system

Abstract

The system according to the embodiment aims to efficiently introduce, analyze, and document past cases related to business improvement and system development projects. [Solution] The system according to the embodiment includes a learning unit, a reception unit, an introduction unit, an analysis unit, and a documentation unit. The learning unit learns and accumulates data. The reception unit accepts user input. The introduction unit introduces past cases. The analysis unit analyzes and evaluates cases. The documentation unit documents cases.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not efficiently introduce, analyze, and document past cases related to business improvement and system development projects, so there is room for improvement.

[0005] The system according to the embodiment aims to efficiently introduce, analyze, and document past cases related to business improvement and system development projects. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a reception unit, an introduction unit, an analysis unit, and a documentation unit. The learning unit learns and accumulates data. The reception unit receives user input. The introduction unit introduces past cases. The analysis unit analyzes and evaluates cases. The documentation unit documents cases. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently introduce, analyze, and document past cases related to business improvement and system development projects. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business improvement support system according to an embodiment of the present invention uses AI to learn and accumulate data on business improvement, business, system development projects, manuals, security guidelines, and other data for a corporate group. The system then introduces past cases, analyzes and evaluates projects, and documents them for the project content the user wants to research, thereby reducing internal work hours. This business improvement support system improves business efficiency by learning and accumulating data, accepting user input, introducing past cases, analyzing and evaluating projects, and documenting them. For example, AI learns and accumulates data on business improvement, business, system development projects, manuals, security guidelines, and other data. Next, the AI ​​introduces past cases for the project content the user wants to research, analyzes and evaluates the project, and documents it. This reduces internal work hours. Furthermore, by selling this platform to external parties, other companies can achieve similar benefits. For example, data types include business improvement, business, system development projects, manuals, security guidelines, and more. AI learns and accumulates this data. Next, the AI ​​introduces past cases for the project content the user wants to research, analyzes and evaluates the project, and documents it. This reduces internal work hours. Furthermore, by selling this platform externally, other companies can achieve similar benefits. As a result, the business improvement support system uses AI to learn and accumulate data such as business improvement, business, system development projects, manuals, and security guidelines from the corporate group, and can introduce past cases, analyze and evaluate projects, and document them for the project content that users want to research, thereby reducing internal work hours. Furthermore, by selling this platform externally, other companies can achieve similar benefits.

[0029] A business improvement support system according to an embodiment includes a learning unit, a reception unit, an introduction unit, an analysis unit, and a documentation unit. The learning unit learns and accumulates data such as business improvement, business, system development projects, manuals, and security guidelines. For example, the learning unit can learn and accumulate text data, numerical data, image data, and the like. The learning unit can also use AI to analyze data and extract important information. The reception unit receives the details of the project that a user wants to research. For example, the reception unit can receive user input in the form of text input, voice input, image input, and the like. The reception unit can also analyze the user's input and perform appropriate processing. The introduction unit introduces past cases. For example, the introduction unit can introduce success cases, failure cases, cases in specific industries, and the like. The introduction unit can also use AI to search past cases and provide appropriate cases to the user. The analysis unit analyzes and evaluates the project. For example, the analysis unit can analyze and evaluate the project using methods such as quantitative evaluation, qualitative evaluation, and risk analysis. The analysis unit can also use AI to analyze data and understand the details of the project. The documentation unit documents the analysis and evaluation results of projects. For example, the documentation unit can create documents in a report format, a presentation format, a digital document format, or other formats. The documentation unit can also automatically generate documents using AI. This enables the business improvement support system according to the embodiment to learn and accumulate data, accept user input, introduce past cases, analyze and evaluate projects, and document them.

[0030] The business improvement support system includes a sales department for external sales. The sales department provides functions for externally selling the system. For example, the sales department can sell the system through online sales, direct sales, agency sales, and other methods. The sales department can also select the optimal external sales method by taking into account the attribute information of the target company. For example, the sales department selects the optimal external sales method based on attribute information such as the industry, size, and region of the target company. This allows the business improvement support system to achieve similar effects for other companies.

[0031] The learning unit can learn and accumulate data on business improvement, business, system development projects, manuals, and security guidelines. The learning unit learns and accumulates data such as specific business improvement methods, business types, system development phases, manual scope, and security guideline details. For example, the learning unit learns data on process improvement, cost reduction, and quality improvement as specific business improvement methods. The learning unit also learns data on business types such as manufacturing, services, and IT. The learning unit also learns data on system development phases such as requirements definition, design, implementation, testing, and operation. Manual scope includes operation manuals, training manuals, and maintenance manuals. Security guideline details include access control, data protection, and vulnerability management. This allows the learning unit to learn and accumulate diverse data, enabling it to handle a wide range of projects.

[0032] The reception unit can receive the details of the project that the user wants to research. The reception unit receives details of the project, such as a specific project, a specific technology, or a specific market. For example, if the user wants to research a specific project, the reception unit receives detailed information about the project. Also, if the user wants to research a specific technology, the reception unit receives detailed information about the technology. Furthermore, if the user wants to research a specific market, the reception unit receives detailed information about the market. In this way, the reception unit can provide appropriate information by receiving the details of the project that the user wants to research.

[0033] The introduction section can introduce past cases. The introduction section introduces, for example, success cases, failure cases, and cases from specific industries. For example, the introduction section introduces the reasons and results of a specific project as a success case. Also, the introduction section introduces the reasons and lessons learned from a specific project as a failure case. Furthermore, the introduction section introduces industry-specific challenges and solutions as cases from specific industries. In this way, the introduction section provides information that users can refer to by introducing past cases.

[0034] The analysis unit can analyze and evaluate cases. The analysis unit analyzes and evaluates cases using methods such as quantitative evaluation, qualitative evaluation, and risk analysis. For example, the analysis unit evaluates the performance of a case using numerical data as a quantitative evaluation. The analysis unit also evaluates the qualitative aspects of a case using text data as a qualitative evaluation. Furthermore, the analysis unit identifies risks related to a case and evaluates their impact and probability of occurrence as a risk analysis. In this way, the analysis unit can analyze and evaluate a case, allowing the user to understand the details of the case.

[0035] The documentation unit can document the analysis and evaluation results of a project. The documentation unit creates materials in formats such as a report, a presentation, or a digital document. For example, the documentation unit compiles the analysis results of a project in a report format. The documentation unit also compiles the evaluation results of a project in a presentation format. Furthermore, the documentation unit compiles detailed information about the project as a digital document. In this way, the documentation unit documents the analysis and evaluation results of a project, making it easier for users to organize information.

[0036] During learning, the learning unit can adjust the learning priority based on the importance of the data. For example, the learning unit prioritizes learning of business improvement data with high importance. For example, the learning unit learns process improvement and cost reduction methods as business improvement data. Furthermore, the learning unit prioritizes learning of security guideline data with high importance. For example, the learning unit learns access control and data protection methods as security guideline data. Furthermore, the learning unit prioritizes learning of business project data with high importance. For example, the learning unit learns revenue forecasting and market analysis methods as business project data. This enables the learning unit to efficiently learn based on importance.

[0037] The learning unit can apply different learning algorithms to each data category during learning. For example, the learning unit applies an efficiency algorithm to business improvement data. For example, the learning unit uses an efficiency algorithm to learn process improvement and cost reduction methods for the business improvement data. The learning unit also applies a risk assessment algorithm to security guideline data. For example, the learning unit uses a risk assessment algorithm to learn access control and data protection methods for the security guideline data. Furthermore, the learning unit applies a revenue forecasting algorithm to business project data. For example, the learning unit uses a revenue forecasting algorithm to learn revenue forecasting and market analysis methods for the business project data. This enables the learning unit to perform optimal learning for each category.

[0038] During learning, the learning unit can weight the learning data based on the time of data submission. For example, the learning unit weights the most recent business improvement data highly. For example, the learning unit weights recent process improvement and cost reduction techniques highly as the most recent business improvement data, and causes the data to be learned. Furthermore, the learning unit weights the most recent security guideline data highly. For example, the learning unit weights recent access control and data protection methods highly as the most recent security guideline data, and causes the data to be learned. Furthermore, the learning unit weights the most recent business project data highly. For example, the learning unit weights recent revenue forecasts and market analysis techniques highly as the most recent business project data, and causes the data to be learned. This enables the learning unit to perform efficient learning based on the time of submission.

[0039] During learning, the learning unit can adjust the order of learning based on the relevance of the data. For example, the learning unit prioritizes learning of highly relevant business improvement data. For example, the learning unit learns process improvement and cost reduction methods as business improvement data in order of relevance. Furthermore, the learning unit prioritizes learning of highly relevant security guideline data. For example, the learning unit learns access control and data protection methods as security guideline data in order of relevance. Furthermore, the learning unit prioritizes learning of highly relevant business project data. For example, the learning unit learns revenue forecasting and market analysis methods as business project data in order of relevance. This enables the learning unit to perform efficient learning based on relevance.

[0040] The reception unit can select the optimal reception method by referring to the user's past inquiry history when receiving a call. The reception unit, for example, prioritizes providing a reception method that the user has frequently used in the past. For example, the reception unit prioritizes providing an online reception method that the user has frequently used in the past. The reception unit also suggests a related reception method based on the content of the user's past inquiry. For example, the reception unit suggests a related telephone reception method based on the content of the user's past inquiry. Furthermore, the reception unit predicts the optimal reception method from the user's past inquiry history. For example, the reception unit predicts the optimal face-to-face reception from the user's past inquiry history. This enables the reception unit to provide the optimal reception based on the past inquiry history.

[0041] The reception unit can perform filtering based on the user's current work situation when receiving requests. For example, the reception unit prioritizes receiving requests related to work that the user is currently working on. For example, the reception unit prioritizes receiving requests related to a project that the user is currently working on. The reception unit also filters related requests based on the user's current work situation. For example, the reception unit filters related technical requests based on the user's current work situation. Furthermore, the reception unit provides an optimal reception method in consideration of the user's current work situation. For example, the reception unit provides an optimal market request in consideration of the user's current work situation. This enables the reception unit to filter based on the current work situation.

[0042] The reception unit can prioritize receiving highly relevant cases during reception, taking into account the user's geographical location information. For example, the reception unit prioritizes receiving cases close to the user's current location. For example, the reception unit prioritizes receiving project cases close to the user's current location. The reception unit also filters highly relevant cases based on the user's geographical location information. For example, the reception unit filters highly relevant technical cases based on the user's geographical location information. Furthermore, the reception unit provides an optimal reception method taking into account the user's geographical location information. For example, the reception unit provides optimal market cases taking into account the user's geographical location information. This enables the reception unit to optimally receive cases based on the geographical location information.

[0043] The reception unit can analyze the user's social media activity at the time of reception and receive related cases. For example, the reception unit prioritizes reception of related cases based on the user's social media activity. For example, the reception unit prioritizes reception of related project cases based on the user's social media activity. The reception unit also analyzes the user's social media activity to provide an optimal reception method. For example, the reception unit analyzes the user's social media activity to provide an optimal technology case. Furthermore, the reception unit filters highly relevant cases based on the user's social media activity. For example, the reception unit filters highly relevant market cases based on the user's social media activity. This enables the reception unit to optimally receive cases based on social media activity.

[0044] When making an introduction, the introduction department can adjust the level of detail of the introduction based on the importance of the past case. For example, the introduction department introduces past cases of high importance in detail. For example, the introduction department introduces project cases of high importance in detail. Furthermore, the introduction department briefly introduces past cases of low importance. For example, the introduction department briefly introduces technology cases of low importance. Furthermore, the introduction department moderately introduces past cases of medium importance. For example, the introduction department moderately introduces market cases of medium importance. This enables the introduction department to make optimal introductions based on importance.

[0045] When introducing a case, the introduction department can apply different introduction algorithms depending on the category of the case. For example, the introduction department applies an efficiency algorithm to a business improvement case. For example, the introduction department introduces process improvement and cost reduction methods as a business improvement case using an efficiency algorithm. Furthermore, the introduction department applies a risk assessment algorithm to a security guideline case. For example, the introduction department introduces access control and data protection methods as a security guideline case using a risk assessment algorithm. Furthermore, the introduction department applies a revenue forecasting algorithm to a business project case. For example, the introduction department introduces revenue forecasting and market analysis methods as a business project case using a revenue forecasting algorithm. This enables the introduction department to make the most appropriate introduction for each category.

[0046] At the time of introduction, the introduction department can determine the priority of introduction based on the time of submission of the case. For example, the introduction department prioritizes introducing the latest case. For example, the introduction department prioritizes introducing the latest project case. In addition, the introduction department postpones older case. For example, the introduction department postpones older technology case. Furthermore, the introduction department introduces the case in an appropriate order based on the time of submission. For example, the introduction department introduces market case in an appropriate order based on the time of submission. This enables the introduction department to make optimal introductions based on the time of submission.

[0047] The introduction department can adjust the order of introduction based on the relevance of the cases when making an introduction. For example, the introduction department prioritizes introducing highly relevant cases. For example, the introduction department prioritizes introducing highly relevant project cases. Furthermore, the introduction department postpones less relevant cases. For example, the introduction department postpones less relevant technology cases. Furthermore, the introduction department introduces cases in an appropriate order based on relevance. For example, the introduction department introduces market cases in an appropriate order based on relevance. This enables the introduction department to make optimal introductions based on relevance.

[0048] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between cases during analysis. For example, the analysis unit groups related cases and analyzes them. For example, the analysis unit groups related project cases and analyzes them. The analysis unit also performs a highly accurate analysis by taking into account the interrelationships between cases. For example, the analysis unit performs a highly accurate analysis by taking into account the interrelationships between project cases. Furthermore, the analysis unit provides an optimal analysis method based on the interrelationships between cases. For example, the analysis unit provides an optimal analysis method based on the interrelationships between project cases. This enables the analysis unit to perform a highly accurate analysis by taking into account the interrelationships between cases.

[0049] When performing analysis, the analysis department can take into consideration the attribute information of the submitter of the case. The analysis department performs analysis based on, for example, the submitter's job title. For example, the analysis department analyzes project cases based on the submitter's job title. The analysis department also performs analysis based on the submitter's department. For example, the analysis department analyzes technology cases based on the submitter's department. The analysis department also performs analysis based on the submitter's years of experience. For example, the analysis department analyzes market cases based on the submitter's years of experience. This enables the analysis department to perform optimal analysis based on the submitter's attribute information.

[0050] The analysis department can perform the analysis taking into consideration the geographical distribution of the projects. For example, the analysis department performs an analysis by region based on the geographical distribution of the projects. For example, the analysis department performs a project analysis by region based on the geographical distribution of the projects. The analysis department also provides an optimal analysis method taking into consideration the geographical distribution. For example, the analysis department provides an optimal technology analysis method taking into consideration the geographical distribution. Furthermore, the analysis department groups related projects based on the geographical distribution for analysis. For example, the analysis department groups related market projects based on the geographical distribution for analysis. This enables the analysis department to perform an optimal analysis based on the geographical distribution.

[0051] The analysis department can improve the accuracy of the analysis by referring to literature related to the project during the analysis. The analysis department, for example, performs a highly accurate analysis based on the related literature. For example, the analysis department performs a highly accurate project analysis based on the related literature. The analysis department also refers to the related literature to provide an optimal analysis method. For example, the analysis department refers to the related literature to provide an optimal technology analysis method. Furthermore, the analysis department performs an analysis based on the related literature, taking into account the interrelationships between projects. For example, the analysis department performs a market analysis based on the related literature, taking into account the interrelationships between projects. This enables the analysis department to perform a highly accurate analysis based on the related literature.

[0052] The documentation department can adjust the level of detail of the materials when documenting based on the importance of the project. For example, the documentation department creates detailed materials for projects with high importance. For example, the documentation department creates detailed materials for project projects with high importance. Furthermore, the documentation department creates concise materials for projects with low importance. For example, the documentation department creates concise materials for technology projects with low importance. Furthermore, the documentation department creates materials with a moderate level of detail for projects with medium importance. For example, the documentation department creates materials with a moderate level of detail for market projects with medium importance. This enables the documentation department to create optimal materials based on importance.

[0053] When documenting, the documentation department can apply different documentation algorithms depending on the category of the case. For example, the documentation department applies an efficiency algorithm to business improvement cases. For example, the documentation department documents process improvement and cost reduction methods for business improvement cases using an efficiency algorithm. The documentation department also applies a risk assessment algorithm to security guideline cases. For example, the documentation department documents access control and data protection methods for security guideline cases using a risk assessment algorithm. Furthermore, the documentation department applies a revenue forecasting algorithm to business cases. For example, the documentation department documents revenue forecasting and market analysis methods for business cases using a revenue forecasting algorithm. This enables the documentation department to create optimal documentation for each category.

[0054] When documenting, the documentation department can adjust the order of documents based on the submission date of the case. For example, the documentation department prioritizes documenting the latest case. For example, the documentation department prioritizes documenting the latest project case. In addition, the documentation department postpones older cases. For example, the documentation department postpones older technology cases. Furthermore, the documentation department creates documents in an appropriate order based on the submission date. For example, the documentation department creates documents for market cases in an appropriate order based on the submission date. This enables the documentation department to optimally document based on the submission date.

[0055] When documenting, the documentation department can adjust the display method of the materials based on the relevance of the cases. For example, the documentation department prioritizes documenting highly relevant cases. For example, the documentation department prioritizes documenting highly relevant project cases. In addition, the documentation department postpones less relevant cases. For example, the documentation department postpones less relevant technical cases. Furthermore, the documentation department creates materials in an appropriate order based on relevance. For example, the documentation department creates materials for market cases in an appropriate order based on relevance. This enables the documentation department to perform optimal documenting based on relevance.

[0056] When making external sales, the external sales department can select the optimal external sales method by taking into consideration the attribute information of the target company. The external sales department, for example, selects the external sales method based on the industry of the target company. For example, the external sales department selects the optimal online sales method for a target company in the manufacturing industry. The external sales department also selects the external sales method based on the size of the target company. For example, the external sales department selects the optimal direct sales method for a target company that is a large corporation. Furthermore, the external sales department selects the external sales method based on the region of the target company. For example, the external sales department selects the optimal agency sales method for a target company in a specific region. This enables the external sales department to make optimal external sales based on the attribute information of the target company.

[0057] When making an external sale, the external sales department can select the optimal external sales method by taking into consideration the geographical location information of the target company. The external sales department, for example, selects the external sales method based on the location of the target company. For example, the external sales department selects the optimal online sales method based on the location of the target company. The external sales department also prioritizes external sales to geographically nearby companies. For example, the external sales department selects the optimal direct sales method for geographically nearby target companies. Furthermore, the external sales department provides the optimal external sales method by taking into consideration geographical characteristics. For example, the external sales department provides the optimal agency sales method by taking into consideration the geographical characteristics of a specific region. This enables the external sales department to make optimal external sales based on geographical location information.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] During learning, the learning unit can adjust the learning priority based on the importance of the data. For example, it prioritizes learning of business improvement data with high importance. For example, it can learn methods for process improvement and cost reduction. It can also prioritize learning of security guideline data with high importance. For example, it can learn methods for access control and data protection. It can also prioritize learning of business project data with high importance. For example, it can learn methods for revenue forecasting and market analysis. This enables the learning unit to efficiently learn based on importance.

[0060] When accepting a call, the reception unit can select the optimal reception method by referring to the user's past inquiry history. For example, it can prioritize the reception method that the user has used frequently in the past. For example, it can prioritize online reception. It can also suggest a related reception method based on the content of the user's past inquiries. For example, it can suggest telephone reception. It can also predict the optimal reception method from the user's past inquiry history. For example, it can predict face-to-face reception. This enables the reception unit to select the optimal reception method based on the past inquiry history.

[0061] The reception unit can perform filtering based on the user's current work situation when receiving requests. For example, it prioritizes receiving requests related to work that the user is currently doing. For example, it prioritizes receiving requests related to a project that is currently underway. It also filters related requests based on the user's current work situation. For example, it filters related technical requests. It also provides the optimal reception method taking into account the user's current work situation. For example, it provides the optimal market request. This enables the reception unit to filter requests based on the current work situation.

[0062] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between cases during analysis. For example, related cases are grouped and analyzed. For example, related project cases are grouped and analyzed. Furthermore, a highly accurate analysis is performed by taking into account the interrelationships between cases. For example, a highly accurate analysis is performed by taking into account the interrelationships between project cases. Furthermore, an optimal analysis method is provided based on the interrelationships between cases. For example, an optimal analysis method is provided based on the interrelationships between project cases. This enables the analysis unit to perform a highly accurate analysis by taking into account the interrelationships between cases.

[0063] When documenting, the documentation department can adjust the level of detail of the documents based on the importance of the project. For example, detailed documents are created for projects with high importance. For example, detailed documents are created for project projects with high importance. Furthermore, concise documents are created for projects with low importance. For example, concise documents are created for technology projects with low importance. Furthermore, documents with a moderate level of detail are created for projects with medium importance. For example, documents with a moderate level of detail are created for market projects with medium importance. This enables the documentation department to create optimal documents based on importance.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The learning unit learns and accumulates data such as business improvement, business, system development projects, manuals, and security guidelines. For example, the learning unit can learn and accumulate text data, numerical data, image data, etc. The learning unit can also use AI to analyze the data and extract important information. Step 2: The reception unit receives the details of the case that the user wants to check. For example, the reception unit can receive user input in the form of text input, voice input, image input, etc. The reception unit can also analyze the user's input and perform appropriate processing. Step 3: The introduction department introduces past cases. For example, the introduction department can introduce success stories, failure stories, cases in specific industries, etc. The introduction department can also use AI to search past cases and provide appropriate cases to the user. Step 4: The analysis department analyzes and evaluates the case. For example, the analysis department can analyze and evaluate the case using methods such as quantitative evaluation, qualitative evaluation, and risk analysis. The analysis department can also use AI to analyze data and understand the details of the case. Step 5: The Documentation Department documents the analysis and evaluation results of the project. For example, the Documentation Department can create documents in the form of reports, presentations, digital documents, etc. The Documentation Department can also use AI to automatically generate documents.

[0066] (Example 2) A business improvement support system according to an embodiment of the present invention uses AI to learn and accumulate data on business improvement, business, system development projects, manuals, security guidelines, and other data for a corporate group. The system then introduces past cases, analyzes and evaluates projects, and documents them for the project content the user wants to research, thereby reducing internal work hours. This business improvement support system improves business efficiency by learning and accumulating data, accepting user input, introducing past cases, analyzing and evaluating projects, and documenting them. For example, AI learns and accumulates data on business improvement, business, system development projects, manuals, security guidelines, and other data. Next, the AI ​​introduces past cases for the project content the user wants to research, analyzes and evaluates the project, and documents it. This reduces internal work hours. Furthermore, by selling this platform to external parties, other companies can achieve similar benefits. For example, data types include business improvement, business, system development projects, manuals, security guidelines, and more. AI learns and accumulates this data. Next, the AI ​​introduces past cases for the project content the user wants to research, analyzes and evaluates the project, and documents it. This reduces internal work hours. Furthermore, by selling this platform externally, other companies can achieve similar benefits. As a result, the business improvement support system uses AI to learn and accumulate data such as business improvement, business, system development projects, manuals, and security guidelines from the corporate group, and can introduce past cases, analyze and evaluate projects, and document them for the project content that users want to research, thereby reducing internal work hours. Furthermore, by selling this platform externally, other companies can achieve similar benefits.

[0067] A business improvement support system according to an embodiment includes a learning unit, a reception unit, an introduction unit, an analysis unit, and a documentation unit. The learning unit learns and accumulates data such as business improvement, business, system development projects, manuals, and security guidelines. For example, the learning unit can learn and accumulate text data, numerical data, image data, and the like. The learning unit can also use AI to analyze data and extract important information. The reception unit receives the details of the project that a user wants to research. For example, the reception unit can receive user input in the form of text input, voice input, image input, and the like. The reception unit can also analyze the user's input and perform appropriate processing. The introduction unit introduces past cases. For example, the introduction unit can introduce success cases, failure cases, cases in specific industries, and the like. The introduction unit can also use AI to search past cases and provide appropriate cases to the user. The analysis unit analyzes and evaluates the project. For example, the analysis unit can analyze and evaluate the project using methods such as quantitative evaluation, qualitative evaluation, and risk analysis. The analysis unit can also use AI to analyze data and understand the details of the project. The documentation unit documents the analysis and evaluation results of projects. For example, the documentation unit can create documents in a report format, a presentation format, a digital document format, or other formats. The documentation unit can also automatically generate documents using AI. This enables the business improvement support system according to the embodiment to learn and accumulate data, accept user input, introduce past cases, analyze and evaluate projects, and document them.

[0068] The business improvement support system includes a sales department for external sales. The sales department provides functions for externally selling the system. For example, the sales department can sell the system through online sales, direct sales, agency sales, and other methods. The sales department can also select the optimal external sales method by taking into account the attribute information of the target company. For example, the sales department selects the optimal external sales method based on attribute information such as the industry, size, and region of the target company. This allows the business improvement support system to achieve similar effects for other companies.

[0069] The learning unit can learn and accumulate data on business improvement, business, system development projects, manuals, and security guidelines. The learning unit learns and accumulates data such as specific business improvement methods, business types, system development phases, manual scope, and security guideline details. For example, the learning unit learns data on process improvement, cost reduction, and quality improvement as specific business improvement methods. The learning unit also learns data on business types such as manufacturing, services, and IT. The learning unit also learns data on system development phases such as requirements definition, design, implementation, testing, and operation. Manual scope includes operation manuals, training manuals, and maintenance manuals. Security guideline details include access control, data protection, and vulnerability management. This allows the learning unit to learn and accumulate diverse data, enabling it to handle a wide range of projects.

[0070] The reception unit can receive the details of the project that the user wants to research. The reception unit receives details of the project, such as a specific project, a specific technology, or a specific market. For example, if the user wants to research a specific project, the reception unit receives detailed information about the project. Also, if the user wants to research a specific technology, the reception unit receives detailed information about the technology. Furthermore, if the user wants to research a specific market, the reception unit receives detailed information about the market. In this way, the reception unit can provide appropriate information by receiving the details of the project that the user wants to research.

[0071] The introduction section can introduce past cases. The introduction section introduces, for example, success cases, failure cases, and cases from specific industries. For example, the introduction section introduces the reasons and results of a specific project as a success case. Also, the introduction section introduces the reasons and lessons learned from a specific project as a failure case. Furthermore, the introduction section introduces industry-specific challenges and solutions as cases from specific industries. In this way, the introduction section provides information that users can refer to by introducing past cases.

[0072] The analysis unit can analyze and evaluate cases. The analysis unit analyzes and evaluates cases using methods such as quantitative evaluation, qualitative evaluation, and risk analysis. For example, the analysis unit evaluates the performance of a case using numerical data as a quantitative evaluation. The analysis unit also evaluates the qualitative aspects of a case using text data as a qualitative evaluation. Furthermore, the analysis unit identifies risks related to a case and evaluates their impact and probability of occurrence as a risk analysis. In this way, the analysis unit can analyze and evaluate a case, allowing the user to understand the details of the case.

[0073] The documentation unit can document the analysis and evaluation results of a project. The documentation unit creates materials in formats such as a report, a presentation, or a digital document. For example, the documentation unit compiles the analysis results of a project in a report format. The documentation unit also compiles the evaluation results of a project in a presentation format. Furthermore, the documentation unit compiles detailed information about the project as a digital document. In this way, the documentation unit documents the analysis and evaluation results of a project, making it easier for users to organize information.

[0074] The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit prioritizes learning data with relaxing content. For example, the learning unit may learn relaxation techniques and stress management methods as data with relaxing content. Furthermore, if the user is excited, the learning unit prioritizes learning data with stimulating content. For example, the learning unit may learn the latest technological trends and innovative business models as data with stimulating content. Furthermore, if the user is tired, the learning unit prioritizes learning data with simple and easy-to-understand content. For example, the learning unit may learn basic concepts and introductory information as data with simple and easy-to-understand content. This enables the learning unit to select data according to the user's emotions.

[0075] During learning, the learning unit can adjust the learning priority based on the importance of the data. For example, the learning unit prioritizes learning of business improvement data with high importance. For example, the learning unit learns process improvement and cost reduction methods as business improvement data. Furthermore, the learning unit prioritizes learning of security guideline data with high importance. For example, the learning unit learns access control and data protection methods as security guideline data. Furthermore, the learning unit prioritizes learning of business project data with high importance. For example, the learning unit learns revenue forecasting and market analysis methods as business project data. This enables the learning unit to efficiently learn based on importance.

[0076] The learning unit can apply different learning algorithms to each data category during learning. For example, the learning unit applies an efficiency algorithm to business improvement data. For example, the learning unit uses an efficiency algorithm to learn process improvement and cost reduction methods for the business improvement data. The learning unit also applies a risk assessment algorithm to security guideline data. For example, the learning unit uses a risk assessment algorithm to learn access control and data protection methods for the security guideline data. Furthermore, the learning unit applies a revenue forecasting algorithm to business project data. For example, the learning unit uses a revenue forecasting algorithm to learn revenue forecasting and market analysis methods for the business project data. This enables the learning unit to perform optimal learning for each category.

[0077] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user's emotions. For example, if the user is feeling stressed, the learning unit sets the learning frequency low. For example, the learning unit sets the learning frequency to once a week for a user who is feeling stressed. Furthermore, if the user is relaxed, the learning unit sets the learning frequency high. For example, the learning unit sets the learning frequency to daily for a relaxed user. Furthermore, if the user is tired, the learning unit sets the learning frequency to medium. For example, the learning unit sets the learning frequency to three times a week for a user who is tired. This enables the learning unit to adjust the learning frequency according to the user's emotions.

[0078] During learning, the learning unit can weight the learning data based on the time of data submission. For example, the learning unit weights the most recent business improvement data highly. For example, the learning unit weights recent process improvement and cost reduction techniques highly as the most recent business improvement data, and causes the data to be learned. Furthermore, the learning unit weights the most recent security guideline data highly. For example, the learning unit weights recent access control and data protection methods highly as the most recent security guideline data, and causes the data to be learned. Furthermore, the learning unit weights the most recent business project data highly. For example, the learning unit weights recent revenue forecasts and market analysis techniques highly as the most recent business project data, and causes the data to be learned. This enables the learning unit to perform efficient learning based on the time of submission.

[0079] During learning, the learning unit can adjust the order of learning based on the relevance of the data. For example, the learning unit prioritizes learning of highly relevant business improvement data. For example, the learning unit learns process improvement and cost reduction methods as business improvement data in order of relevance. Furthermore, the learning unit prioritizes learning of highly relevant security guideline data. For example, the learning unit learns access control and data protection methods as security guideline data in order of relevance. Furthermore, the learning unit prioritizes learning of highly relevant business project data. For example, the learning unit learns revenue forecasting and market analysis methods as business project data in order of relevance. This enables the learning unit to perform efficient learning based on relevance.

[0080] The reception unit can estimate the user's emotions and adjust the reception method based on the estimated user's emotions. For example, the reception unit provides a simple interface when the user is feeling stressed. For example, the reception unit provides a simple, highly visible interface to a user who is feeling stressed. Furthermore, the reception unit provides detailed input options when the user is relaxed. For example, the reception unit provides detailed input options to a relaxed user. Furthermore, the reception unit prioritizes voice input when the user is in a hurry. For example, the reception unit prioritizes voice input to a user who is in a hurry. This enables the reception unit to adjust the reception method according to the user's emotions.

[0081] The reception unit can select the optimal reception method by referring to the user's past inquiry history when receiving a call. The reception unit, for example, prioritizes providing a reception method that the user has frequently used in the past. For example, the reception unit prioritizes providing an online reception method that the user has frequently used in the past. The reception unit also suggests a related reception method based on the content of the user's past inquiry. For example, the reception unit suggests a related telephone reception method based on the content of the user's past inquiry. Furthermore, the reception unit predicts the optimal reception method from the user's past inquiry history. For example, the reception unit predicts the optimal face-to-face reception from the user's past inquiry history. This enables the reception unit to provide the optimal reception based on the past inquiry history.

[0082] The reception unit can perform filtering based on the user's current work situation when receiving requests. For example, the reception unit prioritizes receiving requests related to work that the user is currently working on. For example, the reception unit prioritizes receiving requests related to a project that the user is currently working on. The reception unit also filters related requests based on the user's current work situation. For example, the reception unit filters related technical requests based on the user's current work situation. Furthermore, the reception unit provides an optimal reception method in consideration of the user's current work situation. For example, the reception unit provides an optimal market request in consideration of the user's current work situation. This enables the reception unit to filter based on the current work situation.

[0083] The reception unit can estimate the user's emotions and determine the priority of the cases to be received based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit prioritizes receiving cases of high importance. For example, the reception unit prioritizes receiving project cases of high importance for a user who is feeling stressed. Furthermore, when the user is relaxed, the reception unit also accepts cases of low importance. For example, the reception unit also accepts technical cases of low importance for a relaxed user. Furthermore, when the user is in a hurry, the reception unit prioritizes receiving urgent cases. For example, the reception unit prioritizes receiving market cases of high importance for a user who is in a hurry. This enables the reception unit to determine the priority of cases according to the user's emotions.

[0084] The reception unit can prioritize receiving highly relevant cases during reception, taking into account the user's geographical location information. For example, the reception unit prioritizes receiving cases close to the user's current location. For example, the reception unit prioritizes receiving project cases close to the user's current location. The reception unit also filters highly relevant cases based on the user's geographical location information. For example, the reception unit filters highly relevant technical cases based on the user's geographical location information. Furthermore, the reception unit provides an optimal reception method taking into account the user's geographical location information. For example, the reception unit provides optimal market cases taking into account the user's geographical location information. This enables the reception unit to optimally receive cases based on the geographical location information.

[0085] The reception unit can analyze the user's social media activity at the time of reception and receive related cases. For example, the reception unit prioritizes reception of related cases based on the user's social media activity. For example, the reception unit prioritizes reception of related project cases based on the user's social media activity. The reception unit also analyzes the user's social media activity to provide an optimal reception method. For example, the reception unit analyzes the user's social media activity to provide an optimal technology case. Furthermore, the reception unit filters highly relevant cases based on the user's social media activity. For example, the reception unit filters highly relevant market cases based on the user's social media activity. This enables the reception unit to optimally receive cases based on social media activity.

[0086] The introduction unit can estimate the user's emotions and adjust the presentation method of the introduction based on the estimated user's emotions. For example, if the user is feeling stressed, the introduction unit provides a simple and highly visible introduction method. For example, the introduction unit introduces simple and highly visible project examples to a user who is feeling stressed. Furthermore, if the user is relaxed, the introduction unit provides an introduction method including detailed information. For example, the introduction unit introduces technology examples including detailed information to a relaxed user. Furthermore, if the user is in a hurry, the introduction unit provides an introduction method that focuses on the main points. For example, the introduction unit introduces market examples that focus on the main points to a user who is in a hurry. This enables the introduction unit to adjust the introduction method according to the user's emotions.

[0087] When making an introduction, the introduction department can adjust the level of detail of the introduction based on the importance of the past case. For example, the introduction department introduces past cases of high importance in detail. For example, the introduction department introduces project cases of high importance in detail. Furthermore, the introduction department briefly introduces past cases of low importance. For example, the introduction department briefly introduces technology cases of low importance. Furthermore, the introduction department moderately introduces past cases of medium importance. For example, the introduction department moderately introduces market cases of medium importance. This enables the introduction department to make optimal introductions based on importance.

[0088] When introducing a case, the introduction department can apply different introduction algorithms depending on the category of the case. For example, the introduction department applies an efficiency algorithm to a business improvement case. For example, the introduction department introduces process improvement and cost reduction methods as a business improvement case using an efficiency algorithm. Furthermore, the introduction department applies a risk assessment algorithm to a security guideline case. For example, the introduction department introduces access control and data protection methods as a security guideline case using a risk assessment algorithm. Furthermore, the introduction department applies a revenue forecasting algorithm to a business project case. For example, the introduction department introduces revenue forecasting and market analysis methods as a business project case using a revenue forecasting algorithm. This enables the introduction department to make the most appropriate introduction for each category.

[0089] The introduction unit can estimate the user's emotions and adjust the length of the introduction based on the estimated user's emotions. For example, if the user is in a hurry, the introduction unit provides a short and to-the-point introduction. For example, the introduction unit introduces a short and to-the-point project example to a user who is in a hurry. Furthermore, if the user is relaxed, the introduction unit provides a longer introduction including detailed explanations. For example, the introduction unit introduces a longer technology example including detailed explanations to a relaxed user. Furthermore, if the user is excited, the introduction unit provides an introduction with visually stimulating effects. For example, the introduction unit introduces a market example with visually stimulating effects to an excited user. This enables the introduction unit to adjust the length of the introduction according to the user's emotions.

[0090] At the time of introduction, the introduction department can determine the priority of introduction based on the time of submission of the case. For example, the introduction department prioritizes introducing the latest case. For example, the introduction department prioritizes introducing the latest project case. In addition, the introduction department postpones older case. For example, the introduction department postpones older technology case. Furthermore, the introduction department introduces the case in an appropriate order based on the time of submission. For example, the introduction department introduces market case in an appropriate order based on the time of submission. This enables the introduction department to make optimal introductions based on the time of submission.

[0091] The introduction department can adjust the order of introduction based on the relevance of the cases when making an introduction. For example, the introduction department prioritizes introducing highly relevant cases. For example, the introduction department prioritizes introducing highly relevant project cases. Furthermore, the introduction department postpones less relevant cases. For example, the introduction department postpones less relevant technology cases. Furthermore, the introduction department introduces cases in an appropriate order based on relevance. For example, the introduction department introduces market cases in an appropriate order based on relevance. This enables the introduction department to make optimal introductions based on relevance.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit provides simple analysis criteria. For example, the analysis unit provides simple project analysis criteria to a user who is feeling stressed. Furthermore, if the user is relaxed, the analysis unit provides detailed analysis criteria. For example, the analysis unit provides detailed technology analysis criteria to a relaxed user. Furthermore, if the user is in a hurry, the analysis unit provides analysis criteria that focus on the main points. For example, the analysis unit provides market analysis criteria that focus on the main points to a user who is in a hurry. This enables the analysis unit to adjust the analysis criteria according to the user's emotions.

[0093] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between cases during analysis. For example, the analysis unit groups related cases and analyzes them. For example, the analysis unit groups related project cases and analyzes them. The analysis unit also performs a highly accurate analysis by taking into account the interrelationships between cases. For example, the analysis unit performs a highly accurate analysis by taking into account the interrelationships between project cases. Furthermore, the analysis unit provides an optimal analysis method based on the interrelationships between cases. For example, the analysis unit provides an optimal analysis method based on the interrelationships between project cases. This enables the analysis unit to perform a highly accurate analysis by taking into account the interrelationships between cases.

[0094] When performing analysis, the analysis department can take into consideration the attribute information of the submitter of the case. The analysis department performs analysis based on, for example, the submitter's job title. For example, the analysis department analyzes project cases based on the submitter's job title. The analysis department also performs analysis based on the submitter's department. For example, the analysis department analyzes technology cases based on the submitter's department. The analysis department also performs analysis based on the submitter's years of experience. For example, the analysis department analyzes market cases based on the submitter's years of experience. This enables the analysis department to perform optimal analysis based on the submitter's attribute information.

[0095] The analysis unit can estimate the user's emotions and adjust the order in which analysis results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit displays important results first. For example, the analysis unit displays important project analysis results first to a user who is feeling stressed. Furthermore, if the user is relaxed, the analysis unit sequentially displays detailed results. For example, the analysis unit sequentially displays detailed technical analysis results to a relaxed user. Furthermore, if the user is in a hurry, the analysis unit displays results that emphasize the main points first. For example, the analysis unit displays market analysis results that emphasize the main points first to a user who is in a hurry. This enables the analysis unit to adjust the order in which results are displayed according to the user's emotions.

[0096] The analysis department can perform the analysis taking into consideration the geographical distribution of the projects. For example, the analysis department performs an analysis by region based on the geographical distribution of the projects. For example, the analysis department performs a project analysis by region based on the geographical distribution of the projects. The analysis department also provides an optimal analysis method taking into consideration the geographical distribution. For example, the analysis department provides an optimal technology analysis method taking into consideration the geographical distribution. Furthermore, the analysis department groups related projects based on the geographical distribution for analysis. For example, the analysis department groups related market projects based on the geographical distribution for analysis. This enables the analysis department to perform an optimal analysis based on the geographical distribution.

[0097] The analysis department can improve the accuracy of the analysis by referring to literature related to the project during the analysis. The analysis department, for example, performs a highly accurate analysis based on the related literature. For example, the analysis department performs a highly accurate project analysis based on the related literature. The analysis department also refers to the related literature to provide an optimal analysis method. For example, the analysis department refers to the related literature to provide an optimal technology analysis method. Furthermore, the analysis department performs an analysis based on the related literature, taking into account the interrelationships between projects. For example, the analysis department performs a market analysis based on the related literature, taking into account the interrelationships between projects. This enables the analysis department to perform a highly accurate analysis based on the related literature.

[0098] The documentation unit can estimate the user's emotions and adjust the documentation method based on the estimated user's emotions. For example, if the user is feeling stressed, the documentation unit creates simple materials. For example, the documentation unit creates simple project materials for a user who is feeling stressed. Furthermore, if the user is relaxed, the documentation unit creates detailed materials. For example, the documentation unit creates detailed technical materials for a relaxed user. Furthermore, if the user is in a hurry, the documentation unit creates materials that focus on the main points. For example, the documentation unit creates market materials that focus on the main points for a user who is in a hurry. This enables the documentation unit to adjust the documentation method according to the user's emotions.

[0099] The documentation department can adjust the level of detail of the materials when documenting based on the importance of the project. For example, the documentation department creates detailed materials for projects with high importance. For example, the documentation department creates detailed materials for project projects with high importance. Furthermore, the documentation department creates concise materials for projects with low importance. For example, the documentation department creates concise materials for technology projects with low importance. Furthermore, the documentation department creates materials with a moderate level of detail for projects with medium importance. For example, the documentation department creates materials with a moderate level of detail for market projects with medium importance. This enables the documentation department to create optimal materials based on importance.

[0100] When documenting, the documentation department can apply different documentation algorithms depending on the category of the case. For example, the documentation department applies an efficiency algorithm to business improvement cases. For example, the documentation department documents process improvement and cost reduction methods for business improvement cases using an efficiency algorithm. The documentation department also applies a risk assessment algorithm to security guideline cases. For example, the documentation department documents access control and data protection methods for security guideline cases using a risk assessment algorithm. Furthermore, the documentation department applies a revenue forecasting algorithm to business cases. For example, the documentation department documents revenue forecasting and market analysis methods for business cases using a revenue forecasting algorithm. This enables the documentation department to create optimal documentation for each category.

[0101] The documentation unit can estimate the user's emotions and determine the priority of materials based on the estimated user's emotions. For example, if the user is feeling stressed, the documentation unit prioritizes creating important materials. For example, the documentation unit prioritizes creating important project materials for a user who is feeling stressed. Furthermore, if the user is relaxed, the documentation unit also creates detailed materials. For example, the documentation unit also creates detailed technical materials for a relaxed user. Furthermore, if the user is in a hurry, the documentation unit prioritizes creating materials with high urgency. For example, the documentation unit prioritizes creating market materials with high urgency for a user who is in a hurry. This enables the documentation unit to determine the priority of materials according to the user's emotions.

[0102] When documenting, the documentation department can adjust the order of documents based on the submission date of the case. For example, the documentation department prioritizes documenting the latest case. For example, the documentation department prioritizes documenting the latest project case. In addition, the documentation department postpones older cases. For example, the documentation department postpones older technology cases. Furthermore, the documentation department creates documents in an appropriate order based on the submission date. For example, the documentation department creates documents for market cases in an appropriate order based on the submission date. This enables the documentation department to optimally document based on the submission date.

[0103] When documenting, the documentation department can adjust the display method of the materials based on the relevance of the cases. For example, the documentation department prioritizes documenting highly relevant cases. For example, the documentation department prioritizes documenting highly relevant project cases. In addition, the documentation department postpones less relevant cases. For example, the documentation department postpones less relevant technical cases. Furthermore, the documentation department creates materials in an appropriate order based on relevance. For example, the documentation department creates materials for market cases in an appropriate order based on relevance. This enables the documentation department to perform optimal documenting based on relevance.

[0104] The sales department can estimate the user's emotions and adjust the sales method based on the estimated user's emotions. For example, if the user is feeling stressed, the sales department provides a simple sales method. For example, the sales department provides a simple online sales method to a user who is feeling stressed. Furthermore, if the user is relaxed, the sales department provides a detailed sales method. For example, the sales department provides a detailed direct sales method to a relaxed user. Furthermore, if the user is in a hurry, the sales department provides a quick sales method. For example, the sales department provides a quick agency sales method to a user who is in a hurry. This allows the sales department to adjust the sales method according to the user's emotions.

[0105] When making external sales, the external sales department can select the optimal external sales method by taking into consideration the attribute information of the target company. The external sales department, for example, selects the external sales method based on the industry of the target company. For example, the external sales department selects the optimal online sales method for a target company in the manufacturing industry. The external sales department also selects the external sales method based on the size of the target company. For example, the external sales department selects the optimal direct sales method for a target company that is a large corporation. Furthermore, the external sales department selects the external sales method based on the region of the target company. For example, the external sales department selects the optimal agency sales method for a target company in a specific region. This enables the external sales department to make optimal external sales based on the attribute information of the target company.

[0106] The sales department can estimate the user's emotions and determine the priority of external sales based on the estimated user's emotions. For example, if the user is feeling stressed, the sales department prioritizes important external sales cases. For example, the sales department prioritizes important project external sales cases for a user who is feeling stressed. Furthermore, if the user is relaxed, the sales department also handles detailed external sales cases. For example, the sales department also handles detailed technical external sales cases for a relaxed user. Furthermore, if the user is in a hurry, the sales department prioritizes external sales cases with high urgency. For example, the sales department prioritizes market external sales cases with high urgency for a user who is in a hurry. This enables the sales department to determine the priority of external sales according to the user's emotions.

[0107] When making an external sale, the external sales department can select the optimal external sales method by taking into consideration the geographical location information of the target company. The external sales department, for example, selects the external sales method based on the location of the target company. For example, the external sales department selects the optimal online sales method based on the location of the target company. The external sales department also prioritizes external sales to geographically nearby companies. For example, the external sales department selects the optimal direct sales method for geographically nearby target companies. Furthermore, the external sales department provides the optimal external sales method by taking into consideration geographical characteristics. For example, the external sales department provides the optimal agency sales method by taking into consideration the geographical characteristics of a specific region. This enables the external sales department to make optimal external sales based on geographical location information. === Hard Collateral 1-1 === Each of the multiple elements, including the learning unit, reception unit, introduction unit, analysis unit, documentation unit, and external sales unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns and accumulates data such as business improvement, business, system development projects, manuals, and security guidelines. The reception unit is implemented, for example, by the control unit 46A of the smart device 14 and receives project details that the user wants to research. The introduction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and introduces past cases. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes and evaluates projects. The documentation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and documents the results of the project analysis and evaluation. The external sales unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides functions for externally selling the system. === Hard Collateral 1-2 === Each of the multiple elements, including the learning unit, reception unit, introduction unit, analysis unit, documentation unit, and external sales unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns and accumulates data such as business improvement, business, system development projects, manuals, and security guidelines. The reception unit is realized, for example, by the control unit 46A of the smart glasses 214 and receives project details that the user wants to research. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces past cases. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes and evaluates projects. The documentation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and documents the results of the project analysis and evaluation. The external sales unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides functions for externally selling the system. === Hard Collateral 1-3 === Each of the multiple elements, including the learning unit, reception unit, introduction unit, analysis unit, documentation unit, and external sales unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns and accumulates data such as business improvement, business, system development projects, manuals, and security guidelines. The reception unit is implemented, for example, by the control unit 46A of the headset terminal 314 and receives project details that the user wants to research. The introduction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and introduces past cases. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes and evaluates projects. The documentation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and documents the results of project analysis and evaluation. The external sales unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides functions for externally selling the system. === Hard Collateral 1-4 === Each of the multiple elements, including the learning unit, reception unit, introduction unit, analysis unit, documentation unit, and external sales unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns and accumulates data such as business improvement, business, system development projects, manuals, and security guidelines. The reception unit is realized, for example, by the control unit 46A of the robot 414 and receives project details that the user wants to research. The introduction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and introduces past cases. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes and evaluates projects. The documentation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and documents the results of the project analysis and evaluation. The external sales unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides functions for externally selling the system.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is feeling stressed, data with relaxing content is given priority for learning. For example, relaxation techniques and stress management methods are taught. If the user is excited, data with stimulating content is given priority for learning. For example, the latest technological trends and innovative business models are taught. Furthermore, if the user is tired, data with simple and easy-to-understand content is given priority for learning. For example, basic concepts and introductory information are taught. This enables the learning unit to select data according to the user's emotions.

[0110] The reception unit can estimate the user's emotions and adjust the reception method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple interface is provided. For example, a simple and highly visible interface is provided. Also, if the user is relaxed, detailed input options are provided. For example, detailed input options are provided. Furthermore, if the user is in a hurry, voice input is given priority. For example, voice input is given priority. This enables the reception unit to adjust the reception method according to the user's emotions.

[0111] The introduction unit can estimate the user's emotions and adjust the way the introduction is presented based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and highly visible introduction method is provided. For example, a simple and highly visible project example is introduced. Also, if the user is relaxed, a introduction method including detailed information is provided. For example, a technology example including detailed information is introduced. Furthermore, if the user is in a hurry, a presentation method that focuses on the main points is provided. For example, a market example that focuses on the main points is introduced. This enables the introduction unit to adjust the presentation method according to the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. For example, if the user is feeling stressed, simple analysis criteria can be provided, such as simple project analysis criteria. If the user is relaxed, detailed analysis criteria can be provided, such as detailed technology analysis criteria. If the user is in a hurry, analysis criteria that focuses on the main points can be provided, such as market analysis criteria that focuses on the main points. This enables the analysis unit to adjust the analysis criteria according to the user's emotions.

[0113] The documentation unit can estimate the user's emotions and adjust the documentation method based on the estimated user's emotions. For example, if the user is feeling stressed, simple materials are created, such as simple project materials. If the user is relaxed, detailed materials are created, such as detailed technical materials. If the user is in a hurry, materials that focus on the main points are created, such as market materials that focus on the main points. This allows the documentation unit to adjust the documentation method according to the user's emotions.

[0114] During learning, the learning unit can adjust the learning priority based on the importance of the data. For example, it prioritizes learning of business improvement data with high importance. For example, it can learn methods for process improvement and cost reduction. It can also prioritize learning of security guideline data with high importance. For example, it can learn methods for access control and data protection. It can also prioritize learning of business project data with high importance. For example, it can learn methods for revenue forecasting and market analysis. This enables the learning unit to efficiently learn based on importance.

[0115] When accepting a call, the reception unit can select the optimal reception method by referring to the user's past inquiry history. For example, it can prioritize the reception method that the user has used frequently in the past. For example, it can prioritize online reception. It can also suggest a related reception method based on the content of the user's past inquiries. For example, it can suggest telephone reception. It can also predict the optimal reception method from the user's past inquiry history. For example, it can predict face-to-face reception. This enables the reception unit to select the optimal reception method based on the past inquiry history.

[0116] The reception unit can perform filtering based on the user's current work situation when receiving requests. For example, it prioritizes receiving requests related to work that the user is currently doing. For example, it prioritizes receiving requests related to a project that is currently underway. It also filters related requests based on the user's current work situation. For example, it filters related technical requests. It also provides the optimal reception method taking into account the user's current work situation. For example, it provides the optimal market request. This enables the reception unit to filter requests based on the current work situation.

[0117] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between cases during analysis. For example, related cases are grouped and analyzed. For example, related project cases are grouped and analyzed. Furthermore, a highly accurate analysis is performed by taking into account the interrelationships between cases. For example, a highly accurate analysis is performed by taking into account the interrelationships between project cases. Furthermore, an optimal analysis method is provided based on the interrelationships between cases. For example, an optimal analysis method is provided based on the interrelationships between project cases. This enables the analysis unit to perform a highly accurate analysis by taking into account the interrelationships between cases.

[0118] When documenting, the documentation department can adjust the level of detail of the documents based on the importance of the project. For example, detailed documents are created for projects with high importance. For example, detailed documents are created for project projects with high importance. Furthermore, concise documents are created for projects with low importance. For example, concise documents are created for technology projects with low importance. Furthermore, documents with a moderate level of detail are created for projects with medium importance. For example, documents with a moderate level of detail are created for market projects with medium importance. This enables the documentation department to create optimal documents based on importance.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The learning unit learns and accumulates data such as business improvement, business, system development projects, manuals, and security guidelines. For example, the learning unit can learn and accumulate text data, numerical data, image data, etc. The learning unit can also use AI to analyze the data and extract important information. Step 2: The reception unit receives the details of the case that the user wants to check. For example, the reception unit can receive user input in the form of text input, voice input, image input, etc. The reception unit can also analyze the user's input and perform appropriate processing. Step 3: The introduction department introduces past cases. For example, the introduction department can introduce success stories, failure stories, cases in specific industries, etc. The introduction department can also use AI to search past cases and provide appropriate cases to the user. Step 4: The analysis department analyzes and evaluates the case. For example, the analysis department can analyze and evaluate the case using methods such as quantitative evaluation, qualitative evaluation, and risk analysis. The analysis department can also use AI to analyze data and understand the details of the case. Step 5: The Documentation Department documents the analysis and evaluation results of the project. For example, the Documentation Department can create documents in the form of reports, presentations, digital documents, etc. The Documentation Department can also use AI to automatically generate documents.

[0121] 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.

[0122] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A learning unit that learns and accumulates data; a reception unit that receives input from a user; An introduction section introducing past cases, An analysis department that analyzes and evaluates projects; A documentation section for documenting the data. A system characterized by:

2. Equipping an external sales department for external sales The system of claim 1 .

3. The learning unit Learn and accumulate data on business improvement, business, system development projects, manuals, and security guidelines The system of claim 1 .

4. The reception unit Accepts the details of the case that the user wants to research The system of claim 1 .

5. The introduction unit Introducing past cases The system of claim 1 .

6. The analysis unit Analyze and evaluate the project The system of claim 1 .

7. The documentation unit Documenting the results of project analysis and evaluation The system of claim 1 .

8. The learning unit Estimate the user's emotions and select training data based on the estimated user emotions. The system of claim 1 .

9. The learning unit During training, adjust training priorities based on data importance The system of claim 1 .

10. The learning unit During training, different learning algorithms are applied to different categories of data. The system of claim 1 .

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