System

Abstract

An object of the system according to the embodiment is to perform demand prediction and market trend prediction using generated AI and to improve the success probability of new businesses and product development.SOLUTION: A system includes a demand prediction unit, a market trend prediction unit, and a success probability improvement unit. The demand prediction unit performs demand prediction using the generated AI. The market trend prediction unit predicts a market trend using the generated AI. The success probability improvement unit improves the success probability of a new business or product development based on the prediction results obtained by the demand prediction unit and the market trend prediction 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 technologies are inaccurate in forecasting demand and market trends, and there is room for improvement in terms of increasing the success rate of new businesses and product development.

[0005] The system of the embodiment aims to use generative AI to forecast demand and market trends, thereby improving the probability of success in new businesses and product development. [Means for solving the problem]

[0006] The system according to the embodiment includes a demand forecasting unit, a market trend forecasting unit, and a success probability improvement unit. The demand forecasting unit performs demand forecasting using a generation AI. The market trend forecasting unit predicts market trends using the generation AI. The success probability improvement unit improves the success probability of new businesses and product development based on the prediction results obtained by the demand forecasting unit and the market trend forecasting unit. [Effects of the Invention]

[0007] The system according to the embodiment uses generative AI to forecast demand and market trends, thereby improving the success rate of new businesses and product development. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The business forecasting AI system according to an embodiment of the present invention utilizes generative AI to accurately forecast demand and market trends when planning new services and developing market introduction strategies. This allows the business forecasting AI system to improve the success rate of new businesses and product development, and to enhance user satisfaction and customer loyalty. It also enables inventory optimization and appropriate resource allocation.

[0029] The business forecasting AI system according to the embodiment includes a demand forecasting unit, a market trend forecasting unit, and a success probability improvement unit. The demand forecasting unit performs demand forecasting using a generation AI. For example, the generation AI analyzes past sales data, market data, and consumer behavior data to predict how much a particular product will sell and when. The generation AI can also analyze weather data and event information to improve the accuracy of the demand forecast. The market trend forecasting unit predicts market trends using the generation AI. For example, the generation AI analyzes economic indicators, industry trends, and competitor trends to predict future market growth and risks. The generation AI can also analyze the impact of political events and policy changes to improve the accuracy of market trend forecasts. The success probability improvement unit improves the success probability of new businesses and product development based on the prediction results obtained by the demand forecasting unit and the market trend forecasting unit. For example, the generation AI analyzes past successes and failures to identify factors for increasing the success probability. The generation AI can also analyze consumer purchasing history and behavioral data to propose product development optimal for the target market. As a result, the business forecasting AI system according to the embodiment can improve the probability of success in new businesses and product development by combining demand forecasting and market trend forecasting.

[0030] The demand forecasting unit uses the generation AI to analyze weather data and event information in addition to sales data, thereby improving the accuracy of demand forecasts. For example, the demand forecasting unit uses the generation AI to combine and analyze past sales data and weather data to predict demand fluctuations under specific weather conditions. For example, it identifies products whose sales increase on rainy days and predicts their demand. The generation AI also analyzes event information and predicts demand during periods when specific events are held. For example, it identifies products whose demand increases during sporting events or music festivals. The generation AI also combines and analyzes weather data and event information to predict demand fluctuations due to multiple factors. For example, it predicts the impact that an event held on a rainy day will have on demand. In this way, the accuracy of demand forecasts is improved by analyzing weather data and event information.

[0031] The demand forecasting unit uses the generation AI to analyze consumer social media posts and generate demand forecasts that reflect consumer interests in real time. For example, the generation AI analyzes social media posts to understand consumer interest in specific products and services in real time. For example, when a specific hashtag suddenly increases in popularity, the demand for that product is predicted. The generation AI also analyzes the content of social media posts to identify trends in consumer interests. For example, when interest in a new fashion item increases, the demand for that item is predicted. The generation AI also predicts consumer purchasing intent in real time based on social media post data. For example, it predicts demand for a product that is seeing an increase in positive posts. This makes it possible to generate demand forecasts that reflect consumer interests in real time by analyzing social media posts.

[0032] The market trend prediction unit can use the generation AI to analyze the impact of political events and policy changes in addition to economic indicators to predict market trends. For example, the market trend prediction unit uses the generation AI to combine and analyze past economic indicators and political event data to predict the impact of a specific policy change on the market. For example, it predicts the impact of tax reform on consumer spending. The generation AI also analyzes political event data to predict the impact of a specific event on market trends. For example, it predicts the impact of election results on the market. The generation AI also combines and analyzes economic indicators and policy change data to predict market trends due to multiple factors. For example, it predicts the impact of interest rate fluctuations and policy changes on the market. In this way, by analyzing the impact of political events and policy changes in addition to economic indicators, the accuracy of market trend predictions is improved.

[0033] The market trend prediction unit uses the generation AI to analyze the timing of competitors' new product announcements and market entry, and can predict market trends based on that. For example, the market trend prediction unit uses the generation AI to analyze competitors' new product announcement data and reflect that impact in market trends. For example, it predicts the impact that a competitor's new product will have on the market. The generation AI also analyzes competitors' market entry data and predicts the impact that new competitors will have on the market. For example, it predicts the impact that a new entrant will have on market share. The generation AI also combines and analyzes competitors' new product announcements and market entry data to predict market trends due to multiple factors. For example, it predicts the simultaneous impact that a competitor's new product and market entry will have on the market. In this way, by analyzing the timing of competitors' new product announcements and market entry, the accuracy of market trend predictions is improved.

[0034] The success probability improvement unit can use the generative AI to analyze past success and failure cases and identify factors that increase the success probability. For example, the success probability improvement unit uses the generative AI to analyze past success and failure cases and identify factors that lead to success and failure. For example, it extracts commonalities between successful product developments. The generative AI also proposes strategies to increase the success probability based on data on success and failure cases. For example, it creates product development plans that incorporate success factors. The generative AI also analyzes past data in real time and dynamically identifies factors that increase the success probability. For example, it adjusts strategies based on the most recent success cases. In this way, factors that increase the success probability can be identified by analyzing past success and failure cases.

[0035] The success probability improvement unit can use the generation AI to analyze consumer purchasing history and behavioral data and propose product development that is optimal for the target market. For example, the success probability improvement unit uses the generation AI to analyze consumer purchasing history data and propose product development that is optimal for a specific consumer group. For example, it predicts demand for new products based on past purchasing patterns. The generation AI also proposes a product development strategy that is optimal for the target market based on consumer behavioral data. For example, it analyzes website browsing history and click data to identify consumer interests. The generation AI also combines and analyzes purchase history and behavioral data to propose product development that is optimal for the target market in real time. For example, it adjusts product development plans in response to changes in consumer behavior. In this way, by analyzing consumer purchasing history and behavioral data, it is possible to propose product development that is optimal for the target market.

[0036] The success probability improvement unit can use the generation AI to propose the optimal allocation of resources required to launch a new business. For example, the generation AI analyzes resource data required to launch a new business and proposes the optimal allocation. For example, it optimizes the allocation of necessary human resources and funds. The generation AI also analyzes the resources required to launch a new business in real time and dynamically adjusts the allocation. For example, it changes resource allocation in response to fluctuations in demand. The generation AI also proposes a resource allocation strategy to increase the probability of success based on past new business data. For example, it creates a resource allocation plan based on success cases. This makes it possible to propose the optimal allocation of resources required to launch a new business.

[0037] The success probability improvement unit can use the generative AI to analyze success cases in different markets and propose possible applications to other markets. For example, the success probability improvement unit uses the generative AI to analyze success cases in different markets and identify possible applications to other markets. For example, it applies success cases in the technology field to the consumer goods market. The generative AI also proposes application strategies for other markets based on success cases in different markets. For example, it applies technology in the medical field to everyday life. The generative AI also analyzes success cases in different markets in real time and dynamically proposes possible applications to other markets. For example, it plans application strategies that meet new market needs. In this way, it is possible to propose possible applications to other markets by analyzing success cases in different markets.

[0038] The success probability improvement unit can use the generation AI to analyze customer feedback data and propose specific measures to improve satisfaction. For example, the success probability improvement unit uses the generation AI to analyze customer feedback data and propose measures to improve satisfaction. For example, it identifies customer dissatisfaction points and proposes improvement measures. The generation AI also proposes specific measures to improve satisfaction based on customer feedback data. For example, it proposes service improvements in response to customer requests. The generation AI also analyzes feedback data in real time and dynamically proposes measures to improve satisfaction. For example, it adjusts measures according to customer opinions. In this way, it is possible to propose specific measures to improve satisfaction by analyzing customer feedback data.

[0039] The success probability improvement unit can use the generation AI to analyze customer purchase history and behavioral data and propose customized services that meet individual needs. For example, the success probability improvement unit uses the generation AI to analyze customer purchase history data and propose customized services that meet individual needs. For example, personalized services are provided based on past purchasing patterns. The generation AI also proposes customized services that meet individual needs based on customer behavioral data. For example, it analyzes website browsing history and click data to provide services that meet the customer's interests. The generation AI also combines and analyzes purchase history and behavioral data to propose customized services that meet individual needs in real time. For example, it adjusts services according to changes in customer behavior. In this way, by analyzing customer purchase history and behavioral data, it is possible to propose customized services that meet individual needs.

[0040] The success probability improvement unit can use the generation AI to propose the optimal design of a customer loyalty program. For example, the success probability improvement unit uses the generation AI to analyze customer behavioral data and design the optimal customer loyalty program. For example, it proposes benefits based on the customer's purchasing frequency and amount. The generation AI also identifies areas for improvement in the customer loyalty program based on customer feedback data and proposes the optimal design. For example, it provides benefits and services that meet customer requests. The generation AI also analyzes customer data in real time and dynamically adjusts the design of the customer loyalty program. For example, it changes the program in response to changes in customer behavior. This makes it possible to propose the optimal design of a customer loyalty program.

[0041] The success probability improvement unit can use the generation AI to analyze the satisfaction levels of different customer segments and propose measures for each segment. For example, the success probability improvement unit uses the generation AI to analyze data on different customer segments and propose measures to improve satisfaction for each segment. For example, it provides services customized according to age and gender. The generation AI also proposes measures to improve satisfaction based on feedback data for each customer segment. For example, it strengthens benefits and services for specific segments. The generation AI also analyzes customer segment data in real time and dynamically adjusts measures for each segment. For example, it changes measures in response to changes in the behavior of the segment. In this way, it is possible to propose measures for each segment by analyzing the satisfaction levels of different customer segments.

[0042] The success probability improvement unit can use the generation AI to analyze past inventory data and demand forecasts and propose optimal inventory levels. For example, the generation AI analyzes past inventory data and demand forecast data to propose optimal inventory levels. For example, it adjusts inventory according to periods of high demand. The generation AI also proposes optimal inventory levels for specific products and services based on inventory data and demand forecasts. For example, it increases inventory for products whose demand is rapidly increasing. The generation AI also analyzes inventory data and demand forecasts in real time and dynamically adjusts inventory levels. For example, it changes inventory in response to fluctuations in demand. In this way, it is possible to propose optimal inventory levels by analyzing past inventory data and demand forecasts.

[0043] The success probability improvement unit can use the generation AI to analyze resource usage history and propose efficient resource allocation. For example, the success probability improvement unit uses the generation AI to analyze resource usage history data and propose efficient resource allocation. For example, it optimizes resources based on past usage patterns. The generation AI also proposes resource allocation for specific projects or tasks based on resource usage history. For example, it adjusts resource surpluses and shortages. The generation AI also analyzes resource usage history data in real time and dynamically adjusts resource allocation. For example, it changes resources in response to fluctuations in demand. In this way, efficient resource allocation can be proposed by analyzing resource usage history.

[0044] The success probability improvement unit uses the generation AI to optimize inventory management in different regions and propose inventory management that meets the demand in each region. For example, the generation AI analyzes inventory data from different regions and proposes inventory optimization that meets the demand in each region. For example, it optimizes inventory management in urban and rural areas. The generation AI also proposes inventory management strategies for specific regions based on demand forecast data for each region. For example, it places inventory in areas where demand is increasing. The generation AI also analyzes inventory data for each region in real time and dynamically adjusts inventory management. For example, it changes inventory in response to fluctuations in demand. In this way, by optimizing inventory management in different regions, it is possible to propose inventory management that meets the demand in each region.

[0045] The success probability improvement unit uses generative AI to analyze inventory management methods from different industries and apply best practices from those industries. For example, the success probability improvement unit uses generative AI to analyze inventory management data from different industries and identify best practices from those industries. For example, inventory management methods from the technology industry are applied to the consumer goods industry. The generative AI also proposes application strategies for other industries based on inventory management methods from different industries. For example, inventory management methods from the medical field are applied to everyday life. The generative AI also analyzes inventory management data from different industries in real time and dynamically applies best practices from those industries. For example, it develops inventory management strategies that meet new industry needs. In this way, best practices from those industries can be applied by analyzing inventory management methods from different industries.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The business forecasting AI system can also be equipped with an application unit that analyzes success stories from other industries and proposes applicability to other industries. In the application unit, for example, the generative AI analyzes success stories from other industries and identifies applicability to other industries. For example, applying success stories from the technology field to the consumer goods market. The generative AI also proposes application strategies for other industries based on success stories from other industries. For example, applying medical technology to everyday life. The generative AI also analyzes success stories from other industries in real time and dynamically proposes applicability to other industries. For example, it plans application strategies that meet new market needs. In this way, it is possible to propose applicability to other industries by analyzing success stories from other industries.

[0048] The business prediction AI system can also be equipped with a customization unit that analyzes consumer purchasing history and behavioral data and proposes product development optimal for the target market. In the customization unit, for example, the generation AI analyzes consumer purchasing history data and proposes product development optimal for a specific consumer group. For example, it predicts demand for new products based on past purchasing patterns. The generation AI also proposes product development strategies optimal for the target market based on consumer behavioral data. For example, it analyzes website browsing history and click data to identify consumer interests. The generation AI also combines and analyzes the purchase history and behavioral data to propose product development optimal for the target market in real time. For example, it adjusts product development plans in response to changes in consumer behavior. In this way, it is possible to propose product development optimal for the target market by analyzing consumer purchasing history and behavioral data.

[0049] The business prediction AI system can also be equipped with a feedback analysis unit that analyzes customer feedback data and proposes specific measures to improve satisfaction. In the feedback analysis unit, for example, the generation AI analyzes customer feedback data and proposes measures to improve satisfaction. For example, it identifies customer dissatisfaction points and proposes improvement measures. The generation AI also proposes specific measures to improve satisfaction based on customer feedback data. For example, it proposes service improvements in response to customer requests. The generation AI also analyzes feedback data in real time and dynamically proposes measures to improve satisfaction. For example, it adjusts measures according to customer opinions. In this way, specific measures to improve satisfaction can be proposed by analyzing customer feedback data.

[0050] The business prediction AI system can also be equipped with a regional inventory management unit that optimizes inventory management in different regions and proposes inventory management according to regional demand. In the regional inventory management unit, for example, the generation AI analyzes inventory data from different regions and proposes inventory optimization according to regional demand. For example, optimizing inventory management in urban and rural areas. The generation AI also proposes inventory management strategies for specific regions based on demand forecast data for each region. For example, it may prioritize inventory allocation in areas where demand is increasing. The generation AI also analyzes inventory data for each region in real time and dynamically adjusts inventory management. For example, it may change inventory in response to fluctuations in demand. This makes it possible to propose inventory management according to regional demand by optimizing inventory management in different regions.

[0051] The business prediction AI system can also include an inventory management application unit that analyzes inventory management methods from different industries and applies best practices from those industries. In the inventory management application unit, for example, the generation AI analyzes inventory management data from different industries and identifies best practices from those industries. For example, inventory management methods from the technology industry are applied to the consumer goods industry. The generation AI also proposes application strategies for other industries based on the inventory management methods from those industries. For example, inventory management methods from the medical field are applied to everyday life. The generation AI also analyzes inventory management data from different industries in real time and dynamically applies best practices from those industries. For example, it develops inventory management strategies that meet new industry needs. This makes it possible to apply best practices from those industries by analyzing inventory management methods from different industries.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The demand forecasting unit uses generation AI to make demand forecasts. The generation AI analyzes past sales data, market data, and consumer behavior data to predict how much a specific product will sell and when. The generation AI can also analyze weather data and event information to improve the accuracy of demand forecasts. Step 2: The market trend forecasting department uses generative AI to predict market trends. Generative AI analyzes economic indicators, industry trends, and competitor trends to predict future market growth and risks. Generative AI can also analyze the impact of political events and policy changes to improve the accuracy of market trend forecasts. Step 3: The success probability improvement unit improves the success probability of new businesses and product development based on the prediction results obtained by the demand forecasting unit and market trend forecasting unit. The generation AI analyzes past successes and failures to identify factors that increase the success probability. The generation AI can also analyze consumer purchasing history and behavioral data to propose product development that is optimal for the target market.

[0054] (Example 2) The business forecasting AI system according to an embodiment of the present invention utilizes generative AI to accurately forecast demand and market trends when planning new services and developing market introduction strategies. This allows the business forecasting AI system to improve the success rate of new businesses and product development, and to enhance user satisfaction and customer loyalty. It also enables inventory optimization and appropriate resource allocation.

[0055] The business forecasting AI system according to the embodiment includes a demand forecasting unit, a market trend forecasting unit, and a success probability improvement unit. The demand forecasting unit performs demand forecasting using a generation AI. For example, the generation AI analyzes past sales data, market data, and consumer behavior data to predict how much a particular product will sell and when. The generation AI can also analyze weather data and event information to improve the accuracy of the demand forecast. The market trend forecasting unit predicts market trends using the generation AI. For example, the generation AI analyzes economic indicators, industry trends, and competitor trends to predict future market growth and risks. The generation AI can also analyze the impact of political events and policy changes to improve the accuracy of market trend forecasts. The success probability improvement unit improves the success probability of new businesses and product development based on the prediction results obtained by the demand forecasting unit and the market trend forecasting unit. For example, the generation AI analyzes past successes and failures to identify factors for increasing the success probability. The generation AI can also analyze consumer purchasing history and behavioral data to propose product development optimal for the target market. As a result, the business forecasting AI system according to the embodiment can improve the probability of success in new businesses and product development by combining demand forecasting and market trend forecasting.

[0056] The demand forecasting unit uses the generation AI to analyze weather data and event information in addition to sales data, thereby improving the accuracy of demand forecasts. For example, the demand forecasting unit uses the generation AI to combine and analyze past sales data and weather data to predict demand fluctuations under specific weather conditions. For example, it identifies products whose sales increase on rainy days and predicts their demand. The generation AI also analyzes event information and predicts demand during periods when specific events are held. For example, it identifies products whose demand increases during sporting events or music festivals. The generation AI also combines and analyzes weather data and event information to predict demand fluctuations due to multiple factors. For example, it predicts the impact that an event held on a rainy day will have on demand. In this way, the accuracy of demand forecasts is improved by analyzing weather data and event information.

[0057] The demand forecasting unit uses the generation AI to analyze consumer social media posts and generate demand forecasts that reflect consumer interests in real time. For example, the generation AI analyzes social media posts to understand consumer interest in specific products and services in real time. For example, when a specific hashtag suddenly increases in popularity, the demand for that product is predicted. The generation AI also analyzes the content of social media posts to identify trends in consumer interests. For example, when interest in a new fashion item increases, the demand for that item is predicted. The generation AI also predicts consumer purchasing intent in real time based on social media post data. For example, it predicts demand for a product that is seeing an increase in positive posts. This makes it possible to generate demand forecasts that reflect consumer interests in real time by analyzing social media posts.

[0058] The demand forecasting unit can use the generation AI to analyze consumer emotion data and make demand forecasts based on emotional fluctuations. The demand forecasting unit, for example, uses an emotion estimation function to analyze consumer emotion data and understand emotional fluctuations toward a specific product. For example, it predicts demand for a product for which positive emotions are increasing. The generation AI also predicts the impact of a specific event or campaign on demand based on consumer emotion data. For example, it identifies products for which demand will increase during an event when emotions are high. The generation AI also analyzes emotion estimation data and makes demand forecasts based on consumer emotion fluctuations. For example, it predicts demand for a product for which negative emotions are decreasing. In this way, by analyzing emotion data, it becomes possible to make demand forecasts based on emotional fluctuations.

[0059] The market trend prediction unit can use the generation AI to analyze the impact of political events and policy changes in addition to economic indicators to predict market trends. For example, the market trend prediction unit uses the generation AI to combine and analyze past economic indicators and political event data to predict the impact of a specific policy change on the market. For example, it predicts the impact of tax reform on consumer spending. The generation AI also analyzes political event data to predict the impact of a specific event on market trends. For example, it predicts the impact of election results on the market. The generation AI also combines and analyzes economic indicators and policy change data to predict market trends due to multiple factors. For example, it predicts the impact of interest rate fluctuations and policy changes on the market. In this way, by analyzing the impact of political events and policy changes in addition to economic indicators, the accuracy of market trend predictions is improved.

[0060] The market trend prediction unit uses the generation AI to analyze the timing of competitors' new product announcements and market entry, and can predict market trends based on that. For example, the market trend prediction unit uses the generation AI to analyze competitors' new product announcement data and reflect that impact in market trends. For example, it predicts the impact that a competitor's new product will have on the market. The generation AI also analyzes competitors' market entry data and predicts the impact that new competitors will have on the market. For example, it predicts the impact that a new entrant will have on market share. The generation AI also combines and analyzes competitors' new product announcements and market entry data to predict market trends due to multiple factors. For example, it predicts the simultaneous impact that a competitor's new product and market entry will have on the market. In this way, by analyzing the timing of competitors' new product announcements and market entry, the accuracy of market trend predictions is improved.

[0061] The market trend prediction unit can use the generation AI to analyze consumer emotion data and predict market trends based on emotional fluctuations. The market trend prediction unit, for example, uses an emotion estimation function to analyze consumer emotion data and predict market trends at times when positive emotions are rising. For example, it predicts that the market will become active during an event period when consumer emotions are high. The generation AI also predicts the impact that a specific event or campaign will have on market trends based on consumer emotion data. For example, it predicts the impact that an emotionally high event will have on the market. The generation AI also analyzes emotion estimation data and predicts market trends based on fluctuations in consumer emotions. For example, it predicts that the market will stabilize during a period when negative emotions are decreasing. This makes it possible to predict market trends based on emotional fluctuations by analyzing emotion data.

[0062] The success probability improvement unit can use the generative AI to analyze past success and failure cases and identify factors that increase the success probability. For example, the success probability improvement unit uses the generative AI to analyze past success and failure cases and identify factors that lead to success and failure. For example, it extracts commonalities between successful product developments. The generative AI also proposes strategies to increase the success probability based on data on success and failure cases. For example, it creates product development plans that incorporate success factors. The generative AI also analyzes past data in real time and dynamically identifies factors that increase the success probability. For example, it adjusts strategies based on the most recent success cases. In this way, factors that increase the success probability can be identified by analyzing past success and failure cases.

[0063] The success probability improvement unit can use the generation AI to analyze consumer purchasing history and behavioral data and propose product development that is optimal for the target market. For example, the success probability improvement unit uses the generation AI to analyze consumer purchasing history data and propose product development that is optimal for a specific consumer group. For example, it predicts demand for new products based on past purchasing patterns. The generation AI also proposes a product development strategy that is optimal for the target market based on consumer behavioral data. For example, it analyzes website browsing history and click data to identify consumer interests. The generation AI also combines and analyzes purchase history and behavioral data to propose product development that is optimal for the target market in real time. For example, it adjusts product development plans in response to changes in consumer behavior. In this way, by analyzing consumer purchasing history and behavioral data, it is possible to propose product development that is optimal for the target market.

[0064] The success probability improvement unit can use the generation AI to analyze consumer emotional data and propose product development that is likely to resonate emotionally. The success probability improvement unit, for example, uses the emotion estimation function to analyze consumer emotional data and propose product development that will increase positive emotions. For example, by incorporating product characteristics with high emotion scores. The generation AI also proposes product development strategies that are likely to resonate emotionally based on consumer emotional data. For example, by incorporating designs and functions that arouse emotions. The generation AI also analyzes the emotion estimation data and proposes product development based on consumer emotions in real time. For example, by adjusting product development plans in response to emotional fluctuations. In this way, by analyzing emotional data, it is possible to propose product development that is likely to resonate emotionally.

[0065] The success probability improvement unit can use the generation AI to propose the optimal allocation of resources required to launch a new business. For example, the generation AI analyzes resource data required to launch a new business and proposes the optimal allocation. For example, it optimizes the allocation of necessary human resources and funds. The generation AI also analyzes the resources required to launch a new business in real time and dynamically adjusts the allocation. For example, it changes resource allocation in response to fluctuations in demand. The generation AI also proposes a resource allocation strategy to increase the probability of success based on past new business data. For example, it creates a resource allocation plan based on success cases. This makes it possible to propose the optimal allocation of resources required to launch a new business.

[0066] The success probability improvement unit can use the generative AI to analyze success cases in different markets and propose possible applications to other markets. For example, the success probability improvement unit uses the generative AI to analyze success cases in different markets and identify possible applications to other markets. For example, it applies success cases in the technology field to the consumer goods market. The generative AI also proposes application strategies for other markets based on success cases in different markets. For example, it applies technology in the medical field to everyday life. The generative AI also analyzes success cases in different markets in real time and dynamically proposes possible applications to other markets. For example, it plans application strategies that meet new market needs. In this way, it is possible to propose possible applications to other markets by analyzing success cases in different markets.

[0067] The success probability improvement unit can use the generation AI to analyze consumer emotional data and propose a marketing strategy based on consumer emotions. The success probability improvement unit, for example, uses an emotion estimation function to analyze consumer emotional data and propose a marketing strategy that increases positive emotions. For example, it implements an advertising campaign with a high emotion score. The generation AI also proposes a marketing strategy that is likely to resonate emotionally based on consumer emotional data. For example, it uses messages and visuals that stimulate emotions. The generation AI also analyzes the emotion estimation data and proposes a marketing strategy based on consumer emotions in real time. For example, it adjusts a marketing plan according to emotional fluctuations. In this way, by analyzing emotional data, it is possible to propose a marketing strategy based on consumer emotions.

[0068] The success probability improvement unit can use the generation AI to analyze customer feedback data and propose specific measures to improve satisfaction. For example, the success probability improvement unit uses the generation AI to analyze customer feedback data and propose measures to improve satisfaction. For example, it identifies customer dissatisfaction points and proposes improvement measures. The generation AI also proposes specific measures to improve satisfaction based on customer feedback data. For example, it proposes service improvements in response to customer requests. The generation AI also analyzes feedback data in real time and dynamically proposes measures to improve satisfaction. For example, it adjusts measures according to customer opinions. In this way, it is possible to propose specific measures to improve satisfaction by analyzing customer feedback data.

[0069] The success probability improvement unit can use the generation AI to analyze customer purchase history and behavioral data and propose customized services that meet individual needs. For example, the success probability improvement unit uses the generation AI to analyze customer purchase history data and propose customized services that meet individual needs. For example, personalized services are provided based on past purchasing patterns. The generation AI also proposes customized services that meet individual needs based on customer behavioral data. For example, it analyzes website browsing history and click data to provide services that meet the customer's interests. The generation AI also combines and analyzes purchase history and behavioral data to propose customized services that meet individual needs in real time. For example, it adjusts services according to changes in customer behavior. In this way, by analyzing customer purchase history and behavioral data, it is possible to propose customized services that meet individual needs.

[0070] The success probability improvement unit can use the generation AI to analyze customer emotional data and propose measures to emotionally increase satisfaction. The success probability improvement unit, for example, uses the emotion estimation function to analyze customer emotional data and propose measures to increase positive emotions. For example, it implements service improvements that result in high emotion scores. The generation AI also proposes measures to emotionally increase satisfaction based on customer emotional data. For example, it provides emotionally uplifting customer support. The generation AI also analyzes emotion estimation data and proposes satisfaction improvement measures based on customer emotions in real time. For example, it adjusts measures according to emotional fluctuations. In this way, it is possible to propose measures to emotionally increase satisfaction by analyzing customer emotional data.

[0071] The success probability improvement unit can use the generation AI to propose the optimal design of a customer loyalty program. For example, the success probability improvement unit uses the generation AI to analyze customer behavioral data and design the optimal customer loyalty program. For example, it proposes benefits based on the customer's purchasing frequency and amount. The generation AI also identifies areas for improvement in the customer loyalty program based on customer feedback data and proposes the optimal design. For example, it provides benefits and services that meet customer requests. The generation AI also analyzes customer data in real time and dynamically adjusts the design of the customer loyalty program. For example, it changes the program in response to changes in customer behavior. This makes it possible to propose the optimal design of a customer loyalty program.

[0072] The success probability improvement unit can use the generation AI to analyze the satisfaction levels of different customer segments and propose measures for each segment. For example, the success probability improvement unit uses the generation AI to analyze data on different customer segments and propose measures to improve satisfaction for each segment. For example, it provides services customized according to age and gender. The generation AI also proposes measures to improve satisfaction based on feedback data for each customer segment. For example, it strengthens benefits and services for specific segments. The generation AI also analyzes customer segment data in real time and dynamically adjusts measures for each segment. For example, it changes measures in response to changes in the behavior of the segment. In this way, it is possible to propose measures for each segment by analyzing the satisfaction levels of different customer segments.

[0073] The success probability improvement unit can use the generation AI to analyze customer emotional data and propose loyalty improvement measures based on the customer's emotions. The success probability improvement unit, for example, uses an emotion estimation function to analyze customer emotional data and propose loyalty improvement measures that will increase positive emotions. For example, it provides benefits and services with high emotion scores. The generation AI also proposes measures to emotionally increase loyalty based on the customer's emotional data. For example, it provides customer support that will uplift emotions. The generation AI also analyzes the emotion estimation data and proposes loyalty improvement measures based on the customer's emotions in real time. For example, it adjusts the measures according to emotional fluctuations. In this way, by analyzing customer emotional data, it is possible to propose loyalty improvement measures based on emotions.

[0074] The success probability improvement unit can use the generation AI to analyze past inventory data and demand forecasts and propose optimal inventory levels. For example, the generation AI analyzes past inventory data and demand forecast data to propose optimal inventory levels. For example, it adjusts inventory according to periods of high demand. The generation AI also proposes optimal inventory levels for specific products and services based on inventory data and demand forecasts. For example, it increases inventory for products whose demand is rapidly increasing. The generation AI also analyzes inventory data and demand forecasts in real time and dynamically adjusts inventory levels. For example, it changes inventory in response to fluctuations in demand. In this way, it is possible to propose optimal inventory levels by analyzing past inventory data and demand forecasts.

[0075] The success probability improvement unit can use the generation AI to analyze resource usage history and propose efficient resource allocation. For example, the success probability improvement unit uses the generation AI to analyze resource usage history data and propose efficient resource allocation. For example, it optimizes resources based on past usage patterns. The generation AI also proposes resource allocation for specific projects or tasks based on resource usage history. For example, it adjusts resource surpluses and shortages. The generation AI also analyzes resource usage history data in real time and dynamically adjusts resource allocation. For example, it changes resources in response to fluctuations in demand. In this way, efficient resource allocation can be proposed by analyzing resource usage history.

[0076] The success probability improvement unit can use the generation AI to analyze employee emotional data and propose resource allocation that emotionally increases satisfaction. The success probability improvement unit, for example, uses an emotion estimation function to analyze employee emotional data and propose resource allocation that increases positive emotions. For example, it concentrates resources on tasks with high emotion scores. The generation AI also proposes a resource allocation strategy that emotionally increases satisfaction based on employee emotional data. For example, it allocates resources to projects that increase emotions. The generation AI also analyzes emotion estimation data and proposes resource allocation based on employee emotions in real time. For example, it adjusts resource allocation according to emotional fluctuations. In this way, by analyzing employee emotional data, it is possible to propose resource allocation that emotionally increases satisfaction.

[0077] The success probability improvement unit uses the generation AI to optimize inventory management in different regions and propose inventory management that meets the demand in each region. For example, the generation AI analyzes inventory data from different regions and proposes inventory optimization that meets the demand in each region. For example, it optimizes inventory management in urban and rural areas. The generation AI also proposes inventory management strategies for specific regions based on demand forecast data for each region. For example, it places inventory in areas where demand is increasing. The generation AI also analyzes inventory data for each region in real time and dynamically adjusts inventory management. For example, it changes inventory in response to fluctuations in demand. In this way, by optimizing inventory management in different regions, it is possible to propose inventory management that meets the demand in each region.

[0078] The success probability improvement unit uses generative AI to analyze inventory management methods from different industries and apply best practices from those industries. For example, the success probability improvement unit uses generative AI to analyze inventory management data from different industries and identify best practices from those industries. For example, inventory management methods from the technology industry are applied to the consumer goods industry. The generative AI also proposes application strategies for other industries based on inventory management methods from different industries. For example, inventory management methods from the medical field are applied to everyday life. The generative AI also analyzes inventory management data from different industries in real time and dynamically applies best practices from those industries. For example, it develops inventory management strategies that meet new industry needs. In this way, best practices from those industries can be applied by analyzing inventory management methods from different industries.

[0079] The success probability improvement unit uses the generation AI to analyze employee emotional data and propose resource allocation based on employee emotions, thereby improving employee motivation. The success probability improvement unit, for example, uses an emotion estimation function to analyze employee emotional data and propose resource allocation that increases positive emotions. For example, resources are concentrated on tasks with high emotion scores. The generation AI also proposes a resource allocation strategy that increases emotional satisfaction based on employee emotional data. For example, resources are allocated to projects that elevate emotions. The generation AI also analyzes emotion estimation data and proposes resource allocation based on employee emotions in real time. For example, resource allocation is adjusted according to emotional fluctuations. In this way, by analyzing employee emotional data, resource allocation based on emotions is proposed and employee motivation is improved.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The business forecasting AI system can also be equipped with an application unit that analyzes success stories from other industries and proposes applicability to other industries. In the application unit, for example, the generative AI analyzes success stories from other industries and identifies applicability to other industries. For example, applying success stories from the technology field to the consumer goods market. The generative AI also proposes application strategies for other industries based on success stories from other industries. For example, applying medical technology to everyday life. The generative AI also analyzes success stories from other industries in real time and dynamically proposes applicability to other industries. For example, it plans application strategies that meet new market needs. In this way, it is possible to propose applicability to other industries by analyzing success stories from other industries.

[0082] The business prediction AI system can also be equipped with a customization unit that analyzes consumer purchasing history and behavioral data and proposes product development optimal for the target market. In the customization unit, for example, the generation AI analyzes consumer purchasing history data and proposes product development optimal for a specific consumer group. For example, it predicts demand for new products based on past purchasing patterns. The generation AI also proposes product development strategies optimal for the target market based on consumer behavioral data. For example, it analyzes website browsing history and click data to identify consumer interests. The generation AI also combines and analyzes the purchase history and behavioral data to propose product development optimal for the target market in real time. For example, it adjusts product development plans in response to changes in consumer behavior. In this way, it is possible to propose product development optimal for the target market by analyzing consumer purchasing history and behavioral data.

[0083] The business prediction AI system can also be equipped with a feedback analysis unit that analyzes customer feedback data and proposes specific measures to improve satisfaction. In the feedback analysis unit, for example, the generation AI analyzes customer feedback data and proposes measures to improve satisfaction. For example, it identifies customer dissatisfaction points and proposes improvement measures. The generation AI also proposes specific measures to improve satisfaction based on customer feedback data. For example, it proposes service improvements in response to customer requests. The generation AI also analyzes feedback data in real time and dynamically proposes measures to improve satisfaction. For example, it adjusts measures according to customer opinions. In this way, specific measures to improve satisfaction can be proposed by analyzing customer feedback data.

[0084] The business prediction AI system can also be equipped with a regional inventory management unit that optimizes inventory management in different regions and proposes inventory management according to regional demand. In the regional inventory management unit, for example, the generation AI analyzes inventory data from different regions and proposes inventory optimization according to regional demand. For example, optimizing inventory management in urban and rural areas. The generation AI also proposes inventory management strategies for specific regions based on demand forecast data for each region. For example, it may prioritize inventory allocation in areas where demand is increasing. The generation AI also analyzes inventory data for each region in real time and dynamically adjusts inventory management. For example, it may change inventory in response to fluctuations in demand. This makes it possible to propose inventory management according to regional demand by optimizing inventory management in different regions.

[0085] The business prediction AI system can also include an inventory management application unit that analyzes inventory management methods from different industries and applies best practices from those industries. In the inventory management application unit, for example, the generation AI analyzes inventory management data from different industries and identifies best practices from those industries. For example, inventory management methods from the technology industry are applied to the consumer goods industry. The generation AI also proposes application strategies for other industries based on the inventory management methods from those industries. For example, inventory management methods from the medical field are applied to everyday life. The generation AI also analyzes inventory management data from different industries in real time and dynamically applies best practices from those industries. For example, it develops inventory management strategies that meet new industry needs. This makes it possible to apply best practices from those industries by analyzing inventory management methods from different industries.

[0086] The business forecasting AI system can also be equipped with an emotion analysis unit that analyzes consumer emotion data and performs demand forecasting based on emotional fluctuations. The emotion analysis unit, for example, uses an emotion estimation function to analyze consumer emotion data and understand emotional fluctuations toward a specific product. For example, it predicts demand for a product where positive emotions are increasing. The generation AI also predicts the impact of a specific event or campaign on demand based on consumer emotion data. For example, it identifies products whose demand will increase during an event when emotions are high. The generation AI also analyzes emotion estimation data and performs demand forecasting based on consumer emotion fluctuations. For example, it predicts demand for a product where negative emotions are decreasing. In this way, analyzing emotion data makes it possible to perform demand forecasting based on emotional fluctuations.

[0087] The business prediction AI system can also be equipped with an emotion market prediction unit that analyzes consumer emotion data and predicts market trends based on emotional fluctuations. The emotion market prediction unit, for example, uses an emotion estimation function to analyze consumer emotion data and predict market trends when positive emotions are rising. For example, it predicts that the market will become active during an event period when consumer emotions are high. The generation AI also predicts the impact that a specific event or campaign will have on market trends based on consumer emotion data. For example, it predicts the impact that an emotionally high event will have on the market. The generation AI also analyzes emotion estimation data and predicts market trends based on fluctuations in consumer emotion. For example, it predicts that the market will stabilize during a period when negative emotions are decreasing. This makes it possible to predict market trends based on emotional fluctuations by analyzing emotion data.

[0088] The business prediction AI system can also be equipped with an emotional product development department that analyzes consumer emotional data and proposes product development that is likely to resonate emotionally. The emotional product development department, for example, uses an emotion estimation function to analyze consumer emotional data and proposes product development that will increase positive emotions. For example, it would incorporate product characteristics that have a high emotion score. The generative AI also proposes product development strategies that are likely to resonate emotionally based on consumer emotional data. For example, it would incorporate designs and functions that will arouse emotions. The generative AI also analyzes the emotion estimation data and proposes product development based on consumer emotions in real time. For example, it adjusts product development plans according to emotional fluctuations. In this way, by analyzing emotional data, it is possible to propose product development that is likely to resonate emotionally.

[0089] The business prediction AI system can also be equipped with an emotional marketing unit that analyzes consumer emotional data and proposes marketing strategies based on consumer emotions. The emotional marketing unit, for example, uses an emotion estimation function to analyze consumer emotional data and proposes marketing strategies that increase positive emotions. For example, it implements advertising campaigns with high emotion scores. The generation AI also proposes marketing strategies that are likely to resonate emotionally based on consumer emotional data. For example, it uses messages and visuals that stimulate emotions. The generation AI also analyzes the emotion estimation data and proposes marketing strategies based on consumer emotions in real time. For example, it adjusts marketing plans according to emotional fluctuations. In this way, by analyzing emotional data, it is possible to propose marketing strategies based on consumer emotions.

[0090] The business prediction AI system can also be equipped with an employee emotion analysis unit that analyzes employee emotion data and proposes resource allocation that emotionally increases satisfaction. The employee emotion analysis unit, for example, uses an emotion estimation function to analyze employee emotion data and proposes resource allocation that increases positive emotions. For example, it may concentrate resources on tasks with high emotion scores. The generation AI also proposes a resource allocation strategy that emotionally increases satisfaction based on employee emotion data. For example, it may allocate resources to projects that generate high emotions. The generation AI also analyzes emotion estimation data and proposes resource allocation based on employee emotions in real time. For example, it adjusts resource allocation according to emotional fluctuations. In this way, by analyzing employee emotion data, it proposes emotion-based resource allocation and improves employee motivation.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The demand forecasting unit uses generation AI to make demand forecasts. The generation AI analyzes past sales data, market data, and consumer behavior data to predict how much a specific product will sell and when. The generation AI can also analyze weather data and event information to improve the accuracy of demand forecasts. Step 2: The market trend forecasting department uses generative AI to predict market trends. Generative AI analyzes economic indicators, industry trends, and competitor trends to predict future market growth and risks. Generative AI can also analyze the impact of political events and policy changes to improve the accuracy of market trend forecasts. Step 3: The success probability improvement unit improves the success probability of new businesses and product development based on the prediction results obtained by the demand forecasting unit and market trend forecasting unit. The generation AI analyzes past successes and failures to identify factors that increase the success probability. The generation AI can also analyze consumer purchasing history and behavioral data to propose product development that is optimal for the target market.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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 AI 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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, in order to avoid confusion and to 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a demand forecasting unit that uses generation AI to make demand forecasts; A market trend forecasting department that uses generative AI to forecast market trends; a success probability improvement unit that improves the success probability of new business and product development based on the prediction results obtained by the demand prediction unit and the market trend prediction unit. A system characterized by:

2. The demand forecasting unit The generative AI will be used to analyze sales data, weather data, and event information to improve the accuracy of demand forecasts.

2. The system of claim 1.

3. The demand forecasting unit The generative AI is used to analyze consumer social media posts and make demand forecasts that reflect the consumer's interests and concerns in real time.

2. The system of claim 1.

4. The demand forecasting unit Using the generative AI, analyze consumer emotional data and make demand predictions based on emotional fluctuations.

2. The system of claim 1.

5. The market trend forecasting unit The generative AI will be used to analyze the impact of economic indicators, as well as political events and policy changes, to forecast market trends.

2. The system of claim 1.

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