cell vehicles. The analysis unit can also propose the promotion of renewable energy use. For example, the analysis unit proposes the use of renewable energy such as solar power generation, wind power generation, and biomass power generation. This makes it possible to reduce carbon emissions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute the selection of an energy-efficient means of transportation or the promotion of renewable energy use.

[0032] The execution unit can introduce energy-efficient transportation means and utilize renewable energy. The execution unit, for example, introduces energy-efficient transportation means. For example, the execution unit introduces transportation means such as electric vehicles, hybrid vehicles, and fuel cell vehicles. The execution unit can also utilize renewable energy. For example, the execution unit utilizes renewable energy such as solar power generation, wind power generation, and biomass power generation. This can reduce actual carbon emissions. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can cause the generation AI to introduce energy-efficient transportation means and utilize renewable energy.

[0033] The execution unit can monitor carbon emissions. The execution unit, for example, uses sensors to monitor carbon emissions. For example, the execution unit monitors carbon emissions using a temperature sensor, a humidity sensor, a position sensor, etc. The execution unit can also adjust the frequency of data collection. For example, the execution unit increases the frequency of data collection to monitor carbon emissions in real time. This enables management of carbon emissions. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input data obtained from a sensor into a generation AI and cause the generation AI to monitor carbon emissions.

[0034] The collection unit can adjust sensor placement to collect energy consumption and carbon emissions at each step in real time. The collection unit, for example, places sensors in optimal positions to monitor energy consumption at each step in real time. For example, the collection unit places temperature sensors, humidity sensors, position sensors, etc. in optimal positions. The collection unit can also adjust sensor placement to accurately measure carbon emissions at each step. For example, the collection unit optimizes sensor placement to improve the accuracy of data collection. This improves the accuracy of data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to execute an algorithm to optimize sensor placement.

[0035] The collection unit can integrate multiple data sources during data collection to improve accuracy. For example, the collection unit can integrate satellite data and meteorological data to improve the accuracy of carbon emissions. For example, the collection unit can use satellite data to obtain geographical information and meteorological data to understand climate conditions. The collection unit can also integrate data from different data sources to improve the accuracy of energy consumption. For example, the collection unit can integrate sensor data, external databases, user-input data, etc. to obtain a more accurate carbon footprint. This allows for a more accurate carbon footprint to be obtained. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data obtained from different data sources into the generation AI and have the generation AI integrate and analyze the data.

[0036] The collection unit can add a function to detect abnormal values ​​by comparing data with past data when collecting data and issue an alert. The collection unit, for example, detects abnormal energy consumption by comparing data with past data and issues an alert. For example, the collection unit sets a statistical threshold based on past data and detects energy consumption that exceeds the threshold. The collection unit can also detect abnormal carbon emissions by comparing data with past data and issue an alert. For example, the collection unit detects abnormal values ​​using a machine learning model and issues an alert in real time. This allows for early detection of abnormal carbon emissions or energy consumption. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to execute an algorithm that detects abnormal values ​​by comparing data with past data.

[0037] The collection unit can customize the collection method based on geographical conditions when collecting data. For example, in urban areas, the collection unit customizes the data collection method taking into account traffic volume and population density. For example, the collection unit adjusts the data collection method using traffic volume sensors and population density data. The collection unit can also customize the data collection method in rural areas taking into account agricultural activities and the natural environment. For example, the collection unit adjusts the data collection method using agricultural sensors and environmental data. This enables optimal data collection according to geographical conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to execute an algorithm that customizes the data collection method based on geographical conditions.

[0038] When collecting data, the collection unit can evaluate the work efficiency at each step in the supply chain and identify areas of low efficiency. The collection unit, for example, monitors the work efficiency at each step in real time and identifies areas of low efficiency. For example, the collection unit monitors work time and energy consumption and identifies areas of low efficiency. The collection unit can also propose areas for improvement based on the results of the work efficiency evaluation. For example, the collection unit identifies areas of low work efficiency and takes measures to improve efficiency. This identifies areas for improvement in work efficiency and enables efficiency to be improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have a generation AI evaluate the work efficiency and propose areas for improvement.

[0039] When collecting data, the collection unit can adjust the collection frequency taking into account seasonal fluctuations in energy consumption and carbon emissions. The collection unit, for example, adjusts the data collection frequency for energy consumption in accordance with seasonal fluctuations. For example, the collection unit increases the data collection frequency during periods when energy consumption increases, such as summer and winter. The collection unit can also adjust the data collection frequency for carbon emissions in accordance with seasonal fluctuations. For example, the collection unit grasps seasonal carbon emission patterns and sets an optimal data collection frequency. This enables optimal data collection in accordance with seasonal fluctuations. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can cause the generation AI to execute an algorithm that adjusts the data collection frequency based on seasonal fluctuations.

[0040] During the analysis, the analysis unit can specifically analyze the correlation between energy consumption and carbon emissions at each step. The analysis unit, for example, analyzes the correlation between energy consumption and carbon emissions at each step in detail. For example, the analysis unit analyzes the correlation using statistical methods or regression analysis. The analysis unit can also propose improvements to energy efficiency based on the results of the correlation analysis. For example, the analysis unit proposes a method for reducing carbon emissions based on the results of the correlation analysis. This makes it possible to propose improvements to energy efficiency and methods for reducing carbon emissions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI perform the correlation analysis and propose improvements.

[0041] The analysis unit can simulate multiple scenarios during analysis. For example, the analysis unit simulates scenarios with different renewable energy utilization rates and proposes an optimal energy usage method. For example, the analysis unit simulates best-case, worst-case, realistic scenarios, etc., and evaluates the carbon emission reduction effect. The analysis unit can also propose an optimal energy usage method based on the simulation results. This makes it possible to evaluate the optimal energy usage method and the carbon emission reduction effect. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute the scenario simulation and propose the optimal energy usage method.

[0042] During analysis, the analysis unit can predict future carbon emissions using past data and propose appropriate measures. The analysis unit, for example, predicts future carbon emissions based on past data. For example, the analysis unit predicts future carbon emissions using trend analysis of past data or a simulation model. The analysis unit can also propose optimal carbon emission reduction measures based on the prediction results. For example, the analysis unit proposes improvements to energy efficiency based on the future carbon emission prediction results. This makes it possible to predict future carbon emissions and take optimal measures. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute an algorithm that predicts future carbon emissions based on past data and proposes optimal measures.

[0043] During analysis, the analysis unit can evaluate the efficiency of the entire supply chain and identify problem areas. The analysis unit, for example, evaluates the efficiency of the entire supply chain and identifies bottlenecks. For example, the analysis unit evaluates the work time, energy consumption, cost, etc. of each step and identifies areas with low efficiency. The analysis unit can also propose measures to improve efficiency based on the results of identifying bottlenecks. For example, the analysis unit evaluates the efficiency of the entire supply chain and proposes areas for improvement. In this way, the efficiency of the entire supply chain can be evaluated and proposed areas for improvement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute an algorithm that evaluates the efficiency of the entire supply chain and identifies bottlenecks.

[0044] During the analysis, the analysis unit can compare the utilization effects of multiple energy sources. For example, the analysis unit compares the utilization effects of solar energy and wind energy and proposes the optimal energy source. For example, the analysis unit compares the utilization effects of solar power generation and wind power generation and evaluates the carbon emission reduction effect. The analysis unit can also propose the optimal energy source based on the utilization effect comparison results. This makes it possible to propose the optimal energy source. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute an algorithm that compares the utilization effects of energy sources.

[0045] During analysis, the analysis unit can evaluate the effectiveness of carbon offsets and propose an appropriate offset method. The analysis unit, for example, evaluates the effectiveness of carbon offsets and proposes the optimal offset method. For example, the analysis unit evaluates carbon offset methods such as reforestation activities and purchasing carbon credits and proposes the optimal method. The analysis unit can also propose measures to reduce carbon emissions based on the results of the offset effectiveness evaluation. This makes it possible to evaluate the effectiveness of carbon offsets and select the optimal offset method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute an algorithm that evaluates the effectiveness of carbon offsets and proposes the optimal offset method.

[0046] During execution, the execution unit can monitor the effects of introducing energy-efficient transportation means in real time. The execution unit, for example, monitors the effects of introducing energy-efficient transportation means in real time and evaluates the effects. For example, the execution unit monitors the effects of introducing electric vehicles, hybrid vehicles, fuel cell vehicles, etc. The execution unit can also propose improvements based on the results of monitoring the effects of the introduction. For example, the execution unit monitors the effects of introducing energy-efficient transportation means in real time and selects the optimal transportation means. This makes it possible to evaluate the effects of energy-efficient transportation means and select the optimal transportation means. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can cause the generation AI to execute an algorithm that monitors the effects of introducing energy-efficient transportation means.

[0047] During execution, the execution unit can periodically evaluate the usage status of renewable energy and identify problems. The execution unit, for example, periodically evaluates the usage status of renewable energy and identifies areas for improvement. For example, the execution unit evaluates the amount of energy generated, usage efficiency, cost, etc., and identifies problems. The execution unit can also propose measures to promote the use of renewable energy based on the results of the evaluation of the usage status. For example, the execution unit periodically evaluates the usage status of renewable energy and proposes the optimal usage method. This makes it possible to propose measures to promote the use of renewable energy. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can cause the generation AI to execute an algorithm that evaluates the usage status of renewable energy and identifies areas for improvement.

[0048] During execution, the execution unit can add a function to integrate and manage carbon emissions across the entire supply chain and report them in real time. The execution unit, for example, adds a function to centrally manage carbon emissions across the entire supply chain and report them in real time. For example, the execution unit sets the data collection method, data integration method, management system specifications, etc., and centrally manages carbon emissions. The execution unit can also suggest improvements based on the results of the centralized management of carbon emissions. For example, the execution unit reports carbon emissions across the entire supply chain in real time and takes optimal measures. This makes it possible to centrally manage carbon emissions across the entire supply chain and take optimal measures. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can have a generation AI perform integrated management of carbon emissions and real-time reporting.

[0049] The execution unit can select the optimal execution method based on geographical conditions during execution. For example, in urban areas, the execution unit selects the optimal execution method taking into account traffic volume and population density. For example, the execution unit selects the optimal execution method using traffic volume sensors and population density data. In rural areas, the execution unit can also select the optimal execution method taking into account agricultural activities and the natural environment. For example, the execution unit selects the optimal execution method using agricultural sensors and environmental data. This makes it possible to select the optimal execution method according to the geographical conditions. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can cause the generation AI to execute an algorithm that selects the optimal execution method based on geographical conditions.

[0050] During execution, the execution unit can evaluate the work efficiency at each step in the supply chain and improve areas where efficiency is low. For example, the execution unit can monitor the work efficiency at each step in real time and improve areas where efficiency is low. For example, the execution unit can monitor work time and energy consumption and identify areas where efficiency is low. The execution unit can also propose areas for improvement based on the results of the work efficiency evaluation. For example, the execution unit can identify areas where work efficiency is low and take measures to improve efficiency. This identifies areas where work efficiency can be improved and efficiency can be improved. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the execution unit can have a generation AI evaluate work efficiency and propose areas for improvement.

[0051] During execution, the execution unit can adjust the execution frequency taking into account seasonal fluctuations in energy consumption and carbon emissions. The execution unit, for example, adjusts the execution frequency for energy consumption in accordance with seasonal fluctuations. For example, the execution unit increases the execution frequency during periods when energy consumption increases, such as summer and winter. The execution unit can also adjust the execution frequency for carbon emissions in accordance with seasonal fluctuations. For example, the execution unit grasps seasonal carbon emission patterns and sets an optimal execution frequency. This makes it possible to set an optimal execution frequency in accordance with seasonal fluctuations. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can cause the generation AI to execute an algorithm that adjusts the execution frequency based on seasonal fluctuations.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When collecting data, the collection unit not only collects data on energy consumption and carbon emissions at each step, but can also simultaneously collect work efficiency and costs at each step. For example, the collection unit can monitor work time and energy consumption at each step and identify areas of low efficiency. The collection unit can also collect cost data at each step and evaluate cost efficiency. This allows for optimization not only in terms of energy consumption and carbon emissions, but also in terms of work efficiency and costs.

[0054] The implementation unit can not only introduce energy-efficient transportation means but also optimize the maintenance schedules of transportation means. For example, the implementation unit can propose a maintenance schedule to extend the battery life of electric vehicles. It can also propose a maintenance schedule to maximize the engine efficiency of hybrid vehicles. It can also propose a maintenance schedule to optimize the fuel efficiency of fuel cell vehicles. This maximizes the efficiency of transportation means and reduces carbon emissions.

[0055] The implementation unit not only monitors carbon emissions, but also provides real-time feedback based on the monitoring results. For example, the implementation unit can issue an alert if carbon emissions exceed the target value. It can also report progress if carbon emissions fall below the target value. Furthermore, the implementation unit can share success stories if carbon emissions are successfully reduced. This allows for more effective management of carbon emissions.

[0056] The collection unit can not only adjust the sensor placement to collect energy consumption and carbon emissions at each step in real time, but also optimize the type and accuracy of the sensors. For example, the collection unit can optimize the types of temperature sensors, humidity sensors, position sensors, etc. to improve the accuracy of data collection. The collection unit can also adjust the accuracy of the sensors to accurately collect the required data. Furthermore, the collection unit can combine and optimize the placement and type of sensors to maximize the efficiency of data collection. This improves the accuracy and efficiency of data collection.

[0057] When collecting data, the collection unit can not only integrate multiple data sources to improve accuracy, but also evaluate the reliability of the data. For example, the collection unit can compare data from different data sources and select highly reliable data. The collection unit can also ensure data quality using an algorithm to evaluate the reliability of the data. Furthermore, the collection unit can improve data collection methods based on the results of the data reliability evaluation. This improves the accuracy and reliability of the data.

[0058] When collecting data, the collection unit not only adds the function of detecting abnormal values ​​by comparing them with past data and issuing alerts, but also identifies the cause of the abnormal values. For example, the collection unit can identify the cause of abnormal energy consumption and propose countermeasures. It can also identify the cause of abnormal carbon emissions and propose improvement measures. Furthermore, the collection unit can improve data collection methods based on the results of identifying the cause of abnormal values. This enables early detection of abnormal values ​​and countermeasures.

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

[0060] Step 1: The collection unit performs a detailed analysis of the carbon footprint in the supply chain from food producers to consumers. For example, the collection unit uses IoT sensors to measure energy consumption and carbon emissions at each step. The collection unit can also collect data by utilizing existing databases. For example, the collection unit uses IoT sensors and existing databases to obtain a detailed understanding of the carbon footprint at each step, such as production at farms, processing at processing plants, transportation, storage in warehouses, and sales in stores. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal method for minimizing carbon emissions at each step. For example, the analysis unit uses AI to analyze energy consumption and carbon emissions at each step. For example, the analysis unit proposes the selection of energy-efficient transportation methods and the promotion of renewable energy use. Step 3: The execution unit executes the optimization method proposed by the analysis unit and monitors actual carbon emissions. For example, the execution unit introduces energy-efficient transportation means and uses renewable energy. The execution unit also monitors actual carbon emissions. For example, the execution unit reduces actual carbon emissions by introducing energy-efficient transportation means and using renewable energy.

[0061] (Example 2) A carbon-neutral food supply network system according to an embodiment of the present invention performs a detailed analysis of the carbon footprint of the entire supply chain from food producers to consumers, proposes optimal methods for minimizing carbon emissions at each step, and monitors actual carbon emissions. For example, the carbon-neutral food supply network system collects data using IoT sensors and existing databases, analyzes energy consumption and carbon emissions at each step using AI, and proposes the selection of energy-efficient transportation methods and the promotion of renewable energy use. Furthermore, the proposed optimization method is implemented and actual carbon emissions are monitored. This results in a carbon-neutral food supply network. This carbon-neutral food supply network system can reduce carbon emissions throughout the entire food supply network and achieve carbon neutrality. For example, introducing energy-efficient transportation methods reduces carbon emissions during transportation. Furthermore, using renewable energy reduces energy consumption. This results in reduced carbon emissions throughout the entire food supply network and achieves carbon neutrality.

[0062] A carbon-neutral food supply network system according to an embodiment includes a collection unit, an analysis unit, and an execution unit. The collection unit performs a detailed analysis of the carbon footprint of the supply chain from food producers to consumers. The collection unit, for example, uses IoT sensors to measure energy consumption and carbon emissions at each step. The collection unit can also collect data using an existing database. For example, the collection unit uses IoT sensors and an existing database to obtain a detailed understanding of the carbon footprint at each step, such as production at farms, processing at processing plants, transportation, storage in warehouses, and sales at stores. The analysis unit analyzes the data collected by the collection unit and proposes optimal methods for minimizing carbon emissions at each step. For example, the analysis unit uses AI to analyze the energy consumption and carbon emissions at each step. For example, the analysis unit proposes the selection of energy-efficient transportation methods and the promotion of renewable energy use. The execution unit executes the optimization method proposed by the analysis unit and monitors actual carbon emissions. For example, the execution unit introduces energy-efficient transportation methods and utilizes renewable energy. The execution unit also monitors actual carbon emissions. For example, the execution unit reduces actual carbon emissions by introducing energy-efficient transportation methods and using renewable energy. As a result, the carbon-neutral food supply network system according to the embodiment can reduce carbon emissions throughout the food supply network and achieve carbon neutrality.

[0063] The collection unit can collect data using IoT sensors or an existing database. For example, the collection unit measures energy consumption and carbon emissions at each step using IoT sensors. For example, the collection unit collects data using temperature sensors, humidity sensors, position sensors, etc. The collection unit can also collect data using an existing database. For example, the collection unit collects data using government statistical data or internal company data. This improves the accuracy of data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from IoT sensors into a generation AI and have the generation AI analyze the data.

[0064] The analysis unit can propose the selection of an energy-efficient means of transportation or the promotion of renewable energy use. The analysis unit, for example, proposes the selection of an energy-efficient means of transportation. For example, the analysis unit proposes transportation means such as electric vehicles, hybrid vehicles, and fuel cell vehicles. The analysis unit can also propose the promotion of renewable energy use. For example, the analysis unit proposes the use of renewable energy such as solar power generation, wind power generation, and biomass power generation. This makes it possible to reduce carbon emissions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute the selection of an energy-efficient means of transportation or the promotion of renewable energy use.

[0065] The execution unit can introduce energy-efficient transportation means and utilize renewable energy. The execution unit, for example, introduces energy-efficient transportation means. For example, the execution unit introduces transportation means such as electric vehicles, hybrid vehicles, and fuel cell vehicles. The execution unit can also utilize renewable energy. For example, the execution unit utilizes renewable energy such as solar power generation, wind power generation, and biomass power generation. This can reduce actual carbon emissions. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can cause the generation AI to introduce energy-efficient transportation means and utilize renewable energy.

[0066] The execution unit can monitor carbon emissions. The execution unit, for example, uses sensors to monitor carbon emissions. For example, the execution unit monitors carbon emissions using a temperature sensor, a humidity sensor, a position sensor, etc. The execution unit can also adjust the frequency of data collection. For example, the execution unit increases the frequency of data collection to monitor carbon emissions in real time. This enables management of carbon emissions. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input data obtained from a sensor into a generation AI and cause the generation AI to monitor carbon emissions.

[0067] The collection unit can analyze the user's emotions and adjust the timing of data collection based on the analyzed user emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The collection unit also adjusts the timing of data collection based on the user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. This reduces the burden on the user and enables efficient data collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0068] The collection unit can adjust sensor placement to collect energy consumption and carbon emissions at each step in real time. The collection unit, for example, places sensors in optimal positions to monitor energy consumption at each step in real time. For example, the collection unit places temperature sensors, humidity sensors, position sensors, etc. in optimal positions. The collection unit can also adjust sensor placement to accurately measure carbon emissions at each step. For example, the collection unit optimizes sensor placement to improve the accuracy of data collection. This improves the accuracy of data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to execute an algorithm to optimize sensor placement.

[0069] The collection unit can integrate multiple data sources during data collection to improve accuracy. For example, the collection unit can integrate satellite data and meteorological data to improve the accuracy of carbon emissions. For example, the collection unit can use satellite data to obtain geographical information and meteorological data to understand climate conditions. The collection unit can also integrate data from different data sources to improve the accuracy of energy consumption. For example, the collection unit can integrate sensor data, external databases, user-input data, etc. to obtain a more accurate carbon footprint. This allows for a more accurate carbon footprint to be obtained. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data obtained from different data sources into the generation AI and have the generation AI integrate and analyze the data.

[0070] The collection unit can add a function to detect abnormal values ​​by comparing data with past data when collecting data and issue an alert. The collection unit, for example, detects abnormal energy consumption by comparing data with past data and issues an alert. For example, the collection unit sets a statistical threshold based on past data and detects energy consumption that exceeds the threshold. The collection unit can also detect abnormal carbon emissions by comparing data with past data and issue an alert. For example, the collection unit detects abnormal values ​​using a machine learning model and issues an alert in real time. This allows for early detection of abnormal carbon emissions or energy consumption. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to execute an algorithm that detects abnormal values ​​by comparing data with past data.

[0071] The collection unit can analyze the user's emotions and determine the priority of data to be collected based on the analyzed user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The collection unit also determines the priority of data to be collected based on the user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting only important data. Also, if the user is relaxed, the collection unit can prioritize collecting detailed data. This enables data collection according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0072] The collection unit can customize the collection method based on geographical conditions when collecting data. For example, in urban areas, the collection unit customizes the data collection method taking into account traffic volume and population density. For example, the collection unit adjusts the data collection method using traffic volume sensors and population density data. The collection unit can also customize the data collection method in rural areas taking into account agricultural activities and the natural environment. For example, the collection unit adjusts the data collection method using agricultural sensors and environmental data. This enables optimal data collection according to geographical conditions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause the generation AI to execute an algorithm that customizes the data collection method based on geographical conditions.

[0073] When collecting data, the collection unit can evaluate the work efficiency at each step in the supply chain and identify areas of low efficiency. The collection unit, for example, monitors the work efficiency at each step in real time and identifies areas of low efficiency. For example, the collection unit monitors work time and energy consumption and identifies areas of low efficiency. The collection unit can also propose areas for improvement based on the results of the work efficiency evaluation. For example, the collection unit identifies areas of low work efficiency and takes measures to improve efficiency. This identifies areas for improvement in work efficiency and enables efficiency to be improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can have a generation AI evaluate the work efficiency and propose areas for improvement.

[0074] When collecting data, the collection unit can adjust the collection frequency taking into account seasonal fluctuations in energy consumption and carbon emissions. The collection unit, for example, adjusts the data collection frequency for energy consumption in accordance with seasonal fluctuations. For example, the collection unit increases the data collection frequency during periods when energy consumption increases, such as summer and winter. The collection unit can also adjust the data collection frequency for carbon emissions in accordance with seasonal fluctuations. For example, the collection unit grasps seasonal carbon emission patterns and sets an optimal data collection frequency. This enables optimal data collection in accordance with seasonal fluctuations. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can cause the generation AI to execute an algorithm that adjusts the data collection frequency based on seasonal fluctuations.

[0075] The analysis unit can analyze the user's emotions and adjust the display method of the analysis results based on the analyzed user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The analysis unit also adjusts the display method of the analysis results based on the user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. This enables the display of analysis results according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0076] During the analysis, the analysis unit can specifically analyze the correlation between energy consumption and carbon emissions at each step. The analysis unit, for example, analyzes the correlation between energy consumption and carbon emissions at each step in detail. For example, the analysis unit analyzes the correlation using statistical methods or regression analysis. The analysis unit can also propose improvements to energy efficiency based on the results of the correlation analysis. For example, the analysis unit proposes a method for reducing carbon emissions based on the results of the correlation analysis. This makes it possible to propose improvements to energy efficiency and methods for reducing carbon emissions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI perform the correlation analysis and propose improvements.

[0077] The analysis unit can simulate multiple scenarios during analysis. For example, the analysis unit simulates scenarios with different renewable energy utilization rates and proposes an optimal energy usage method. For example, the analysis unit simulates best-case, worst-case, realistic scenarios, etc., and evaluates the carbon emission reduction effect. The analysis unit can also propose an optimal energy usage method based on the simulation results. This makes it possible to evaluate the optimal energy usage method and the carbon emission reduction effect. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute the scenario simulation and propose the optimal energy usage method.

[0078] During analysis, the analysis unit can predict future carbon emissions using past data and propose appropriate measures. The analysis unit, for example, predicts future carbon emissions based on past data. For example, the analysis unit predicts future carbon emissions using trend analysis of past data or a simulation model. The analysis unit can also propose optimal carbon emission reduction measures based on the prediction results. For example, the analysis unit proposes improvements to energy efficiency based on the future carbon emission prediction results. This makes it possible to predict future carbon emissions and take optimal measures. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute an algorithm that predicts future carbon emissions based on past data and proposes optimal measures.

[0079] The analysis unit can analyze the user's emotions and prioritize the analysis results based on the analyzed user's emotions. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The analysis unit also prioritizes the analysis results based on the user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying only important analysis results. Also, if the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. This allows the prioritization of analysis results according to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0080] During analysis, the analysis unit can evaluate the efficiency of the entire supply chain and identify problem areas. The analysis unit, for example, evaluates the efficiency of the entire supply chain and identifies bottlenecks. For example, the analysis unit evaluates the work time, energy consumption, cost, etc. of each step and identifies areas with low efficiency. The analysis unit can also propose measures to improve efficiency based on the results of identifying bottlenecks. For example, the analysis unit evaluates the efficiency of the entire supply chain and proposes areas for improvement. In this way, the efficiency of the entire supply chain can be evaluated and proposed areas for improvement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute an algorithm that evaluates the efficiency of the entire supply chain and identifies bottlenecks.

[0081] During the analysis, the analysis unit can compare the utilization effects of multiple energy sources. For example, the analysis unit compares the utilization effects of solar energy and wind energy and proposes the optimal energy source. For example, the analysis unit compares the utilization effects of solar power generation and wind power generation and evaluates the carbon emission reduction effect. The analysis unit can also propose the optimal energy source based on the utilization effect comparison results. This makes it possible to propose the optimal energy source. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute an algorithm that compares the utilization effects of energy sources.

[0082] During analysis, the analysis unit can evaluate the effectiveness of carbon offsets and propose an appropriate offset method. The analysis unit, for example, evaluates the effectiveness of carbon offsets and proposes the optimal offset method. For example, the analysis unit evaluates carbon offset methods such as reforestation activities and purchasing carbon credits and proposes the optimal method. The analysis unit can also propose measures to reduce carbon emissions based on the results of the offset effectiveness evaluation. This makes it possible to evaluate the effectiveness of carbon offsets and select the optimal offset method. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause the generation AI to execute an algorithm that evaluates the effectiveness of carbon offsets and proposes the optimal offset method.

[0083] The execution unit can estimate the user's emotions and adjust the execution procedure based on the estimated user's emotions. The execution unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the execution unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The execution unit can also estimate the user's emotions using voice analysis technology. For example, the execution unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The execution unit further adjusts the execution procedure based on the user's emotions. For example, if the user is nervous, the execution unit provides simple, highly visible execution procedures. If the user is relaxed, the execution unit can also provide execution procedures including detailed information. This makes it possible to provide execution procedures tailored to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or without AI. For example, the execution unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0084] During execution, the execution unit can monitor the effects of introducing energy-efficient transportation means in real time. The execution unit, for example, monitors the effects of introducing energy-efficient transportation means in real time and evaluates the effects. For example, the execution unit monitors the effects of introducing electric vehicles, hybrid vehicles, fuel cell vehicles, etc. The execution unit can also propose improvements based on the results of monitoring the effects of the introduction. For example, the execution unit monitors the effects of introducing energy-efficient transportation means in real time and selects the optimal transportation means. This makes it possible to evaluate the effects of energy-efficient transportation means and select the optimal transportation means. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can cause the generation AI to execute an algorithm that monitors the effects of introducing energy-efficient transportation means.

[0085] During execution, the execution unit can periodically evaluate the usage status of renewable energy and identify problems. The execution unit, for example, periodically evaluates the usage status of renewable energy and identifies areas for improvement. For example, the execution unit evaluates the amount of energy generated, usage efficiency, cost, etc., and identifies problems. The execution unit can also propose measures to promote the use of renewable energy based on the results of the evaluation of the usage status. For example, the execution unit periodically evaluates the usage status of renewable energy and proposes the optimal usage method. This makes it possible to propose measures to promote the use of renewable energy. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can cause the generation AI to execute an algorithm that evaluates the usage status of renewable energy and identifies areas for improvement.

[0086] During execution, the execution unit can add a function to integrate and manage carbon emissions across the entire supply chain and report them in real time. The execution unit, for example, adds a function to centrally manage carbon emissions across the entire supply chain and report them in real time. For example, the execution unit sets the data collection method, data integration method, management system specifications, etc., and centrally manages carbon emissions. The execution unit can also suggest improvements based on the results of the centralized management of carbon emissions. For example, the execution unit reports carbon emissions across the entire supply chain in real time and takes optimal measures. This makes it possible to centrally manage carbon emissions across the entire supply chain and take optimal measures. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can have a generation AI perform integrated management of carbon emissions and real-time reporting.

[0087] The execution unit can analyze the user's emotions and determine the priority of execution based on the analyzed user's emotions. The execution unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the execution unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The execution unit can also estimate the user's emotions using voice analysis technology. For example, the execution unit records the user's voice with a microphone and estimates the emotions using a voice analysis algorithm. The execution unit further determines the priority of execution based on the user's emotions. For example, if the user is feeling stressed, the execution unit can prioritize executing only important execution steps. Also, if the user is relaxed, the execution unit can prioritize executing detailed execution steps. This makes it possible to determine the execution priority according to the user's situation. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, an AI, or without an AI. For example, the execution unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0088] The execution unit can select the optimal execution method based on geographical conditions during execution. For example, in urban areas, the execution unit selects the optimal execution method taking into account traffic volume and population density. For example, the execution unit selects the optimal execution method using traffic volume sensors and population density data. In rural areas, the execution unit can also select the optimal execution method taking into account agricultural activities and the natural environment. For example, the execution unit selects the optimal execution method using agricultural sensors and environmental data. This makes it possible to select the optimal execution method according to the geographical conditions. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can cause the generation AI to execute an algorithm that selects the optimal execution method based on geographical conditions.

[0089] During execution, the execution unit can evaluate the work efficiency at each step in the supply chain and improve areas where efficiency is low. For example, the execution unit can monitor the work efficiency at each step in real time and improve areas where efficiency is low. For example, the execution unit can monitor work time and energy consumption and identify areas where efficiency is low. The execution unit can also propose areas for improvement based on the results of the work efficiency evaluation. For example, the execution unit can identify areas where work efficiency is low and take measures to improve efficiency. This identifies areas where work efficiency can be improved and efficiency can be improved. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the execution unit can have a generation AI evaluate work efficiency and propose areas for improvement.

[0090] During execution, the execution unit can adjust the execution frequency taking into account seasonal fluctuations in energy consumption and carbon emissions. The execution unit, for example, adjusts the execution frequency for energy consumption in accordance with seasonal fluctuations. For example, the execution unit increases the execution frequency during periods when energy consumption increases, such as summer and winter. The execution unit can also adjust the execution frequency for carbon emissions in accordance with seasonal fluctuations. For example, the execution unit grasps seasonal carbon emission patterns and sets an optimal execution frequency. This makes it possible to set an optimal execution frequency in accordance with seasonal fluctuations. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can cause the generation AI to execute an algorithm that adjusts the execution frequency based on seasonal fluctuations. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and execution unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit measures energy consumption and carbon emissions at each step using the camera 42 and microphone 38B of the smart device 14, and the data is analyzed in detail by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes energy consumption and carbon emissions at each step using AI and proposes the selection of energy-efficient transportation means and the promotion of renewable energy use. The execution unit, realized, for example, by the control unit 46A of the smart device 14, executes the optimization method proposed by the analysis unit and monitors actual carbon emissions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and execution unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit measures energy consumption and carbon emissions at each step using the camera 42 and microphone 238 of the smart glasses 214, and the data is analyzed in detail by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes energy consumption and carbon emissions at each step using AI and proposes the selection of energy-efficient transportation means and the promotion of renewable energy use. The execution unit, realized, for example, by the control unit 46A of the smart glasses 214, executes the optimization method proposed by the analysis unit and monitors actual carbon emissions. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and execution unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit measures energy consumption and carbon emissions at each step using the camera 42 and microphone 238 of the headset terminal 314, and the data is analyzed in detail by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes energy consumption and carbon emissions at each step using AI, and proposes the selection of energy-efficient transportation means and the promotion of renewable energy use. The execution unit is realized, for example, by the control unit 46A of the headset terminal 314, and executes the optimization method proposed by the analysis unit and monitors actual carbon emissions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and execution unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit measures the energy consumption and carbon emissions at each step using the camera 42 and microphone 238 of the robot 414, and the data is analyzed in detail by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the energy consumption and carbon emissions at each step using AI, and proposes the selection of energy-efficient transportation means and the promotion of renewable energy use. The execution unit is realized, for example, by the control unit 46A of the robot 414, and executes the optimization method proposed by the analysis unit and monitors the actual carbon emissions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can estimate the user's emotions and, based on the estimated user's emotions, suggest the selection of energy-efficient transportation means and the promotion of renewable energy use. For example, if the user shows a strong interest in the environment, the analysis unit can suggest more proactive use of renewable energy. Also, if the user is sensitive to costs, the analysis unit can prioritize suggesting cost-efficient transportation means. Furthermore, if the user is feeling stressed, the analysis unit can make simple and easy-to-implement suggestions. This makes it possible to make optimal suggestions according to the user's emotions.

[0093] When collecting data, the collection unit not only collects data on energy consumption and carbon emissions at each step, but can also simultaneously collect work efficiency and costs at each step. For example, the collection unit can monitor work time and energy consumption at each step and identify areas of low efficiency. The collection unit can also collect cost data at each step and evaluate cost efficiency. This allows for optimization not only in terms of energy consumption and carbon emissions, but also in terms of work efficiency and costs.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is excited, the analysis unit can also provide an interactive display method. In this way, the optimal display method according to the user's emotions can be provided.

[0095] The implementation unit can not only introduce energy-efficient transportation means but also optimize the maintenance schedules of transportation means. For example, the implementation unit can propose a maintenance schedule to extend the battery life of electric vehicles. It can also propose a maintenance schedule to maximize the engine efficiency of hybrid vehicles. It can also propose a maintenance schedule to optimize the fuel efficiency of fuel cell vehicles. This maximizes the efficiency of transportation means and reduces carbon emissions.

[0096] The implementation unit not only monitors carbon emissions, but also provides real-time feedback based on the monitoring results. For example, the implementation unit can issue an alert if carbon emissions exceed the target value. It can also report progress if carbon emissions fall below the target value. Furthermore, the implementation unit can share success stories if carbon emissions are successfully reduced. This allows for more effective management of carbon emissions.

[0097] The collection unit can analyze the user's emotions and adjust the timing of data collection based on the analyzed user's emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is excited, the timing of data collection can be adjusted to collect data that attracts the user's interest. This reduces the burden on the user and enables efficient data collection.

[0098] The collection unit can not only adjust the sensor placement to collect energy consumption and carbon emissions at each step in real time, but also optimize the type and accuracy of the sensors. For example, the collection unit can optimize the types of temperature sensors, humidity sensors, position sensors, etc. to improve the accuracy of data collection. The collection unit can also adjust the accuracy of the sensors to accurately collect the required data. Furthermore, the collection unit can combine and optimize the placement and type of sensors to maximize the efficiency of data collection. This improves the accuracy and efficiency of data collection.

[0099] When collecting data, the collection unit can not only integrate multiple data sources to improve accuracy, but also evaluate the reliability of the data. For example, the collection unit can compare data from different data sources and select highly reliable data. The collection unit can also ensure data quality using an algorithm to evaluate the reliability of the data. Furthermore, the collection unit can improve data collection methods based on the results of the data reliability evaluation. This improves the accuracy and reliability of the data.

[0100] When collecting data, the collection unit not only adds the function of detecting abnormal values ​​by comparing them with past data and issuing alerts, but also identifies the cause of the abnormal values. For example, the collection unit can identify the cause of abnormal energy consumption and propose countermeasures. It can also identify the cause of abnormal carbon emissions and propose improvement measures. Furthermore, the collection unit can improve data collection methods based on the results of identifying the cause of abnormal values. This enables early detection of abnormal values ​​and countermeasures.

[0101] The collection unit can analyze the user's emotions and determine the priority of data to be collected based on the analyzed user's emotions. For example, if the user is feeling stressed, only important data can be collected with priority. Also, if the user is relaxed, detailed data can be collected with priority. Furthermore, if the user is excited, data that attracts interest can be collected with priority. This makes it possible to collect data according to the user's situation.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit performs a detailed analysis of the carbon footprint in the supply chain from food producers to consumers. For example, the collection unit uses IoT sensors to measure energy consumption and carbon emissions at each step. The collection unit can also collect data by utilizing existing databases. For example, the collection unit uses IoT sensors and existing databases to obtain a detailed understanding of the carbon footprint at each step, such as production at farms, processing at processing plants, transportation, storage in warehouses, and sales in stores. Step 2: The analysis unit analyzes the data collected by the collection unit and proposes the optimal method for minimizing carbon emissions at each step. For example, the analysis unit uses AI to analyze energy consumption and carbon emissions at each step. For example, the analysis unit proposes the selection of energy-efficient transportation methods and the promotion of renewable energy use. Step 3: The execution unit executes the optimization method proposed by the analysis unit and monitors actual carbon emissions. For example, the execution unit introduces energy-efficient transportation means and uses renewable energy. The execution unit also monitors actual carbon emissions. For example, the execution unit reduces actual carbon emissions by introducing energy-efficient transportation means and using renewable energy.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

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

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that conducts detailed analysis of the carbon footprint of the food supply chain from producers to consumers; an analysis unit that analyzes the data collected by the collection unit and proposes an optimal method for minimizing carbon emissions at each step; an execution unit that executes the optimization method proposed by the analysis unit and monitors carbon emissions. A system characterized by:

2. The collecting unit Leverage IoT sensors or existing databases to collect data The system of claim 1 .

3. The analysis unit Propose the selection of energy-efficient transportation methods or the promotion of renewable energy use The system of claim 1 .

4. The execution unit: Adopt energy-efficient transport and use renewable energy The system of claim 1 .

5. The execution unit: Monitor your carbon emissions The system of claim 1 .

6. The collecting unit Analyze user sentiment and adjust the timing of data collection based on the analyzed user sentiment. The system of claim 1 .

7. The collecting unit Adjusting sensor placement to collect real-time energy consumption and carbon emissions data for each step The system of claim 1 .

8. The collecting unit When collecting data, integrate multiple data sources to improve accuracy The system of claim 1 .

Images