system

Abstract

An object of the system according to the embodiment is to unitarily provide necessary business intelligence tools and support for financing and financial planning to entrepreneurs.SOLUTION: A system according to an embodiment includes a BI tool providing unit, a financing advice unit, and a financial plan planning unit. The BI tool provider provides business intelligence tools to entrepreneurs. The financing advice unit provides advice on financing to the entrepreneur. The financial plan drafting unit assists the entrepreneur in drafting a financial plan.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] Existing technology lacks a single system that provides the business intelligence tools, fundraising, and financial planning support entrepreneurs need, leaving room for improvement.

[0005] The system according to the embodiment aims to provide entrepreneurs with the necessary business intelligence tools, fundraising, and financial planning support in a unified manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a BI tool provider, a fundraising advice unit, and a financial planning unit. The BI tool provider provides business intelligence tools to entrepreneurs. The fundraising advice unit provides advice on fundraising to entrepreneurs. The financial planning unit supports entrepreneurs in formulating financial plans. [Effects of the Invention]

[0007] The system according to the embodiment can provide entrepreneurs with the necessary business intelligence tools, fundraising, and financial planning support in a unified manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The assistant tool according to the embodiment of the present invention is a system that provides entrepreneurs with business intelligence tools, gives advice on fundraising, and supports the development of financial plans. As a result, the assistant tool can provide entrepreneurs with the information they need and support them in fundraising methods, financial planning, and improving the efficiency of their daily work.

[0029] The assistant tool according to the embodiment includes a BI tool providing unit, a fundraising advice unit, and a financial planning unit. The BI tool providing unit provides business intelligence tools to entrepreneurs. For example, the BI tool providing unit uses a generation AI to analyze market trends and generate a report for the entrepreneur showing which markets are growing and which products and services are in high demand. The BI tool providing unit can also use the generation AI to perform competitive analysis and provide the entrepreneur with a report showing the activities of competitors. The BI tool providing unit can also use the generation AI to perform customer analysis and provide the entrepreneur with a report showing customer attributes and purchasing behavior. For example, the generation AI can analyze market trends and generate a report using a text generation AI (e.g., LLM). The generation AI can also perform competitive analysis and customer analysis using a multimodal generation AI. The generation AI can analyze competitor activities and customer attributes using natural language processing technology and generate a report. The fundraising advice unit provides fundraising advice to entrepreneurs. For example, the fundraising advice unit can use the generation AI to analyze the entrepreneur's business plan and suggest which investors are likely to be interested. The fundraising advice department can also use the generative AI to analyze successful crowdfunding cases and provide advice on effective campaign methods. The fundraising advice department can also use the generative AI to support the loan application process. For example, the generative AI analyzes a business plan and identifies points that will attract investors' interest. The generative AI analyzes successful crowdfunding cases and suggests effective campaign methods. The generative AI guides entrepreneurs through the necessary documents and procedures to support the loan application process. The financial planning department supports entrepreneurs in developing financial plans. For example, the financial planning department can use the generative AI to analyze past data and predict future income and expenses. The financial planning department can also use the generative AI to suggest specific methods for cost reduction. The financial planning department can also use the generative AI to support cash flow management. For example, the generative AI can analyze past data and predict future income and expenses.The generation AI proposes specific methods for reducing costs. The generation AI proposes methods for optimizing the balance between income and expenses to support cash flow management. As a result, the assistant tool according to the embodiment can provide entrepreneurs with the information they need and support them in fundraising methods, financial planning, and streamlining their daily operations. For example, entrepreneurs can understand market trends based on reports provided by the BI tool provider and develop appropriate business strategies. With advice from the fundraising advice department, they can secure the necessary funds and create financial plans. Furthermore, with support from the financial planning department, they can efficiently carry out their daily operations.

[0030] The BI tool provider uses the generation AI to analyze the user's past business decisions and their results, identify patterns of success and failure, and reflect these in the next business decision. For example, the BI tool provider stores the user's past business decisions and their results in a database, and the generation AI analyzes them. For example, the generation AI identifies the factors that led to the success or failure of past marketing campaigns and reflects these in the next campaign. The BI tool provider also uses the generation AI to analyze the user's business decision history and extract patterns of success and failure. For example, if a particular market strategy is successful, that strategy can be applied to the next business decision. The BI tool provider also analyzes the user's past business data and develops an algorithm to identify patterns of success and failure. For example, if a particular product line is successful, a proposal can be made to expand that product line. This allows the user's past business decisions to be analyzed and reflected in the next business decision.

[0031] The BI tool provider can use the generation AI to monitor market fluctuations in real time and send immediate alerts to entrepreneurs. For example, the generation AI collects market fluctuation data in real time and sends immediate alerts when it detects abnormal fluctuations. For example, it notifies entrepreneurs when a specific stock price suddenly drops. The BI tool provider can also build a system that monitors market fluctuations in real time and sends alerts to entrepreneurs when the generation AI detects an abnormality. For example, it can immediately notify entrepreneurs of the release of a new product by a competitor. The BI tool provider can also use the generation AI to analyze market fluctuations and send alerts to entrepreneurs when important fluctuations occur. For example, it can notify entrepreneurs when a specific market segment is growing rapidly. This makes it possible to monitor market fluctuations in real time and send immediate alerts.

[0032] The BI tool provider uses the generation AI to generate visual data, making it easier to visually understand market trends. For example, the generation AI in the BI tool provider analyzes market data and automatically generates graphs and charts that are easy to understand visually. For example, sales trends and fluctuations in market share can be displayed in graphs. The BI tool provider also develops algorithms that generate visual data, allowing users to intuitively understand market trends. For example, data can be visualized using heat maps and bubble charts. The BI tool provider also analyzes market data using the generation AI and automatically generates visual reports that are easy to understand visually. For example, competitive analysis and the distribution of customer segments can be displayed in charts. This allows visual data to be generated, making it easier to visually understand market trends.

[0033] The BI tool provider can cross-reference data from different industries and analyze trends across those industries. For example, the BI tool provider develops algorithms that cross-reference data from different industries and analyze trends across those industries. For example, it compares trends in the technology industry and the consumer market. The BI tool provider also uses generative AI to analyze data from different industries and identify common trends and differences. For example, it can compare trends in the medical industry and the entertainment industry. The BI tool provider also builds a system that cross-references data from different industries and discovers new business opportunities. For example, it can propose new business models based on success stories from different industries. This makes it possible to cross-reference data from different industries and analyze trends across those industries.

[0034] The fundraising advice department can use the generation AI to analyze investors' past investment history and list the most suitable investors. For example, the generation AI in the fundraising advice department analyzes investors' past investment history and develops an algorithm to list the most suitable investors. For example, it identifies investors who invest in a specific industry. The fundraising advice department also collects investors' investment history data, and the generation AI analyzes it to list suitable investors. For example, it can select investors based on past successful investment projects. The fundraising advice department also uses the generation AI to analyze investors' investment history and list investors who are most suitable for the entrepreneur's business plan. For example, it can identify investors who have invested in the same business model. This makes it possible to analyze investors' past investment history and list the most suitable investors.

[0035] The fundraising advice unit can use the generation AI to automatically generate visual content for crowdfunding campaigns. For example, the fundraising advice unit develops a system in which the generation AI automatically generates visual content for crowdfunding campaigns. For example, the generation AI automatically generates promotional videos and campaign images. The fundraising advice unit also has the generation AI automatically generate visual content for crowdfunding campaigns and make it easy for users to use. For example, the generation AI can generate videos and images based on templates. The fundraising advice unit also has the generation AI analyze the visual content of crowdfunding campaigns and automatically generate optimal content. For example, the generation AI can generate content based on visual elements of successful campaigns. This makes it possible to automatically generate visual content for crowdfunding campaigns.

[0036] The fundraising advice department can use the generating AI to compare different fundraising methods and propose the optimal method. For example, the generating AI can analyze different fundraising methods and develop an algorithm to propose the optimal method. For example, it can compare the advantages and disadvantages of angel investment and venture capital. The fundraising advice department also collects data on fundraising methods, and the generating AI analyzes it to propose the optimal method. For example, it can select the fundraising method that is most suitable for an entrepreneur's business model. The fundraising advice department also builds a system in which the generating AI compares different fundraising methods and proposes the optimal method to the user. For example, it can make proposals based on the success rate and cost of fundraising. This makes it possible to compare different fundraising methods and propose the optimal method.

[0037] The financial planning department can use the generative AI to analyze past financial data, predict future risks, and propose risk avoidance measures. For example, the generative AI in the financial planning department analyzes past financial data and develops an algorithm to predict future risks. For example, risks are identified based on fluctuations in revenue and market fluctuations. The financial planning department also collects financial data, and the generative AI analyzes it to propose risk avoidance measures. For example, specific cost reduction measures and investment strategies can be proposed. The financial planning department also builds a system in which the generative AI analyzes past financial data, predicts future risks, and proposes specific risk avoidance measures. For example, advice can be provided to avoid high-risk investments. This makes it possible to analyze past financial data, predict future risks, and propose risk avoidance measures.

[0038] The financial planning department can use the generation AI to monitor cash flow in real time and issue immediate alerts if an abnormality occurs. For example, the financial planning department builds a system in which the generation AI monitors cash flow in real time and issues immediate alerts if an abnormality is detected. For example, a notification is sent if an unexpected expense occurs. The financial planning department also collects cash flow data in real time and develops an algorithm in which the generation AI analyzes it to detect anomalies. For example, an alert can be sent if income suddenly drops. The financial planning department also adds a function in which the generation AI monitors cash flow in real time and issues immediate alerts if an abnormality occurs. For example, a notification can be sent if a specific expense exceeds the budget. This makes it possible to monitor cash flow in real time and issue immediate alerts if an abnormality occurs.

[0039] The financial planning department can use the generation AI to automatically generate visual reports of financial plans. For example, the financial planning department develops a system in which the generation AI analyzes financial data and automatically generates visual reports that are easy to understand visually. For example, it generates graphs of income and expenditure forecasts and cash flow. The financial planning department also develops an algorithm to automatically generate visual reports, allowing users to intuitively understand financial plans. For example, it can visualize data using infographics. The financial planning department also analyzes financial data using the generation AI and automatically generates visual reports that are easy to understand visually. For example, it can display cost reduction proposals and revenue forecasts in graphs. In this way, it is possible to automatically generate visual reports of financial plans.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The assistant tool may further include a health management unit that monitors the user's health status. For example, the health management unit may monitor the user's heart rate and sleep patterns and analyze the user's health status. The health management unit may also collect the user's health data, which the generative AI may analyze to identify health risks. For example, the health management unit may provide advice on how to relax during times of high stress. Furthermore, the health management unit may analyze the user's health status and suggest specific actions to maintain health. For example, it may recommend improvements in exercise or diet. This allows the user's health status to be monitored, health risks to be identified, and advice on maintaining health to be provided.

[0042] The assistant tool can further include a learning support unit that monitors the user's learning progress. For example, the learning support unit can collect the user's learning data, which the generation AI can analyze to evaluate the user's learning progress. The learning support unit can also suggest the optimal learning method according to the user's learning style. For example, for users who find visual learning effective, it can provide learning materials using graphs and charts. Furthermore, the learning support unit can analyze the user's learning progress and suggest specific actions to maximize the learning effect. For example, it can recommend the timing of review or adjustments to the learning content. In this way, the user's learning progress can be monitored, the optimal learning method can be suggested, and the learning effect can be maximized.

[0043] The assistant tool may further include a networking support unit that supports the user's networking activities. For example, the networking support unit may analyze the user's business network and suggest optimal networking events and contacts. The networking support unit may also analyze the user's past networking activities and identify successful contact methods and events. For example, it may recommend participation in an event in a specific industry. The networking support unit may also analyze the user's networking activities and suggest an effective networking strategy. For example, it may suggest a method and timing for contacting a specific key person. In this way, the networking support unit may support the user's networking activities, suggest optimal networking events and contacts, and provide an effective networking strategy.

[0044] The assistant tool can further analyze the user's business network and suggest optimal collaboration partners. For example, the business network analysis unit can collect the user's past collaboration data, which the generative AI can analyze to identify optimal partners. The business network analysis unit can also analyze the user's business network and suggest partners with common business goals. For example, it can identify partners suitable for a specific project. Furthermore, the business network analysis unit can analyze the user's business network and suggest effective collaboration strategies. For example, it can recommend collaboration with experts in a specific industry. This makes it possible to analyze the user's business network, suggest optimal collaboration partners, and provide an effective collaboration strategy.

[0045] The assistant tool can further analyze the user's business plan and propose an optimal marketing strategy. For example, the marketing strategy proposal unit can collect the user's business plan, and the generation AI can analyze it to identify the optimal marketing strategy. The marketing strategy proposal unit can also analyze the user's business plan and propose a marketing strategy according to the target market and customer segment. For example, it can propose an effective advertising campaign for a specific market segment. Furthermore, the marketing strategy proposal unit can analyze the user's business plan and propose a specific marketing strategy to differentiate the user from competitors. For example, it can propose unique sales channels and promotion methods. In this way, the assistant tool can analyze the user's business plan and propose the optimal marketing strategy.

[0046] The assistant tool can further analyze the user's business plan and suggest the optimal fundraising method. For example, the fundraising method suggestion unit can collect the user's business plan, and the generation AI can analyze it to identify the optimal fundraising method. The fundraising method suggestion unit can also analyze the user's business plan and compare the advantages and disadvantages of different fundraising methods. For example, it can explain the difference between angel investment and venture capital. Furthermore, the fundraising method suggestion unit can analyze the user's business plan and suggest specific actions to propose the optimal fundraising method. For example, it can suggest how and when to contact specific investors. This allows the tool to analyze the user's business plan and suggest the optimal fundraising method.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The BI tool provider provides entrepreneurs with business intelligence tools. For example, they use generative AI to analyze market trends and generate reports showing which markets are growing and which products and services are in high demand. They also conduct competitive and customer analysis and provide reports showing competitor trends, customer attributes, and purchasing behavior. Step 2: The Funding Advice Department provides entrepreneurs with advice on fundraising. For example, it uses generative AI to analyze business plans and suggest which investors are likely to be interested. It also analyzes successful crowdfunding cases and provides advice on effective campaign methods. It also assists with the loan application process. Step 3: The financial planning department helps entrepreneurs develop financial plans. For example, it uses generative AI to analyze past data and predict future income and expenses. It also suggests specific ways to reduce costs and supports cash flow management.

[0049] (Example 2) The assistant tool according to the embodiment of the present invention is a system that provides entrepreneurs with business intelligence tools, gives advice on fundraising, and supports the development of financial plans. As a result, the assistant tool can provide entrepreneurs with the information they need and support them in fundraising methods, financial planning, and improving the efficiency of their daily work.

[0050] The assistant tool according to the embodiment includes a BI tool providing unit, a fundraising advice unit, and a financial planning unit. The BI tool providing unit provides business intelligence tools to entrepreneurs. For example, the BI tool providing unit uses a generation AI to analyze market trends and generate a report for the entrepreneur showing which markets are growing and which products and services are in high demand. The BI tool providing unit can also use the generation AI to perform competitive analysis and provide the entrepreneur with a report showing the activities of competitors. The BI tool providing unit can also use the generation AI to perform customer analysis and provide the entrepreneur with a report showing customer attributes and purchasing behavior. For example, the generation AI can analyze market trends and generate a report using a text generation AI (e.g., LLM). The generation AI can also perform competitive analysis and customer analysis using a multimodal generation AI. The generation AI can analyze competitor activities and customer attributes using natural language processing technology and generate a report. The fundraising advice unit provides fundraising advice to entrepreneurs. For example, the fundraising advice unit can use the generation AI to analyze the entrepreneur's business plan and suggest which investors are likely to be interested. The fundraising advice department can also use the generative AI to analyze successful crowdfunding cases and provide advice on effective campaign methods. The fundraising advice department can also use the generative AI to support the loan application process. For example, the generative AI analyzes a business plan and identifies points that will attract investors' interest. The generative AI analyzes successful crowdfunding cases and suggests effective campaign methods. The generative AI guides entrepreneurs through the necessary documents and procedures to support the loan application process. The financial planning department supports entrepreneurs in developing financial plans. For example, the financial planning department can use the generative AI to analyze past data and predict future income and expenses. The financial planning department can also use the generative AI to suggest specific methods for cost reduction. The financial planning department can also use the generative AI to support cash flow management. For example, the generative AI can analyze past data and predict future income and expenses.The generation AI proposes specific methods for reducing costs. The generation AI proposes methods for optimizing the balance between income and expenses to support cash flow management. As a result, the assistant tool according to the embodiment can provide entrepreneurs with the information they need and support them in fundraising methods, financial planning, and streamlining their daily operations. For example, entrepreneurs can understand market trends based on reports provided by the BI tool provider and develop appropriate business strategies. With advice from the fundraising advice department, they can secure the necessary funds and create financial plans. Furthermore, with support from the financial planning department, they can efficiently carry out their daily operations.

[0051] The BI tool provider uses the generation AI to analyze the user's past business decisions and their results, identify patterns of success and failure, and reflect these in the next business decision. For example, the BI tool provider stores the user's past business decisions and their results in a database, and the generation AI analyzes them. For example, the generation AI identifies the factors that led to the success or failure of past marketing campaigns and reflects these in the next campaign. The BI tool provider also uses the generation AI to analyze the user's business decision history and extract patterns of success and failure. For example, if a particular market strategy is successful, that strategy can be applied to the next business decision. The BI tool provider also analyzes the user's past business data and develops an algorithm to identify patterns of success and failure. For example, if a particular product line is successful, a proposal can be made to expand that product line. This allows the user's past business decisions to be analyzed and reflected in the next business decision.

[0052] The BI tool provider can use the generation AI to monitor market fluctuations in real time and send immediate alerts to entrepreneurs. For example, the generation AI collects market fluctuation data in real time and sends immediate alerts when it detects abnormal fluctuations. For example, it notifies entrepreneurs when a specific stock price suddenly drops. The BI tool provider can also build a system that monitors market fluctuations in real time and sends alerts to entrepreneurs when the generation AI detects an abnormality. For example, it can immediately notify entrepreneurs of the release of a new product by a competitor. The BI tool provider can also use the generation AI to analyze market fluctuations and send alerts to entrepreneurs when important fluctuations occur. For example, it can notify entrepreneurs when a specific market segment is growing rapidly. This makes it possible to monitor market fluctuations in real time and send immediate alerts.

[0053] The BI tool providing unit can use the emotion estimation function to analyze the user's emotional state and provide advice on how to relax during times of high stress. For example, the BI tool providing unit can use the emotion estimation function to analyze the user's emotional state in real time and provide advice on how to relax during times of high stress. For example, the BI tool providing unit can suggest relaxing music or meditation. The BI tool providing unit can also collect user emotional data and develop an algorithm that identifies times of high stress. For example, the BI tool providing unit can provide advice on how to relax during times of high workload. The BI tool providing unit can also use the emotion estimation function to analyze the user's emotional state and suggest specific actions to take to relax during times of high stress. For example, the BI tool providing unit can recommend short breaks or refreshing activities. This makes it possible to analyze the user's emotional state and provide advice on how to relax during times of high stress.

[0054] The BI tool provider uses the generation AI to generate visual data, making it easier to visually understand market trends. For example, the generation AI in the BI tool provider analyzes market data and automatically generates graphs and charts that are easy to understand visually. For example, sales trends and fluctuations in market share can be displayed in graphs. The BI tool provider also develops algorithms that generate visual data, allowing users to intuitively understand market trends. For example, data can be visualized using heat maps and bubble charts. The BI tool provider also analyzes market data using the generation AI and automatically generates visual reports that are easy to understand visually. For example, competitive analysis and the distribution of customer segments can be displayed in charts. This allows visual data to be generated, making it easier to visually understand market trends.

[0055] The BI tool provider can cross-reference data from different industries and analyze trends across those industries. For example, the BI tool provider develops algorithms that cross-reference data from different industries and analyze trends across those industries. For example, it compares trends in the technology industry and the consumer market. The BI tool provider also uses generative AI to analyze data from different industries and identify common trends and differences. For example, it can compare trends in the medical industry and the entertainment industry. The BI tool provider also builds a system that cross-references data from different industries and discovers new business opportunities. For example, it can propose new business models based on success stories from different industries. This makes it possible to cross-reference data from different industries and analyze trends across those industries.

[0056] The BI tool providing unit can use the emotion estimation function to identify market trends that users are most interested in and provide that information preferentially. For example, the BI tool providing unit can use the emotion estimation function to develop an algorithm that identifies market trends that users are most interested in. For example, trends of interest are preferentially displayed based on the user's emotional response. The BI tool providing unit can also analyze user emotional data and build a system that identifies market trends that users are most interested in. For example, trends with a high number of positive emotional responses can be preferentially provided. The BI tool providing unit can also use the emotion estimation function to identify market trends that users are most interested in in real time and provide that information preferentially. For example, trend information can be updated according to changes in the user's emotions. This makes it possible to identify market trends that users are most interested in and provide that information preferentially.

[0057] The fundraising advice department can use the generation AI to analyze investors' past investment history and list the most suitable investors. For example, the generation AI in the fundraising advice department analyzes investors' past investment history and develops an algorithm to list the most suitable investors. For example, it identifies investors who invest in a specific industry. The fundraising advice department also collects investors' investment history data, and the generation AI analyzes it to list suitable investors. For example, it can select investors based on past successful investment projects. The fundraising advice department also uses the generation AI to analyze investors' investment history and list investors who are most suitable for the entrepreneur's business plan. For example, it can identify investors who have invested in the same business model. This makes it possible to analyze investors' past investment history and list the most suitable investors.

[0058] The fundraising advice unit can use the emotion estimation function to evaluate the user's presentation skills and provide feedback on areas for improvement in real time. The fundraising advice unit, for example, uses the emotion estimation function to build a system that evaluates the user's presentation skills in real time and provides feedback on areas for improvement. For example, the system analyzes the user's facial expressions and tone of voice. The fundraising advice unit also analyzes the user's presentation using the emotion estimation function and suggests areas for improvement in real time. For example, it can provide advice on how to relax if the user is nervous. The fundraising advice unit also uses the emotion estimation function to evaluate the user's presentation skills and provide feedback on specific areas for improvement in real time. For example, it can suggest improvements in the use of eye contact and gestures. This makes it possible to evaluate the user's presentation skills and provide feedback on areas for improvement in real time.

[0059] The fundraising advice unit can use the generation AI to automatically generate visual content for crowdfunding campaigns. For example, the fundraising advice unit develops a system in which the generation AI automatically generates visual content for crowdfunding campaigns. For example, the generation AI automatically generates promotional videos and campaign images. The fundraising advice unit also has the generation AI automatically generate visual content for crowdfunding campaigns and make it easy for users to use. For example, the generation AI can generate videos and images based on templates. The fundraising advice unit also has the generation AI analyze the visual content of crowdfunding campaigns and automatically generate optimal content. For example, the generation AI can generate content based on visual elements of successful campaigns. This makes it possible to automatically generate visual content for crowdfunding campaigns.

[0060] The fundraising advice department can use the generating AI to compare different fundraising methods and propose the optimal method. For example, the generating AI can analyze different fundraising methods and develop an algorithm to propose the optimal method. For example, it can compare the advantages and disadvantages of angel investment and venture capital. The fundraising advice department also collects data on fundraising methods, and the generating AI analyzes it to propose the optimal method. For example, it can select the fundraising method that is most suitable for an entrepreneur's business model. The fundraising advice department also builds a system in which the generating AI compares different fundraising methods and proposes the optimal method to the user. For example, it can make proposals based on the success rate and cost of fundraising. This makes it possible to compare different fundraising methods and propose the optimal method.

[0061] The fundraising advice unit can use the emotion estimation function to identify the timing when a user can make a presentation with the most confidence and recommend that the presentation be made at that timing. The fundraising advice unit, for example, uses the emotion estimation function to develop an algorithm that identifies the timing when a user can make a presentation with the most confidence. For example, the unit analyzes the user's emotion data and suggests the optimal timing. The fundraising advice unit also builds a system that analyzes the user's emotional state in real time and identifies the timing when a user can make a presentation with the most confidence. For example, it can identify times when positive emotions are strong. The fundraising advice unit also uses the emotion estimation function to identify the timing when a user can make a presentation with the most confidence in real time and recommend that the presentation be made at that timing. For example, it can adjust the timing according to changes in the user's emotions. This makes it possible to identify the timing when a user can make a presentation with the most confidence and recommend that the presentation be made at that timing.

[0062] The financial planning department can use the generative AI to analyze past financial data, predict future risks, and propose risk avoidance measures. For example, the generative AI in the financial planning department analyzes past financial data and develops an algorithm to predict future risks. For example, risks are identified based on fluctuations in revenue and market fluctuations. The financial planning department also collects financial data, and the generative AI analyzes it to propose risk avoidance measures. For example, specific cost reduction measures and investment strategies can be proposed. The financial planning department also builds a system in which the generative AI analyzes past financial data, predicts future risks, and proposes specific risk avoidance measures. For example, advice can be provided to avoid high-risk investments. This makes it possible to analyze past financial data, predict future risks, and propose risk avoidance measures.

[0063] The financial planning department can use the generation AI to monitor cash flow in real time and issue immediate alerts if an abnormality occurs. For example, the financial planning department builds a system in which the generation AI monitors cash flow in real time and issues immediate alerts if an abnormality is detected. For example, a notification is sent if an unexpected expense occurs. The financial planning department also collects cash flow data in real time and develops an algorithm in which the generation AI analyzes it to detect anomalies. For example, an alert can be sent if income suddenly drops. The financial planning department also adds a function in which the generation AI monitors cash flow in real time and issues immediate alerts if an abnormality occurs. For example, a notification can be sent if a specific expense exceeds the budget. This makes it possible to monitor cash flow in real time and issue immediate alerts if an abnormality occurs.

[0064] The financial planning unit can use the emotion estimation function to analyze a user's anxiety about financial planning and provide specific advice to alleviate the anxiety. For example, the financial planning unit can use the emotion estimation function to build a system that analyzes a user's anxiety about financial planning in real time and provides specific advice to alleviate the anxiety. For example, it can suggest low-risk investments. The financial planning unit can also collect user emotion data and develop an algorithm that identifies anxiety about financial planning. For example, it can analyze anxiety about specific expenditure items and suggest alternatives. The financial planning unit can also use the emotion estimation function to analyze a user's anxiety about financial planning and suggest specific actions to alleviate the anxiety. For example, it can suggest measures to review expenditures or increase income. In this way, it is possible to analyze a user's anxiety about financial planning and provide specific advice to alleviate the anxiety.

[0065] The financial planning department can use the generation AI to automatically generate visual reports of financial plans. For example, the financial planning department develops a system in which the generation AI analyzes financial data and automatically generates visual reports that are easy to understand visually. For example, it generates graphs of income and expenditure forecasts and cash flow. The financial planning department also develops an algorithm to automatically generate visual reports, allowing users to intuitively understand financial plans. For example, it can visualize data using infographics. The financial planning department also analyzes financial data using the generation AI and automatically generates visual reports that are easy to understand visually. For example, it can display cost reduction proposals and revenue forecasts in graphs. In this way, it is possible to automatically generate visual reports of financial plans.

[0066] The financial planning unit can use the emotion estimation function to identify a financial plan that the user can feel most comfortable with and prioritize proposing that plan. The financial planning unit, for example, uses the emotion estimation function to develop an algorithm that identifies a financial plan that the user can feel most comfortable with. For example, it analyzes the user's emotion data and proposes an optimal plan. The financial planning unit can also build a system that analyzes the user's emotional state in real time and identifies a financial plan that the user can feel most comfortable with. For example, it can prioritize proposing a plan that evokes strong positive emotions. The financial planning unit can also use the emotion estimation function to identify a financial plan that the user can feel most comfortable with in real time and prioritize proposing that plan. For example, it can adjust the plan according to changes in the user's emotions. This allows the financial plan that the user can feel most comfortable with to be identified and prioritize proposing that plan.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The assistant tool may further include a health management unit that monitors the user's health status. For example, the health management unit may monitor the user's heart rate and sleep patterns and analyze the user's health status. The health management unit may also collect the user's health data, which the generative AI may analyze to identify health risks. For example, the health management unit may provide advice on how to relax during times of high stress. Furthermore, the health management unit may analyze the user's health status and suggest specific actions to maintain health. For example, it may recommend improvements in exercise or diet. This allows the user's health status to be monitored, health risks to be identified, and advice on maintaining health to be provided.

[0069] The assistant tool can further include a learning support unit that monitors the user's learning progress. For example, the learning support unit can collect the user's learning data, which the generation AI can analyze to evaluate the user's learning progress. The learning support unit can also suggest the optimal learning method according to the user's learning style. For example, for users who find visual learning effective, it can provide learning materials using graphs and charts. Furthermore, the learning support unit can analyze the user's learning progress and suggest specific actions to maximize the learning effect. For example, it can recommend the timing of review or adjustments to the learning content. In this way, the user's learning progress can be monitored, the optimal learning method can be suggested, and the learning effect can be maximized.

[0070] The assistant tool may further include a networking support unit that supports the user's networking activities. For example, the networking support unit may analyze the user's business network and suggest optimal networking events and contacts. The networking support unit may also analyze the user's past networking activities and identify successful contact methods and events. For example, it may recommend participation in an event in a specific industry. The networking support unit may also analyze the user's networking activities and suggest an effective networking strategy. For example, it may suggest a method and timing for contacting a specific key person. In this way, the networking support unit may support the user's networking activities, suggest optimal networking events and contacts, and provide an effective networking strategy.

[0071] The assistant tool can further analyze the user's emotional state and provide advice on how to relax during times of high stress. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and provide advice on how to relax during times of high stress. For example, the function can suggest relaxing music or meditation. The emotion estimation function can also be used to collect user emotional data and develop algorithms to identify times of high stress. For example, advice on how to relax during times of concentrated work can be provided. The emotion estimation function can also be used to analyze the user's emotional state and suggest specific actions to take to relax during times of high stress. For example, short breaks or refreshing activities can be recommended. This makes it possible to analyze the user's emotional state and provide advice on how to relax during times of high stress.

[0072] The assistant tool can further analyze the user's emotional state, identify the market trend that interests the user most, and provide that information preferentially. For example, an emotion estimation function can be used to develop an algorithm that identifies the market trend that interests the user most. For example, trends of interest can be preferentially displayed based on the user's emotional response. In addition, the emotion estimation function can be used to build a system that analyzes the user's emotional data and identifies the market trend that interests the user most. For example, trends with a high number of positive emotional responses can be preferentially provided. Furthermore, the emotion estimation function can be used to identify the market trend that interests the user most in real time and provide that information preferentially. For example, trend information can be updated according to changes in the user's emotions. This makes it possible to identify the market trend that interests the user most and provide that information preferentially.

[0073] The assistant tool can further analyze the user's emotional state, identify the timing when the user can give a presentation with the most confidence, and recommend that the presentation be given at that timing. For example, an algorithm can be developed using the emotion estimation function to identify the timing when the user can give a presentation with the most confidence. For example, the user's emotional data can be analyzed to suggest the optimal timing. The emotion estimation function can also be used to build a system that analyzes the user's emotional state in real time and identifies the timing when the user can give a presentation with the most confidence. For example, it can identify periods when positive emotions are strong. The emotion estimation function can further identify the timing when the user can give a presentation with the most confidence in real time and recommend that the presentation be given at that timing. For example, the timing can be adjusted according to changes in the user's emotions. This makes it possible to identify the timing when the user can give a presentation with the most confidence and recommend that the presentation be given at that timing.

[0074] The assistant tool can further analyze the user's emotional state, identify the financial plan that the user feels most comfortable with, and prioritize suggesting that plan. For example, an emotion estimation function is used to develop an algorithm that identifies the financial plan that the user feels most comfortable with. For example, the user's emotional data is analyzed to suggest the optimal plan. Also, the emotion estimation function is used to build a system that analyzes the user's emotional state in real time and identifies the financial plan that the user feels most comfortable with. For example, plans that have strong positive emotions can be prioritized. Furthermore, the emotion estimation function can be used to identify the financial plan that the user feels most comfortable with in real time and prioritize suggesting that plan. For example, the plan can be adjusted according to changes in the user's emotions. This makes it possible to identify the financial plan that the user feels most comfortable with and prioritize suggesting that plan.

[0075] The assistant tool can further analyze the user's business network and suggest optimal collaboration partners. For example, the business network analysis unit can collect the user's past collaboration data, which the generative AI can analyze to identify optimal partners. The business network analysis unit can also analyze the user's business network and suggest partners with common business goals. For example, it can identify partners suitable for a specific project. Furthermore, the business network analysis unit can analyze the user's business network and suggest effective collaboration strategies. For example, it can recommend collaboration with experts in a specific industry. This makes it possible to analyze the user's business network, suggest optimal collaboration partners, and provide an effective collaboration strategy.

[0076] The assistant tool can further analyze the user's business plan and propose an optimal marketing strategy. For example, the marketing strategy proposal unit can collect the user's business plan, and the generation AI can analyze it to identify the optimal marketing strategy. The marketing strategy proposal unit can also analyze the user's business plan and propose a marketing strategy according to the target market and customer segment. For example, it can propose an effective advertising campaign for a specific market segment. Furthermore, the marketing strategy proposal unit can analyze the user's business plan and propose a specific marketing strategy to differentiate the user from competitors. For example, it can propose unique sales channels and promotion methods. In this way, the assistant tool can analyze the user's business plan and propose the optimal marketing strategy.

[0077] The assistant tool can further analyze the user's business plan and suggest the optimal fundraising method. For example, the fundraising method suggestion unit can collect the user's business plan, and the generation AI can analyze it to identify the optimal fundraising method. The fundraising method suggestion unit can also analyze the user's business plan and compare the advantages and disadvantages of different fundraising methods. For example, it can explain the difference between angel investment and venture capital. Furthermore, the fundraising method suggestion unit can analyze the user's business plan and suggest specific actions to propose the optimal fundraising method. For example, it can suggest how and when to contact specific investors. This allows the tool to analyze the user's business plan and suggest the optimal fundraising method.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The BI tool provider provides entrepreneurs with business intelligence tools. For example, they use generative AI to analyze market trends and generate reports showing which markets are growing and which products and services are in high demand. They also conduct competitive and customer analysis and provide reports showing competitor trends, customer attributes, and purchasing behavior. Step 2: The Funding Advice Department provides entrepreneurs with advice on fundraising. For example, it uses generative AI to analyze business plans and suggest which investors are likely to be interested. It also analyzes successful crowdfunding cases and provides advice on effective campaign methods. It also assists with the loan application process. Step 3: The financial planning department helps entrepreneurs develop financial plans. For example, it uses generative AI to analyze past data and predict future income and expenses. It also suggests specific ways to reduce costs and supports cash flow management.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0128] The data processing system 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The BI Tool Department provides business intelligence tools to entrepreneurs, A fundraising advice department that provides advice on fundraising to the entrepreneur; A financial planning unit that supports the entrepreneur in formulating a financial plan. A system characterized by:

2. The BI tool providing unit Using generative AI, the system analyzes the user's past business decisions and their outcomes, identifies patterns of success and failure, and reflects these in future business decisions.

2. The system of claim 1.

3. The fundraising advice unit Using generative AI to analyze fundraising success stories and suggest the most effective presentation methods 2. The system of claim 1.

4. The financial planning department Analyze financial data using generative AI to predict future risks and propose risk avoidance measures 2. The system of claim 1.

5. The BI tool providing unit Analyzing the user's emotional state and providing advice on how to relax during times of high stress 2. The system of claim 1.

6. The fundraising advice unit Evaluate users' presentation skills and provide real-time feedback on areas for improvement 2. The system of claim 1.

7. The financial planning department Analyzing the user's concerns about the financial plan and providing specific advice to alleviate the concerns.

2. The system of claim 1.

8. The financial planning department Identify the financial plan that the user feels most comfortable with and prioritize the plan.

2. The system of claim 1.

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