System

Abstract

An object of a system according to an embodiment is to comprehensively analyze past cases and current company data and propose an optimal strategy.SOLUTION: A system according to an embodiment includes a historical case learner, a company data collector, a strategy evaluator, and a strategy suggester. The historical example learning unit learns a past example using the generated AI. The company data collection unit collects current company data. The strategy evaluation unit evaluates the possibility of success or failure of the strategy based on the past cases learned by the historical case learning unit and the company data collected by the company data collection unit. The strategy proposal unit proposes an optimum strategy on the basis of a result evaluated by the strategy evaluation 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 do not adequately evaluate the likelihood of a strategy's success or failure through an integrated analysis of past cases and current corporate data, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively analyze past cases and current company data and propose optimal strategies. [Means for solving the problem]

[0006] The system according to the embodiment includes a historical case learning unit, a company data collection unit, a strategy evaluation unit, and a strategy proposal unit. The historical case learning unit learns past cases using a generative AI. The company data collection unit collects current company data. The strategy evaluation unit evaluates the likelihood of success or failure of a strategy based on the past cases learned by the historical case learning unit and the company data collected by the company data collection unit. The strategy proposal unit proposes an optimal strategy based on the results of the evaluation by the strategy evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively analyze past cases and current company data and propose optimal strategies. [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 management strategy optimization system according to an embodiment of the present invention is a system that learns from past historical cases and analyzes current company data to evaluate the likelihood of success or failure of a company's business strategy and propose an optimal strategy. This allows the management strategy optimization system to evaluate the likelihood of success or failure of a company's business strategy and propose an optimal strategy.

[0029] A management strategy optimization system according to an embodiment includes a historical case learning unit, a company data collection unit, a strategy evaluation unit, and a strategy proposal unit. The historical case learning unit uses a generation AI to learn past cases. For example, the generation AI learns a large number of past successes and failures and analyzes the data. The generation AI also learns past corporate growth strategies, market entry strategies, product development strategies, and the like, and analyzes the conditions under which each case succeeded or failed. The generation AI also performs analysis based on detailed data and literature related to past cases. The company data collection unit collects current company data, such as company financial data, market share, competitor trends, and consumer preferences. The company data collection unit also collects detailed data on the current company situation and converts the data into a format that is easy for the generation AI to analyze. The strategy evaluation unit evaluates the likelihood of success or failure of a strategy based on the past cases learned by the historical case learning unit and the company data collected by the company data collection unit. For example, the generation AI evaluates the likelihood of success of a target company's strategy by referring to strategies that have been successful or unsuccessful under similar conditions in the past. The generation AI also performs evaluation based on the results of comparing past cases with current company data. The strategy proposal unit proposes an optimal strategy based on the results of the evaluation by the strategy evaluation unit. For example, the generation AI proposes a new market entry strategy to promote company growth, or a product development strategy to compete with competitors. The generation AI also generates a strategy based on strategy proposal prompts based on the evaluation results. As a result, the business strategy optimization system according to the embodiment can evaluate the likelihood of success or failure of a strategy based on past cases and current company data, and propose an optimal strategy.

[0030] The historical case study section cross-references cases from different industries and regions to clarify similarities and differences. For example, the historical case study section cross-references successful and unsuccessful cases from different industries to clarify similarities and differences. For example, it compares market entry strategies between manufacturing and service industries. The historical case study section also analyzes cases from different regions to identify region-specific factors. For example, it analyzes differences in consumer behavior between Asian and Western markets. The historical case study section also cross-references cases from different eras to evaluate the impact of historical background. For example, it compares corporate strategies during the bubble economy and after the Lehman shock. In this way, by comparing cases from different industries and regions, strategies can be evaluated from a broader perspective.

[0031] The Historical Case Learning Department can analyze the influence of leadership style and organizational culture and evaluate its impact on the success or failure of a strategy. For example, the Historical Case Learning Department uses generative AI to analyze leadership styles in past cases. For example, it evaluates the influence of charismatic leadership and democratic leadership. To analyze the influence of organizational culture, the Historical Case Learning Department collects data on past corporate cultures and analyzes it with generative AI. For example, it evaluates the difference between an innovative culture and a conservative culture. The Historical Case Learning Department also uses generative AI to analyze changes in leadership in past cases and evaluate their impact on the success or failure of a strategy. For example, it compares corporate performance before and after a CEO change. This improves the accuracy of evaluating the success or failure of a strategy by taking into account the influence of leadership style and organizational culture.

[0032] The historical case learning unit analyzes multimodal information, including image and audio data, to gain insights from visual and audio information. For example, the historical case learning unit analyzes image data to evaluate visual elements in past cases. For example, it analyzes images from an advertising campaign to evaluate consumer reactions. The historical case learning unit also analyzes audio data to evaluate audio elements in past cases. For example, it analyzes audio from a product launch event to evaluate audience reactions. The historical case learning unit also integrates multimodal information to gain comprehensive insights into past cases. For example, it integrates image, audio, and text data to identify the factors that led to the success of a strategy. This allows for more multifaceted insights to be gained by analyzing multimodal information.

[0033] The Historical Case Learning Department can hold workshops that bring together experts from different industries and fields to jointly devise new strategies based on past cases. For example, the Historical Case Learning Department can hold workshops that invite experts from different industries to jointly devise new strategies based on past cases. For example, it can provide a forum for experts from technology, design, and marketing to gather together. The Historical Case Learning Department also records the discussions at the workshops and saves them as audio data. It then performs audio analysis to extract important ideas and opinions and stores them in a database. The Historical Case Learning Department also collects materials and presentation data used in the workshops and saves them in a database as background information for ideas. This clarifies the origins and rationale of ideas. This makes it possible to devise new strategies by utilizing the knowledge of experts from different industries and fields.

[0034] When analyzing current data, the corporate data collection department collects real-time data, allowing it to reflect the latest market trends and competitors' actions. The corporate data collection department, for example, collects real-time data and analyzes the company's current data. For example, it collects the latest financial data and market share data and analyzes it using generative AI. The corporate data collection department also monitors competitors' actions in real time and reflects this in the company's strategy. For example, it collects information on competitors' new product announcements and market entry. The corporate data collection department also analyzes the latest market trends in real time and reflects this in the company's strategy. For example, it monitors consumer purchasing behavior and trends in real time. By reflecting real-time data, it is possible to propose strategies that take into account the latest market trends and competitors' actions.

[0035] The corporate data collection department can analyze supply chain and logistics data and propose strategies for improving efficiency. For example, using generative AI, the corporate data collection department can analyze a company's supply chain data and propose strategies for improving efficiency. For example, optimizing inventory management and the supply chain. The corporate data collection department can also analyze logistics data and propose strategies for improving efficiency. For example, optimizing delivery routes and reducing logistics costs. The corporate data collection department can also use generative AI to integrate data from the entire supply chain and propose strategies for improving efficiency. For example, optimizing supplier selection and procurement strategies. In this way, by analyzing supply chain and logistics data, strategies for improving efficiency can be proposed.

[0036] The corporate data collection department also collects unstructured data such as social media and online reviews, allowing it to reflect consumer opinions. For example, the corporate data collection department collects social media posting data and analyzes it with generative AI. For example, it proposes a strategy that reflects consumer opinions and emotions. The corporate data collection department also collects online review data and analyzes it with generative AI. For example, it reflects consumer feedback on products and services in its strategy. The corporate data collection department also collects unstructured data and analyzes it with generative AI. For example, it analyzes blog articles and forum posts and reflects consumer opinions in its strategy. In this way, by collecting unstructured data, it is possible to reflect consumer opinions in its strategy.

[0037] The corporate data collection department can compare corporate data from different regions and cultural spheres and propose strategies from a global perspective. For example, the corporate data collection department collects corporate data from different regions and analyzes it using generative AI. For example, it compares data from the Asian market with that of the Western market and proposes a global strategy. The corporate data collection department also collects corporate data from different cultural spheres and analyzes it using generative AI. For example, it proposes a strategy that takes cultural differences into account. The corporate data collection department also integrates data from different regions and cultural spheres and analyzes it using generative AI to propose strategies from a global perspective. For example, it proposes a strategy that reflects the market needs of each region. In this way, by comparing data from different regions and cultural spheres, it is possible to propose strategies from a global perspective.

[0038] The strategy evaluation department can simulate different scenarios to identify the most promising scenario when evaluating the likelihood of a strategy's success or failure. For example, the strategy evaluation department simulates different scenarios to evaluate the likelihood of a strategy's success or failure. For example, it simulates multiple market entry strategies to identify the most promising scenario. The strategy evaluation department also uses generative AI to simulate different scenarios to evaluate risk and return. For example, it compares high-risk scenarios with low-risk scenarios. The strategy evaluation department also develops tools to simulate different scenarios and evaluate the likelihood of a strategy's success or failure. For example, it calculates the probability of success for each scenario. This makes it possible to identify the most promising scenario by simulating different scenarios.

[0039] The Strategy Evaluation Department can analyze a company's risk factors in detail and propose a risk management strategy based on that. The Strategy Evaluation Department, for example, uses generative AI to analyze a company's risk factors in detail and propose a risk management strategy. For example, it evaluates financial risk and market risk and proposes risk mitigation measures. The Strategy Evaluation Department also analyzes a company's risk factors and develops tools to propose a risk management strategy based on that analysis. For example, it builds a risk assessment model. The Strategy Evaluation Department also uses generative AI to monitor a company's risk factors in real time and propose a risk management strategy. For example, it predicts the occurrence of risk and takes measures in advance. This allows the department to propose a risk management strategy based on a detailed analysis of risk factors.

[0040] The Strategy Evaluation Department can integrate data from different industries and regions to evaluate the likelihood of a strategy's success or failure from a broader perspective. For example, the Strategy Evaluation Department integrates data from different industries to evaluate the likelihood of a strategy's success or failure. For example, it integrates data from the manufacturing and service industries to evaluate strategies. The Strategy Evaluation Department also integrates data from different regions to evaluate the likelihood of a strategy's success or failure. For example, it integrates data from the Asian market and the European and American markets to evaluate strategies. The Strategy Evaluation Department also integrates data from different industries and regions to develop tools for evaluating the likelihood of a strategy's success or failure from a broader perspective. For example, it builds a strategy evaluation model from a global perspective. This allows strategies to be evaluated from a broader perspective by integrating data from different industries and regions.

[0041] The strategy evaluation unit can convert the strategy evaluation results into visual notes or mind maps to make them easier to understand visually. For example, the strategy evaluation unit converts the strategy evaluation results into visual notes and displays them visually. For example, it shows important points using diagrams or icons. The strategy evaluation unit also converts the strategy evaluation results into mind map format and visually organizes related keywords and concepts. This makes it possible to understand the overall picture of the strategy at a glance. The strategy evaluation unit also develops tools that automatically generate visual notes and mind maps, allowing users to easily visually display the strategy evaluation results. For example, it provides a function to visualize evaluation results using drag and drop. This makes it easier to understand by visually displaying the strategy evaluation results.

[0042] When proposing a strategy, the strategy proposal department can simulate different scenarios and propose a strategy based on the most promising scenario. For example, the strategy proposal department simulates different scenarios and proposes a strategy based on the most promising scenario. For example, the strategy proposal department simulates multiple market entry strategies and identifies the most promising scenario. The strategy proposal department also uses generative AI to simulate different scenarios and evaluate risks and returns. For example, it compares high-risk scenarios with low-risk scenarios. The strategy proposal department also develops tools to simulate different scenarios and propose a strategy based on the most promising scenario. For example, it calculates the probability of success for each scenario. This allows the department to propose a strategy based on the most promising scenario by simulating different scenarios.

[0043] The Strategy Proposal Department can perform a detailed analysis of a company's resources and capabilities and propose feasible strategies based on that analysis. For example, the Strategy Proposal Department uses generative AI to perform a detailed analysis of a company's resources and capabilities and propose feasible strategies based on that analysis. For example, it evaluates a company's financial situation and human resources. The Strategy Proposal Department also analyzes a company's resources and capabilities and develops tools to propose feasible strategies based on that analysis. For example, it builds a resource evaluation model. The Strategy Proposal Department also uses generative AI to monitor a company's resources and capabilities in real time and propose feasible strategies based on that analysis. For example, it predicts resource fluctuations and adjusts the strategy. This allows the department to propose feasible strategies based on a detailed analysis of a company's resources and capabilities.

[0044] The Strategy Proposal Department can discover new market needs by combining strategies from different industries and applications. For example, the Strategy Proposal Department builds a system that combines strategies from different industries to discover new market needs. For example, it proposes a strategy that combines the technology field with the consumer market. The Strategy Proposal Department also proposes new products and services by combining strategies from different applications. For example, it proposes a strategy to apply medical technology to everyday life. The Strategy Proposal Department also conducts market research based on the combined strategies to identify new market needs. For example, it conducts market research that combines the needs of different industries. In this way, new market needs can be discovered by combining strategies from different industries and applications.

[0045] The Strategy Proposal Department can introduce agile methods, which implement strategic proposals as prototypes and improve them based on feedback. For example, the Strategy Proposal Department introduces agile methods, which implement strategic proposals as prototypes and improve them based on user feedback. For example, a prototype is developed in a short period of time and user opinions are reflected. The Strategy Proposal Department also collects user feedback in real time during the prototype development process and makes immediate improvements. For example, it conducts user interviews and surveys. The Strategy Proposal Department also uses agile methods to iteratively develop prototypes and improve the quality of the final product or service. For example, it utilizes sprint reviews and daily scrums. In this way, the introduction of agile methods allows strategic proposals to be improved quickly.

[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 strategy optimization system can also cross-reference successful and unsuccessful cases from different industries to identify similarities and differences. For example, by comparing market entry strategies in the manufacturing and service industries, it is possible to understand the characteristics of the different industries and propose more effective strategies. It can also analyze cases from different regions to identify factors unique to each region. For example, it can analyze differences in consumer behavior between Asian and Western markets and optimize strategies for each region. It can also cross-reference cases from different eras to evaluate the impact of historical background. For example, it can compare corporate strategies from the bubble economy and the post-Lehman shock to propose strategies that respond to changes in economic conditions. By comparing cases from different industries and regions, it is possible to evaluate strategies from a broader perspective.

[0048] The business strategy optimization system can also analyze the influence of leadership style and organizational culture and evaluate their impact on strategic success or failure. For example, by evaluating the influence of charismatic leadership and democratic leadership, it can clarify which leadership style is effective in a particular situation. To analyze the influence of organizational culture, it can also collect data on past corporate cultures and evaluate the differences between innovative and conservative cultures. It can also analyze leadership transitions and evaluate their impact on strategic success or failure. For example, it can compare corporate performance before and after a CEO change and clarify the impact of leadership changes on corporate strategy. By taking into account the influence of leadership style and organizational culture, the accuracy of assessments of strategic success or failure can be improved.

[0049] The business strategy optimization system can also analyze multimodal information, including image and audio data, to gain insights from visual and audio information. For example, analyzing images from an advertising campaign and evaluating consumer responses can clarify the impact of visual elements on a strategy. Similarly, analyzing the audio from a product launch event and evaluating the audience's responses can clarify the impact of audio elements on a strategy. Furthermore, it is possible to integrate image, audio, and text data to gain comprehensive insights from past cases. For example, integrating multiple data sources can identify the factors that led to the success of a strategy. This allows for more multifaceted insights to be gained by analyzing multimodal information.

[0050] The business strategy optimization system can also hold workshops that bring together experts from different industries and fields to jointly devise new strategies based on past cases. For example, a forum can be created to bring together experts from technical, design, and marketing fields to combine ideas from different perspectives. In addition, workshop discussions can be recorded and saved as audio data, allowing important ideas and opinions to be stored in a database. Furthermore, materials and presentation data used in workshops can be collected and saved in a database as background information for ideas. This makes it possible to devise new strategies by utilizing the knowledge of experts from different industries and fields.

[0051] The business strategy optimization system can further simulate different scenarios and propose a strategy based on the most promising one. For example, it can simulate multiple market entry strategies and identify the most promising one to evaluate risk and return. It can also compare high-risk scenarios with low-risk scenarios to propose the optimal strategy for a company. Furthermore, it is possible to develop tools to simulate different scenarios and evaluate the likelihood of a strategy's success or failure. For example, it can calculate the probability of success for each scenario and propose the most promising strategy for a company. This makes it possible to simulate different scenarios and propose a strategy based on the most promising scenario.

[0052] The business strategy optimization system can also use the corporate data collection unit to collect real-time data and reflect the latest market trends and competitors' actions. For example, by collecting the latest financial data and market share data and analyzing it with generative AI, the current company data can be grasped in real time. It can also monitor competitors' actions in real time and reflect this in the company's strategy. For example, by collecting information on competitors' new product announcements and market entry, competitors' actions can be understood. Furthermore, it is possible to analyze the latest market trends in real time and reflect this in the company's strategy. For example, by monitoring consumer purchasing behavior and trends in real time, it is possible to propose strategies that take into account the latest market trends and competitors' actions.

[0053] The management strategy optimization system can also use the company data collection unit to analyze supply chain and logistics data and propose strategies for improving efficiency. For example, by analyzing a company's supply chain data and optimizing inventory management and the supply chain, it can propose strategies for improving efficiency. It can also analyze logistics data to optimize delivery routes and reduce logistics costs. It can also integrate data from the entire supply chain and optimize supplier selection and procurement strategies. This makes it possible to propose strategies for improving efficiency by analyzing supply chain and logistics data.

[0054] The business strategy optimization system can also use the corporate data collection section to collect unstructured data such as social media and online reviews, allowing it to reflect consumer opinions. For example, by collecting social media posting data and analyzing it with generative AI, it can propose strategies that reflect consumer opinions and emotions. Also, by collecting online review data and analyzing it with generative AI, it is possible to reflect consumer feedback on products and services in strategies. Furthermore, by collecting unstructured data and analyzing it with generative AI, it is also possible to analyze blog articles and forum posts and reflect consumer opinions in strategies. In this way, by collecting unstructured data, it is possible to reflect consumer opinions in strategies.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The historical case learning unit uses generative AI to learn from past cases. For example, generative AI learns a large number of past successes and failures and analyzes that data. Generative AI also learns about past corporate growth strategies, market entry strategies, product development strategies, etc., and analyzes the conditions under which each case succeeded or failed. Furthermore, generative AI performs analysis based on detailed data and literature on past cases. Step 2: The corporate data collection department collects current corporate data, such as the company's financial data, market share, competitor trends, and consumer preferences. The corporate data collection department also collects detailed data on the company's current situation and converts it into a format that is easy for the generation AI to analyze. Step 3: The strategy evaluation unit evaluates the likelihood of success or failure of the strategy based on past cases learned by the historical case learning unit and the company data collected by the company data collection unit. For example, the generative AI refers to strategies that have been successful or unsuccessful under similar conditions in the past and evaluates the likelihood of success of the target company's strategy. The generative AI also makes an evaluation based on the results of comparing past cases with current company data. Step 4: The strategy proposal unit proposes optimal strategies based on the results of the evaluation by the strategy evaluation unit. For example, the generation AI may propose a new market entry strategy to promote corporate growth or a product development strategy to compete with competitors. The generation AI also generates strategies based on prompts for strategy proposals based on the evaluation results.

[0057] (Example 2) The management strategy optimization system according to an embodiment of the present invention is a system that learns from past historical cases and analyzes current company data to evaluate the likelihood of success or failure of a company's business strategy and propose an optimal strategy. This allows the management strategy optimization system to evaluate the likelihood of success or failure of a company's business strategy and propose an optimal strategy.

[0058] A management strategy optimization system according to an embodiment includes a historical case learning unit, a company data collection unit, a strategy evaluation unit, and a strategy proposal unit. The historical case learning unit uses a generation AI to learn past cases. For example, the generation AI learns a large number of past successes and failures and analyzes the data. The generation AI also learns past corporate growth strategies, market entry strategies, product development strategies, and the like, and analyzes the conditions under which each case succeeded or failed. The generation AI also performs analysis based on detailed data and literature related to past cases. The company data collection unit collects current company data, such as company financial data, market share, competitor trends, and consumer preferences. The company data collection unit also collects detailed data on the current company situation and converts the data into a format that is easy for the generation AI to analyze. The strategy evaluation unit evaluates the likelihood of success or failure of a strategy based on the past cases learned by the historical case learning unit and the company data collected by the company data collection unit. For example, the generation AI evaluates the likelihood of success of a target company's strategy by referring to strategies that have been successful or unsuccessful under similar conditions in the past. The generation AI also performs evaluation based on the results of comparing past cases with current company data. The strategy proposal unit proposes an optimal strategy based on the results of the evaluation by the strategy evaluation unit. For example, the generation AI proposes a new market entry strategy to promote company growth, or a product development strategy to compete with competitors. The generation AI also generates a strategy based on strategy proposal prompts based on the evaluation results. As a result, the business strategy optimization system according to the embodiment can evaluate the likelihood of success or failure of a strategy based on past cases and current company data, and propose an optimal strategy.

[0059] The historical case learning unit can analyze emotional factors in past cases and evaluate their impact on the success or failure of a strategy. For example, the historical case learning unit uses generative AI to analyze consumer emotions in past cases. For example, it analyzes social media posts and reviews to track changes in consumer emotions. The historical case learning unit also uses generative AI to analyze news articles and reports to extract emotional trends in order to analyze past market reactions. For example, it evaluates market reactions to specific products and services. The historical case learning unit also uses generative AI to analyze employee emotions in past cases and evaluate their impact on the success or failure of a strategy. For example, it analyzes employee satisfaction surveys and internal reports. This improves the accuracy of evaluating the success or failure of a strategy by taking emotional factors into account.

[0060] The historical case study section cross-references cases from different industries and regions to clarify similarities and differences. For example, the historical case study section cross-references successful and unsuccessful cases from different industries to clarify similarities and differences. For example, it compares market entry strategies between manufacturing and service industries. The historical case study section also analyzes cases from different regions to identify region-specific factors. For example, it analyzes differences in consumer behavior between Asian and Western markets. The historical case study section also cross-references cases from different eras to evaluate the impact of historical background. For example, it compares corporate strategies during the bubble economy and after the Lehman shock. In this way, by comparing cases from different industries and regions, strategies can be evaluated from a broader perspective.

[0061] The Historical Case Learning Department can analyze the influence of leadership style and organizational culture and evaluate its impact on the success or failure of a strategy. For example, the Historical Case Learning Department uses generative AI to analyze leadership styles in past cases. For example, it evaluates the influence of charismatic leadership and democratic leadership. To analyze the influence of organizational culture, the Historical Case Learning Department collects data on past corporate cultures and analyzes it with generative AI. For example, it evaluates the difference between an innovative culture and a conservative culture. The Historical Case Learning Department also uses generative AI to analyze changes in leadership in past cases and evaluate their impact on the success or failure of a strategy. For example, it compares corporate performance before and after a CEO change. This improves the accuracy of evaluating the success or failure of a strategy by taking into account the influence of leadership style and organizational culture.

[0062] The historical case learning unit analyzes multimodal information, including image and audio data, to gain insights from visual and audio information. For example, the historical case learning unit analyzes image data to evaluate visual elements in past cases. For example, it analyzes images from an advertising campaign to evaluate consumer reactions. The historical case learning unit also analyzes audio data to evaluate audio elements in past cases. For example, it analyzes audio from a product launch event to evaluate audience reactions. The historical case learning unit also integrates multimodal information to gain comprehensive insights into past cases. For example, it integrates image, audio, and text data to identify the factors that led to the success of a strategy. This allows for more multifaceted insights to be gained by analyzing multimodal information.

[0063] The Historical Case Learning Department can hold workshops that bring together experts from different industries and fields to jointly devise new strategies based on past cases. For example, the Historical Case Learning Department can hold workshops that invite experts from different industries to jointly devise new strategies based on past cases. For example, it can provide a forum for experts from technology, design, and marketing to gather together. The Historical Case Learning Department also records the discussions at the workshops and saves them as audio data. It then performs audio analysis to extract important ideas and opinions and stores them in a database. The Historical Case Learning Department also collects materials and presentation data used in the workshops and saves them in a database as background information for ideas. This clarifies the origins and rationale of ideas. This makes it possible to devise new strategies by utilizing the knowledge of experts from different industries and fields.

[0064] The historical case learning unit uses the emotion estimation function to analyze consumer and employee emotions in past cases and, based on that, identify the success factors of a strategy. For example, the historical case learning unit uses the emotion estimation function to analyze consumer emotions in past cases. For example, it analyzes social media posts and reviews to track changes in consumer emotions. The historical case learning unit also analyzes employee emotions in past cases and evaluates how they affected the success factors of a strategy. For example, it analyzes employee satisfaction surveys and internal reports. The historical case learning unit also uses the emotion estimation function to analyze market emotions in past cases and, based on that, identify the success factors of a strategy. For example, it analyzes news articles and reports to extract trends in market emotions. In this way, the emotion estimation function can be used to more accurately identify the success factors of a strategy.

[0065] The corporate data collection department can analyze emotional elements in current data and evaluate their impact on strategy. For example, the corporate data collection department uses generative AI to analyze a company's employee satisfaction survey data and evaluate the impact of employee emotions on strategy. For example, it analyzes the strategic success rate of companies with highly motivated employees. The corporate data collection department also analyzes customer feedback data and evaluates the impact of customer emotions on strategy. For example, it analyzes the market success rate of products with a lot of positive feedback. The corporate data collection department also uses generative AI to analyze a company's internal communication data and evaluate the impact of employee emotions on strategy. For example, it analyzes the contents of internal emails and chats. In this way, by taking emotional elements into consideration, the accuracy of strategy evaluation is improved.

[0066] When analyzing current data, the corporate data collection department collects real-time data, allowing it to reflect the latest market trends and competitors' actions. The corporate data collection department, for example, collects real-time data and analyzes the company's current data. For example, it collects the latest financial data and market share data and analyzes it using generative AI. The corporate data collection department also monitors competitors' actions in real time and reflects this in the company's strategy. For example, it collects information on competitors' new product announcements and market entry. The corporate data collection department also analyzes the latest market trends in real time and reflects this in the company's strategy. For example, it monitors consumer purchasing behavior and trends in real time. By reflecting real-time data, it is possible to propose strategies that take into account the latest market trends and competitors' actions.

[0067] The corporate data collection department can analyze supply chain and logistics data and propose strategies for improving efficiency. For example, using generative AI, the corporate data collection department can analyze a company's supply chain data and propose strategies for improving efficiency. For example, optimizing inventory management and the supply chain. The corporate data collection department can also analyze logistics data and propose strategies for improving efficiency. For example, optimizing delivery routes and reducing logistics costs. The corporate data collection department can also use generative AI to integrate data from the entire supply chain and propose strategies for improving efficiency. For example, optimizing supplier selection and procurement strategies. In this way, by analyzing supply chain and logistics data, strategies for improving efficiency can be proposed.

[0068] The corporate data collection department also collects unstructured data such as social media and online reviews, allowing it to reflect consumer opinions. For example, the corporate data collection department collects social media posting data and analyzes it with generative AI. For example, it proposes a strategy that reflects consumer opinions and emotions. The corporate data collection department also collects online review data and analyzes it with generative AI. For example, it reflects consumer feedback on products and services in its strategy. The corporate data collection department also collects unstructured data and analyzes it with generative AI. For example, it analyzes blog articles and forum posts and reflects consumer opinions in its strategy. In this way, by collecting unstructured data, it is possible to reflect consumer opinions in its strategy.

[0069] The corporate data collection department can compare corporate data from different regions and cultural spheres and propose strategies from a global perspective. For example, the corporate data collection department collects corporate data from different regions and analyzes it using generative AI. For example, it compares data from the Asian market with that of the Western market and proposes a global strategy. The corporate data collection department also collects corporate data from different cultural spheres and analyzes it using generative AI. For example, it proposes a strategy that takes cultural differences into account. The corporate data collection department also integrates data from different regions and cultural spheres and analyzes it using generative AI to propose strategies from a global perspective. For example, it proposes a strategy that reflects the market needs of each region. In this way, by comparing data from different regions and cultural spheres, it is possible to propose strategies from a global perspective.

[0070] The corporate data collection unit can use the emotion estimation function to monitor the emotions of the company's employees and customers in real time and adjust strategies based on that. For example, the corporate data collection unit uses the emotion estimation function to monitor the emotions of the company's employees in real time. For example, it analyzes employee motivation and stress levels and adjusts strategies. The corporate data collection unit also monitors customer emotions in real time and adjusts strategies based on that. For example, it analyzes customer feedback and reviews and adjusts strategies. The corporate data collection unit also uses the emotion estimation function to monitor the company's internal communication data in real time and adjust strategies based on that. For example, it analyzes the content of internal emails and chats. In this way, it is possible to dynamically adjust strategies by monitoring the emotions of employees and customers in real time.

[0071] The strategy evaluation department can take emotional factors into account when comparing past cases with current company data and evaluate the likelihood of a strategy's success or failure. For example, the strategy evaluation department uses generative AI to compare past cases with current company data and evaluate the likelihood of a strategy's success or failure by taking consumer emotions into account. For example, it prioritizes strategies that evoke strong positive consumer emotions. The strategy evaluation department also evaluates the likelihood of a strategy's success or failure by taking market reactions into account. For example, it predicts current market reactions based on past market reaction data. The strategy evaluation department also uses generative AI to compare past cases with current company data and evaluate the likelihood of a strategy's success or failure by taking employee emotions into account. For example, it prioritizes strategies that highly motivate employees. In this way, by taking emotional factors into account, the accuracy of evaluations of strategy success or failure is improved.

[0072] The strategy evaluation department can simulate different scenarios to identify the most promising scenario when evaluating the likelihood of a strategy's success or failure. For example, the strategy evaluation department simulates different scenarios to evaluate the likelihood of a strategy's success or failure. For example, it simulates multiple market entry strategies to identify the most promising scenario. The strategy evaluation department also uses generative AI to simulate different scenarios to evaluate risk and return. For example, it compares high-risk scenarios with low-risk scenarios. The strategy evaluation department also develops tools to simulate different scenarios and evaluate the likelihood of a strategy's success or failure. For example, it calculates the probability of success for each scenario. This makes it possible to identify the most promising scenario by simulating different scenarios.

[0073] The Strategy Evaluation Department can analyze a company's risk factors in detail and propose a risk management strategy based on that. The Strategy Evaluation Department, for example, uses generative AI to analyze a company's risk factors in detail and propose a risk management strategy. For example, it evaluates financial risk and market risk and proposes risk mitigation measures. The Strategy Evaluation Department also analyzes a company's risk factors and develops tools to propose a risk management strategy based on that analysis. For example, it builds a risk assessment model. The Strategy Evaluation Department also uses generative AI to monitor a company's risk factors in real time and propose a risk management strategy. For example, it predicts the occurrence of risk and takes measures in advance. This allows the department to propose a risk management strategy based on a detailed analysis of risk factors.

[0074] The Strategy Evaluation Department can integrate data from different industries and regions to evaluate the likelihood of a strategy's success or failure from a broader perspective. For example, the Strategy Evaluation Department integrates data from different industries to evaluate the likelihood of a strategy's success or failure. For example, it integrates data from the manufacturing and service industries to evaluate strategies. The Strategy Evaluation Department also integrates data from different regions to evaluate the likelihood of a strategy's success or failure. For example, it integrates data from the Asian market and the European and American markets to evaluate strategies. The Strategy Evaluation Department also integrates data from different industries and regions to develop tools for evaluating the likelihood of a strategy's success or failure from a broader perspective. For example, it builds a strategy evaluation model from a global perspective. This allows strategies to be evaluated from a broader perspective by integrating data from different industries and regions.

[0075] The strategy evaluation unit can convert the strategy evaluation results into visual notes or mind maps to make them easier to understand visually. For example, the strategy evaluation unit converts the strategy evaluation results into visual notes and displays them visually. For example, it shows important points using diagrams or icons. The strategy evaluation unit also converts the strategy evaluation results into mind map format and visually organizes related keywords and concepts. This makes it possible to understand the overall picture of the strategy at a glance. The strategy evaluation unit also develops tools that automatically generate visual notes and mind maps, allowing users to easily visually display the strategy evaluation results. For example, it provides a function to visualize evaluation results using drag and drop. This makes it easier to understand by visually displaying the strategy evaluation results.

[0076] The strategy evaluation unit uses the emotion estimation function to collect users' emotional reactions to the strategy evaluation results and can improve the accuracy of the evaluation based on that. The strategy evaluation unit, for example, collects users' emotional reactions to the strategy evaluation results in real time and improves the accuracy of the evaluation based on that data. For example, it prioritizes the adoption of evaluation results with a high number of positive reactions. The strategy evaluation unit also uses the emotion estimation function to collect feedback on the strategy evaluation results and regenerates the evaluation results if there are a high number of negative reactions. The strategy evaluation unit also analyzes users' emotional reaction data and identifies areas for improvement in the evaluation results based on the results. For example, it makes suggestions to correct parts with low emotional scores. In this way, the accuracy of the evaluation can be improved by collecting users' emotional reactions.

[0077] The strategy proposal unit can use the emotion estimation function to consider the user's emotions and propose a strategy that elicits positive emotions. The strategy proposal unit, for example, uses the emotion estimation function to propose a strategy that takes the user's emotions into consideration. For example, it prioritizes proposing strategies that elicit positive emotions from the user. The strategy proposal unit also analyzes the user's emotional reactions in real time and proposes a strategy based on the results. For example, it prioritizes proposing themes that evoke strong positive emotions. The strategy proposal unit also develops a system that proposes strategies based on the emotion estimation data, tailored to the user's preferences and interests. For example, it dynamically adjusts the content of the proposal according to changes in the user's emotions. This makes it possible to propose a strategy that elicits positive emotions by considering the user's emotions.

[0078] When proposing a strategy, the strategy proposal department can simulate different scenarios and propose a strategy based on the most promising scenario. For example, the strategy proposal department simulates different scenarios and proposes a strategy based on the most promising scenario. For example, the strategy proposal department simulates multiple market entry strategies and identifies the most promising scenario. The strategy proposal department also uses generative AI to simulate different scenarios and evaluate risks and returns. For example, it compares high-risk scenarios with low-risk scenarios. The strategy proposal department also develops tools to simulate different scenarios and propose a strategy based on the most promising scenario. For example, it calculates the probability of success for each scenario. This allows the department to propose a strategy based on the most promising scenario by simulating different scenarios.

[0079] The Strategy Proposal Department can perform a detailed analysis of a company's resources and capabilities and propose feasible strategies based on that analysis. For example, the Strategy Proposal Department uses generative AI to perform a detailed analysis of a company's resources and capabilities and propose feasible strategies based on that analysis. For example, it evaluates a company's financial situation and human resources. The Strategy Proposal Department also analyzes a company's resources and capabilities and develops tools to propose feasible strategies based on that analysis. For example, it builds a resource evaluation model. The Strategy Proposal Department also uses generative AI to monitor a company's resources and capabilities in real time and propose feasible strategies based on that analysis. For example, it predicts resource fluctuations and adjusts the strategy. This allows the department to propose feasible strategies based on a detailed analysis of a company's resources and capabilities.

[0080] The Strategy Proposal Department can discover new market needs by combining strategies from different industries and applications. For example, the Strategy Proposal Department builds a system that combines strategies from different industries to discover new market needs. For example, it proposes a strategy that combines the technology field with the consumer market. The Strategy Proposal Department also proposes new products and services by combining strategies from different applications. For example, it proposes a strategy to apply medical technology to everyday life. The Strategy Proposal Department also conducts market research based on the combined strategies to identify new market needs. For example, it conducts market research that combines the needs of different industries. In this way, new market needs can be discovered by combining strategies from different industries and applications.

[0081] The Strategy Proposal Department can introduce agile methods, which implement strategic proposals as prototypes and improve them based on feedback. For example, the Strategy Proposal Department introduces agile methods, which implement strategic proposals as prototypes and improve them based on user feedback. For example, a prototype is developed in a short period of time and user opinions are reflected. The Strategy Proposal Department also collects user feedback in real time during the prototype development process and makes immediate improvements. For example, it conducts user interviews and surveys. The Strategy Proposal Department also uses agile methods to iteratively develop prototypes and improve the quality of the final product or service. For example, it utilizes sprint reviews and daily scrums. In this way, the introduction of agile methods allows strategic proposals to be improved quickly.

[0082] The strategy proposal unit uses the emotion estimation function to monitor the user's emotional reactions to proposed strategies in real time, and is able to continuously search for optimal strategies. The strategy proposal unit, for example, uses the emotion estimation function to develop a system that monitors the user's emotional reactions to proposed strategies in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The strategy proposal unit also evaluates proposed strategies based on the user's emotional reaction data and continuously searches for optimal strategies. For example, it prioritizes the adoption of strategies with a high number of positive emotional reactions. The strategy proposal unit also collects emotion estimation data in real time and builds a system that uses the data to improve proposed strategies. For example, it dynamically adjusts strategies in response to changes in the user's emotions. In this way, it is possible to continuously search for optimal strategies by monitoring the user's emotional reactions in real time.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The business strategy optimization system can also cross-reference successful and unsuccessful cases from different industries to identify similarities and differences. For example, by comparing market entry strategies in the manufacturing and service industries, it is possible to understand the characteristics of the different industries and propose more effective strategies. It can also analyze cases from different regions to identify factors unique to each region. For example, it can analyze differences in consumer behavior between Asian and Western markets and optimize strategies for each region. It can also cross-reference cases from different eras to evaluate the impact of historical background. For example, it can compare corporate strategies from the bubble economy and the post-Lehman shock to propose strategies that respond to changes in economic conditions. By comparing cases from different industries and regions, it is possible to evaluate strategies from a broader perspective.

[0085] The business strategy optimization system can also analyze the influence of leadership style and organizational culture and evaluate their impact on strategic success or failure. For example, by evaluating the influence of charismatic leadership and democratic leadership, it can clarify which leadership style is effective in a particular situation. To analyze the influence of organizational culture, it can also collect data on past corporate cultures and evaluate the differences between innovative and conservative cultures. It can also analyze leadership transitions and evaluate their impact on strategic success or failure. For example, it can compare corporate performance before and after a CEO change and clarify the impact of leadership changes on corporate strategy. By taking into account the influence of leadership style and organizational culture, the accuracy of assessments of strategic success or failure can be improved.

[0086] The business strategy optimization system can also analyze multimodal information, including image and audio data, to gain insights from visual and audio information. For example, analyzing images from an advertising campaign and evaluating consumer responses can clarify the impact of visual elements on a strategy. Similarly, analyzing the audio from a product launch event and evaluating the audience's responses can clarify the impact of audio elements on a strategy. Furthermore, it is possible to integrate image, audio, and text data to gain comprehensive insights from past cases. For example, integrating multiple data sources can identify the factors that led to the success of a strategy. This allows for more multifaceted insights to be gained by analyzing multimodal information.

[0087] The business strategy optimization system can also hold workshops that bring together experts from different industries and fields to jointly devise new strategies based on past cases. For example, a forum can be created to bring together experts from technical, design, and marketing fields to combine ideas from different perspectives. In addition, workshop discussions can be recorded and saved as audio data, allowing important ideas and opinions to be stored in a database. Furthermore, materials and presentation data used in workshops can be collected and saved in a database as background information for ideas. This makes it possible to devise new strategies by utilizing the knowledge of experts from different industries and fields.

[0088] The business strategy optimization system can further simulate different scenarios and propose a strategy based on the most promising one. For example, it can simulate multiple market entry strategies and identify the most promising one to evaluate risk and return. It can also compare high-risk scenarios with low-risk scenarios to propose the optimal strategy for a company. Furthermore, it is possible to develop tools to simulate different scenarios and evaluate the likelihood of a strategy's success or failure. For example, it can calculate the probability of success for each scenario and propose the most promising strategy for a company. This makes it possible to simulate different scenarios and propose a strategy based on the most promising scenario.

[0089] The business strategy optimization system can also use the emotion estimation function to analyze consumer and employee emotions from past cases and identify the success factors of a strategy based on that information. For example, by analyzing social media posts and reviews and tracking changes in consumer emotions, it can clarify the impact of consumer emotions on a strategy. It can also analyze employee satisfaction surveys and internal reports to evaluate how employee emotions influenced the success factors of a strategy. It can also analyze news articles and reports to extract trends in market emotions and identify the success factors of a strategy. As a result, the emotion estimation function can more accurately identify the success factors of a strategy.

[0090] The management strategy optimization system can also use the company data collection unit to analyze emotional factors in current data and evaluate their impact on strategy. For example, by analyzing a company's employee satisfaction survey data and evaluating the impact of employee emotions on strategy, the system can analyze the strategic success rate of companies with highly motivated employees. It can also analyze customer feedback data and evaluate the impact of customer emotions on strategy. For example, it can analyze the market success rate of products with a lot of positive feedback. It can also analyze a company's internal communication data and evaluate the impact of employee emotions on strategy. For example, analyzing the content of internal emails and chats can take emotional factors into account, improving the accuracy of strategy evaluation.

[0091] The business strategy optimization system can also use the corporate data collection unit to collect real-time data and reflect the latest market trends and competitors' actions. For example, by collecting the latest financial data and market share data and analyzing it with generative AI, the current company data can be grasped in real time. It can also monitor competitors' actions in real time and reflect this in the company's strategy. For example, by collecting information on competitors' new product announcements and market entry, competitors' actions can be understood. Furthermore, it is possible to analyze the latest market trends in real time and reflect this in the company's strategy. For example, by monitoring consumer purchasing behavior and trends in real time, it is possible to propose strategies that take into account the latest market trends and competitors' actions.

[0092] The management strategy optimization system can also use the company data collection unit to analyze supply chain and logistics data and propose strategies for improving efficiency. For example, by analyzing a company's supply chain data and optimizing inventory management and the supply chain, it can propose strategies for improving efficiency. It can also analyze logistics data to optimize delivery routes and reduce logistics costs. It can also integrate data from the entire supply chain and optimize supplier selection and procurement strategies. This makes it possible to propose strategies for improving efficiency by analyzing supply chain and logistics data.

[0093] The business strategy optimization system can also use the corporate data collection section to collect unstructured data such as social media and online reviews, allowing it to reflect consumer opinions. For example, by collecting social media posting data and analyzing it with generative AI, it can propose strategies that reflect consumer opinions and emotions. Also, by collecting online review data and analyzing it with generative AI, it is possible to reflect consumer feedback on products and services in strategies. Furthermore, by collecting unstructured data and analyzing it with generative AI, it is also possible to analyze blog articles and forum posts and reflect consumer opinions in strategies. In this way, by collecting unstructured data, it is possible to reflect consumer opinions in strategies.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The historical case learning unit uses generative AI to learn from past cases. For example, generative AI learns a large number of past successes and failures and analyzes that data. Generative AI also learns about past corporate growth strategies, market entry strategies, product development strategies, etc., and analyzes the conditions under which each case succeeded or failed. Furthermore, generative AI performs analysis based on detailed data and literature on past cases. Step 2: The corporate data collection department collects current corporate data, such as the company's financial data, market share, competitor trends, and consumer preferences. The corporate data collection department also collects detailed data on the company's current situation and converts it into a format that is easy for the generation AI to analyze. Step 3: The strategy evaluation unit evaluates the likelihood of success or failure of the strategy based on past cases learned by the historical case learning unit and the company data collected by the company data collection unit. For example, the generative AI refers to strategies that have been successful or unsuccessful under similar conditions in the past and evaluates the likelihood of success of the target company's strategy. The generative AI also makes an evaluation based on the results of comparing past cases with current company data. Step 4: The strategy proposal unit proposes optimal strategies based on the results of the evaluation by the strategy evaluation unit. For example, the generation AI may propose a new market entry strategy to promote corporate growth or a product development strategy to compete with competitors. The generation AI also generates strategies based on prompts for strategy proposals based on the evaluation results.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] 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]

[0163] 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 historical case learning unit that uses generative AI to learn from past cases, A company data collection department that collects current company data; a strategy evaluation unit that evaluates the likelihood of success or failure of a strategy based on the past cases learned by the historical case learning unit and the company data collected by the company data collection unit; a strategy proposal unit that proposes an optimal strategy based on the results of evaluation by the strategy evaluation unit. A system characterized by:

2. The historical case learning unit Analyze the emotional aspects of the past cases and assess their impact on the success or failure of the strategy.

2. The system of claim 1.

3. The historical case learning unit Cross-reference examples from different industries and regions to clarify commonalities and differences 2. The system of claim 1.

4. The historical case learning unit Analyze the impact of leadership style and organizational culture and assess their impact on strategic success or failure 2. The system of claim 1.

5. The historical case learning unit Analyze multimodal information, including image and audio data, to gain insights from visual and auditory information 2. The system of claim 1.

6. The historical case learning unit Hold workshops that bring together experts from different industries and fields to jointly devise new strategies based on past examples.

2. The system of claim 1.

7. The historical case learning unit Analyze consumer and employee sentiment in past cases and identify success factors for your strategy based on that.

2. The system of claim 1.

8. The company data collection unit Analyze the emotional impact of current data and assess its strategic implications 2. The system of claim 1.

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