System

Abstract

An object of a system according to an embodiment is to construct an efficient supply chain by performing a demand prediction of overseas markets and a preference analysis of consumers.SOLUTION: A system according to an embodiment includes a demand prediction unit, a preference analysis unit, and a supply chain construction unit. The demand prediction unit uses the generated AI to collect and analyze overseas market data to perform demand prediction. The preference analyzing unit analyzes the preferences of overseas consumers using the generated AI. The supply chain construction unit constructs an efficient supply chain based on the results of the demand prediction unit and the preference analysis unit.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 did not adequately forecast demand in overseas markets or analyze consumer preferences, making it difficult to build an efficient supply chain.

[0005] The system according to the embodiment aims to forecast demand in overseas markets and analyze consumer preferences, thereby building an efficient supply chain. [Means for solving the problem]

[0006] The system according to the embodiment comprises a demand forecasting unit, a preference analysis unit, and a supply chain construction unit. The demand forecasting unit uses generation AI to collect and analyze data from overseas markets and make demand forecasts. The preference analysis unit uses generation AI to analyze the preferences of overseas consumers. The supply chain construction unit constructs an efficient supply chain based on the results of the demand forecasting unit and preference analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can forecast demand in overseas markets and analyze consumer preferences, thereby building an efficient supply chain. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 export service system according to an embodiment of the present invention is a system that uses generative AI to forecast demand in overseas markets and analyze consumer preferences, building an efficient supply chain for exporting Japanese sweets overseas. This makes it easier for Japanese sweets manufacturers to enter new markets, and allows overseas consumers to easily enjoy Japanese flavors.

[0029] The export service system according to the embodiment includes a demand forecasting unit, a preference analysis unit, and a supply chain construction unit. The demand forecasting unit uses a generation AI to collect and analyze data from overseas markets and perform demand forecasting. For example, the generation AI analyzes past sales data, seasonal consumption trends, economic indicators, and the like to predict how much of each type of sweet will be sold at each time. The generation AI can also predict demand fluctuations associated with specific events, taking into account regional cultural events and holidays. For example, the generation AI can predict peak demand associated with specific events, such as the Chinese New Year or American Thanksgiving. The preference analysis unit uses the generation AI to analyze the preferences of overseas consumers. For example, the generation AI analyzes social media posts, ratings on review sites, survey results, and the like to identify preferred flavors and package designs. The generation AI can also predict changes in preferences by analyzing consumer purchasing histories and review sentiment. For example, the generation AI can identify products with many positive reviews and predict changes in their preferences. The supply chain construction unit constructs an efficient supply chain based on the results of the demand forecasting unit and the preference analysis unit. For example, the generation AI can perform optimal inventory management and propose delivery routes, and collaborate with local logistics companies. The generation AI can also predict logistics delay risks and propose risk avoidance measures. For example, the generation AI can analyze past logistics data, identify delay patterns during specific seasons or weather conditions, and propose risk avoidance measures. This enables the export service system according to the embodiment to forecast demand, analyze preferences, and build a supply chain for efficiently exporting Japanese sweets overseas. For example, appropriate inventory management based on demand forecasts can reduce unnecessary inventory and cut costs. Furthermore, product development tailored to consumer preferences can increase sales. Furthermore, an efficient supply chain can enable faster delivery and increase consumer satisfaction.

[0030] The demand forecasting unit analyzes past sales data, seasonal consumption trends, economic indicators, etc., and can predict how much of which types of sweets will be sold at what time. The demand forecasting unit, for example, uses a generation AI to collect and analyze past sales data. For example, the generation AI predicts how much of which types of sweets will be sold at what time based on sales data and sales quantity data. The demand forecasting unit also uses a generation AI to analyze seasonal sales fluctuations and consumer purchasing patterns in order to analyze seasonal consumption trends. For example, the generation AI predicts a tendency for demand for ice cream and frozen desserts to increase in the summer. The demand forecasting unit also uses a generation AI to analyze economic data such as GDP, unemployment rate, and consumer confidence index in order to analyze economic indicators. For example, the generation AI predicts the impact of fluctuations in economic indicators on consumption. This improves the accuracy of demand forecasts and enables appropriate inventory management.

[0031] The preference analysis unit can analyze social media posts, review site ratings, survey results, etc., to determine what flavors and package designs are preferred. The preference analysis unit, for example, uses a generation AI to collect and analyze social media posts. For example, the generation AI analyzes posts on Twitter, Facebook, Instagram, etc., to determine what flavors and package designs are preferred. The preference analysis unit also uses a generation AI to analyze ratings on review sites, such as Amazon reviews and Yelp reviews. For example, the generation AI performs sentiment analysis on reviews to identify products with many positive reviews. The preference analysis unit also uses a generation AI to analyze the results of online surveys and telephone surveys to analyze survey results. For example, the generation AI identifies consumer preferences based on survey results. This makes it possible to develop products tailored to consumer preferences.

[0032] The supply chain construction unit can perform optimal inventory management, propose delivery routes, and collaborate with local logistics companies. The supply chain construction unit, for example, uses generation AI to optimize inventory management. For example, the generation AI performs inventory management using inventory turnover and inventory optimization algorithms. The supply chain construction unit also uses generation AI to use route optimization algorithms to propose delivery routes. For example, the generation AI proposes optimal delivery routes aimed at shortening delivery time and reducing costs. The supply chain construction unit also uses generation AI to analyze selection criteria and collaboration methods for logistics companies in order to collaborate with local logistics companies. For example, the generation AI selects the optimal logistics company based on indicators such as cost, reliability, and delivery speed. This makes it possible to build an efficient supply chain.

[0033] The demand forecasting unit can predict demand fluctuations that coincide with specific events, taking into account cultural events and holidays in each region. The demand forecasting unit, for example, uses generation AI to register cultural events and holidays in each region in a database and predict demand fluctuations based on that. For example, the generation AI predicts peaks in demand that coincide with specific events, such as Chinese New Year and American Thanksgiving. The demand forecasting unit also uses generation AI to make demand forecasts that coincide with events and holidays. For example, the generation AI predicts demand fluctuations that consider the impact of events and holidays. This makes it possible to make demand forecasts that coincide with events and holidays.

[0034] The demand forecasting unit can analyze past weather data and predict the impact of weather fluctuations on consumption. The demand forecasting unit, for example, uses a generation AI to collect and analyze past weather data. For example, the generation AI predicts the impact of weather fluctuations on consumption based on weather data such as temperature, precipitation, and wind speed. The demand forecasting unit also uses a generation AI to analyze the impact of weather fluctuations on consumption. For example, the generation AI predicts a tendency for demand for ice cream and frozen desserts to increase during high temperatures in the summer. This makes it possible to forecast demand based on weather fluctuations.

[0035] The demand forecasting unit can refer to consumption data of other foods and beverages and predict demand based on correlations. The demand forecasting unit, for example, uses generation AI to collect consumption data of other foods and beverages and analyze the correlations between that data and consumption data of sweets. For example, the generation AI identifies a tendency for demand for sweets to increase during periods when consumption of a specific beverage increases. The demand forecasting unit also uses generation AI to refer to consumption data of other foods and beverages and build a system that predicts demand based on correlations. For example, the generation AI predicts a tendency for demand for sweets to increase during periods when consumption of a specific beverage increases. This makes it possible to forecast demand based on consumption data of other foods and beverages.

[0036] The demand forecasting unit can compare demand forecasts from different countries and regions to identify global demand trends. The demand forecasting unit, for example, uses generation AI to collect demand forecast data from different countries and regions and compare and analyze them. For example, the generation AI compares demand trends from Asia and Europe to identify global demand trends. The demand forecasting unit also uses generation AI to build a system that compares demand forecasts from different countries and regions to identify global demand trends. For example, the generation AI compares demand trends from Asia and Europe to identify global demand trends. This makes it possible to grasp global demand trends.

[0037] The preference analysis unit can analyze consumer social media posts and identify trends and fads. The preference analysis unit, for example, uses a generation AI to collect and analyze consumer social media posts. For example, the generation AI analyzes posts on Twitter, Instagram, etc. and identifies trends and fads. The preference analysis unit also uses a generation AI to analyze the frequency of appearance of hashtags and keywords on social media and identify fads. For example, the generation AI identifies trends and fads based on the frequency of appearance of specific hashtags and keywords. This makes it possible to identify consumer trends and fads.

[0038] The preference analysis unit can compare preferences for different age groups and genders to conduct targeted marketing. The preference analysis unit, for example, uses generation AI to collect and analyze consumer data for different age groups and genders. For example, generation AI can identify differences in preferences between young people and middle-aged and elderly people to conduct targeted marketing. The preference analysis unit also uses generation AI to analyze purchasing patterns by age and differences in preferences by gender. For example, generation AI can identify differences in preferences between young people and middle-aged and elderly people to conduct targeted marketing. This makes targeted marketing possible.

[0039] The preference analysis unit can compare the preferences of consumers in different countries and regions to identify global preference trends. The preference analysis unit, for example, uses generative AI to collect and analyze consumer data from different countries and regions. For example, generative AI can identify differences in the preferences of consumers in Asia and Europe to identify global preference trends. The preference analysis unit also uses generative AI to build a system that compares the preferences of consumers in different countries and regions to identify global preference trends. For example, generative AI can identify differences in the preferences of consumers in Asia and Europe to identify global preference trends. This makes it possible to understand global preference trends.

[0040] The supply chain construction unit can predict the risk of logistics delays and propose risk avoidance measures. The supply chain construction unit, for example, uses generation AI to analyze past logistics data and predict the risk of delays. For example, the generation AI identifies delay patterns during specific seasons or weather conditions and proposes risk avoidance measures. The supply chain construction unit also uses generation AI to build a system that predicts the risk of logistics delays and proposes risk avoidance measures. For example, the generation AI analyzes past logistics data, identifies delay patterns during specific seasons or weather conditions and proposes risk avoidance measures. This makes it possible to predict the risk of logistics delays and propose risk avoidance measures.

[0041] The supply chain construction unit can propose optimal inventory allocation by taking into account product expiration dates and risk of deterioration. The supply chain construction unit, for example, uses generation AI to collect product expiration date data and reflect this in inventory management. For example, the generation AI proposes inventory allocation so that products with approaching expiration dates are shipped first. The supply chain construction unit also uses generation AI to propose optimal inventory allocation by taking into account the risk of product deterioration. For example, generation AI predicts the risk of product deterioration based on temperature and humidity control and proposes optimal inventory allocation. This makes it possible to allocate inventory by taking into account product expiration dates and risk of deterioration.

[0042] The supply chain construction unit can compare the performance of different logistics companies and select the most suitable company. For example, the supply chain construction unit uses generation AI to collect and compare performance data from different logistics companies. For example, the generation AI selects the most suitable company based on performance indicators such as delivery time, delay rate, and cost. The supply chain construction unit also uses generation AI to build a system that compares the performance of different logistics companies and selects the most suitable company. For example, the generation AI selects the most suitable company based on performance indicators such as delivery time, delay rate, and cost. This makes it possible to select the most suitable logistics company.

[0043] The supply chain construction unit can compare supply chains in different countries and regions and perform global optimization. The supply chain construction unit, for example, uses generation AI to collect and compare supply chain data from different countries and regions. For example, the generation AI compares logistics costs and delivery times in each region and performs global optimization. The supply chain construction unit also uses generation AI to build a system that compares supply chains in different countries and regions and performs global optimization. For example, the generation AI compares logistics costs and delivery times in each region and performs global optimization. This makes it possible to optimize the global supply chain.

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

[0045] The export service system can further include a health analysis unit that analyzes the consumer's health data. For example, the health analysis unit collects and analyzes the consumer's health data. The generative AI can suggest appropriate products based on the consumer's health condition and dietary restrictions. For example, it can suggest low-sugar sweets to consumers with diabetes, and allergen-free products to consumers with allergies. The health analysis unit can also analyze the consumer's fitness data and suggest health-conscious products. For example, it can suggest high-protein sweets to consumers who exercise a lot. This makes it possible to suggest products tailored to the consumer's health condition.

[0046] The export service system can further include an environmental impact assessment unit. For example, the environmental impact assessment unit analyzes the environmental impact from product manufacturing to delivery. The generative AI can propose an environmentally friendly supply chain based on carbon dioxide emissions and energy consumption. For example, it can select a factory that uses renewable energy and propose an eco-friendly delivery method. The environmental impact assessment unit can also analyze the environmental impact of product packaging and propose sustainable packaging methods. For example, it can propose packaging using recyclable materials. This makes it possible to build an environmentally friendly supply chain.

[0047] The export service system can further include a cultural adaptation section. For example, the cultural adaptation section analyzes the cultural background and customs of each region and makes product suggestions based on that. The generative AI can suggest appropriate products by taking into account the cultural events and eating habits of each region. For example, it can suggest flavors and package designs that are popular in a particular region. The cultural adaptation section can also suggest appropriate products by taking into account religious restrictions in each region. For example, it can suggest halal-certified products to regions with a large Muslim population. This makes it possible to suggest products that are adapted to the culture of each region.

[0048] The export service system can further include a promotion department to increase consumer purchasing motivation. For example, the promotion department analyzes consumer purchasing history and preference data to propose effective promotion strategies. The generative AI can propose promotions customized for specific consumer groups. For example, it can offer a new version of a product at a discounted price to consumers who have previously purchased that product. The promotion department can also propose seasonal promotion strategies. For example, it can propose special packages for the Christmas season. This enables effective promotions that increase consumer purchasing motivation.

[0049] The export service system can further include a feedback collection unit that collects consumer feedback. For example, the feedback collection unit collects and analyzes consumer reviews and survey results. The generative AI can identify areas for product improvement based on consumer feedback. For example, it can analyze negative consumer reviews and suggest product improvements. The feedback collection unit can also collect consumer feedback in real time and respond quickly. For example, it can collect consumer feedback after the release of a new product and immediately reflect improvements. This enables rapid product improvement based on consumer feedback.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The demand forecasting department uses generation AI to collect and analyze data from overseas markets and make demand forecasts. For example, generation AI analyzes past sales data, seasonal consumption trends, economic indicators, etc. to predict how much of each type of product will be sold at what time. Generation AI can also predict fluctuations in demand that coincide with specific events, taking into account cultural events and holidays in each region. For example, it can predict peaks in demand that coincide with specific events, such as the Chinese New Year or American Thanksgiving. Step 2: The preference analysis unit uses the generation AI to analyze the preferences of overseas consumers. For example, the generation AI analyzes social media posts, ratings on review sites, survey results, etc. to identify preferred flavors and package designs. The generation AI can also analyze consumer purchasing history and reviews to predict changes in preferences. For example, it can identify products with many positive reviews and predict changes in those preferences. Step 3: The supply chain construction unit builds an efficient supply chain based on the results of the demand forecasting unit and preference analysis unit. For example, the generation AI proposes optimal inventory management and delivery routes, and coordinates with local logistics companies. The generation AI can also predict the risk of logistics delays and propose risk avoidance measures. For example, it analyzes past logistics data, identifies delay patterns under specific seasons or weather conditions, and proposes risk avoidance measures.

[0052] (Example 2) The export service system according to an embodiment of the present invention is a system that uses generative AI to forecast demand in overseas markets and analyze consumer preferences, building an efficient supply chain for exporting Japanese sweets overseas. This makes it easier for Japanese sweets manufacturers to enter new markets, and allows overseas consumers to easily enjoy Japanese flavors.

[0053] The export service system according to the embodiment includes a demand forecasting unit, a preference analysis unit, and a supply chain construction unit. The demand forecasting unit uses a generation AI to collect and analyze data from overseas markets and perform demand forecasting. For example, the generation AI analyzes past sales data, seasonal consumption trends, economic indicators, and the like to predict how much of each type of sweet will be sold at each time. The generation AI can also predict demand fluctuations associated with specific events, taking into account regional cultural events and holidays. For example, the generation AI can predict peak demand associated with specific events, such as the Chinese New Year or American Thanksgiving. The preference analysis unit uses the generation AI to analyze the preferences of overseas consumers. For example, the generation AI analyzes social media posts, ratings on review sites, survey results, and the like to identify preferred flavors and package designs. The generation AI can also predict changes in preferences by analyzing consumer purchasing histories and review sentiment. For example, the generation AI can identify products with many positive reviews and predict changes in their preferences. The supply chain construction unit constructs an efficient supply chain based on the results of the demand forecasting unit and the preference analysis unit. For example, the generation AI can perform optimal inventory management and propose delivery routes, and collaborate with local logistics companies. The generation AI can also predict logistics delay risks and propose risk avoidance measures. For example, the generation AI can analyze past logistics data, identify delay patterns during specific seasons or weather conditions, and propose risk avoidance measures. This enables the export service system according to the embodiment to forecast demand, analyze preferences, and build a supply chain for efficiently exporting Japanese sweets overseas. For example, appropriate inventory management based on demand forecasts can reduce unnecessary inventory and cut costs. Furthermore, product development tailored to consumer preferences can increase sales. Furthermore, an efficient supply chain can enable faster delivery and increase consumer satisfaction.

[0054] The demand forecasting unit analyzes past sales data, seasonal consumption trends, economic indicators, etc., and can predict how much of which types of sweets will be sold at what time. The demand forecasting unit, for example, uses a generation AI to collect and analyze past sales data. For example, the generation AI predicts how much of which types of sweets will be sold at what time based on sales data and sales quantity data. The demand forecasting unit also uses a generation AI to analyze seasonal sales fluctuations and consumer purchasing patterns in order to analyze seasonal consumption trends. For example, the generation AI predicts a tendency for demand for ice cream and frozen desserts to increase in the summer. The demand forecasting unit also uses a generation AI to analyze economic data such as GDP, unemployment rate, and consumer confidence index in order to analyze economic indicators. For example, the generation AI predicts the impact of fluctuations in economic indicators on consumption. This improves the accuracy of demand forecasts and enables appropriate inventory management.

[0055] The preference analysis unit can analyze social media posts, review site ratings, survey results, etc., to determine what flavors and package designs are preferred. The preference analysis unit, for example, uses a generation AI to collect and analyze social media posts. For example, the generation AI analyzes posts on Twitter, Facebook, Instagram, etc., to determine what flavors and package designs are preferred. The preference analysis unit also uses a generation AI to analyze ratings on review sites, such as Amazon reviews and Yelp reviews. For example, the generation AI performs sentiment analysis on reviews to identify products with many positive reviews. The preference analysis unit also uses a generation AI to analyze the results of online surveys and telephone surveys to analyze survey results. For example, the generation AI identifies consumer preferences based on survey results. This makes it possible to develop products tailored to consumer preferences.

[0056] The supply chain construction unit can perform optimal inventory management, propose delivery routes, and collaborate with local logistics companies. The supply chain construction unit, for example, uses generation AI to optimize inventory management. For example, the generation AI performs inventory management using inventory turnover and inventory optimization algorithms. The supply chain construction unit also uses generation AI to use route optimization algorithms to propose delivery routes. For example, the generation AI proposes optimal delivery routes aimed at shortening delivery time and reducing costs. The supply chain construction unit also uses generation AI to analyze selection criteria and collaboration methods for logistics companies in order to collaborate with local logistics companies. For example, the generation AI selects the optimal logistics company based on indicators such as cost, reliability, and delivery speed. This makes it possible to build an efficient supply chain.

[0057] The demand forecasting unit can predict demand fluctuations that coincide with specific events, taking into account cultural events and holidays in each region. The demand forecasting unit, for example, uses generation AI to register cultural events and holidays in each region in a database and predict demand fluctuations based on that. For example, the generation AI predicts peaks in demand that coincide with specific events, such as Chinese New Year and American Thanksgiving. The demand forecasting unit also uses generation AI to make demand forecasts that coincide with events and holidays. For example, the generation AI predicts demand fluctuations that consider the impact of events and holidays. This makes it possible to make demand forecasts that coincide with events and holidays.

[0058] The demand forecasting unit can analyze past weather data and predict the impact of weather fluctuations on consumption. The demand forecasting unit, for example, uses a generation AI to collect and analyze past weather data. For example, the generation AI predicts the impact of weather fluctuations on consumption based on weather data such as temperature, precipitation, and wind speed. The demand forecasting unit also uses a generation AI to analyze the impact of weather fluctuations on consumption. For example, the generation AI predicts a tendency for demand for ice cream and frozen desserts to increase during high temperatures in the summer. This makes it possible to forecast demand based on weather fluctuations.

[0059] The demand forecasting unit can use the emotion estimation function to analyze consumer emotion data and predict the impact of emotional fluctuations on demand. For example, the demand forecasting unit uses the emotion estimation function to analyze emotion data from consumer social media posts and reviews and predict the impact of emotional fluctuations on demand. For example, the generation AI identifies a tendency for demand to increase during events that heighten positive emotions. The demand forecasting unit also uses the emotion estimation function to build a system that analyzes consumer emotion data and predicts the impact of emotional fluctuations on demand. For example, the generation AI predicts a tendency for demand to increase during events that heighten consumer emotions. This makes it possible to forecast demand based on consumer emotions.

[0060] The demand forecasting unit can refer to consumption data of other foods and beverages and predict demand based on correlations. The demand forecasting unit, for example, uses generation AI to collect consumption data of other foods and beverages and analyze the correlations between that data and consumption data of sweets. For example, the generation AI identifies a tendency for demand for sweets to increase during periods when consumption of a specific beverage increases. The demand forecasting unit also uses generation AI to refer to consumption data of other foods and beverages and build a system that predicts demand based on correlations. For example, the generation AI predicts a tendency for demand for sweets to increase during periods when consumption of a specific beverage increases. This makes it possible to forecast demand based on consumption data of other foods and beverages.

[0061] The demand forecasting unit can compare demand forecasts from different countries and regions to identify global demand trends. The demand forecasting unit, for example, uses generation AI to collect demand forecast data from different countries and regions and compare and analyze them. For example, the generation AI compares demand trends from Asia and Europe to identify global demand trends. The demand forecasting unit also uses generation AI to build a system that compares demand forecasts from different countries and regions to identify global demand trends. For example, the generation AI compares demand trends from Asia and Europe to identify global demand trends. This makes it possible to grasp global demand trends.

[0062] The demand forecasting unit can use the emotion estimation function to collect consumer emotion data in real time and reflect it in the demand forecast. The demand forecasting unit, for example, uses the emotion estimation function to collect consumer emotion data in real time and builds a system that performs demand forecasting based on that data. For example, the generation AI predicts a tendency for demand to increase during events that heighten consumer emotions. The demand forecasting unit also uses the emotion estimation function to collect consumer emotion data in real time and reflect it in the demand forecast. For example, the generation AI predicts a tendency for demand to increase during events that heighten consumer emotions. This makes it possible to perform demand forecasting based on emotion data in real time.

[0063] The preference analysis unit performs sentiment analysis of consumer purchase history and reviews, and can predict changes in preferences. The preference analysis unit uses, for example, a generation AI to collect and analyze consumer purchase history data. For example, the generation AI predicts changes in consumer preferences based on POS data and online purchase history. The preference analysis unit also uses a generation AI to perform sentiment analysis of reviews. For example, the generation AI uses text mining and sentiment scoring to analyze the sentiment of reviews and predict changes in preferences. This makes it possible to predict changes in consumer preferences.

[0064] The preference analysis unit can analyze consumer social media posts and identify trends and fads. The preference analysis unit, for example, uses a generation AI to collect and analyze consumer social media posts. For example, the generation AI analyzes posts on Twitter, Instagram, etc. and identifies trends and fads. The preference analysis unit also uses a generation AI to analyze the frequency of appearance of hashtags and keywords on social media and identify fads. For example, the generation AI identifies trends and fads based on the frequency of appearance of specific hashtags and keywords. This makes it possible to identify consumer trends and fads.

[0065] The preference analysis unit can use the emotion estimation function to analyze consumer emotion data and identify the impact of emotional fluctuations on preferences. The preference analysis unit, for example, uses the emotion estimation function to analyze consumer emotion data and identify the impact of emotional fluctuations on preferences. For example, the generation AI identifies a tendency for consumers to prefer certain products when their emotions are high. The preference analysis unit also uses the emotion estimation function to build a system that analyzes consumer emotion data and identifies the impact of emotional fluctuations on preferences. For example, the generation AI identifies a tendency for consumers to prefer certain products when their positive emotions are high. This makes it possible to analyze preferences based on consumer emotions.

[0066] The preference analysis unit can compare preferences for different age groups and genders to conduct targeted marketing. The preference analysis unit, for example, uses generation AI to collect and analyze consumer data for different age groups and genders. For example, generation AI can identify differences in preferences between young people and middle-aged and elderly people to conduct targeted marketing. The preference analysis unit also uses generation AI to analyze purchasing patterns by age and differences in preferences by gender. For example, generation AI can identify differences in preferences between young people and middle-aged and elderly people to conduct targeted marketing. This makes targeted marketing possible.

[0067] The preference analysis unit can compare the preferences of consumers in different countries and regions to identify global preference trends. The preference analysis unit, for example, uses generative AI to collect and analyze consumer data from different countries and regions. For example, generative AI can identify differences in the preferences of consumers in Asia and Europe to identify global preference trends. The preference analysis unit also uses generative AI to build a system that compares the preferences of consumers in different countries and regions to identify global preference trends. For example, generative AI can identify differences in the preferences of consumers in Asia and Europe to identify global preference trends. This makes it possible to understand global preference trends.

[0068] The preference analysis unit can use the emotion estimation function to collect consumer emotion data in real time and reflect it in the preference analysis. The preference analysis unit, for example, uses the emotion estimation function to collect consumer emotion data in real time and build a system that performs preference analysis based on that data. For example, the generation AI identifies a tendency for consumers to prefer specific products during events when their emotions are heightened. The preference analysis unit also uses the emotion estimation function to collect consumer emotion data in real time and reflect it in the preference analysis. For example, the generation AI identifies a tendency for consumers to prefer specific products during events when their emotions are heightened. This makes it possible to perform preference analysis based on emotion data in real time.

[0069] The supply chain construction unit can predict the risk of logistics delays and propose risk avoidance measures. The supply chain construction unit, for example, uses generation AI to analyze past logistics data and predict the risk of delays. For example, the generation AI identifies delay patterns during specific seasons or weather conditions and proposes risk avoidance measures. The supply chain construction unit also uses generation AI to build a system that predicts the risk of logistics delays and proposes risk avoidance measures. For example, the generation AI analyzes past logistics data, identifies delay patterns during specific seasons or weather conditions and proposes risk avoidance measures. This makes it possible to predict the risk of logistics delays and propose risk avoidance measures.

[0070] The supply chain construction unit can propose optimal inventory allocation by taking into account product expiration dates and risk of deterioration. The supply chain construction unit, for example, uses generation AI to collect product expiration date data and reflect this in inventory management. For example, the generation AI proposes inventory allocation so that products with approaching expiration dates are shipped first. The supply chain construction unit also uses generation AI to propose optimal inventory allocation by taking into account the risk of product deterioration. For example, generation AI predicts the risk of product deterioration based on temperature and humidity control and proposes optimal inventory allocation. This makes it possible to allocate inventory by taking into account product expiration dates and risk of deterioration.

[0071] The supply chain construction unit can use the emotion estimation function to analyze consumer emotion data and predict the impact of emotional fluctuations on the supply chain. The supply chain construction unit, for example, uses the emotion estimation function to analyze consumer emotion data and predict the impact of emotional fluctuations on the supply chain. For example, the generation AI identifies a tendency for demand to increase during periods when consumer emotions are high. The supply chain construction unit also uses the emotion estimation function to analyze consumer emotion data and build a system that predicts the impact of emotional fluctuations on the supply chain. For example, the generation AI identifies a tendency for demand to increase during periods when consumer emotions are high. This makes it possible to optimize the supply chain based on consumer emotions.

[0072] The supply chain construction unit can compare the performance of different logistics companies and select the most suitable company. For example, the supply chain construction unit uses generation AI to collect and compare performance data from different logistics companies. For example, the generation AI selects the most suitable company based on performance indicators such as delivery time, delay rate, and cost. The supply chain construction unit also uses generation AI to build a system that compares the performance of different logistics companies and selects the most suitable company. For example, the generation AI selects the most suitable company based on performance indicators such as delivery time, delay rate, and cost. This makes it possible to select the most suitable logistics company.

[0073] The supply chain construction unit can compare supply chains in different countries and regions and perform global optimization. The supply chain construction unit, for example, uses generation AI to collect and compare supply chain data from different countries and regions. For example, the generation AI compares logistics costs and delivery times in each region and performs global optimization. The supply chain construction unit also uses generation AI to build a system that compares supply chains in different countries and regions and performs global optimization. For example, the generation AI compares logistics costs and delivery times in each region and performs global optimization. This makes it possible to optimize the global supply chain.

[0074] The supply chain construction unit can use the emotion estimation function to collect consumer emotion data in real time and reflect it in the optimization of the supply chain. The supply chain construction unit, for example, uses the emotion estimation function to collect consumer emotion data in real time and builds a system that optimizes the supply chain based on that data. For example, the generation AI predicts a tendency for demand to increase during events when consumer emotions are high and reflects this in the supply chain. The supply chain construction unit also uses the emotion estimation function to collect consumer emotion data in real time and reflects this in the optimization of the supply chain. For example, the generation AI predicts a tendency for demand to increase during events when consumer emotions are high and reflects this in the supply chain. This makes it possible to optimize the supply chain based on emotion data in real time.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The export service system can further include a health analysis unit that analyzes the consumer's health data. For example, the health analysis unit collects and analyzes the consumer's health data. The generative AI can suggest appropriate products based on the consumer's health condition and dietary restrictions. For example, it can suggest low-sugar sweets to consumers with diabetes, and allergen-free products to consumers with allergies. The health analysis unit can also analyze the consumer's fitness data and suggest health-conscious products. For example, it can suggest high-protein sweets to consumers who exercise a lot. This makes it possible to suggest products tailored to the consumer's health condition.

[0077] The export service system can further include an environmental impact assessment unit. For example, the environmental impact assessment unit analyzes the environmental impact from product manufacturing to delivery. The generative AI can propose an environmentally friendly supply chain based on carbon dioxide emissions and energy consumption. For example, it can select a factory that uses renewable energy and propose an eco-friendly delivery method. The environmental impact assessment unit can also analyze the environmental impact of product packaging and propose sustainable packaging methods. For example, it can propose packaging using recyclable materials. This makes it possible to build an environmentally friendly supply chain.

[0078] The export service system can further include a cultural adaptation section. For example, the cultural adaptation section analyzes the cultural background and customs of each region and makes product suggestions based on that. The generative AI can suggest appropriate products by taking into account the cultural events and eating habits of each region. For example, it can suggest flavors and package designs that are popular in a particular region. The cultural adaptation section can also suggest appropriate products by taking into account religious restrictions in each region. For example, it can suggest halal-certified products to regions with a large Muslim population. This makes it possible to suggest products that are adapted to the culture of each region.

[0079] The export service system can further include a promotion department to increase consumer purchasing motivation. For example, the promotion department analyzes consumer purchasing history and preference data to propose effective promotion strategies. The generative AI can propose promotions customized for specific consumer groups. For example, it can offer a new version of a product at a discounted price to consumers who have previously purchased that product. The promotion department can also propose seasonal promotion strategies. For example, it can propose special packages for the Christmas season. This enables effective promotions that increase consumer purchasing motivation.

[0080] The export service system can further include a feedback collection unit that collects consumer feedback. For example, the feedback collection unit collects and analyzes consumer reviews and survey results. The generative AI can identify areas for product improvement based on consumer feedback. For example, it can analyze negative consumer reviews and suggest product improvements. The feedback collection unit can also collect consumer feedback in real time and respond quickly. For example, it can collect consumer feedback after the release of a new product and immediately reflect improvements. This enables rapid product improvement based on consumer feedback.

[0081] The demand forecasting unit uses the emotion estimation function to analyze consumer emotional data and predict the impact of emotional fluctuations on demand. For example, the generative AI analyzes emotional data from consumers' social media posts and reviews and predicts the impact of emotional fluctuations on demand. For example, it identifies a tendency for demand to increase during events that heighten positive emotions. The demand forecasting unit also uses the emotion estimation function to analyze consumer emotional data and build a system that predicts the impact of emotional fluctuations on demand. For example, it predicts a tendency for demand to increase during events that heighten consumer emotions. This makes it possible to forecast demand based on consumer emotions.

[0082] The preference analysis unit uses the emotion estimation function to analyze consumer emotional data and identify the impact that emotional fluctuations have on preferences. For example, the generative AI identifies a tendency for consumers to prefer certain products when their emotions are high. The preference analysis unit also uses the emotion estimation function to build a system that analyzes consumer emotional data and identifies the impact that emotional fluctuations have on preferences. For example, it identifies a tendency for consumers to prefer certain products when their positive emotions are high. This makes it possible to analyze preferences based on consumer emotions.

[0083] The supply chain construction unit uses the emotion estimation function to analyze consumer emotional data and predict the impact of emotional fluctuations on the supply chain. For example, the generative AI identifies a tendency for demand to increase during periods when consumer emotions are high. The supply chain construction unit also uses the emotion estimation function to analyze consumer emotional data and build a system that predicts the impact of emotional fluctuations on the supply chain. For example, it identifies a tendency for demand to increase during periods when consumer emotions are high. This makes it possible to optimize the supply chain based on consumer emotions.

[0084] The demand forecasting unit can use the emotion estimation function to collect consumer emotion data in real time and reflect it in the demand forecast. For example, the generative AI predicts a tendency for demand to increase during events that heighten consumer emotions. The demand forecasting unit can also use the emotion estimation function to collect consumer emotion data in real time and reflect it in the demand forecast. For example, it predicts a tendency for demand to increase during events that heighten consumer emotions. This makes it possible to forecast demand based on emotion data in real time.

[0085] The preference analysis unit uses the emotion estimation function to collect consumer emotional data in real time and reflect it in the preference analysis. For example, the generative AI identifies a tendency for consumers to prefer certain products during events when their emotions are heightened. The preference analysis unit also uses the emotion estimation function to collect consumer emotional data in real time and reflect it in the preference analysis. For example, it identifies a tendency for consumers to prefer certain products during events when their emotions are heightened. This makes it possible to perform preference analysis based on emotional data in real time.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The demand forecasting department uses generation AI to collect and analyze data from overseas markets and make demand forecasts. For example, generation AI analyzes past sales data, seasonal consumption trends, economic indicators, etc. to predict how much of each type of product will be sold at what time. Generation AI can also predict fluctuations in demand that coincide with specific events, taking into account cultural events and holidays in each region. For example, it can predict peaks in demand that coincide with specific events, such as the Chinese New Year or American Thanksgiving. Step 2: The preference analysis unit uses the generation AI to analyze the preferences of overseas consumers. For example, the generation AI analyzes social media posts, ratings on review sites, survey results, etc. to identify preferred flavors and package designs. The generation AI can also analyze consumer purchasing history and reviews to predict changes in preferences. For example, it can identify products with many positive reviews and predict changes in those preferences. Step 3: The supply chain construction unit builds an efficient supply chain based on the results of the demand forecasting unit and preference analysis unit. For example, the generation AI proposes optimal inventory management and delivery routes, and coordinates with local logistics companies. The generation AI can also predict the risk of logistics delays and propose risk avoidance measures. For example, it analyzes past logistics data, identifies delay patterns under specific seasons or weather conditions, and proposes risk avoidance measures.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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. The demand forecasting department uses AI to collect and analyze data from overseas markets and make demand forecasts. A preference analysis department that uses generative AI to analyze the preferences of overseas consumers, a supply chain construction unit that constructs an efficient supply chain based on the results of the demand forecasting unit and the preference analysis unit. A system characterized by:

2. The demand forecasting unit Analyze past sales data, seasonal consumption trends, economic indicators, etc. to predict how much of each type of sweet will be sold at what time.

2. The system of claim 1.

3. The preference analysis unit Analyze social media posts, review site ratings, survey results, etc. to identify preferred flavors and packaging designs 2. The system of claim 1.

4. The supply chain construction unit Propose optimal inventory management and delivery routes, and collaborate with local logistics companies 2. The system of claim 1.

5. The demand forecasting unit Compare demand forecasts from different countries and regions to identify global demand trends 2. The system of claim 1.

6. The preference analysis unit Analyze consumer sentiment data using emotion estimation to identify how emotional fluctuations affect preferences 2. The system of claim 1.

7. The supply chain construction unit Predicting logistics delay risks and proposing risk avoidance measures 2. The system of claim 1.

8. The supply chain construction unit Use sentiment estimation to collect consumer sentiment data in real time and use it to optimize the supply chain.

2. The system of claim 1.

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