system

Abstract

The system according to the embodiment aims to automatically generate a business plan by utilizing past success stories and lessons learned, and to provide review and feedback. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, a feedback unit, and a learning unit. The analysis unit analyzes past success stories and lessons learned documents. The generation unit automatically generates a business plan based on the information analyzed by the analysis unit. The feedback unit automatically reviews the business plan generated by the generation unit and provides feedback. The learning unit learns based on the feedback provided by the feedback unit.

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] With conventional technology, the process of creating and reviewing a business plan by effectively utilizing past success stories and lessons learned was tedious and time-consuming.

[0005] The system according to the embodiment aims to automatically generate a business plan by utilizing past success stories and lessons learned, and to provide review and feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a feedback unit, and a learning unit. The analysis unit analyzes past success stories and lessons learned documents. The generation unit automatically generates a business plan based on the information analyzed by the analysis unit. The feedback unit automatically reviews and provides feedback based on the business plan generated by the generation unit. The learning unit learns based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a business plan by utilizing past success stories and lessons learned, and can provide review and feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A brush-up service system according to an embodiment of the present invention utilizes past success stories and lessons learned documents to automatically generate business plans and automatically provide feedback before presentations. In the brush-up service system, when a user prepares a business plan or presentation, a generation AI analyzes past success stories and lessons learned documents to automatically generate an optimal business plan. The generation AI then automatically provides feedback based on the generated business plan and provides feedback to the user. For example, in the brush-up service system, when a user prepares a business plan or presentation, the generation AI analyzes past success stories and lessons learned documents. The generation AI then automatically generates an optimal business plan, taking into account information such as the type of business, market environment, and competitive situation. For example, when launching a new product, a business plan is created by referring to past examples of successful similar products. The generation AI then automatically provides feedback based on the generated business plan. The generation AI analyzes the contents of the business plan and identifies key points and areas for improvement in the presentation. For example, it provides feedback on the presentation structure, slide design, and speaking style. This allows users to improve the quality of their presentations. This allows the brush-up service system to enable startups to efficiently create business plans and prepare for presentations. This allows the brush-up service system to enable startups to efficiently create business plans and prepare for presentations, increasing the probability of success. For example, by utilizing past success stories and lessons learned, users can create more effective business plans and improve the quality of their presentations. Furthermore, the generation AI learns based on user feedback and improves the accuracy of the service. This allows users to create more effective business plans and prepare for presentations by continuously using the service.

[0029] A brush-up service system according to an embodiment includes an analysis unit, a generation unit, a feedback unit, and a learning unit. The analysis unit analyzes past success stories and lessons learned documents. The analysis unit performs its analysis by taking into account information such as the type of business, the market environment, and the competitive situation. For example, the analysis unit performs its analysis based on success stories in a specific industry or market. The analysis unit can also perform its analysis based on analysis reports and improvement proposals of past failure stories. The generation unit automatically generates a business plan based on the information analyzed by the analysis unit. The generation unit performs, for example, template-based generation or machine learning generation. For example, the generation unit automatically generates a business plan by referring to past success stories. The generation unit can also generate a business plan that includes elements such as a business model, a financial plan, and a marketing strategy. The feedback unit automatically reviews the business plan generated by the generation unit and provides feedback. The feedback unit provides feedback on, for example, the structure of a presentation, the design of slides, and speaking style. For example, the feedback unit provides feedback based on a checklist or evaluation criteria. The feedback unit can also provide text-based comments and scoring. The learning unit learns based on the feedback provided by the feedback unit. The learning unit performs learning using, for example, a machine learning algorithm. For example, the learning unit learns new success stories and lessons based on user feedback. The learning unit can also perform learning using a data feedback loop. As a result, the brush-up service system according to the embodiment can provide a brush-up service for startups by utilizing past success stories and lesson-learning documents to automatically generate business plans and automatically provide advice before presentations.

[0030] The analysis unit can perform analysis based on information on the type of business, the market environment, and the competitive situation. The analysis unit performs analysis, for example, taking into account information on the type of business, the market environment, and the competitive situation. For example, the analysis unit performs analysis based on the type of business, such as manufacturing, services, or IT. The analysis unit can also perform analysis taking into account market environment factors such as market size, growth rate, and competitive situation. Furthermore, the analysis unit can also perform analysis based on information on the competitive situation, such as the market share and competitive advantage of competitors. This makes it possible to provide more appropriate analysis results by taking into account information on the type of business, the market environment, and the competitive situation.

[0031] The generation unit can automatically generate a business plan based on past success stories. The generation unit, for example, automatically generates a business plan by referring to past success stories. For example, the generation unit generates a business plan based on success stories in a specific industry or market. The generation unit can also generate a business plan based on analysis reports and improvement proposals of past success stories. Furthermore, the generation unit can generate a business plan that includes elements such as a business model, financial plan, and marketing strategy. In this way, by referring to past success stories, a more effective business plan can be automatically generated.

[0032] The feedback unit can provide feedback on the structure of the presentation, the design of the slides, and the speaking style. The feedback unit provides feedback on, for example, the structure of the presentation, the design of the slides, the speaking style, etc. For example, the feedback unit provides feedback on components of the presentation such as the introduction, main content, and conclusion. The feedback unit can also provide feedback on slide design elements such as layout, fonts, and color usage. Furthermore, the feedback unit can also provide feedback on speaking style elements such as tone of voice, speed, and gestures. In this way, by providing feedback on the structure of the presentation, the design of the slides, the speaking style, etc., the quality of the presentation can be improved.

[0033] The learning unit can learn new success stories and lessons based on user feedback. The learning unit learns new success stories and lessons based on user feedback, for example. For example, the learning unit learns success stories based on recent market trends. The learning unit can also learn lessons based on analysis reports of failure stories and improvement suggestions. Furthermore, the learning unit can also perform learning using a machine learning algorithm. This allows the accuracy of the service to be improved by learning new success stories and lessons based on user feedback.

[0034] During the analysis, the analysis unit can perform the analysis based on the type of business, market environment, and competitive situation, as well as the success rate of the user's past business plans. For example, the analysis unit performs the analysis by taking into account the type of business, market environment, and competitive situation, as well as the success rate of the user's past business plans. For example, if the success rate of the user's past business plans is high, the analysis unit prioritizes analysis of similar approaches. Furthermore, if the success rate of the user's past business plans is low, the analysis unit can also perform analysis to propose a new approach. Furthermore, the analysis unit can perform the analysis by taking into account the optimal market environment and competitive situation based on the success rate of the user's past business plans. This allows for more appropriate analysis results to be provided by taking into account the success rate of the user's past business plans.

[0035] The analysis unit can use different analysis algorithms for each type of business during analysis. The analysis unit applies different analysis algorithms to each type of business, for example. For example, in the case of a product development business, the analysis unit performs analysis based on success stories related to product market launch. In addition, in the case of a service industry, the analysis unit can also perform analysis based on success stories related to improving customer satisfaction. Furthermore, in the case of a technology business, the analysis unit can also perform analysis based on success stories related to technological innovation. In this way, by applying different analysis algorithms to each type of business, more appropriate analysis results can be provided.

[0036] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past feedback. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past feedback. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past feedback. Furthermore, the analysis unit can adjust the priority of the analysis based on the user's feedback. In this way, the accuracy of the analysis can be improved by referring to the user's past feedback.

[0037] During analysis, the analysis unit can prioritize analysis of highly relevant cases based on the user's geographical location information. For example, the analysis unit prioritizes analysis of highly relevant cases taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit prioritizes analysis of successful cases related to that region. In addition, if the user is in a different region, the analysis unit can also perform analysis taking into account the market environment of that region. Furthermore, the analysis unit can perform analysis to propose an optimal business plan based on the user's geographical location information. In this way, by taking into account the user's geographical location information, more relevant analysis results can be provided.

[0038] During the analysis, the analysis unit can analyze the user's social media activities and analyze related cases. For example, the analysis unit analyzes the user's social media activities and analyzes related cases. For example, the analysis unit analyzes related success cases based on information shared by the user on social media. The analysis unit can also analyze the user's social media activities to propose an optimal business plan. Furthermore, the analysis unit can customize the analysis results by referring to the user's social media activities. This makes it possible to provide more relevant analysis results by analyzing the user's social media activities.

[0039] During analysis, the analysis unit can customize the analysis method based on the user's past feedback. The analysis unit customizes the analysis method by, for example, reflecting the user's past feedback. For example, the analysis unit adjusts the analysis method based on feedback provided by the user in the past. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past feedback. Furthermore, the analysis unit can adjust the priority of the analysis based on the user's feedback. In this way, the analysis method can be customized and accuracy can be improved by reflecting the user's past feedback.

[0040] The generation unit can change the level of detail of the generation based on the importance of the business plan when generating it. The generation unit adjusts the level of detail of the generation based on, for example, the importance of the business plan. For example, the generation unit generates a plan that includes detailed information for a business plan with high importance. The generation unit can also generate a plan that includes concise information for a business plan with low importance. Furthermore, the generation unit can also generate a business plan that appropriately includes necessary information depending on the importance. In this way, by adjusting the level of detail of the generation based on the importance of the business plan, it is possible to provide a more appropriate business plan.

[0041] The generation unit can use different generation algorithms depending on the business category when generating the business plan. For example, the generation unit applies different generation algorithms depending on the business category. For example, in the case of a product development business, the generation unit generates the business plan based on success stories related to product market launch. In addition, in the case of a service industry, the generation unit can generate the business plan based on success stories related to improving customer satisfaction. Furthermore, in the case of a technology business, the generation unit can generate the business plan based on success stories related to technological innovation. In this way, by applying different generation algorithms depending on the business category, it is possible to provide a more appropriate business plan.

[0042] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit adjusts the generation algorithm based on business plans generated by the user in the past. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. Furthermore, the generation unit can adjust the generation priority based on user feedback. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0043] The generation unit can set a generation priority based on the submission date of the business plan when generating the business plan. The generation unit determines the generation priority based on, for example, the submission date of the business plan. For example, when the submission deadline is approaching, the generation unit prioritizes generating the business plan. Also, when the submission deadline is far away, the generation unit can generate the business plan in parallel with other tasks. Furthermore, the generation unit can generate the business plan at the optimal timing depending on the submission date. In this way, by determining the generation priority based on the submission date of the business plan, it is possible to provide the business plan at a more appropriate timing.

[0044] The generation unit can change the order of generation based on the relevance of the business plans during generation. The generation unit adjusts the order of generation based on, for example, the relevance of the business plans. For example, the generation unit generates business plans with high relevance first. The generation unit can also generate business plans with low relevance later. Furthermore, the generation unit can generate business plans in an optimal order according to the relevance. In this way, by adjusting the order of generation based on the relevance of the business plans, it is possible to provide a more appropriate business plan.

[0045] The generation unit can change the use of technical terms in the business plan during generation according to the user's level of expertise. The generation unit, for example, adjusts the use of technical terms in the business plan according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a business plan that uses a lot of technical terms. Also, if the user does not have technical expertise, the generation unit can generate a concise and easy-to-understand business plan. Furthermore, the generation unit can adjust the use of optimal technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, a more appropriate business plan can be provided.

[0046] The feedback unit can change the level of detail of the feedback based on the importance of the presentation when providing feedback. The feedback unit adjusts the level of detail of the feedback based on, for example, the importance of the presentation. For example, the feedback unit provides detailed feedback for a presentation with high importance. The feedback unit can also provide concise feedback for a presentation with low importance. Furthermore, the feedback unit can also provide feedback that appropriately includes necessary information depending on the importance. In this way, by adjusting the level of detail of the feedback based on the importance of the presentation, it is possible to provide more appropriate feedback.

[0047] The feedback unit can use different feedback algorithms depending on the category of the presentation when providing feedback. For example, the feedback unit applies different feedback algorithms depending on the category of the presentation. For example, in a product development presentation, the feedback unit provides feedback regarding product market launch. In addition, in a service industry presentation, the feedback unit can provide feedback regarding customer satisfaction improvement. Furthermore, in a technology presentation, the feedback unit can provide feedback regarding technological innovation. In this way, by applying different feedback algorithms depending on the category of the presentation, more appropriate feedback can be provided.

[0048] The feedback unit can improve the accuracy of the feedback based on the user's past feedback results when providing feedback. For example, the feedback unit improves the accuracy of the feedback by referring to the user's past feedback results. For example, the feedback unit adjusts a feedback algorithm based on feedback provided by the user in the past. The feedback unit can also improve the accuracy of the feedback by referring to the user's past feedback results. Furthermore, the feedback unit can adjust the priority of the feedback based on the user's feedback. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results.

[0049] When providing feedback, the feedback unit can set a priority of the feedback based on the time of submission of the presentation. The feedback unit determines the priority of the feedback based on, for example, the time of submission of the presentation. For example, the feedback unit provides feedback preferentially when the submission deadline is approaching. Also, when the submission deadline is far away, the feedback unit can provide feedback in parallel with other tasks. Furthermore, the feedback unit can provide feedback at the optimal timing depending on the time of submission. In this way, by determining the priority of the feedback based on the time of submission of the presentation, it is possible to provide feedback at a more appropriate time.

[0050] The feedback unit can change the order of feedback based on the relevance of the presentation when providing feedback. The feedback unit adjusts the order of feedback based on, for example, the relevance of the presentation. For example, the feedback unit provides feedback preferentially to presentations with high relevance. The feedback unit can also provide feedback later to presentations with low relevance. Furthermore, the feedback unit can provide feedback in an optimal order according to the relevance. In this way, by adjusting the order of feedback based on the relevance of the presentation, more appropriate feedback can be provided.

[0051] The feedback unit may change the use of technical terminology in the feedback depending on the user's level of expertise when providing feedback. For example, the feedback unit may adjust the use of technical terminology in the feedback depending on the user's level of expertise. For example, if the user has technical expertise, the feedback unit may provide feedback that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the feedback unit may provide concise and easy-to-understand feedback. Furthermore, the feedback unit may adjust the use of optimal technical terminology depending on the user's level of expertise. In this way, more appropriate feedback can be provided by adjusting the use of technical terminology depending on the user's level of expertise.

[0052] During learning, the learning unit can optimize the learning algorithm based on past learning data. The learning unit, for example, optimizes the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. Furthermore, the learning unit can adjust the priority of the learning algorithm based on past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and accuracy can be improved.

[0053] The learning unit can update the learning data based on user feedback during learning. For example, the learning unit updates the learning data by reflecting user feedback. For example, the learning unit updates the learning data based on feedback provided by the user. The learning unit can also adjust the learning algorithm by reflecting user feedback. Furthermore, the learning unit can adjust the priority of the learning data based on user feedback. In this way, the learning data can be updated by reflecting user feedback, thereby improving accuracy.

[0054] During learning, the learning unit can weight the learning data based on the time of submission of the business plan. The learning unit weights the learning data based on, for example, the time of submission of the business plan. For example, the learning unit weights the learning data more when the submission deadline is approaching. The learning unit can also weight the learning data less when the submission deadline is far away. Furthermore, the learning unit can also weight the learning data optimally depending on the time of submission. In this way, by weighting the learning data based on the time of submission of the business plan, more appropriate learning can be provided.

[0055] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit integrates information from different data sources to enrich the learning data. For example, the learning unit acquires data from an external database or API and integrates it into the learning data. The learning unit can also adjust the learning algorithm based on information from different data sources. Furthermore, the learning unit can improve the accuracy of the learning data by referring to information from different data sources. In this way, by integrating information from different data sources, the learning data can be enriched and its accuracy improved.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The analysis unit can analyze the user's past behavioral history and optimize the generation of the business plan based on the behavioral pattern. For example, the analysis unit can analyze what kind of business plans the user has created in the past and what kind of feedback they have received. The analysis unit can also analyze what market environments the user has succeeded or failed in in the past. Furthermore, the analysis unit can suggest an optimal method for generating a business plan based on the user's past behavioral history. In this way, a more appropriate business plan can be provided by taking the user's past behavioral history into consideration.

[0058] The generation unit can analyze the user's current market trends in real time and generate a business plan based on the trends. For example, the generation unit can analyze current market trends and propose a marketing strategy based on the trends. The generation unit can also generate a business plan that takes into account the trends of competitors based on current market trends. Furthermore, the generation unit can dynamically update the content of the business plan to respond to market trends that change in real time. This makes it possible to provide a more appropriate business plan by taking current market trends into account.

[0059] The analysis unit can analyze the user's geographic location information and generate a business plan based on the region's specific market environment. For example, if the user is in a specific region, the analysis unit generates a business plan taking into account the market environment of that region. The analysis unit can also generate a business plan taking into account the region's specific competitive situation and consumer preferences. Furthermore, the analysis unit can propose an optimal business plan based on success stories for each region. In this way, a more appropriate business plan can be provided by taking into account the user's geographic location information.

[0060] The feedback unit can analyze the user's past feedback history and optimize the content of the feedback based on the history. For example, the feedback unit can adjust the current feedback content based on feedback the user has received in the past. The feedback unit can also set a priority of the feedback by referring to the user's past feedback history. Furthermore, the feedback unit can suggest an optimal feedback method for the user based on the past feedback history. In this way, more appropriate feedback can be provided by taking the user's past feedback history into consideration.

[0061] The analysis unit can analyze the user's social media activities and customize the business plan based on the activities. For example, the analysis unit can analyze related success stories based on information shared by the user on social media. The analysis unit can also customize the content of the business plan by referring to the user's social media activities. Furthermore, the analysis unit can suggest optimal marketing strategies based on the user's social media activities. This makes it possible to provide a more appropriate business plan by taking the user's social media activities into consideration.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The analysis department analyzes past success stories and lessons learned documents. The analysis department conducts its analysis taking into account information such as the type of business, market environment, and competitive situation. For example, the analysis can be based on success stories in a specific industry or market, or on analysis reports and improvement proposals for past failures. Step 2: The generation unit automatically generates a business plan based on the information analyzed by the analysis unit. The generation unit generates a business plan using templates or machine learning, including elements such as a business model, financial plan, and marketing strategy. Step 3: The feedback section performs an automatic review of the business plan generated by the generation section and provides feedback on the presentation structure, slide design, speaking style, etc., and provides text comments and a score based on a checklist and evaluation criteria. Step 4: The learning unit learns based on the feedback provided by the feedback unit. The learning unit uses machine learning algorithms to learn new success stories and lessons learned based on user feedback, using a data feedback loop.

[0064] (Example 2) A brush-up service system according to an embodiment of the present invention utilizes past success stories and lessons learned documents to automatically generate business plans and automatically provide feedback before presentations. In the brush-up service system, when a user prepares a business plan or presentation, a generation AI analyzes past success stories and lessons learned documents to automatically generate an optimal business plan. The generation AI then automatically provides feedback based on the generated business plan and provides feedback to the user. For example, in the brush-up service system, when a user prepares a business plan or presentation, the generation AI analyzes past success stories and lessons learned documents. The generation AI then automatically generates an optimal business plan, taking into account information such as the type of business, market environment, and competitive situation. For example, when launching a new product, a business plan is created by referring to past examples of successful similar products. The generation AI then automatically provides feedback based on the generated business plan. The generation AI analyzes the contents of the business plan and identifies key points and areas for improvement in the presentation. For example, it provides feedback on the presentation structure, slide design, and speaking style. This allows users to improve the quality of their presentations. This allows the brush-up service system to enable startups to efficiently create business plans and prepare for presentations. This allows the brush-up service system to enable startups to efficiently create business plans and prepare for presentations, increasing the probability of success. For example, by utilizing past success stories and lessons learned, users can create more effective business plans and improve the quality of their presentations. Furthermore, the generation AI learns based on user feedback and improves the accuracy of the service. This allows users to create more effective business plans and prepare for presentations by continuously using the service.

[0065] A brush-up service system according to an embodiment includes an analysis unit, a generation unit, a feedback unit, and a learning unit. The analysis unit analyzes past success stories and lessons learned documents. The analysis unit performs its analysis by taking into account information such as the type of business, the market environment, and the competitive situation. For example, the analysis unit performs its analysis based on success stories in a specific industry or market. The analysis unit can also perform its analysis based on analysis reports and improvement proposals of past failure stories. The generation unit automatically generates a business plan based on the information analyzed by the analysis unit. The generation unit performs, for example, template-based generation or machine learning generation. For example, the generation unit automatically generates a business plan by referring to past success stories. The generation unit can also generate a business plan that includes elements such as a business model, a financial plan, and a marketing strategy. The feedback unit automatically reviews the business plan generated by the generation unit and provides feedback. The feedback unit provides feedback on, for example, the structure of a presentation, the design of slides, and speaking style. For example, the feedback unit provides feedback based on a checklist or evaluation criteria. The feedback unit can also provide text-based comments and scoring. The learning unit learns based on the feedback provided by the feedback unit. The learning unit performs learning using, for example, a machine learning algorithm. For example, the learning unit learns new success stories and lessons based on user feedback. The learning unit can also perform learning using a data feedback loop. As a result, the brush-up service system according to the embodiment can provide a brush-up service for startups by utilizing past success stories and lesson-learning documents to automatically generate business plans and automatically provide advice before presentations.

[0066] The analysis unit can perform analysis based on information on the type of business, the market environment, and the competitive situation. The analysis unit performs analysis, for example, taking into account information on the type of business, the market environment, and the competitive situation. For example, the analysis unit performs analysis based on the type of business, such as manufacturing, services, or IT. The analysis unit can also perform analysis taking into account market environment factors such as market size, growth rate, and competitive situation. Furthermore, the analysis unit can also perform analysis based on information on the competitive situation, such as the market share and competitive advantage of competitors. This makes it possible to provide more appropriate analysis results by taking into account information on the type of business, the market environment, and the competitive situation.

[0067] The generation unit can automatically generate a business plan based on past success stories. The generation unit, for example, automatically generates a business plan by referring to past success stories. For example, the generation unit generates a business plan based on success stories in a specific industry or market. The generation unit can also generate a business plan based on analysis reports and improvement proposals of past success stories. Furthermore, the generation unit can generate a business plan that includes elements such as a business model, financial plan, and marketing strategy. In this way, by referring to past success stories, a more effective business plan can be automatically generated.

[0068] The feedback unit can provide feedback on the structure of the presentation, the design of the slides, and the speaking style. The feedback unit provides feedback on, for example, the structure of the presentation, the design of the slides, the speaking style, etc. For example, the feedback unit provides feedback on components of the presentation such as the introduction, main content, and conclusion. The feedback unit can also provide feedback on slide design elements such as layout, fonts, and color usage. Furthermore, the feedback unit can also provide feedback on speaking style elements such as tone of voice, speed, and gestures. In this way, by providing feedback on the structure of the presentation, the design of the slides, the speaking style, etc., the quality of the presentation can be improved.

[0069] The learning unit can learn new success stories and lessons based on user feedback. The learning unit learns new success stories and lessons based on user feedback, for example. For example, the learning unit learns success stories based on recent market trends. The learning unit can also learn lessons based on analysis reports of failure stories and improvement suggestions. Furthermore, the learning unit can also perform learning using a machine learning algorithm. This allows the accuracy of the service to be improved by learning new success stories and lessons based on user feedback.

[0070] The analysis unit can estimate the user's emotions and adjust the order of analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize providing analysis results that help the user relax. Furthermore, if the user is excited, the analysis unit can prioritize providing detailed analysis results. Furthermore, if the user is tired, the analysis unit can prioritize providing concise and to-the-point analysis results. In this way, by adjusting the analysis priority according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] During the analysis, the analysis unit can perform the analysis based on the type of business, market environment, and competitive situation, as well as the success rate of the user's past business plans. For example, the analysis unit performs the analysis by taking into account the type of business, market environment, and competitive situation, as well as the success rate of the user's past business plans. For example, if the success rate of the user's past business plans is high, the analysis unit prioritizes analysis of similar approaches. Furthermore, if the success rate of the user's past business plans is low, the analysis unit can also perform analysis to propose a new approach. Furthermore, the analysis unit can perform the analysis by taking into account the optimal market environment and competitive situation based on the success rate of the user's past business plans. This allows for more appropriate analysis results to be provided by taking into account the success rate of the user's past business plans.

[0072] The analysis unit can use different analysis algorithms for each type of business during analysis. The analysis unit applies different analysis algorithms to each type of business, for example. For example, in the case of a product development business, the analysis unit performs analysis based on success stories related to product market launch. In addition, in the case of a service industry, the analysis unit can also perform analysis based on success stories related to improving customer satisfaction. Furthermore, in the case of a technology business, the analysis unit can also perform analysis based on success stories related to technological innovation. In this way, by applying different analysis algorithms to each type of business, more appropriate analysis results can be provided.

[0073] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past feedback. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past feedback. For example, the analysis unit adjusts the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past feedback. Furthermore, the analysis unit can adjust the priority of the analysis based on the user's feedback. In this way, the accuracy of the analysis can be improved by referring to the user's past feedback.

[0074] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] During analysis, the analysis unit can prioritize analysis of highly relevant cases based on the user's geographical location information. For example, the analysis unit prioritizes analysis of highly relevant cases taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit prioritizes analysis of successful cases related to that region. In addition, if the user is in a different region, the analysis unit can also perform analysis taking into account the market environment of that region. Furthermore, the analysis unit can perform analysis to propose an optimal business plan based on the user's geographical location information. In this way, by taking into account the user's geographical location information, more relevant analysis results can be provided.

[0076] During the analysis, the analysis unit can analyze the user's social media activities and analyze related cases. For example, the analysis unit analyzes the user's social media activities and analyzes related cases. For example, the analysis unit analyzes related success cases based on information shared by the user on social media. The analysis unit can also analyze the user's social media activities to propose an optimal business plan. Furthermore, the analysis unit can customize the analysis results by referring to the user's social media activities. This makes it possible to provide more relevant analysis results by analyzing the user's social media activities.

[0077] During analysis, the analysis unit can customize the analysis method based on the user's past feedback. The analysis unit customizes the analysis method by, for example, reflecting the user's past feedback. For example, the analysis unit adjusts the analysis method based on feedback provided by the user in the past. The analysis unit can also improve the accuracy of the analysis results by referring to the user's past feedback. Furthermore, the analysis unit can adjust the priority of the analysis based on the user's feedback. In this way, the analysis method can be customized and accuracy can be improved by reflecting the user's past feedback.

[0078] The generation unit can estimate the user's emotions and change the presentation style of the business plan based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the presentation style of the business plan based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a business plan that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a concise and to-the-point business plan. Furthermore, if the user is excited, the generation unit can generate a business plan that includes a visually stimulating design. This allows for a more appropriate business plan to be provided by adjusting the presentation style of the business plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The generation unit can change the level of detail of the generation based on the importance of the business plan when generating it. The generation unit adjusts the level of detail of the generation based on, for example, the importance of the business plan. For example, the generation unit generates a plan that includes detailed information for a business plan with high importance. The generation unit can also generate a plan that includes concise information for a business plan with low importance. Furthermore, the generation unit can also generate a business plan that appropriately includes necessary information depending on the importance. In this way, by adjusting the level of detail of the generation based on the importance of the business plan, it is possible to provide a more appropriate business plan.

[0080] The generation unit can use different generation algorithms depending on the business category when generating the business plan. For example, the generation unit applies different generation algorithms depending on the business category. For example, in the case of a product development business, the generation unit generates the business plan based on success stories related to product market launch. In addition, in the case of a service industry, the generation unit can generate the business plan based on success stories related to improving customer satisfaction. Furthermore, in the case of a technology business, the generation unit can generate the business plan based on success stories related to technological innovation. In this way, by applying different generation algorithms depending on the business category, it is possible to provide a more appropriate business plan.

[0081] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, improves the accuracy of generation by referring to the user's past generation results. For example, the generation unit adjusts the generation algorithm based on business plans generated by the user in the past. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. Furthermore, the generation unit can adjust the generation priority based on user feedback. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0082] The generation unit can estimate the user's emotions and change the length of the business plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the business plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise business plan. If the user is relaxed, the generation unit can generate a longer business plan with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a business plan with a visually stimulating design. This allows the user to adjust the length of the business plan according to the user's emotions, thereby providing a more appropriate business plan. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The generation unit can set a generation priority based on the submission date of the business plan when generating the business plan. The generation unit determines the generation priority based on, for example, the submission date of the business plan. For example, when the submission deadline is approaching, the generation unit prioritizes generating the business plan. Also, when the submission deadline is far away, the generation unit can generate the business plan in parallel with other tasks. Furthermore, the generation unit can generate the business plan at the optimal timing depending on the submission date. In this way, by determining the generation priority based on the submission date of the business plan, it is possible to provide the business plan at a more appropriate timing.

[0084] The generation unit can change the order of generation based on the relevance of the business plans during generation. The generation unit adjusts the order of generation based on, for example, the relevance of the business plans. For example, the generation unit generates business plans with high relevance first. The generation unit can also generate business plans with low relevance later. Furthermore, the generation unit can generate business plans in an optimal order according to the relevance. In this way, by adjusting the order of generation based on the relevance of the business plans, it is possible to provide a more appropriate business plan.

[0085] The generation unit can change the use of technical terms in the business plan during generation according to the user's level of expertise. The generation unit, for example, adjusts the use of technical terms in the business plan according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a business plan that uses a lot of technical terms. Also, if the user does not have technical expertise, the generation unit can generate a concise and easy-to-understand business plan. Furthermore, the generation unit can adjust the use of optimal technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, a more appropriate business plan can be provided.

[0086] The feedback unit can estimate the user's emotion and change the way the feedback is expressed based on the estimated user's emotion. For example, the feedback unit can estimate the user's emotion and adjust the way the feedback is expressed based on the estimated user's emotion. For example, if the user is nervous, the feedback unit can provide gentle feedback. If the user is relaxed, the feedback unit can also provide detailed feedback. If the user is in a hurry, the feedback unit can also provide concise and to-the-point feedback. This allows for more appropriate feedback to be provided by adjusting the way the feedback is expressed depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The feedback unit can change the level of detail of the feedback based on the importance of the presentation when providing feedback. The feedback unit adjusts the level of detail of the feedback based on, for example, the importance of the presentation. For example, the feedback unit provides detailed feedback for a presentation with high importance. The feedback unit can also provide concise feedback for a presentation with low importance. Furthermore, the feedback unit can also provide feedback that appropriately includes necessary information depending on the importance. In this way, by adjusting the level of detail of the feedback based on the importance of the presentation, it is possible to provide more appropriate feedback.

[0088] The feedback unit can use different feedback algorithms depending on the category of the presentation when providing feedback. For example, the feedback unit applies different feedback algorithms depending on the category of the presentation. For example, in a product development presentation, the feedback unit provides feedback regarding product market launch. In addition, in a service industry presentation, the feedback unit can provide feedback regarding customer satisfaction improvement. Furthermore, in a technology presentation, the feedback unit can provide feedback regarding technological innovation. In this way, by applying different feedback algorithms depending on the category of the presentation, more appropriate feedback can be provided.

[0089] The feedback unit can improve the accuracy of the feedback based on the user's past feedback results when providing feedback. For example, the feedback unit improves the accuracy of the feedback by referring to the user's past feedback results. For example, the feedback unit adjusts a feedback algorithm based on feedback provided by the user in the past. The feedback unit can also improve the accuracy of the feedback by referring to the user's past feedback results. Furthermore, the feedback unit can adjust the priority of the feedback based on the user's feedback. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results.

[0090] The feedback unit can estimate the user's emotion and change the length of the feedback based on the estimated user's emotion. The feedback unit, for example, estimates the user's emotion and adjusts the length of the feedback based on the estimated user's emotion. For example, the feedback unit can provide short and to-the-point feedback when the user is in a hurry. The feedback unit can also provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide visually stimulating feedback when the user is excited. This allows for more appropriate feedback to be provided by adjusting the length of the feedback according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When providing feedback, the feedback unit can set a priority of the feedback based on the time of submission of the presentation. The feedback unit determines the priority of the feedback based on, for example, the time of submission of the presentation. For example, the feedback unit provides feedback preferentially when the submission deadline is approaching. Also, when the submission deadline is far away, the feedback unit can provide feedback in parallel with other tasks. Furthermore, the feedback unit can provide feedback at the optimal timing depending on the time of submission. In this way, by determining the priority of the feedback based on the time of submission of the presentation, it is possible to provide feedback at a more appropriate time.

[0092] The feedback unit can change the order of feedback based on the relevance of the presentation when providing feedback. The feedback unit adjusts the order of feedback based on, for example, the relevance of the presentation. For example, the feedback unit provides feedback preferentially to presentations with high relevance. The feedback unit can also provide feedback later to presentations with low relevance. Furthermore, the feedback unit can provide feedback in an optimal order according to the relevance. In this way, by adjusting the order of feedback based on the relevance of the presentation, more appropriate feedback can be provided.

[0093] The feedback unit may change the use of technical terminology in the feedback depending on the user's level of expertise when providing feedback. For example, the feedback unit may adjust the use of technical terminology in the feedback depending on the user's level of expertise. For example, if the user has technical expertise, the feedback unit may provide feedback that uses a lot of technical terminology. Alternatively, if the user does not have technical expertise, the feedback unit may provide concise and easy-to-understand feedback. Furthermore, the feedback unit may adjust the use of optimal technical terminology depending on the user's level of expertise. In this way, more appropriate feedback can be provided by adjusting the use of technical terminology depending on the user's level of expertise.

[0094] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, the learning unit estimates the user's emotions and selects training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects detailed training data. If the user is in a hurry, the learning unit can also select concise and to-the-point training data. Furthermore, if the user is excited, the learning unit can also select visually stimulating training data. In this way, by selecting training data according to the user's emotions, more appropriate training data can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] During learning, the learning unit can optimize the learning algorithm based on past learning data. The learning unit, for example, optimizes the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. Furthermore, the learning unit can adjust the priority of the learning algorithm based on past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and accuracy can be improved.

[0096] The learning unit can update the learning data based on user feedback during learning. For example, the learning unit updates the learning data by reflecting user feedback. For example, the learning unit updates the learning data based on feedback provided by the user. The learning unit can also adjust the learning algorithm by reflecting user feedback. Furthermore, the learning unit can adjust the priority of the learning data based on user feedback. In this way, the learning data can be updated by reflecting user feedback, thereby improving accuracy.

[0097] The learning unit can estimate the user's emotions and change the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. For example, the learning unit performs learning more frequently when the user is relaxed. The learning unit can also reduce the frequency of learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is excited. In this way, by adjusting the frequency of learning according to the user's emotions, more appropriate learning can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] During learning, the learning unit can weight the learning data based on the time of submission of the business plan. The learning unit weights the learning data based on, for example, the time of submission of the business plan. For example, the learning unit weights the learning data more when the submission deadline is approaching. The learning unit can also weight the learning data less when the submission deadline is far away. Furthermore, the learning unit can also weight the learning data optimally depending on the time of submission. In this way, by weighting the learning data based on the time of submission of the business plan, more appropriate learning can be provided.

[0099] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit integrates information from different data sources to enrich the learning data. For example, the learning unit acquires data from an external database or API and integrates it into the learning data. The learning unit can also adjust the learning algorithm based on information from different data sources. Furthermore, the learning unit can improve the accuracy of the learning data by referring to information from different data sources. In this way, by integrating information from different data sources, the learning data can be enriched and its accuracy improved. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, feedback unit, and learning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, feedback unit, and learning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, feedback unit, and learning unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, feedback unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can analyze the user's past behavioral history and optimize the generation of the business plan based on the behavioral pattern. For example, the analysis unit can analyze what kind of business plans the user has created in the past and what kind of feedback they have received. The analysis unit can also analyze what market environments the user has succeeded or failed in in the past. Furthermore, the analysis unit can suggest an optimal method for generating a business plan based on the user's past behavioral history. In this way, a more appropriate business plan can be provided by taking the user's past behavioral history into consideration.

[0102] The generation unit can analyze the user's current market trends in real time and generate a business plan based on the trends. For example, the generation unit can analyze current market trends and propose a marketing strategy based on the trends. The generation unit can also generate a business plan that takes into account the trends of competitors based on current market trends. Furthermore, the generation unit can dynamically update the content of the business plan to respond to market trends that change in real time. This makes it possible to provide a more appropriate business plan by taking current market trends into account.

[0103] The feedback unit can estimate the user's emotion and adjust the timing of the feedback based on the estimated user's emotion. For example, the feedback unit delays feedback when the user is feeling stressed. The feedback unit can also provide immediate feedback when the user is relaxed. Furthermore, the feedback unit can provide detailed feedback when the user is excited. In this way, by adjusting the timing of feedback according to the user's emotion, more appropriate feedback can be provided.

[0104] The learning unit can estimate the user's emotions and customize the learning content based on the estimated user's emotions. For example, the learning unit can provide detailed learning content when the user is relaxed. The learning unit can also provide concise and to-the-point learning content when the user is in a hurry. Furthermore, the learning unit can provide visually stimulating learning content when the user is excited. In this way, the learning content can be customized according to the user's emotions, thereby providing more appropriate learning.

[0105] The analysis unit can analyze the user's geographic location information and generate a business plan based on the region's specific market environment. For example, if the user is in a specific region, the analysis unit generates a business plan taking into account the market environment of that region. The analysis unit can also generate a business plan taking into account the region's specific competitive situation and consumer preferences. Furthermore, the analysis unit can propose an optimal business plan based on success stories for each region. In this way, a more appropriate business plan can be provided by taking into account the user's geographic location information.

[0106] The generation unit can estimate the user's emotions and change the design of the business plan based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can provide a visually calming design. If the user is excited, the generation unit can also provide a visually stimulating design. Furthermore, if the user is stressed, the generation unit can also provide a simple, highly visible design. In this way, by changing the design of the business plan according to the user's emotions, a more appropriate business plan can be provided.

[0107] The feedback unit can analyze the user's past feedback history and optimize the content of the feedback based on the history. For example, the feedback unit can adjust the current feedback content based on feedback the user has received in the past. The feedback unit can also set a priority of the feedback by referring to the user's past feedback history. Furthermore, the feedback unit can suggest an optimal feedback method for the user based on the past feedback history. In this way, more appropriate feedback can be provided by taking the user's past feedback history into consideration.

[0108] The learning unit can estimate the user's emotions and adjust the learning progress speed based on the estimated user's emotions. For example, if the user is relaxed, the learning unit can proceed with the learning at a normal speed. If the user is in a hurry, the learning unit can also speed up the learning progress speed. Furthermore, if the user is excited, the learning unit can also adjust the learning progress speed. In this way, by adjusting the learning progress speed according to the user's emotions, more appropriate learning can be provided.

[0109] The analysis unit can analyze the user's social media activities and customize the business plan based on the activities. For example, the analysis unit can analyze related success stories based on information shared by the user on social media. The analysis unit can also customize the content of the business plan by referring to the user's social media activities. Furthermore, the analysis unit can suggest optimal marketing strategies based on the user's social media activities. This makes it possible to provide a more appropriate business plan by taking the user's social media activities into consideration.

[0110] The generation unit can estimate the user's emotions and dynamically change the content of the business plan based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a business plan that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a business plan that is concise and to the point. Furthermore, if the user is excited, the generation unit can also generate a business plan that includes a visually stimulating design. In this way, by dynamically changing the content of the business plan according to the user's emotions, a more appropriate business plan can be provided.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The analysis department analyzes past success stories and lessons learned documents. The analysis department conducts its analysis taking into account information such as the type of business, market environment, and competitive situation. For example, the analysis can be based on success stories in a specific industry or market, or on analysis reports and improvement proposals for past failures. Step 2: The generation unit automatically generates a business plan based on the information analyzed by the analysis unit. The generation unit generates a business plan using templates or machine learning, including elements such as a business model, financial plan, and marketing strategy. Step 3: The feedback section performs an automatic review of the business plan generated by the generation section and provides feedback on the presentation structure, slide design, speaking style, etc., and provides text comments and a score based on a checklist and evaluation criteria. Step 4: The learning unit learns based on the feedback provided by the feedback unit. The learning unit uses machine learning algorithms to learn new success stories and lessons learned based on user feedback, using a data feedback loop.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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. an analysis department that analyzes past success stories and lessons learned documents; a generation unit that automatically generates a business plan based on the information analyzed by the analysis unit; a feedback unit that performs an automatic review based on the business plan generated by the generation unit and provides feedback; a learning unit that learns based on the feedback provided by the feedback unit. A system characterized by:

2. The analysis unit Conduct analysis based on information on the type of business, market environment, and competitive situation 2. The system of claim 1.

3. The generation unit Automatically generate business plans based on past success stories 2. The system of claim 1.

4. The feedback unit Provide feedback on presentation structure, slide design, and speaking style 2. The system of claim 1.

5. The learning unit Learn new success stories and lessons learned from user feedback 2. The system of claim 1.

6. The analysis unit Inferring user emotions and adjusting the order of analysis based on the estimated user emotions 2. The system of claim 1.

7. The analysis unit The analysis is based on the type of business, market environment, and competitive situation, as well as the success rate of the user's past business plans.

2. The system of claim 1.

8. The analysis unit When analyzing, different analysis algorithms are used for different types of businesses.

2. The system of claim 1.

Images