System

Abstract

An object of a system according to an embodiment is to predict a consumption trend on the basis of weather data and optimize purchase.SOLUTION: A system includes a data acquisition part, a prediction model generation part, a weather prediction part, a consumption trend prediction part, and a purchase optimization part. The data acquisition unit acquires past data of the Meteorological Agency. The prediction model generation unit generates a prediction model based on the data acquired by the data acquisition unit. The weather prediction unit predicts weather based on the satellite data. The consumption trend prediction unit predicts a consumption trend based on the weather data predicted by the weather prediction unit. The purchase optimization part optimizes purchase based on the consumption trend predicted by the consumption trend prediction part.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 technology does not adequately predict consumption trends and optimize purchasing based on weather data, and there is room for improvement.

[0005] The system according to the embodiment aims to predict consumption trends based on weather data and optimize purchasing. [Means for solving the problem]

[0006] The system according to the embodiment includes a data acquisition unit, a prediction model generation unit, a climate forecasting unit, a consumption trend forecasting unit, and a purchasing optimization unit. The data acquisition unit acquires past data from the Japan Meteorological Agency. The prediction model generation unit generates a prediction model based on the data acquired by the data acquisition unit. The climate forecasting unit predicts the climate based on satellite data. The consumption trend forecasting unit predicts consumption trends based on the climate data predicted by the climate forecasting unit. The purchasing optimization unit optimizes purchasing based on the consumption trends predicted by the consumption trend forecasting unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict consumption trends based on weather data and optimize purchasing. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The climate forecasting system according to an embodiment of the present invention incorporates past data from the Japan Meteorological Agency, uses a generation AI to establish a forecasting model, predicts the weather for several months based on satellite data, and predicts consumption trends based on the forecast data, eliminating overstocking and shortages. As a result, the climate forecasting system can predict future weather, predict consumption trends based on the forecast data, and eliminate overstocking and shortages.

[0029] The climate prediction system according to the embodiment includes a data acquisition unit, a prediction model generation unit, a climate prediction unit, a consumption trend prediction unit, and a procurement optimization unit. The data acquisition unit acquires past data from the Japan Meteorological Agency. For example, it collects data such as temperature, precipitation, and wind speed. The data acquisition unit can also acquire data directly from the Japan Meteorological Agency's database. For example, it acquires data using an API. The prediction model generation unit generates a prediction model based on the data acquired by the data acquisition unit. For example, a generation AI analyzes past weather data and generates a model for predicting future weather. The prediction model generation unit can also generate a prediction model using a machine learning algorithm. For example, it uses regression analysis or a neural network. The climate prediction unit predicts weather based on satellite data. For example, it analyzes information such as temperature, precipitation, and wind speed obtained from satellite data and predicts future weather. The climate prediction unit can also make weather predictions based on the prediction model established by the generation AI. For example, it inputs satellite data as prompts into the generation AI to make weather predictions. The consumption trend prediction unit predicts consumption trends based on the weather data predicted by the climate prediction unit. For example, it predicts that demand for cold drinks and ice cream will increase during the hot summer months. The consumption trend prediction unit can also use an algorithm that the generation AI uses to predict consumption trends. For example, it makes demand predictions based on past consumption data. The purchase optimization unit optimizes purchases based on the consumption trends predicted by the consumption trend prediction unit. For example, it reduces purchases during periods of low demand and increases purchases during periods of high demand, thereby reducing inventory waste. The purchase optimization unit can also use an algorithm that the generation AI uses to optimize purchases. For example, it adjusts purchase amounts in cooperation with an inventory management system. This enables the climate prediction system according to the embodiment to optimize purchases based on weather data and consumption trends. For example, it can predict consumption trends based on predicted weather data and eliminate excesses and shortages in purchases.

[0030] The data acquisition unit collects regional microclimate data in addition to past data from the Japan Meteorological Agency, allowing the forecast model generation unit to generate more detailed forecast models. For example, the data acquisition unit collects regional microclimate data in addition to past data from the Japan Meteorological Agency and inputs this data into the generation AI. For example, it generates a forecast model that takes into account differences in temperature and precipitation between urban and rural areas. The data acquisition unit can also acquire data from local weather observation stations to collect regional microclimate data. For example, it acquires data from the database of local weather observation stations. This enables detailed climate forecasts for each region. For example, by making separate climate forecasts for urban and rural areas, more accurate forecasts are possible.

[0031] The data acquisition unit can introduce an algorithm that automatically detects and complements outliers and missing values ​​in the data when importing weather data. For example, the data acquisition unit introduces an algorithm that automatically detects outliers and missing values ​​when importing weather data. For example, it detects and complements data with abnormally high temperatures or zero precipitation. The data acquisition unit can also use statistical methods or machine learning algorithms to complement outliers and missing values. For example, it performs mean value imputation or regression imputation. This can improve the quality of the data and increase the accuracy of the forecast model. For example, using data without outliers or missing values ​​enables more accurate climate forecasts.

[0032] The data acquisition unit can incorporate industry data such as agriculture and fishing in addition to data from the Japan Meteorological Agency, and the prediction model generation unit can perform industry-specific climate impact predictions. The data acquisition unit, for example, incorporates agricultural data in addition to data from the Japan Meteorological Agency, and performs industry-specific climate impact predictions. For example, it predicts the impact of temperature and precipitation fluctuations on agricultural crop yields. The data acquisition unit can also incorporate fishing data and predict the impact on fish catches. For example, it predicts the impact of seawater temperature fluctuations on fish catches. This makes it possible to perform industry-specific climate impact predictions. For example, it is possible to predict the impact of climate change on industries such as agriculture and fishing, and take measures.

[0033] The data acquisition unit imports meteorological data in real time, and the prediction model generation unit can constantly update the prediction model with the latest data. The data acquisition unit, for example, imports meteorological data in real time, building a system that constantly updates the prediction model with the latest data. For example, data from the Japan Meteorological Agency is collected in real time and reflected in the generation AI. The data acquisition unit can also use weather observation station and satellite data to acquire data in real time. For example, data is acquired in real time from sensors at weather observation stations. This allows the prediction model to constantly be updated with the latest data. For example, making climate predictions based on the latest weather data enables more accurate predictions.

[0034] The climate prediction unit can improve the accuracy of the climate prediction by combining data from ground sensors with climate predictions based on satellite data. The climate prediction unit improves the accuracy of the climate prediction, for example, by combining data from ground sensors in addition to satellite data. For example, ground temperature and humidity data is integrated with satellite data. The climate prediction unit can also use temperature and humidity sensors to collect data from ground sensors. For example, data is obtained from sensors installed at ground weather observation stations. This can improve the accuracy of the climate prediction. For example, combining data from ground sensors enables more detailed climate predictions.

[0035] The climate prediction unit can capture subtle changes in climate change by using data of different wavelengths when analyzing satellite data. The climate prediction unit can capture subtle changes in climate change by, for example, using data of different wavelengths when analyzing satellite data. For example, visible light and infrared data can be combined for analysis. The climate prediction unit can also use multiple satellite sensors to collect data of different wavelengths. For example, data can be collected using a combination of visible light sensors and infrared sensors. This makes it possible to capture subtle changes in climate change. For example, using data of different wavelengths makes it possible to perform a detailed analysis of climate change.

[0036] The climate prediction department can share the results of satellite data analysis with weather forecasters and experts to jointly improve the accuracy of the forecast model. For example, the climate prediction department can build a system that shares the results of satellite data analysis with weather forecasters and experts to improve the accuracy of the forecast model. For example, it can generate a forecast model that reflects the opinions of experts. The climate prediction department can also use an online platform to collaborate with weather forecasters and experts. For example, it can use a cloud-based data sharing system. This can improve the accuracy of the forecast model. For example, incorporating the knowledge of experts can enable more accurate climate forecasts.

[0037] The consumer trend prediction unit can combine past sales data and marketing data to make more accurate consumer trend predictions. The consumer trend prediction unit, for example, collects past sales data and combines it with weather forecast data to predict consumer trends. For example, based on past sales trends, it predicts the impact of fluctuations in temperature and precipitation on consumption. The consumer trend prediction unit can also predict consumer trends using marketing data. For example, it makes demand predictions based on advertising effectiveness data and customer attribute data. This can improve the accuracy of consumer trend predictions. For example, by combining past sales data and marketing data, more accurate consumer trend predictions are possible.

[0038] The consumer trend forecasting unit can predict fluctuations in demand by taking into account the influence of seasonal events, holidays, and the like. The consumer trend forecasting unit, for example, predicts consumer trends by taking into account seasonal events and holidays. For example, it makes consumption predictions in line with Christmas and New Year events. The consumer trend forecasting unit can also predict fluctuations in demand based on data from past events and holidays. For example, it makes demand predictions based on sales data from past events. This allows for more accurate predictions of demand fluctuations. For example, by taking into account seasonal events and holidays, it is possible to predict peaks and troughs in demand and optimize purchasing.

[0039] The consumption trend prediction unit can predict consumption trends for different regions or countries and identify consumption patterns specific to each region. The consumption trend prediction unit, for example, predicts consumption trends for different regions or countries and identifies consumption patterns specific to each region. For example, consumption trends in urban and rural areas can be compared. The consumption trend prediction unit can also collect consumption data for each region and analyze consumption patterns specific to each region. For example, seasonal consumption trends in a specific region can be identified. This makes it possible to identify consumption patterns specific to each region. For example, by understanding consumption trends for each region, it is possible to create a purchasing strategy tailored to the region.

[0040] The consumer trend prediction unit can share the results of consumer trend predictions throughout the supply chain, thereby optimizing everything from purchasing to sales. The consumer trend prediction unit, for example, shares the results of consumer trend predictions throughout the supply chain, thereby building a system that optimizes everything from purchasing to sales. For example, inventory management is optimized based on the predicted data. The consumer trend prediction unit can also use a cloud-based platform to share data throughout the supply chain. For example, data is shared in real time at each stage of the supply chain. This enables optimization throughout the supply chain. For example, the results of consumer trend predictions can be used to streamline the entire process from purchasing to sales.

[0041] The climate prediction unit uses satellite data to separately predict the climate in urban and rural areas, making it possible to predict climate changes specific to each region. The climate prediction unit, for example, uses satellite data to build a system that separately predicts the climate in urban and rural areas. For example, it predicts the heat island effect in urban areas and the risk of frost damage in rural areas. The climate prediction unit can also collect meteorological data for each region in order to separately predict the climate in urban and rural areas. For example, it collects data on temperature and precipitation in urban and rural areas. This makes it possible to predict climate changes specific to each region. For example, by separately predicting climate changes in urban and rural areas, more accurate climate predictions become possible.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The climate prediction system can further include an energy consumption prediction unit. The energy consumption prediction unit predicts energy consumption trends based on climate prediction data. For example, it predicts that air conditioning use will increase during the hot summer months, leading to increased energy consumption. The energy consumption prediction unit can also perform demand prediction based on past energy consumption data. For example, it can combine past temperature data and energy consumption data to predict future energy demand. This makes it possible to optimize energy supply. For example, energy supply companies can create supply plans based on the predicted energy demand.

[0044] The climate forecasting system can further include a tourism trend forecasting unit. The tourism trend forecasting unit predicts tourist trends based on climate forecast data. For example, it predicts that the number of tourists visiting beaches and resort areas will increase during the summer when temperatures are high. The tourism trend forecasting unit can also forecast demand based on past tourism data. For example, it can combine past temperature data and tourist count data to predict future tourism demand. This makes planning in the tourism industry easier. For example, tourism facilities can create service provision plans based on the predicted tourism demand.

[0045] The climate forecasting system can further include a health risk prediction unit. The health risk prediction unit predicts health risks based on climate forecast data. For example, it predicts that the risk of heatstroke increases during the summer when temperatures are high. The health risk prediction unit can also make risk predictions based on past health data. For example, it combines past temperature data with health damage data to predict future health risks. This allows medical institutions and local governments to take measures based on the predicted health risks. For example, measures to prevent heatstroke could include installing cooling facilities and conducting awareness-raising activities.

[0046] The weather forecasting system can further include a traffic trend forecasting unit. The traffic trend forecasting unit predicts traffic trends based on weather forecast data. For example, it predicts that the risk of traffic congestion and accidents increases during the winter when there is heavy snowfall. The traffic trend forecasting unit can also perform demand forecasting based on past traffic data. For example, it can combine past snowfall data with traffic accident data to predict future traffic risks. This allows traffic management organizations to take measures based on the predicted traffic risks. For example, it can plan snow removal operations and implement traffic regulations.

[0047] The climate prediction system can further include an agricultural production prediction unit. The agricultural production prediction unit predicts agricultural crop production based on climate prediction data. For example, it predicts that crop yields will decrease in years with low precipitation. The agricultural production prediction unit can also make production predictions based on past agricultural data. For example, it combines past precipitation data and crop yield data to predict future agricultural production. This allows farmers to make cultivation plans based on the predicted production. For example, in years with low precipitation, they can consider introducing irrigation equipment.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The data acquisition unit acquires past data from the Japan Meteorological Agency. For example, it collects data such as temperature, precipitation, and wind speed. The data acquisition unit can also acquire data directly from the Japan Meteorological Agency's database. For example, it can acquire data using an API. Step 2: The predictive model generation unit generates a predictive model based on the data acquired by the data acquisition unit. For example, the generation AI analyzes past weather data and generates a model for predicting future climate. The predictive model generation unit can also generate a predictive model using a machine learning algorithm. For example, it uses regression analysis or neural networks. Step 3: The climate prediction unit predicts the climate based on satellite data. For example, it analyzes information such as temperature, precipitation, and wind speed obtained from satellite data to predict future climate. The climate prediction unit can also make climate predictions based on the prediction model established by the generation AI. For example, it inputs satellite data as prompts into the generation AI to make climate predictions. Step 4: The consumption trend forecasting unit predicts consumption trends based on the weather data predicted by the climate forecasting unit. For example, it predicts that demand for cold drinks and ice cream will increase during the hot summer months. The consumption trend forecasting unit can also use algorithms that the generation AI uses to predict consumption trends. For example, it can forecast demand based on past consumption data. Step 5: The Purchasing Optimization Unit optimizes purchasing based on the consumption trends predicted by the Consumption Trend Forecasting Unit. For example, reducing inventory waste by reducing purchasing during periods of low demand and increasing purchasing during periods of high demand. The Purchasing Optimization Unit can also use algorithms that the Generative AI uses to optimize purchasing. For example, it can work with an inventory management system to adjust purchasing quantities.

[0050] (Example 2) The climate forecasting system according to an embodiment of the present invention incorporates past data from the Japan Meteorological Agency, uses a generation AI to establish a forecasting model, predicts the weather for several months based on satellite data, and predicts consumption trends based on the forecast data, eliminating overstocking and shortages. As a result, the climate forecasting system can predict future weather, predict consumption trends based on the forecast data, and eliminate overstocking and shortages.

[0051] The climate prediction system according to the embodiment includes a data acquisition unit, a prediction model generation unit, a climate prediction unit, a consumption trend prediction unit, and a procurement optimization unit. The data acquisition unit acquires past data from the Japan Meteorological Agency. For example, it collects data such as temperature, precipitation, and wind speed. The data acquisition unit can also acquire data directly from the Japan Meteorological Agency's database. For example, it acquires data using an API. The prediction model generation unit generates a prediction model based on the data acquired by the data acquisition unit. For example, a generation AI analyzes past weather data and generates a model for predicting future weather. The prediction model generation unit can also generate a prediction model using a machine learning algorithm. For example, it uses regression analysis or a neural network. The climate prediction unit predicts weather based on satellite data. For example, it analyzes information such as temperature, precipitation, and wind speed obtained from satellite data and predicts future weather. The climate prediction unit can also make weather predictions based on the prediction model established by the generation AI. For example, it inputs satellite data as prompts into the generation AI to make weather predictions. The consumption trend prediction unit predicts consumption trends based on the weather data predicted by the climate prediction unit. For example, it predicts that demand for cold drinks and ice cream will increase during the hot summer months. The consumption trend prediction unit can also use an algorithm that the generation AI uses to predict consumption trends. For example, it makes demand predictions based on past consumption data. The purchase optimization unit optimizes purchases based on the consumption trends predicted by the consumption trend prediction unit. For example, it reduces purchases during periods of low demand and increases purchases during periods of high demand, thereby reducing inventory waste. The purchase optimization unit can also use an algorithm that the generation AI uses to optimize purchases. For example, it adjusts purchase amounts in cooperation with an inventory management system. This enables the climate prediction system according to the embodiment to optimize purchases based on weather data and consumption trends. For example, it can predict consumption trends based on predicted weather data and eliminate excesses and shortages in purchases.

[0052] The data acquisition unit collects regional microclimate data in addition to past data from the Japan Meteorological Agency, allowing the forecast model generation unit to generate more detailed forecast models. For example, the data acquisition unit collects regional microclimate data in addition to past data from the Japan Meteorological Agency and inputs this data into the generation AI. For example, it generates a forecast model that takes into account differences in temperature and precipitation between urban and rural areas. The data acquisition unit can also acquire data from local weather observation stations to collect regional microclimate data. For example, it acquires data from the database of local weather observation stations. This enables detailed climate forecasts for each region. For example, by making separate climate forecasts for urban and rural areas, more accurate forecasts are possible.

[0053] The data acquisition unit can introduce an algorithm that automatically detects and complements outliers and missing values ​​in the data when importing weather data. For example, the data acquisition unit introduces an algorithm that automatically detects outliers and missing values ​​when importing weather data. For example, it detects and complements data with abnormally high temperatures or zero precipitation. The data acquisition unit can also use statistical methods or machine learning algorithms to complement outliers and missing values. For example, it performs mean value imputation or regression imputation. This can improve the quality of the data and increase the accuracy of the forecast model. For example, using data without outliers or missing values ​​enables more accurate climate forecasts.

[0054] The data acquisition unit can incorporate industry data such as agriculture and fishing in addition to data from the Japan Meteorological Agency, and the prediction model generation unit can perform industry-specific climate impact predictions. The data acquisition unit, for example, incorporates agricultural data in addition to data from the Japan Meteorological Agency, and performs industry-specific climate impact predictions. For example, it predicts the impact of temperature and precipitation fluctuations on agricultural crop yields. The data acquisition unit can also incorporate fishing data and predict the impact on fish catches. For example, it predicts the impact of seawater temperature fluctuations on fish catches. This makes it possible to perform industry-specific climate impact predictions. For example, it is possible to predict the impact of climate change on industries such as agriculture and fishing, and take measures.

[0055] The data acquisition unit imports meteorological data in real time, and the prediction model generation unit can constantly update the prediction model with the latest data. The data acquisition unit, for example, imports meteorological data in real time, building a system that constantly updates the prediction model with the latest data. For example, data from the Japan Meteorological Agency is collected in real time and reflected in the generation AI. The data acquisition unit can also use weather observation station and satellite data to acquire data in real time. For example, data is acquired in real time from sensors at weather observation stations. This allows the prediction model to constantly be updated with the latest data. For example, making climate predictions based on the latest weather data enables more accurate predictions.

[0056] The climate prediction unit can improve the accuracy of the climate prediction by combining data from ground sensors with climate predictions based on satellite data. The climate prediction unit improves the accuracy of the climate prediction, for example, by combining data from ground sensors in addition to satellite data. For example, ground temperature and humidity data is integrated with satellite data. The climate prediction unit can also use temperature and humidity sensors to collect data from ground sensors. For example, data is obtained from sensors installed at ground weather observation stations. This can improve the accuracy of the climate prediction. For example, combining data from ground sensors enables more detailed climate predictions.

[0057] The climate prediction unit can capture subtle changes in climate change by using data of different wavelengths when analyzing satellite data. The climate prediction unit can capture subtle changes in climate change by, for example, using data of different wavelengths when analyzing satellite data. For example, visible light and infrared data can be combined for analysis. The climate prediction unit can also use multiple satellite sensors to collect data of different wavelengths. For example, data can be collected using a combination of visible light sensors and infrared sensors. This makes it possible to capture subtle changes in climate change. For example, using data of different wavelengths makes it possible to perform a detailed analysis of climate change.

[0058] The climate prediction department can share the results of satellite data analysis with weather forecasters and experts to jointly improve the accuracy of the forecast model. For example, the climate prediction department can build a system that shares the results of satellite data analysis with weather forecasters and experts to improve the accuracy of the forecast model. For example, it can generate a forecast model that reflects the opinions of experts. The climate prediction department can also use an online platform to collaborate with weather forecasters and experts. For example, it can use a cloud-based data sharing system. This can improve the accuracy of the forecast model. For example, incorporating the knowledge of experts can enable more accurate climate forecasts.

[0059] The consumer trend prediction unit can combine past sales data and marketing data to make more accurate consumer trend predictions. The consumer trend prediction unit, for example, collects past sales data and combines it with weather forecast data to predict consumer trends. For example, based on past sales trends, it predicts the impact of fluctuations in temperature and precipitation on consumption. The consumer trend prediction unit can also predict consumer trends using marketing data. For example, it makes demand predictions based on advertising effectiveness data and customer attribute data. This can improve the accuracy of consumer trend predictions. For example, by combining past sales data and marketing data, more accurate consumer trend predictions are possible.

[0060] The consumer trend forecasting unit can predict fluctuations in demand by taking into account the influence of seasonal events, holidays, and the like. The consumer trend forecasting unit, for example, predicts consumer trends by taking into account seasonal events and holidays. For example, it makes consumption predictions in line with Christmas and New Year events. The consumer trend forecasting unit can also predict fluctuations in demand based on data from past events and holidays. For example, it makes demand predictions based on sales data from past events. This allows for more accurate predictions of demand fluctuations. For example, by taking into account seasonal events and holidays, it is possible to predict peaks and troughs in demand and optimize purchasing.

[0061] The consumption trend prediction unit can predict consumption trends for different regions or countries and identify consumption patterns specific to each region. The consumption trend prediction unit, for example, predicts consumption trends for different regions or countries and identifies consumption patterns specific to each region. For example, consumption trends in urban and rural areas can be compared. The consumption trend prediction unit can also collect consumption data for each region and analyze consumption patterns specific to each region. For example, seasonal consumption trends in a specific region can be identified. This makes it possible to identify consumption patterns specific to each region. For example, by understanding consumption trends for each region, it is possible to create a purchasing strategy tailored to the region.

[0062] The consumer trend prediction unit can share the results of consumer trend predictions throughout the supply chain, thereby optimizing everything from purchasing to sales. The consumer trend prediction unit, for example, shares the results of consumer trend predictions throughout the supply chain, thereby building a system that optimizes everything from purchasing to sales. For example, inventory management is optimized based on the predicted data. The consumer trend prediction unit can also use a cloud-based platform to share data throughout the supply chain. For example, data is shared in real time at each stage of the supply chain. This enables optimization throughout the supply chain. For example, the results of consumer trend predictions can be used to streamline the entire process from purchasing to sales.

[0063] The consumer trend prediction unit can use the emotion estimation function to analyze consumer emotions and predict consumer trends based on those emotions. The consumer trend prediction unit, for example, uses the emotion estimation function to analyze consumer emotions and predict consumer trends. For example, demand for specific products or services is predicted based on consumer emotion data. The consumer trend prediction unit can also use the emotion estimation function to perform text analysis or voice analysis to analyze consumer emotions. For example, consumer reviews and feedback are analyzed to calculate an emotion score. This makes it possible to predict consumer trends based on consumer emotions. For example, taking consumer emotions into consideration enables more accurate demand forecasting.

[0064] The consumer trend prediction unit can use the emotion estimation function to collect consumer reactions to the consumer trend prediction and improve the accuracy of the prediction model. The consumer trend prediction unit, for example, uses the emotion estimation function to collect consumer reactions to the consumer trend prediction and improve the accuracy of the prediction model. For example, the prediction model is adjusted based on consumer emotion data. The consumer trend prediction unit can also perform text analysis and voice analysis to collect consumer reactions. For example, consumer reviews and feedback can be analyzed to calculate an emotion score. This makes it possible to improve the accuracy of the prediction model based on consumer reactions. For example, by reflecting consumer emotion data, more accurate consumer trend predictions are possible.

[0065] The weather forecasting unit can use the emotion estimation function to analyze users' emotional reactions to weather data and improve the reliability of the forecasting model. The weather forecasting unit can, for example, use the emotion estimation function to analyze users' emotional reactions to weather data and improve the reliability of the forecasting model. For example, it can collect users' emotional data on past weather forecasts and reflect it in the generation AI. The weather forecasting unit can also perform text analysis and voice analysis to collect users' emotional reactions. For example, it can analyze user reviews and feedback and calculate an emotion score. This can improve the reliability of the forecasting model based on users' emotional reactions. For example, reflecting users' emotional data can enable more reliable weather forecasts.

[0066] The weather forecasting unit can use the emotion estimation function to collect local residents' emotions regarding weather forecasts and improve the accuracy of forecasts for each region. The weather forecasting unit, for example, uses the emotion estimation function to collect local residents' emotions regarding weather forecasts and improve the accuracy of forecasts. For example, the weather forecasting unit adjusts a forecasting model based on emotion data for each region. The weather forecasting unit can also perform text analysis and voice analysis to collect the emotions of local residents. For example, it can analyze reviews and feedback from local residents and calculate an emotion score. This makes it possible to improve the accuracy of forecasts for each region based on the emotions of local residents. For example, by reflecting emotion data for each region, more accurate weather forecasts are possible.

[0067] The weather forecasting unit can use the emotion estimation function to analyze user emotions regarding weather forecasts based on satellite data and improve the reliability of the forecast results. The weather forecasting unit, for example, analyzes user emotions regarding weather forecasts based on satellite data and improves the reliability of the forecast results. For example, it collects user emotion data regarding past weather forecasts and reflects this in the generation AI. The weather forecasting unit can also perform text analysis and voice analysis to collect user emotions. For example, it analyzes user reviews and feedback and calculates an emotion score. This makes it possible to improve the reliability of the forecast results based on user emotions. For example, reflecting user emotion data enables more reliable weather forecasts.

[0068] The climate prediction unit uses satellite data to separately predict the climate in urban and rural areas, making it possible to predict climate changes specific to each region. The climate prediction unit, for example, uses satellite data to build a system that separately predicts the climate in urban and rural areas. For example, it predicts the heat island effect in urban areas and the risk of frost damage in rural areas. The climate prediction unit can also collect meteorological data for each region in order to separately predict the climate in urban and rural areas. For example, it collects data on temperature and precipitation in urban and rural areas. This makes it possible to predict climate changes specific to each region. For example, by separately predicting climate changes in urban and rural areas, more accurate climate predictions become possible.

[0069] The consumer trend prediction unit can use the emotion estimation function to collect reactions from different industries to the consumer trend prediction, thereby improving prediction accuracy. The consumer trend prediction unit, for example, uses the emotion estimation function to collect reactions from different industries to the consumer trend prediction, thereby improving prediction accuracy. For example, the prediction model is adjusted based on consumer emotion data. The consumer trend prediction unit can also perform text analysis or voice analysis to collect reactions from different industries. For example, reviews and feedback from the industry are analyzed and an emotion score is calculated. This makes it possible to improve prediction accuracy based on reactions from different industries. For example, by reflecting emotion data for each industry, more accurate consumer trend predictions are possible.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The climate prediction system can further include an energy consumption prediction unit. The energy consumption prediction unit predicts energy consumption trends based on climate prediction data. For example, it predicts that air conditioning use will increase during the hot summer months, leading to increased energy consumption. The energy consumption prediction unit can also perform demand prediction based on past energy consumption data. For example, it can combine past temperature data and energy consumption data to predict future energy demand. This makes it possible to optimize energy supply. For example, energy supply companies can create supply plans based on the predicted energy demand.

[0072] The climate forecasting system can further include a tourism trend forecasting unit. The tourism trend forecasting unit predicts tourist trends based on climate forecast data. For example, it predicts that the number of tourists visiting beaches and resort areas will increase during the summer when temperatures are high. The tourism trend forecasting unit can also forecast demand based on past tourism data. For example, it can combine past temperature data and tourist count data to predict future tourism demand. This makes planning in the tourism industry easier. For example, tourism facilities can create service provision plans based on the predicted tourism demand.

[0073] The climate forecasting system can further include a health risk prediction unit. The health risk prediction unit predicts health risks based on climate forecast data. For example, it predicts that the risk of heatstroke increases during the summer when temperatures are high. The health risk prediction unit can also make risk predictions based on past health data. For example, it combines past temperature data with health damage data to predict future health risks. This allows medical institutions and local governments to take measures based on the predicted health risks. For example, measures to prevent heatstroke could include installing cooling facilities and conducting awareness-raising activities.

[0074] The weather forecasting system can further include a traffic trend forecasting unit. The traffic trend forecasting unit predicts traffic trends based on weather forecast data. For example, it predicts that the risk of traffic congestion and accidents increases during the winter when there is heavy snowfall. The traffic trend forecasting unit can also perform demand forecasting based on past traffic data. For example, it can combine past snowfall data with traffic accident data to predict future traffic risks. This allows traffic management organizations to take measures based on the predicted traffic risks. For example, it can plan snow removal operations and implement traffic regulations.

[0075] The climate prediction system can further include an agricultural production prediction unit. The agricultural production prediction unit predicts agricultural crop production based on climate prediction data. For example, it predicts that crop yields will decrease in years with low precipitation. The agricultural production prediction unit can also make production predictions based on past agricultural data. For example, it combines past precipitation data and crop yield data to predict future agricultural production. This allows farmers to make cultivation plans based on the predicted production. For example, in years with low precipitation, they can consider introducing irrigation equipment.

[0076] The consumer trend prediction unit can use the emotion estimation function to analyze consumer emotions and develop emotion-based advertising strategies. For example, advertising can be strengthened for products for which consumers have positive emotions. The consumer trend prediction unit can also use the emotion estimation function to analyze social media posts and reviews to analyze consumer emotions. For example, an emotion score can be calculated from the content of consumer posts and reflected in advertising strategies. This makes it possible to develop effective advertising strategies based on consumer emotions. For example, by taking consumer emotions into consideration, the effectiveness of advertising can be maximized.

[0077] The consumer trend prediction unit can use the emotion estimation function to analyze consumer emotions and develop products based on those emotions. For example, it analyzes product characteristics that consumers have positive emotions about and reflects this in the development of new products. The consumer trend prediction unit can also use the emotion estimation function to analyze survey and interview data to analyze consumer emotions. For example, it can calculate an emotion score from the consumer's responses and reflect this in product development. This makes it possible to develop products based on consumer emotions. For example, by taking consumer emotions into consideration, it is possible to develop more attractive products.

[0078] The consumer trend prediction unit can use the emotion estimation function to analyze consumer emotions and develop a pricing strategy based on those emotions. For example, the price can be increased for products for which consumers have positive emotions. The consumer trend prediction unit can also use the emotion estimation function to analyze purchase history and feedback to analyze consumer emotions. For example, an emotion score can be calculated from the consumer's purchase history and reflected in the pricing strategy. This makes it possible to develop an effective pricing strategy based on consumer emotions. For example, by taking consumer emotions into consideration, the effectiveness of pricing can be maximized.

[0079] The consumer trend prediction unit can use the emotion estimation function to analyze consumer emotions and develop a promotion strategy based on those emotions. For example, it can strengthen promotion for products for which consumers have positive emotions. The consumer trend prediction unit can also use the emotion estimation function to analyze campaign response data in order to analyze consumer emotions. For example, it can calculate an emotion score from the content of consumer responses and reflect that in the promotion strategy. This makes it possible to develop an effective promotion strategy based on consumer emotions. For example, by taking consumer emotions into consideration, it is possible to maximize the effectiveness of promotions.

[0080] The consumer trend prediction unit can use the emotion estimation function to analyze consumer emotions and develop a customer service strategy based on those emotions. For example, customer service can be strengthened for products for which consumers have negative emotions. The consumer trend prediction unit can also use the emotion estimation function to analyze customer support data to analyze consumer emotions. For example, an emotion score can be calculated from the content of a consumer inquiry and reflected in a customer service strategy. This makes it possible to develop an effective customer service strategy based on consumer emotions. For example, customer satisfaction can be improved by taking consumer emotions into consideration.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The data acquisition unit acquires past data from the Japan Meteorological Agency. For example, it collects data such as temperature, precipitation, and wind speed. The data acquisition unit can also acquire data directly from the Japan Meteorological Agency's database. For example, it can acquire data using an API. Step 2: The predictive model generation unit generates a predictive model based on the data acquired by the data acquisition unit. For example, the generation AI analyzes past weather data and generates a model for predicting future climate. The predictive model generation unit can also generate a predictive model using a machine learning algorithm. For example, it uses regression analysis or neural networks. Step 3: The climate prediction unit predicts the climate based on satellite data. For example, it analyzes information such as temperature, precipitation, and wind speed obtained from satellite data to predict future climate. The climate prediction unit can also make climate predictions based on the prediction model established by the generation AI. For example, it inputs satellite data as prompts into the generation AI to make climate predictions. Step 4: The consumption trend forecasting unit predicts consumption trends based on the weather data predicted by the climate forecasting unit. For example, it predicts that demand for cold drinks and ice cream will increase during the hot summer months. The consumption trend forecasting unit can also use algorithms that the generation AI uses to predict consumption trends. For example, it can forecast demand based on past consumption data. Step 5: The Purchasing Optimization Unit optimizes purchasing based on the consumption trends predicted by the Consumption Trend Forecasting Unit. For example, reducing inventory waste by reducing purchasing during periods of low demand and increasing purchasing during periods of high demand. The Purchasing Optimization Unit can also use algorithms that the Generative AI uses to optimize purchasing. For example, it can work with an inventory management system to adjust purchasing quantities.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 data acquisition department that acquires past data from the Japan Meteorological Agency; a prediction model generation unit that generates a prediction model based on the data acquired by the data acquisition unit; a climate forecasting department that forecasts the climate based on satellite data; a consumption trend prediction unit that predicts consumption trends based on the climate data predicted by the climate prediction unit; a purchasing optimization unit that optimizes purchasing based on the consumption trend predicted by the consumption trend prediction unit. A system characterized by:

2. The data acquisition unit In addition to past data from the Japan Meteorological Agency, microclimate data for each region is collected, and a more detailed forecast model is generated by the forecast model generation unit.

2. The system of claim 1.

3. The climate prediction unit Combining data from ground sensors with climate predictions based on satellite data will improve the accuracy of predictions.

2. The system of claim 1.

4. The consumer trend prediction unit Combining past sales data and marketing data to make more accurate predictions of consumer trends 2. The system of claim 1.

5. The consumer trend prediction unit Using emotion estimation function, analyze consumer emotions and predict consumption trends based on emotions.

2. The system of claim 1.

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