system

Abstract

The system according to the embodiment aims to learn past business decisions of the CEO and support future business decisions.SOLUTION: A system according to an embodiment includes a collection unit, a learning unit, and a proposal unit. The collector collects historical business decision data of the CEO. The learning unit learns the data collected by the collection unit. The proposal unit proposes a future business decision based on the data learned by the learning 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 utilize a CEO's past business decisions to support future business decisions, and there is room for improvement.

[0005] The system according to the embodiment aims to learn from the CEO's past business decisions and support future business decisions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, and a proposal unit. The collection unit collects data on past business decisions made by the CEO. The learning unit learns from the data collected by the collection unit. The proposal unit proposes future business decisions based on the data learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can learn from the CEO's past business decisions and support future business decisions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business decision support system according to an embodiment of the present invention learns a CEO's past business decisions and proposes future business decisions. The business decision support system collects data on the CEO's past business decisions, and a generation AI learns from it. The system then responds with, "This is what the CEO would think," when making future business decisions. The generation AI analyzes the CEO's past decision patterns and proposes optimal decisions for similar situations. For example, the business decision support system collects data on the CEO's past business decisions, such as past project decisions, investment decisions, and strategic directions. The business decision support system then trains the generation AI on the collected data. The generation AI analyzes the collected data and learns the CEO's decision patterns. For example, the generation AI analyzes the decisions the CEO made in different situations and learns those patterns. Furthermore, the business decision support system uses the data learned by the generation AI to respond with, "This is what the CEO would think," when making future business decisions. For example, when a new project is proposed, the generation AI suggests the CEO's decision based on similar past situations. This supports the CEO's decision-making. In this way, the business decision support system can support the CEO's decisions and increase the probability of business success. In this way, the business decision support system can support the CEO's decisions and increase the probability of business success. For example, by making current business decisions based on past success stories, the probability of success can be increased. Also, by learning from past failure stories, it can be possible to avoid repeating the same mistakes.

[0029] A business decision support system according to an embodiment includes a collection unit, a learning unit, and a proposal unit. The collection unit collects data on the CEO's past business decisions. For example, the collection unit collects detailed data on business decisions made by the CEO in the past. For example, the collection unit can collect data on past project decisions, investment decisions, strategic directions, and the like. The collection unit can also collect data from databases and document management systems. For example, the collection unit extracts data on past business decisions from a company's database. The collection unit can also collect document data such as the CEO's statements and notes. The learning unit uses a generation AI to learn from the data collected by the collection unit. For example, the learning unit analyzes the collected data and learns the CEO's decision-making patterns. For example, the generation AI uses a machine learning algorithm to analyze the CEO's decisions in different situations and learns those patterns. The generation AI performs learning by taking into account, for example, factors that influenced the CEO's decisions and the results of those decisions. The proposal unit proposes future business decisions based on the data learned by the learning unit. For example, the proposal unit proposes optimal decisions based on similar past situations. For example, the suggestion unit can suggest current business decisions based on past success stories. The suggestion unit can also suggest ways to avoid repeating the same mistakes based on past failure stories. This allows the business decision support system according to the embodiment to support CEO decisions and increase the probability of business success. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the suggestion unit can make suggestions using a generative AI model that receives input data learned by the learning unit and outputs a business decision.

[0030] The collection unit can collect detailed data on business decisions made by the CEO in the past. Examples of detailed data include, but are not limited to, the date, time, location, and people involved. The collection unit can collect detailed data on investment decisions made by the CEO. For example, the collection unit can collect data such as the date and time of the investment decision, the investment target, the investment amount, and the investment results. The collection unit can also collect detailed data on strategic decisions made by the CEO. For example, the collection unit can collect data such as the background of the strategic decision, the reasons for the decision, and the results of the decision. The collection unit can also collect detailed data on project selection made by the CEO. For example, the collection unit can collect data such as the project selection criteria, the selection process, and the selection results. Collecting detailed data on the CEO's past business decisions enables more accurate learning. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input document data on the CEO's past business decisions into the generation AI and have the generation AI extract the detailed data.

[0031] The learning unit can analyze the collected data and learn the CEO's decision-making patterns. The learning unit, for example, analyzes the collected data and learns the CEO's decision-making patterns. For example, the learning unit uses frequency analysis to analyze what decisions the CEO made in what situations. The learning unit can also learn the CEO's decision-making patterns using decision tree analysis. For example, the learning unit performs learning by taking into account factors that influenced the CEO's decisions and the results of the decisions. The learning unit can also learn the CEO's decision-making patterns using a machine learning algorithm. For example, the learning unit performs learning using a machine learning model that inputs data related to the CEO's decisions and outputs decision patterns. By learning the CEO's decision-making patterns, more appropriate proposals can be made. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the collected data to a generation AI and have the generation AI learn the decision patterns.

[0032] The proposal unit can propose future business decisions based on the learned data. The proposal unit, for example, proposes future business decisions based on the learned data. For example, the proposal unit proposes optimal decisions based on similar situations in the past. For example, the proposal unit can propose current business decisions based on past success stories. The proposal unit can also make suggestions to avoid repeating the same mistakes based on past failure stories. The proposal unit, for example, makes proposals regarding investment decisions. For example, the proposal unit proposes optimal decisions for current investment projects based on past investment decision data. The proposal unit can also make proposals regarding strategic directions. For example, the proposal unit proposes future strategic decisions based on past strategic decision data. This makes it possible to support the CEO's decisions by proposing future business decisions based on the learned data. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the proposal unit can make proposals using a generative AI model that uses data learned by the learning unit as input and outputs business decisions.

[0033] The proposal unit can propose appropriate decisions based on similar situations in the past. The proposal unit, for example, proposes appropriate decisions based on similar situations in the past. For example, the proposal unit can propose current business decisions based on past success stories. The proposal unit can also propose ways to avoid repeating the same mistakes based on past failure stories. The proposal unit, for example, proposes optimal decisions for current investment projects based on past investment decision data. The proposal unit can also propose future strategic decisions based on past strategic decision data. In this way, the accuracy of decisions is improved by proposing optimal decisions based on similar situations in the past. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data on similar situations in the past into the generation AI and have the generation AI execute a proposal for an optimal decision.

[0034] The proposal unit can propose current business decisions based on past success stories. The proposal unit, for example, proposes current business decisions based on past success stories. For example, the proposal unit proposes optimal decisions for current investment projects based on past success stories. The proposal unit can also propose current strategic decisions based on past success stories. For example, the proposal unit proposes optimal decisions for current project selection based on past success stories. This increases the probability of success by proposing current business decisions based on past success stories. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data on past success stories into the generation AI and have the generation AI execute a proposal for a current business decision.

[0035] The proposal unit can make suggestions based on past failure cases to prevent the same mistakes from being repeated. For example, the proposal unit makes suggestions based on past failure cases to prevent the same mistakes from being repeated. For example, the proposal unit proposes optimal decisions for current investment projects based on past failure cases. The proposal unit can also propose current strategic decisions based on past failure cases. For example, the proposal unit proposes optimal decisions for current project selection based on past failure cases. In this way, by making suggestions based on past failure cases, the same mistakes are prevented from being repeated. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data on past failure cases into the generation AI and have the generation AI execute suggestions for current business decisions.

[0036] When collecting data on the CEO's past business decisions, the collection unit can simultaneously collect background information on the decisions. For example, when collecting data on the CEO's past business decisions, the collection unit simultaneously collects background information on the decisions (e.g., economic conditions and market trends). For example, the collection unit collects the economic conditions at the time of the CEO's investment decisions. The collection unit can also collect market trends at the time of the CEO's strategic decisions. The collection unit can also collect the trends of competitors at the time of the CEO's project selection. By simultaneously collecting background information on the decisions, more detailed data analysis becomes possible. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data on the CEO's past business decisions and their background information into a generation AI and have the generation AI collect the background information.

[0037] The collection unit can identify and collect external factors that influenced the CEO's decision when collecting business decision data. For example, the collection unit can identify and collect external factors (e.g., competitor trends and regulatory changes) that influenced the CEO's decision when collecting business decision data. For example, the collection unit can collect information on new products from competitors that influenced the CEO's decision. The collection unit can also collect information on regulatory changes that influenced the CEO's decision. The collection unit can also collect information on industry trends that influenced the CEO's decision. By identifying and collecting external factors, more accurate data analysis is possible. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input data on external factors that influenced the CEO's decision into the generation AI and cause the generation AI to identify and collect the external factors.

[0038] The collection unit can also collect feedback and result data regarding the CEO's decisions when collecting the business decision data. For example, the collection unit also collects feedback and result data regarding the CEO's decisions when collecting the business decision data. For example, the collection unit collects internal feedback regarding the decisions made by the CEO. The collection unit can also collect performance data as a result of the decisions made by the CEO. The collection unit can also collect customer reactions to the decisions made by the CEO. In this way, collecting feedback and result data improves the accuracy of decisions. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input feedback and result data regarding the CEO's decisions into the generation AI and cause the generation AI to collect the feedback and result data.

[0039] When collecting business decision data, the collection unit can prioritize collecting highly relevant data by taking into account the CEO's geographic location information. For example, when collecting business decision data, the collection unit prioritizes collecting highly relevant data by taking into account the CEO's geographic location information. For example, when collecting business decision data, the collection unit prioritizes collecting highly relevant data by taking into account the CEO's geographic location information. For example, when the CEO is on a business trip, the collection unit can prioritize collecting market trends in that region. Also, when the CEO is at headquarters, the collection unit can prioritize collecting global market trends. Also, when the CEO is interested in a particular region, the collection unit can prioritize collecting competitive information in that region. In this way, highly relevant data can be collected preferentially by taking into account the geographic location information. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the CEO's geographic location information into the generation AI and cause the generation AI to collect highly relevant data.

[0040] The collection unit can analyze the CEO's social media activity and collect related data when collecting business decision data. For example, the collection unit analyzes the CEO's social media activity and collects related data when collecting business decision data. For example, the collection unit collects data related to topics mentioned by the CEO on social media. The collection unit can also collect statements by industry leaders followed by the CEO. The collection unit can also collect data related to events attended by the CEO. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input data related to the CEO's social media activity into the generation AI and cause the generation AI to collect related data.

[0041] The collection unit can appropriately adjust the collection method when collecting business decision data by reflecting the CEO's past feedback. For example, the collection unit can appropriately adjust the collection method when collecting business decision data by reflecting the CEO's past feedback. For example, the collection unit prioritizes collecting data that the CEO has considered important in the past. The collection unit can also reflect improvements to the collection method that the CEO has previously pointed out. The collection unit can also prioritize using data sources that the CEO has previously evaluated. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input data regarding the CEO's past feedback into the generation AI and have the generation AI adjust the collection method.

[0042] The learning unit can appropriately adjust the learning algorithm during learning, taking into account the success rate and failure rate of past business decision data. For example, the learning unit appropriately adjusts the learning algorithm during learning, taking into account the success rate and failure rate of past business decision data. For example, the learning unit focuses on decision data with a high success rate during learning. The learning unit can also adjust the learning algorithm to reflect decision data with a high failure rate. The learning unit can also select learning data taking into account the balance between success rate and failure rate. In this way, the accuracy of the learning algorithm is improved by taking the success rate and failure rate into account. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data regarding the success rate and failure rate of past business decision data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0043] The learning unit can analyze the transitions in the CEO's decision patterns during learning and learn them as time-series data. For example, the learning unit analyzes the transitions in the CEO's decision patterns over time and learns them as time-series data during learning. For example, the learning unit analyzes the transitions in the CEO's decision patterns over time and reflects the results in learning. The learning unit can also graph the transitions in the CEO's decision patterns and use the graph for learning. The learning unit can also adjust the learning algorithm taking into account the transitions in the CEO's decision patterns. This enables more accurate learning by analyzing the transitions in the decision patterns. Some or all of the above-described processing in the learning unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the learning unit can input data regarding the transitions in the CEO's decision patterns into the generation AI and cause the generation AI to perform learning as time-series data.

[0044] The learning unit can correct the learning data during learning by taking into account external factors that influenced the CEO's decisions. For example, the learning unit corrects the learning data during learning by taking into account external factors that influenced the CEO's decisions. For example, the learning unit reflects economic conditions that influenced the CEO's decisions in the learning data. The learning unit can also reflect market trends that influenced the CEO's decisions in the learning data. The learning unit can also reflect trends of competitors that influenced the CEO's decisions in the learning data. This improves the accuracy of the learning data by taking external factors into account. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input data regarding external factors that influenced the CEO's decisions into the generation AI and cause the generation AI to correct the learning data.

[0045] The learning unit can weight the learning data based on the submission time of the business judgment data during learning. For example, the learning unit weights the learning data based on the submission time of the business judgment data during learning. For example, the learning unit may weight recent business judgment data during learning. The learning unit may also weight past business judgment data during learning. The learning unit may also adjust the weighting of the learning data based on the submission time. In this way, weighting the learning data based on the submission time improves the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit may input data regarding the submission time of the business judgment data into the generation AI and cause the generation AI to weight the learning data.

[0046] The learning unit can integrate information from different data sources to enrich the learning data during learning. For example, the learning unit integrates information from different data sources to enrich the learning data during learning. For example, the learning unit integrates business decision data from different data sources to learn. The learning unit can also integrate market trend data from different data sources to learn. The learning unit can also integrate competitive information from different data sources to learn. In this way, the accuracy of the learning data is improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the information.

[0047] The learning unit can adjust the learning algorithm during learning by reflecting the CEO's past feedback. For example, the learning unit adjusts the learning algorithm during learning by reflecting the CEO's past feedback. For example, the learning unit adjusts the learning algorithm by reflecting the CEO's past feedback. The learning unit can also select learning data based on the CEO's past feedback. The learning unit can also optimize the learning algorithm by taking the CEO's past feedback into consideration. In this way, the accuracy of the learning algorithm is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data regarding the CEO's past feedback into the generation AI and cause the generation AI to adjust the learning algorithm.

[0048] The proposal unit can adjust the level of detail of the proposal based on the importance of the business decision when making a proposal. The proposal unit, for example, adjusts the level of detail of the proposal based on the importance of the business decision when making a proposal. For example, the proposal unit makes a detailed proposal for a business decision with a high level of importance. The proposal unit can also make a concise proposal for a business decision with a low level of importance. The proposal unit can also adjust the level of detail of the proposal based on the importance of the business decision. This enables a more appropriate proposal by adjusting the level of detail of the proposal based on the importance of the business decision. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data regarding the importance of the business decision to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0049] The proposal unit can apply different proposal algorithms depending on the category of the business decision when making a proposal. For example, the proposal unit can apply different proposal algorithms depending on the category of the business decision when making a proposal. For example, the proposal unit can apply a specific proposal algorithm to investment decisions. The proposal unit can also apply a different proposal algorithm to strategic decisions. The proposal unit can also apply a different proposal algorithm to project selection. This enables more appropriate proposals by applying different proposal algorithms depending on the category of the business decision. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, the generation AI, for example. For example, the proposal unit can input data related to the category of the business decision into the generation AI and cause the generation AI to apply the proposal algorithm.

[0050] The proposal unit can improve the accuracy of the proposal by referring to the CEO's past proposal results when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by referring to the CEO's past proposal results when making a proposal. For example, the proposal unit can analyze the CEO's past proposal results and improve the accuracy of the proposal. The proposal unit can also adjust the proposal algorithm based on the CEO's past proposal results. The proposal unit can also improve the accuracy of the proposal by reflecting the CEO's past proposal results. In this way, the accuracy of the proposal is improved by referring to the past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input data regarding the CEO's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0051] The proposal unit can determine the priority of proposals based on the submission time of the business decisions when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission time of the business decisions when making proposals. For example, the proposal unit prioritizes proposals for urgent business decisions. The proposal unit can also prioritize proposals for business decisions with an upcoming submission deadline. The proposal unit can also adjust the priority of proposals based on the submission time. This enables more appropriate proposals by determining the priority of proposals based on the submission time. Some or all of the above-described processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input data regarding the submission time of business decisions into the generation AI and have the generation AI determine the priority of proposals.

[0052] The suggestion unit can adjust the order of proposals based on the relevance of the business judgments when making a proposal. The suggestion unit, for example, adjusts the order of proposals based on the relevance of the business judgments when making a proposal. For example, the suggestion unit first makes a proposal for an important business judgment. The suggestion unit can also prioritize proposals for highly relevant business judgments. The suggestion unit can also adjust the order of proposals based on the relevance of the business judgments. This enables more appropriate proposals by adjusting the order of proposals based on the relevance of the business judgments. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data regarding the relevance of the business judgments into the generation AI and cause the generation AI to adjust the order of proposals.

[0053] The proposal unit can adjust the use of technical terminology in the proposal according to the CEO's level of expertise when making a proposal. For example, the proposal unit can adjust the use of technical terminology in the proposal according to the CEO's level of expertise when making a proposal. For example, if the CEO has technical expertise, the proposal unit can make a proposal that uses a lot of technical terminology. On the other hand, if the CEO does not have technical expertise, the proposal unit can make a concise and easy-to-understand proposal. The proposal unit can also adjust the use of technical terminology in the proposal according to the CEO's level of expertise. This enables more appropriate proposals by adjusting the use of technical terminology according to the CEO's level of expertise. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the proposal unit can input data regarding the CEO's level of expertise into the generation AI and have the generation AI execute the use of technical terminology.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The proposal unit can evaluate the reliability of the proposal based on the CEO's past business decisions. For example, the proposal unit can evaluate the reliability of the current proposal based on past success cases. The proposal unit can also evaluate the risk of the current proposal based on past failure cases. The proposal unit can also evaluate the reliability of the proposal based on the results of past business decisions. This enables more appropriate proposals to be made by evaluating the reliability of the proposal based on past business decisions. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data related to past business decisions into the generation AI and have the generation AI evaluate the reliability of the proposal.

[0056] When collecting data on the CEO's past business decisions, the collection unit can automatically classify background information for the decisions. For example, the collection unit automatically classifies background information such as economic conditions, market trends, and competitor trends. The collection unit can also classify background information based on the type of decision (investment decision, strategic decision, project selection, etc.). The collection unit can also classify background information based on the outcome of the decision. This automatically classifying background information enables more detailed data analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input data on background information into the generation AI and have the generation AI perform the classification.

[0057] The learning unit can evaluate the reliability of the data when learning the CEO's past business decision data. For example, the learning unit can evaluate reliability based on the source of the data and the collection method. The learning unit can also evaluate reliability based on the consistency and accuracy of the data. The learning unit can also evaluate reliability based on the update frequency and recency of the data. This enables more accurate learning by evaluating the reliability of the data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can input information regarding the reliability of the data into the generation AI and have the generation AI perform the reliability evaluation.

[0058] The proposal unit can evaluate the risk of a proposal based on the CEO's past business decision data. For example, the proposal unit can evaluate the risk of a current proposal based on past failure cases. The proposal unit can also evaluate the risk of a current proposal based on past success cases. The proposal unit can also evaluate the risk of a proposal based on the results of past business decisions. This enables more appropriate proposals to be made by evaluating the risk of a proposal based on past business decisions. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI, for example. For example, the proposal unit can input data regarding past business decisions into the generation AI and have the generation AI evaluate the risk of the proposal.

[0059] The collection unit can check the consistency of the data when collecting the CEO's past business decision data. For example, the collection unit checks the consistency based on the format and content of the data. The collection unit can also check the consistency based on the source of the data and the collection method. The collection unit can also check the consistency based on the update frequency and recency of the data. In this way, checking the consistency of the data enables more accurate data collection. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input information about the consistency of the data to the generation AI and have the generation AI perform the consistency check.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection department collects data on the CEO's past business decisions. For example, the collection department can collect data on past project decisions, investment decisions, strategic directions, etc. The collection department can also collect data from databases and document management systems. For example, the collection department can extract data on past business decisions from a company database. The collection department can also collect document data such as the CEO's statements and memos. Step 2: The learning unit uses the generation AI to learn the data collected by the collection unit. For example, the learning unit analyzes the collected data and learns the CEO's decision-making patterns. The generation AI uses a machine learning algorithm to analyze what decisions the CEO made in what situations and learns those patterns. The generation AI learns by taking into account the factors that influenced the CEO's decisions and the results of those decisions. Step 3: The suggestion unit proposes future business decisions based on the data learned by the learning unit. The suggestion unit proposes optimal decisions based on similar situations in the past. For example, it can propose current business decisions based on past successes. It can also propose ways to avoid repeating the same mistakes based on past failures. The processing in the suggestion unit can be performed using or without generative AI.

[0062] (Example 2) A business decision support system according to an embodiment of the present invention learns a CEO's past business decisions and proposes future business decisions. The business decision support system collects data on the CEO's past business decisions, and a generation AI learns from it. The system then responds with, "This is what the CEO would think," when making future business decisions. The generation AI analyzes the CEO's past decision patterns and proposes optimal decisions for similar situations. For example, the business decision support system collects data on the CEO's past business decisions, such as past project decisions, investment decisions, and strategic directions. The business decision support system then trains the generation AI on the collected data. The generation AI analyzes the collected data and learns the CEO's decision patterns. For example, the generation AI analyzes the decisions the CEO made in different situations and learns those patterns. Furthermore, the business decision support system uses the data learned by the generation AI to respond with, "This is what the CEO would think," when making future business decisions. For example, when a new project is proposed, the generation AI suggests the CEO's decision based on similar past situations. This supports the CEO's decision-making. In this way, the business decision support system can support the CEO's decisions and increase the probability of business success. In this way, the business decision support system can support the CEO's decisions and increase the probability of business success. For example, by making current business decisions based on past success stories, the probability of success can be increased. Also, by learning from past failure stories, it can be possible to avoid repeating the same mistakes.

[0063] A business decision support system according to an embodiment includes a collection unit, a learning unit, and a proposal unit. The collection unit collects data on the CEO's past business decisions. For example, the collection unit collects detailed data on business decisions made by the CEO in the past. For example, the collection unit can collect data on past project decisions, investment decisions, strategic directions, and the like. The collection unit can also collect data from databases and document management systems. For example, the collection unit extracts data on past business decisions from a company's database. The collection unit can also collect document data such as the CEO's statements and notes. The learning unit uses a generation AI to learn from the data collected by the collection unit. For example, the learning unit analyzes the collected data and learns the CEO's decision-making patterns. For example, the generation AI uses a machine learning algorithm to analyze the CEO's decisions in different situations and learns those patterns. The generation AI performs learning by taking into account, for example, factors that influenced the CEO's decisions and the results of those decisions. The proposal unit proposes future business decisions based on the data learned by the learning unit. For example, the proposal unit proposes optimal decisions based on similar past situations. For example, the suggestion unit can suggest current business decisions based on past success stories. The suggestion unit can also suggest ways to avoid repeating the same mistakes based on past failure stories. This allows the business decision support system according to the embodiment to support CEO decisions and increase the probability of business success. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the suggestion unit can make suggestions using a generative AI model that receives input data learned by the learning unit and outputs a business decision.

[0064] The collection unit can collect detailed data on business decisions made by the CEO in the past. Examples of detailed data include, but are not limited to, the date, time, location, and people involved. The collection unit can collect detailed data on investment decisions made by the CEO. For example, the collection unit can collect data such as the date and time of the investment decision, the investment target, the investment amount, and the investment results. The collection unit can also collect detailed data on strategic decisions made by the CEO. For example, the collection unit can collect data such as the background of the strategic decision, the reasons for the decision, and the results of the decision. The collection unit can also collect detailed data on project selection made by the CEO. For example, the collection unit can collect data such as the project selection criteria, the selection process, and the selection results. Collecting detailed data on the CEO's past business decisions enables more accurate learning. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input document data on the CEO's past business decisions into the generation AI and have the generation AI extract the detailed data.

[0065] The learning unit can analyze the collected data and learn the CEO's decision-making patterns. The learning unit, for example, analyzes the collected data and learns the CEO's decision-making patterns. For example, the learning unit uses frequency analysis to analyze what decisions the CEO made in what situations. The learning unit can also learn the CEO's decision-making patterns using decision tree analysis. For example, the learning unit performs learning by taking into account factors that influenced the CEO's decisions and the results of the decisions. The learning unit can also learn the CEO's decision-making patterns using a machine learning algorithm. For example, the learning unit performs learning using a machine learning model that inputs data related to the CEO's decisions and outputs decision patterns. By learning the CEO's decision-making patterns, more appropriate proposals can be made. Some or all of the above-described processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input the collected data to a generation AI and have the generation AI learn the decision patterns.

[0066] The proposal unit can propose future business decisions based on the learned data. The proposal unit, for example, proposes future business decisions based on the learned data. For example, the proposal unit proposes optimal decisions based on similar situations in the past. For example, the proposal unit can propose current business decisions based on past success stories. The proposal unit can also make suggestions to avoid repeating the same mistakes based on past failure stories. The proposal unit, for example, makes proposals regarding investment decisions. For example, the proposal unit proposes optimal decisions for current investment projects based on past investment decision data. The proposal unit can also make proposals regarding strategic directions. For example, the proposal unit proposes future strategic decisions based on past strategic decision data. This makes it possible to support the CEO's decisions by proposing future business decisions based on the learned data. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the proposal unit can make proposals using a generative AI model that uses data learned by the learning unit as input and outputs business decisions.

[0067] The proposal unit can propose appropriate decisions based on similar situations in the past. The proposal unit, for example, proposes appropriate decisions based on similar situations in the past. For example, the proposal unit can propose current business decisions based on past success stories. The proposal unit can also propose ways to avoid repeating the same mistakes based on past failure stories. The proposal unit, for example, proposes optimal decisions for current investment projects based on past investment decision data. The proposal unit can also propose future strategic decisions based on past strategic decision data. In this way, the accuracy of decisions is improved by proposing optimal decisions based on similar situations in the past. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data on similar situations in the past into the generation AI and have the generation AI execute a proposal for an optimal decision.

[0068] The proposal unit can propose current business decisions based on past success stories. The proposal unit, for example, proposes current business decisions based on past success stories. For example, the proposal unit proposes optimal decisions for current investment projects based on past success stories. The proposal unit can also propose current strategic decisions based on past success stories. For example, the proposal unit proposes optimal decisions for current project selection based on past success stories. This increases the probability of success by proposing current business decisions based on past success stories. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data on past success stories into the generation AI and have the generation AI execute a proposal for a current business decision.

[0069] The proposal unit can make suggestions based on past failure cases to prevent the same mistakes from being repeated. For example, the proposal unit makes suggestions based on past failure cases to prevent the same mistakes from being repeated. For example, the proposal unit proposes optimal decisions for current investment projects based on past failure cases. The proposal unit can also propose current strategic decisions based on past failure cases. For example, the proposal unit proposes optimal decisions for current project selection based on past failure cases. In this way, by making suggestions based on past failure cases, the same mistakes are prevented from being repeated. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data on past failure cases into the generation AI and have the generation AI execute suggestions for current business decisions.

[0070] The collection unit can estimate the CEO's emotions and adjust the timing of collecting business decision data based on the estimated CEO emotions. The collection unit, for example, estimates the CEO's emotions and adjusts the timing of collecting business decision data based on the estimated CEO emotions. For example, if the CEO is stressed, the collection unit delays the collection timing and collects data in a relaxed state. Furthermore, if the CEO is relaxed, the collection unit can collect data immediately, thereby improving the accuracy of judgments. Furthermore, if the CEO is busy, the collection unit can adjust the collection timing and collect data at the optimal timing. This enables more appropriate data collection by adjusting the collection timing according to the CEO's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI. For example, the collection unit can input the CEO's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0071] When collecting data on the CEO's past business decisions, the collection unit can simultaneously collect background information on the decisions. For example, when collecting data on the CEO's past business decisions, the collection unit simultaneously collects background information on the decisions (e.g., economic conditions and market trends). For example, the collection unit collects the economic conditions at the time of the CEO's investment decisions. The collection unit can also collect market trends at the time of the CEO's strategic decisions. The collection unit can also collect the trends of competitors at the time of the CEO's project selection. By simultaneously collecting background information on the decisions, more detailed data analysis becomes possible. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data on the CEO's past business decisions and their background information into a generation AI and have the generation AI collect the background information.

[0072] The collection unit can identify and collect external factors that influenced the CEO's decision when collecting business decision data. For example, the collection unit can identify and collect external factors (e.g., competitor trends and regulatory changes) that influenced the CEO's decision when collecting business decision data. For example, the collection unit can collect information on new products from competitors that influenced the CEO's decision. The collection unit can also collect information on regulatory changes that influenced the CEO's decision. The collection unit can also collect information on industry trends that influenced the CEO's decision. By identifying and collecting external factors, more accurate data analysis is possible. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input data on external factors that influenced the CEO's decision into the generation AI and cause the generation AI to identify and collect the external factors.

[0073] The collection unit can also collect feedback and result data regarding the CEO's decisions when collecting the business decision data. For example, the collection unit also collects feedback and result data regarding the CEO's decisions when collecting the business decision data. For example, the collection unit collects internal feedback regarding the decisions made by the CEO. The collection unit can also collect performance data as a result of the decisions made by the CEO. The collection unit can also collect customer reactions to the decisions made by the CEO. In this way, collecting feedback and result data improves the accuracy of decisions. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input feedback and result data regarding the CEO's decisions into the generation AI and cause the generation AI to collect the feedback and result data.

[0074] The collection unit can estimate the CEO's emotions and determine the priority of the business decision data to be collected based on the estimated CEO's emotions. The collection unit, for example, estimates the CEO's emotions and determines the priority of the business decision data to be collected based on the estimated CEO's emotions. For example, if the CEO is stressed, the collection unit postpones collecting less important data. Also, if the CEO is relaxed, the collection unit can prioritize collecting more important data. Also, if the CEO is busy, the collection unit can prioritize collecting the most important data. In this way, by determining the priority of data according to the CEO's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI. For example, the collection unit can input the CEO's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] When collecting business decision data, the collection unit can prioritize collecting highly relevant data by taking into account the CEO's geographic location information. For example, when collecting business decision data, the collection unit prioritizes collecting highly relevant data by taking into account the CEO's geographic location information. For example, when collecting business decision data, the collection unit prioritizes collecting highly relevant data by taking into account the CEO's geographic location information. For example, when the CEO is on a business trip, the collection unit can prioritize collecting market trends in that region. Also, when the CEO is at headquarters, the collection unit can prioritize collecting global market trends. Also, when the CEO is interested in a particular region, the collection unit can prioritize collecting competitive information in that region. In this way, highly relevant data can be collected preferentially by taking into account the geographic location information. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the CEO's geographic location information into the generation AI and cause the generation AI to collect highly relevant data.

[0076] The collection unit can analyze the CEO's social media activity and collect related data when collecting business decision data. For example, the collection unit analyzes the CEO's social media activity and collects related data when collecting business decision data. For example, the collection unit collects data related to topics mentioned by the CEO on social media. The collection unit can also collect statements by industry leaders followed by the CEO. The collection unit can also collect data related to events attended by the CEO. In this way, related data can be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input data related to the CEO's social media activity into the generation AI and cause the generation AI to collect related data.

[0077] The collection unit can appropriately adjust the collection method when collecting business decision data by reflecting the CEO's past feedback. For example, the collection unit can appropriately adjust the collection method when collecting business decision data by reflecting the CEO's past feedback. For example, the collection unit prioritizes collecting data that the CEO has considered important in the past. The collection unit can also reflect improvements to the collection method that the CEO has previously pointed out. The collection unit can also prioritize using data sources that the CEO has previously evaluated. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input data regarding the CEO's past feedback into the generation AI and have the generation AI adjust the collection method.

[0078] The learning unit can estimate the CEO's emotions and select training data based on the estimated CEO emotions. The learning unit, for example, estimates the CEO's emotions and selects training data based on the estimated CEO emotions. For example, if the CEO is relaxed, the learning unit can use detailed data for training. Also, if the CEO is stressed, the learning unit can use concise data for training. Also, if the CEO is busy, the learning unit can prioritize important data for training. This improves the accuracy of training by selecting training data according to the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit can input facial expression data of the CEO into the generation AI and have the generation AI perform emotion estimation.

[0079] The learning unit can appropriately adjust the learning algorithm during learning, taking into account the success rate and failure rate of past business decision data. For example, the learning unit appropriately adjusts the learning algorithm during learning, taking into account the success rate and failure rate of past business decision data. For example, the learning unit focuses on decision data with a high success rate during learning. The learning unit can also adjust the learning algorithm to reflect decision data with a high failure rate. The learning unit can also select learning data taking into account the balance between success rate and failure rate. In this way, the accuracy of the learning algorithm is improved by taking the success rate and failure rate into account. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data regarding the success rate and failure rate of past business decision data into the generation AI and cause the generation AI to adjust the learning algorithm.

[0080] The learning unit can analyze the transitions in the CEO's decision patterns during learning and learn them as time-series data. For example, the learning unit analyzes the transitions in the CEO's decision patterns over time and learns them as time-series data during learning. For example, the learning unit analyzes the transitions in the CEO's decision patterns over time and reflects the results in learning. The learning unit can also graph the transitions in the CEO's decision patterns and use the graph for learning. The learning unit can also adjust the learning algorithm taking into account the transitions in the CEO's decision patterns. This enables more accurate learning by analyzing the transitions in the decision patterns. Some or all of the above-described processing in the learning unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the learning unit can input data regarding the transitions in the CEO's decision patterns into the generation AI and cause the generation AI to perform learning as time-series data.

[0081] The learning unit can correct the learning data during learning by taking into account external factors that influenced the CEO's decisions. For example, the learning unit corrects the learning data during learning by taking into account external factors that influenced the CEO's decisions. For example, the learning unit reflects economic conditions that influenced the CEO's decisions in the learning data. The learning unit can also reflect market trends that influenced the CEO's decisions in the learning data. The learning unit can also reflect trends of competitors that influenced the CEO's decisions in the learning data. This improves the accuracy of the learning data by taking external factors into account. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input data regarding external factors that influenced the CEO's decisions into the generation AI and cause the generation AI to correct the learning data.

[0082] The learning unit can estimate the CEO's emotions and adjust the frequency of learning based on the estimated CEO emotions. The learning unit, for example, estimates the CEO's emotions and adjusts the frequency of learning based on the estimated CEO emotions. For example, the learning unit increases the frequency of learning when the CEO is relaxed. The learning unit can also decrease the frequency of learning when the CEO is stressed. The learning unit can also adjust the frequency of learning when the CEO is busy. This improves learning efficiency by adjusting the frequency of learning according to the CEO's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, the generation AI. For example, the learning unit can input facial expression data of the CEO into the generation AI and cause the generation AI to estimate emotions.

[0083] The learning unit can weight the learning data based on the submission time of the business judgment data during learning. For example, the learning unit weights the learning data based on the submission time of the business judgment data during learning. For example, the learning unit may weight recent business judgment data during learning. The learning unit may also weight past business judgment data during learning. The learning unit may also adjust the weighting of the learning data based on the submission time. In this way, weighting the learning data based on the submission time improves the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit may input data regarding the submission time of the business judgment data into the generation AI and cause the generation AI to weight the learning data.

[0084] The learning unit can integrate information from different data sources to enrich the learning data during learning. For example, the learning unit integrates information from different data sources to enrich the learning data during learning. For example, the learning unit integrates business decision data from different data sources to learn. The learning unit can also integrate market trend data from different data sources to learn. The learning unit can also integrate competitive information from different data sources to learn. In this way, the accuracy of the learning data is improved by integrating information from different data sources. Some or all of the above-described processing in the learning unit may be performed using, or without, a generation AI. For example, the learning unit can input information from different data sources into the generation AI and cause the generation AI to integrate the information.

[0085] The learning unit can adjust the learning algorithm during learning by reflecting the CEO's past feedback. For example, the learning unit adjusts the learning algorithm during learning by reflecting the CEO's past feedback. For example, the learning unit adjusts the learning algorithm by reflecting the CEO's past feedback. The learning unit can also select learning data based on the CEO's past feedback. The learning unit can also optimize the learning algorithm by taking the CEO's past feedback into consideration. In this way, the accuracy of the learning algorithm is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the learning unit can input data regarding the CEO's past feedback into the generation AI and cause the generation AI to adjust the learning algorithm.

[0086] The proposal unit can estimate the CEO's emotions and adjust the way the proposal is presented based on the estimated CEO's emotions. The proposal unit, for example, estimates the CEO's emotions and adjusts the way the proposal is presented based on the estimated CEO's emotions. For example, the proposal unit can make a detailed proposal if the CEO is relaxed. Furthermore, the proposal unit can make a concise proposal if the CEO is stressed. Furthermore, the proposal unit can make a proposal that focuses on the main points if the CEO is busy. This allows for adjusting the way the proposal is presented based on the CEO's emotions, thereby enabling more appropriate proposals. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input facial expression data of the CEO into the generation AI and cause the generation AI to estimate the emotion.

[0087] The proposal unit can adjust the level of detail of the proposal based on the importance of the business decision when making a proposal. The proposal unit, for example, adjusts the level of detail of the proposal based on the importance of the business decision when making a proposal. For example, the proposal unit makes a detailed proposal for a business decision with a high level of importance. The proposal unit can also make a concise proposal for a business decision with a low level of importance. The proposal unit can also adjust the level of detail of the proposal based on the importance of the business decision. This enables a more appropriate proposal by adjusting the level of detail of the proposal based on the importance of the business decision. Some or all of the above-described processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data regarding the importance of the business decision to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0088] The proposal unit can apply different proposal algorithms depending on the category of the business decision when making a proposal. For example, the proposal unit can apply different proposal algorithms depending on the category of the business decision when making a proposal. For example, the proposal unit can apply a specific proposal algorithm to investment decisions. The proposal unit can also apply a different proposal algorithm to strategic decisions. The proposal unit can also apply a different proposal algorithm to project selection. This enables more appropriate proposals by applying different proposal algorithms depending on the category of the business decision. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, the generation AI, for example. For example, the proposal unit can input data related to the category of the business decision into the generation AI and cause the generation AI to apply the proposal algorithm.

[0089] The proposal unit can improve the accuracy of the proposal by referring to the CEO's past proposal results when making a proposal. For example, the proposal unit can improve the accuracy of the proposal by referring to the CEO's past proposal results when making a proposal. For example, the proposal unit can analyze the CEO's past proposal results and improve the accuracy of the proposal. The proposal unit can also adjust the proposal algorithm based on the CEO's past proposal results. The proposal unit can also improve the accuracy of the proposal by reflecting the CEO's past proposal results. In this way, the accuracy of the proposal is improved by referring to the past proposal results. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input data regarding the CEO's past proposal results into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0090] The proposal unit can estimate the CEO's emotions and adjust the length of the proposal based on the estimated CEO's emotions. The proposal unit, for example, estimates the CEO's emotions and adjusts the length of the proposal based on the estimated CEO's emotions. For example, the proposal unit can make a detailed proposal if the CEO is relaxed. Furthermore, the proposal unit can make a concise proposal if the CEO is stressed. Furthermore, the proposal unit can make a proposal that focuses on the main points if the CEO is busy. This allows for adjusting the length of the proposal according to the CEO's emotions, thereby enabling more appropriate proposals. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the proposal unit can input facial expression data of the CEO into the generation AI and cause the generation AI to estimate the emotion.

[0091] The proposal unit can determine the priority of proposals based on the submission time of the business decisions when making proposals. The proposal unit, for example, determines the priority of proposals based on the submission time of the business decisions when making proposals. For example, the proposal unit prioritizes proposals for urgent business decisions. The proposal unit can also prioritize proposals for business decisions with an upcoming submission deadline. The proposal unit can also adjust the priority of proposals based on the submission time. This enables more appropriate proposals by determining the priority of proposals based on the submission time. Some or all of the above-described processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input data regarding the submission time of business decisions into the generation AI and have the generation AI determine the priority of proposals.

[0092] The suggestion unit can adjust the order of proposals based on the relevance of the business judgments when making a proposal. The suggestion unit, for example, adjusts the order of proposals based on the relevance of the business judgments when making a proposal. For example, the suggestion unit first makes a proposal for an important business judgment. The suggestion unit can also prioritize proposals for highly relevant business judgments. The suggestion unit can also adjust the order of proposals based on the relevance of the business judgments. This enables more appropriate proposals by adjusting the order of proposals based on the relevance of the business judgments. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data regarding the relevance of the business judgments into the generation AI and cause the generation AI to adjust the order of proposals.

[0093] The proposal unit can adjust the use of technical terminology in the proposal according to the CEO's level of expertise when making a proposal. For example, the proposal unit can adjust the use of technical terminology in the proposal according to the CEO's level of expertise when making a proposal. For example, if the CEO has technical expertise, the proposal unit can make a proposal that uses a lot of technical terminology. On the other hand, if the CEO does not have technical expertise, the proposal unit can make a concise and easy-to-understand proposal. The proposal unit can also adjust the use of technical terminology in the proposal according to the CEO's level of expertise. This enables more appropriate proposals by adjusting the use of technical terminology according to the CEO's level of expertise. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the proposal unit can input data regarding the CEO's level of expertise into the generation AI and have the generation AI execute the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, learning unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the CEO's statements and facial expressions using the camera 42 and microphone 38B of the smart device 14, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI learns the CEO's decision-making patterns based on the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal business decisions based on the learned data. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the learning unit and suggestion unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, learning unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the CEO's remarks and facial expressions using the camera 42 and microphone 238 of the smart glasses 214, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI learns the CEO's decision-making patterns based on the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal business decisions based on the learned data. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the learning unit and suggestion unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, learning unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the CEO's statements and facial expressions using the camera 42 and microphone 238 of the headset-type terminal 314, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI learns the CEO's decision-making patterns based on the collected data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal business decisions based on the learned data. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the learning unit and suggestion unit may be realized, for example, by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, learning unit, and proposal unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the CEO's statements and facial expressions using the camera 42 and microphone 238 of the robot 414, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and the generation AI learns the CEO's decision-making patterns based on the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal business decisions based on the learned data. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the learning unit and proposal unit may be realized, for example, by the control unit 46A of the robot 414.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The proposal unit can estimate the CEO's emotions and adjust the timing of the proposal based on the estimated CEO's emotions. For example, if the CEO is stressed, the proposal can be delayed to make the proposal in a relaxed state. Also, if the CEO is relaxed, the proposal can be made immediately, improving the accuracy of judgment. Also, if the CEO is busy, the proposal can be adjusted to make the proposal at the optimal timing. This allows for more appropriate proposals by adjusting the timing of the proposal according to the CEO's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the proposal unit can input facial expression data of the CEO into the generation AI and have the generation AI perform emotion estimation.

[0096] The collection unit can estimate the CEO's emotions and adjust the type of data to be collected based on the estimated CEO's emotions. For example, if the CEO is relaxed, detailed data can be collected. If the CEO is stressed, concise data can be collected. If the CEO is busy, important data can be prioritized for collection. This allows for more appropriate data collection by adjusting the type of data to be collected according to the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the CEO's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0097] The learning unit can estimate the CEO's emotions and weight the learning data based on the estimated CEO's emotions. For example, if the CEO is relaxed, detailed data can be emphasized in learning. If the CEO is stressed, concise data can be emphasized in learning. If the CEO is busy, important data can be prioritized in learning. This weighting of the learning data according to the CEO's emotions improves the accuracy of learning. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, the generative AI, or without the generative AI. For example, the learning unit can input facial expression data of the CEO into the generative AI and have the generative AI perform emotion estimation.

[0098] The proposal unit can estimate the CEO's emotions and prioritize proposals based on the estimated CEO's emotions. For example, if the CEO is relaxed, important proposals can be prioritized. If the CEO is stressed, concise proposals can be prioritized. If the CEO is busy, the most important proposals can be prioritized. This enables more appropriate proposals by prioritizing proposals according to the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the proposal unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the proposal unit can input facial expression data of the CEO into the generation AI and cause the generation AI to estimate emotions.

[0099] The learning unit can estimate the CEO's emotions and adjust the timing of learning based on the estimated CEO's emotions. For example, if the CEO is relaxed, the learning timing can be advanced. Also, if the CEO is stressed, the learning timing can be delayed. Also, if the CEO is busy, the learning timing can be adjusted. This improves learning efficiency by adjusting the learning timing according to the CEO's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the learning unit can input facial expression data of the CEO into the generation AI and have the generation AI perform emotion estimation.

[0100] The proposal unit can evaluate the reliability of the proposal based on the CEO's past business decisions. For example, the proposal unit can evaluate the reliability of the current proposal based on past success cases. The proposal unit can also evaluate the risk of the current proposal based on past failure cases. The proposal unit can also evaluate the reliability of the proposal based on the results of past business decisions. This enables more appropriate proposals to be made by evaluating the reliability of the proposal based on past business decisions. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input data related to past business decisions into the generation AI and have the generation AI evaluate the reliability of the proposal.

[0101] When collecting data on the CEO's past business decisions, the collection unit can automatically classify background information for the decisions. For example, the collection unit automatically classifies background information such as economic conditions, market trends, and competitor trends. The collection unit can also classify background information based on the type of decision (investment decision, strategic decision, project selection, etc.). The collection unit can also classify background information based on the outcome of the decision. This automatically classifying background information enables more detailed data analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input data on background information into the generation AI and have the generation AI perform the classification.

[0102] The learning unit can evaluate the reliability of the data when learning the CEO's past business decision data. For example, the learning unit can evaluate reliability based on the source of the data and the collection method. The learning unit can also evaluate reliability based on the consistency and accuracy of the data. The learning unit can also evaluate reliability based on the update frequency and recency of the data. This enables more accurate learning by evaluating the reliability of the data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the learning unit can input information regarding the reliability of the data into the generation AI and have the generation AI perform the reliability evaluation.

[0103] The proposal unit can evaluate the risk of a proposal based on the CEO's past business decision data. For example, the proposal unit can evaluate the risk of a current proposal based on past failure cases. The proposal unit can also evaluate the risk of a current proposal based on past success cases. The proposal unit can also evaluate the risk of a proposal based on the results of past business decisions. This enables more appropriate proposals to be made by evaluating the risk of a proposal based on past business decisions. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI, for example. For example, the proposal unit can input data regarding past business decisions into the generation AI and have the generation AI evaluate the risk of the proposal.

[0104] The collection unit can check the consistency of the data when collecting the CEO's past business decision data. For example, the collection unit checks the consistency based on the format and content of the data. The collection unit can also check the consistency based on the source of the data and the collection method. The collection unit can also check the consistency based on the update frequency and recency of the data. In this way, checking the consistency of the data enables more accurate data collection. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the collection unit can input information about the consistency of the data to the generation AI and have the generation AI perform the consistency check.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The collection department collects data on the CEO's past business decisions. For example, the collection department can collect data on past project decisions, investment decisions, strategic directions, etc. The collection department can also collect data from databases and document management systems. For example, the collection department can extract data on past business decisions from a company database. The collection department can also collect document data such as the CEO's statements and memos. Step 2: The learning unit uses the generation AI to learn the data collected by the collection unit. For example, the learning unit analyzes the collected data and learns the CEO's decision-making patterns. The generation AI uses a machine learning algorithm to analyze what decisions the CEO made in what situations and learns those patterns. The generation AI learns by taking into account the factors that influenced the CEO's decisions and the results of those decisions. Step 3: The suggestion unit proposes future business decisions based on the data learned by the learning unit. The suggestion unit proposes optimal decisions based on similar situations in the past. For example, it can propose current business decisions based on past successes. It can also propose ways to avoid repeating the same mistakes based on past failures. The processing in the suggestion unit can be performed using or without generative AI.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 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.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0121] In the 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.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects data on the CEO's past business decisions, a learning unit that learns the data collected by the collection unit; a proposal unit that proposes future business decisions based on the data learned by the learning unit. A system characterized by:

2. The collecting unit Collect detailed data on the CEO's past business decisions 2. The system of claim 1.

3. The learning unit Analyze the collected data and learn the CEO's decision-making patterns 2. The system of claim 1.

4. The proposal unit Suggest future business decisions based on learned data 2. The system of claim 1.

5. The proposal unit Propose appropriate decisions based on similar situations in the past 2. The system of claim 1.

6. The proposal unit Advise current business decisions based on past success stories 2. The system of claim 1.

7. The proposal unit Based on past failures, we make suggestions to prevent the same mistakes from being repeated.

2. The system of claim 1.

8. The collecting unit Estimate CEO sentiment and adjust the timing of business decision data collection based on the estimated CEO sentiment 2. The system of claim 1.

9. The collecting unit When collecting data on the CEO's past business decisions, also collect background information on those decisions.

2. The system of claim 1.

10. The collecting unit When collecting business decision data, identify and collect external factors that influenced the CEO's decision.

2. The system of claim 1.

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