system

Abstract

The system according to the embodiment aims to propose specific ways to utilize generative AI and provide implementation and operational support. [Solution] The system according to the embodiment comprises a hearing unit, a proposal unit, an implementation support unit, and an operation support unit. The hearing unit hears about the client's business operations and needs. The proposal unit proposes specific ways to utilize the generative AI based on the information collected by the hearing unit. The implementation support unit provides implementation support for the generative AI based on the content proposed by the proposal unit. The operation support unit provides operation support for the generative AI introduced by the implementation support unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide sufficient support for specific methods of using generative AI or for its implementation, leaving room for improvement.

[0005] The system according to the embodiment aims to propose specific ways to utilize generative AI and provide implementation and operational support. [Means for solving the problem]

[0006] The system according to the embodiment includes a hearing unit, a proposal unit, an implementation support unit, and an operation support unit. The hearing unit hears about the client's business operations and needs. The proposal unit proposes specific ways to utilize the generative AI based on the information collected by the hearing unit. The implementation support unit provides implementation support for the generative AI based on the content proposed by the proposal unit. The operation support unit provides operation support for the generative AI introduced by the implementation support unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose specific ways to utilize generative AI and provide implementation and operational support. [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 generative AI implementation support system according to an embodiment of the present invention provides consulting services to companies and individuals who wish to implement generative AI but are unsure of specific ways to utilize it. This system interviews clients about their business operations and needs and evaluates the feasibility of implementing generative AI. Next, it proposes specific ways to utilize generative AI based on the evaluation results. Furthermore, it provides support for implementing generative AI based on the proposals and also provides operational support after implementation. This mechanism facilitates the implementation of generative AI, potentially improving business efficiency and productivity. For example, the system interviews clients about their business operations and needs. During this interview, it gains a detailed understanding of the client's operations and the challenges they face. For example, a manufacturing client may have issues with production line efficiency and quality control. This allows the feasibility of implementing generative AI to be evaluated. Next, it proposes specific ways to utilize generative AI based on the evaluation results. For example, for manufacturing clients, it can propose optimizing production lines and automating quality control using generative AI. For service industry clients, it can propose automating customer service and marketing using generative AI. This is expected to improve business efficiency and productivity. Furthermore, we provide support for implementing generative AI based on the proposal. Specifically, we select the generative AI, develop an implementation plan, and build and configure the necessary systems. We also provide operational support after implementation to help clients effectively utilize generative AI. This includes training on generative AI operation and providing a support desk. This facilitates the implementation of generative AI and is expected to improve business efficiency and productivity. Clients can learn specific application methods tailored to their profession, enabling them to effectively implement and operate generative AI. For example, manufacturing clients can expect improved productivity and cost reductions through improved production line efficiency and automated quality control. Similarly, service industry clients can expect improved customer satisfaction and increased sales through automated customer service and optimized marketing.As a result, the generative AI implementation support system can support the implementation and operation of generative AI based on the client's business operations and needs, which is expected to improve business efficiency and productivity.

[0029] A generative AI introduction support system according to an embodiment includes a hearing unit, a proposal unit, an introduction support unit, and an operation support unit. The hearing unit hears about the client's business operations and needs. The client's business operations include, but are not limited to, manufacturing operations, sales operations, and service operations. The hearing unit collects information, such as the client's business flow, current challenges, and expected results. For example, the hearing unit obtains a detailed understanding of the client's operations and challenges. The proposal unit proposes specific ways to utilize generative AI based on the information collected by the hearing unit. For example, for a client in the manufacturing industry, the proposal unit can propose production line optimization and quality control automation using generative AI. For a client in the service industry, the proposal unit can also propose customer response automation and marketing optimization using generative AI. The introduction support unit provides support for the introduction of generative AI based on the content proposed by the proposal unit. For example, the introduction support unit selects a generative AI, develops an introduction plan, and builds and configures the system. For example, the implementation support department clarifies the selection criteria and method for the generative AI and makes the selection taking into consideration performance evaluation, cost evaluation, scope of application, etc. The implementation support department also clarifies the specific content and formulation method of the implementation plan and formulates the plan including the schedule, resource allocation, risk management, etc. The implementation support department also clarifies the system construction method and procedures and performs hardware configuration, software installation, network construction, etc. The operation support department provides operation support for the generative AI implemented by the implementation support department. For example, the operation support department provides training and a support desk for the operation of the generative AI. For example, the operation support department provides explanations on how to operate the generative AI, practical exercises, and training materials. The operation support department also provides inquiry response, troubleshooting, technical support, etc. As a result, the generative AI implementation support system according to the embodiment can be expected to improve business efficiency and productivity by supporting the implementation and operation of generative AI based on the client's business content and needs.

[0030] The interview department can collect information on the client's business flow, current issues, and expected results. For example, the interview department can understand the client's business flow in detail. For example, the interview department can collect details of the client's business procedures and processes. The interview department can also understand the client's current issues. For example, the interview department can collect issues such as decreased efficiency, increased costs, and decreased quality. Furthermore, the interview department can also understand the client's expected results. For example, the interview department can collect results such as improved efficiency, reduced costs, and improved quality. This makes it easier to evaluate the feasibility of introducing generative AI by understanding the client's business flow, issues, and expected results in detail.

[0031] The proposal department can propose specific ways to utilize generative AI based on the collected information. For example, the proposal department can propose to a manufacturing client how to optimize production lines and automate quality control using generative AI. For example, the proposal department can propose ways to maximize production line efficiency and automate quality control processes using generative AI. The proposal department can also propose to a service industry client how to automate customer service and marketing using generative AI. For example, the proposal department can propose ways to use generative AI to automate customer service processes and maximize the effectiveness of marketing campaigns. This allows the proposal department to maximize the effectiveness of implementation by proposing specific ways to utilize generative AI based on the client's business operations and needs.

[0032] The Implementation Support Department may select a generative AI, develop an implementation plan, and build and configure the system. For example, the Implementation Support Department may clarify the selection criteria and method for the generative AI, and make the selection taking into consideration performance evaluation, cost evaluation, and scope of application. For example, the Implementation Support Department may evaluate the performance of the generative AI and select the AI ​​that best meets the client's needs. The Implementation Support Department may also clarify the specific content and formulation method of the implementation plan, and develop a plan that includes schedules, resource allocation, and risk management. For example, the Implementation Support Department may develop an implementation schedule, allocate necessary resources, and develop a risk management plan. Furthermore, the Implementation Support Department may clarify the system construction method and procedures, and perform tasks such as configuring the hardware, installing software, and building the network. For example, the Implementation Support Department may configure the hardware, install the necessary software, and build the network. This allows clients to smoothly implement generative AI by selecting a generative AI, developing an implementation plan, and building and configuring the system.

[0033] The Operations Support Department may provide training and a support desk for the operation of the generative AI. For example, the Operations Support Department may explain how to operate the generative AI, provide practical exercises, and provide training materials. For example, the Operations Support Department may provide detailed explanations of how to operate the generative AI and help clients become familiar with its operation through practical exercises. The Operations Support Department may also provide inquiry support, troubleshooting, technical support, and other services. For example, the Operations Support Department may respond quickly to inquiries from clients and provide appropriate troubleshooting when problems arise. Furthermore, the Operations Support Department may provide regular maintenance and updates. For example, the Operations Support Department may perform regular maintenance on the generative AI and provide updates as needed. This allows clients to effectively utilize the generative AI by providing training and a support desk for the operation of the generative AI.

[0034] The hearing department can analyze the client's past work history and select the optimal hearing method. The hearing department, for example, analyzes the client's past project history and prepares related questions. For example, the hearing department understands the details of the client's past projects and prepares related questions. The hearing department can also analyze the client's work patterns and select the optimal hearing time. For example, the hearing department analyzes the client's work patterns and selects the optimal hearing time. Furthermore, the hearing department can also adjust the way the hearing proceeds by referring to the client's past feedback. For example, the hearing department analyzes the client's past feedback and adjusts the way the hearing proceeds. In this way, a more appropriate hearing method can be selected by analyzing the client's past work history.

[0035] During the interview, the interview department can customize the questions based on the client's current projects and areas of interest. For example, the interview department asks questions related to the client's current ongoing projects. For example, the interview department grasps details of the client's current ongoing projects and asks related questions. The interview department can also ask questions that incorporate specific examples based on the client's areas of interest. For example, the interview department asks questions that incorporate specific examples based on the client's areas of interest. Furthermore, the interview department can also ask questions about future prospects based on trends in the client's industry. For example, the interview department grasps trends in the client's industry and asks questions about future prospects. In this way, by customizing the questions based on the client's current projects and areas of interest, more specific information can be collected.

[0036] During the interview, the interview department can prioritize collecting highly relevant information by taking into account the client's geographical location information. The interview department, for example, asks questions about issues specific to the region based on the characteristics of the region where the client is located. For example, the interview department grasps the characteristics of the client's location and asks questions about issues specific to the region. The interview department can also ask questions by taking into account market trends in the region where the client's business is conducted. For example, the interview department grasps market trends in the region where the client's business is conducted and asks questions based on that. Furthermore, the interview department can also select the optimal interview method based on the client's geographical location. For example, the interview department selects the optimal interview method by taking into account the client's geographical location information. In this way, more relevant information can be collected by taking into account the client's geographical location information.

[0037] The interview department can analyze the client's social media activity and collect relevant information during the interview. For example, the interview department analyzes the client's social media comments and asks questions based on topics of interest. For example, the interview department analyzes the client's social media comments and asks questions based on topics of interest. The interview department can also collect relevant information by referring to the client's social media activity history. For example, the interview department analyzes the client's social media activity history and collects relevant information. Furthermore, the interview department can analyze the reactions of the client's followers on social media and adjust the content of the interview. For example, the interview department analyzes the reactions of the client's followers on social media and adjust the content of the interview. In this way, more relevant information can be collected by analyzing the client's social media activity.

[0038] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the client's work. For example, if the client's work is important, the proposal unit makes a detailed proposal. For example, the proposal unit evaluates the importance of the client's work and makes a detailed proposal for the important work. The proposal unit can also make a concise proposal if the client's work is routine. For example, the proposal unit evaluates the importance of the client's work and makes a concise proposal for the routine work. Furthermore, the proposal unit can adjust the content of the proposal according to the importance of the client's work. For example, the proposal unit evaluates the importance of the client's work and adjusts the content of the proposal according to the importance. In this way, by adjusting the level of detail of the proposal based on the importance of the client's work, more appropriate proposals can be made.

[0039] When making a proposal, the proposal unit can apply different proposal algorithms depending on the industry of the client. For example, the proposal unit makes proposals regarding optimization of production lines to a client in the manufacturing industry. For example, the proposal unit makes proposals to maximize the efficiency of production lines to a client in the manufacturing industry. The proposal unit can also make proposals regarding automation of customer support to a client in the service industry. For example, the proposal unit makes proposals to automate customer support processes to a client in the service industry. Furthermore, the proposal unit can also make proposals regarding improving the efficiency of inventory management to a client in the retail industry. For example, the proposal unit makes proposals to improve the efficiency of inventory management processes to a client in the retail industry. This makes it possible to make more effective proposals by applying different proposal algorithms depending on the industry of the client.

[0040] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of the client's work submission. For example, the proposal unit prioritizes proposals when the client's work submission deadline is approaching. For example, the proposal unit grasps the client's work submission deadline and prioritizes proposals when the submission deadline is approaching. Furthermore, the proposal unit can also prioritize other clients when there is ample time in the client's work submission deadline. For example, the proposal unit grasps the client's work submission deadline and prioritizes other clients when there is ample time in the submission deadline. Furthermore, the proposal unit can also adjust the proposal schedule based on the client's work submission timing. For example, the proposal unit adjusts the proposal schedule taking into account the client's work submission timing. This enables more appropriate proposals to be made by determining the priority of proposals based on the client's work submission timing.

[0041] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the client's business. For example, the proposal unit prioritizes proposals that are directly related to the client's business. For example, the proposal unit evaluates the relevance of the client's business and prioritizes directly related proposals. The proposal unit can also postpone proposals that are indirectly related to the client's business. For example, the proposal unit evaluates the relevance of the client's business and postpones indirectly related proposals. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of the client's business. For example, the proposal unit evaluates the relevance of the client's business and prioritizes highly related business to make proposals. This enables more effective proposals by adjusting the order of proposals based on the relevance of the client's business.

[0042] During installation support, the installation support unit can analyze the client's past installation history and select an appropriate support method. For example, the installation support unit analyzes the client's past installation history and applies a successful method again. For example, the installation support unit analyzes the client's past installation history and applies a successful method again. The installation support unit can also avoid unsuccessful methods based on the client's past installation history. For example, the installation support unit analyzes the client's past installation history and applies a failed method again. Furthermore, the installation support unit can also select an optimal support method by referring to the client's past installation history. For example, the installation support unit analyzes the client's past installation history and selects an optimal support method. In this way, a more appropriate support method can be selected by analyzing the client's past installation history.

[0043] During installation support, the installation support department can customize the installation plan based on the client's current system environment. For example, the installation support department analyzes the client's current system environment and formulates an optimal installation plan. For example, the installation support department analyzes the client's current system environment and formulates an optimal installation plan. The installation support department can also perform settings necessary for installation based on the client's system environment. For example, the installation support department analyzes the client's system environment and performs settings necessary for installation. Furthermore, the installation support department can customize the installation plan to suit the client's system environment. For example, the installation support department customizes the installation plan taking the client's system environment into consideration. In this way, customizing the installation plan based on the client's current system environment enables more effective installation.

[0044] During onboarding support, the onboarding support department can select an appropriate support method by taking into account the client's geographical location information. The onboarding support department, for example, provides support regarding issues specific to the region based on the characteristics of the region where the client is located. For example, the onboarding support department understands the characteristics of the client's location and provides support regarding issues specific to the region. The onboarding support department can also provide support by taking into account market trends in the region where the client's business is conducted. For example, the onboarding support department understands market trends in the region where the client's business is conducted and provides support based on that. Furthermore, the onboarding support department can also select an optimal support method based on the client's geographical location. For example, the onboarding support department selects an optimal support method by taking into account the client's geographical location information. In this way, a more appropriate support method can be selected by taking into account the client's geographical location information.

[0045] During onboarding support, the onboarding support department can analyze the client's social media activity and suggest means of support. For example, the onboarding support department analyzes the client's social media posts and provides support based on topics of interest. For example, the onboarding support department analyzes the client's social media posts and provides support based on topics of interest. The onboarding support department can also provide relevant support by referring to the client's social media activity history. For example, the onboarding support department analyzes the client's social media activity history and provides relevant support. Furthermore, the onboarding support department can analyze the reactions of the client's followers on social media and adjust the support content. For example, the onboarding support department analyzes the reactions of the client's followers on social media and adjust the support content. In this way, by analyzing the client's social media activity, more appropriate means of support can be suggested.

[0046] When providing operational support, the operations support department can analyze the client's past operational history and select the optimal support method. For example, the operations support department analyzes the client's past operational history and applies a successful method again. For example, the operations support department analyzes the client's past operational history and applies a successful method again. The operations support department can also avoid unsuccessful methods based on the client's past operational history. For example, the operations support department analyzes the client's past operational history and avoids unsuccessful methods. Furthermore, the operations support department can also select the optimal support method by referring to the client's past operational history. For example, the operations support department analyzes the client's past operational history and selects the optimal support method. In this way, a more appropriate support method can be selected by analyzing the client's past operational history.

[0047] During operational support, the operational support department can customize the support content based on the client's current system environment. For example, the operational support department analyzes the client's current system environment and provides the optimal support content. For example, the operational support department analyzes the client's current system environment and provides the optimal support content. The operational support department can also make settings necessary for support based on the client's system environment. For example, the operational support department analyzes the client's system environment and makes settings necessary for support. Furthermore, the operational support department can customize the support content to suit the client's system environment. For example, the operational support department customizes the support content taking the client's system environment into consideration. This enables more effective support by customizing the support content based on the client's current system environment.

[0048] When providing operational support, the operations support department can select an appropriate support method by taking into account the client's geographical location information. The operations support department, for example, provides support regarding issues specific to the region based on the characteristics of the region where the client is located. For example, the operations support department understands the characteristics of the client's location and provides support regarding issues specific to the region. The operations support department can also provide support by taking into account market trends in the region where the client's business is conducted. For example, the operations support department understands market trends in the region where the client's business is conducted and provides support based on that. Furthermore, the operations support department can also select the optimal support method based on the client's geographical location. For example, the operations support department selects the optimal support method by taking into account the client's geographical location information. In this way, a more appropriate support method can be selected by taking into account the client's geographical location information.

[0049] When providing operational support, the operations support department can analyze the client's social media activity and suggest support methods. For example, the operations support department analyzes the client's social media comments and provides support based on topics of interest. For example, the operations support department analyzes the client's social media comments and provides support based on topics of interest. The operations support department can also provide related support by referring to the client's social media activity history. For example, the operations support department analyzes the client's social media activity history and provides related support. Furthermore, the operations support department can analyze the reactions of the client's followers on social media and adjust the support content. For example, the operations support department analyzes the reactions of the client's followers on social media and adjust the support content. In this way, by analyzing the client's social media activity, more appropriate support methods can be suggested.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When interviewing clients about their business operations and needs, the Interview Department can collect and analyze external data related to the client's operations. For example, the Interview Department can collect market trend data related to the client's industry and analyze it in relation to the client's business operations. The Interview Department can also research the trends of the client's competitors and provide information that will help improve the client's operations. Furthermore, the Interview Department can research laws, regulations, and guidelines related to the client's operations and identify the matters the client must comply with. This allows for a deeper understanding of the client's business operations and needs, enabling the department to make appropriate proposals.

[0052] When proposing specific ways to utilize generative AI based on collected information, the proposal department can simulate the client's business processes and create optimal AI implementation scenarios. For example, for a manufacturing client, the proposal department can simulate a production line to predict the efficiency benefits of introducing AI. For a service industry client, the proposal department can also simulate customer service to predict the improvement in customer satisfaction that will result from introducing AI. Furthermore, for a retail client, the proposal department can simulate inventory management to predict the effects of inventory optimization through the introduction of AI. This allows the client to see specific effects and understand the benefits of introducing AI.

[0053] When selecting generative AI, formulating an implementation plan, and building and configuring the system, the Implementation Support Department can evaluate the current state of the client's IT infrastructure and propose the optimal implementation method. For example, the Implementation Support Department evaluates the performance of the client's server and network to clarify the hardware and software requirements for AI implementation. The Implementation Support Department can also consider the client's security policy, evaluate the security risks associated with AI implementation, and propose countermeasures. Furthermore, the Implementation Support Department can consider how to integrate with the client's existing systems and clarify any changes to business processes that will result from AI implementation. This enables the Implementation Support Department to help clients smoothly implement AI and utilize it effectively.

[0054] When providing training and support desk services for the operation of generative AI, the Operations Support Department can create training programs tailored to each client's business. For example, the Operations Support Department can provide a training program on optimizing production lines to a manufacturing client. The Operations Support Department can also provide a training program on automating customer support to a service client. Furthermore, the Operations Support Department can provide a training program on improving inventory management efficiency to a retail client. This enables clients to effectively utilize generative AI and achieve improved business efficiency and productivity.

[0055] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the client's work. For example, if the client's work is important, the proposal unit makes a detailed proposal. For example, the proposal unit evaluates the importance of the client's work and makes a detailed proposal for the important work. The proposal unit can also make a concise proposal if the client's work is routine. For example, the proposal unit evaluates the importance of the client's work and makes a concise proposal for the routine work. Furthermore, the proposal unit can adjust the content of the proposal based on the importance of the client's work. For example, the proposal unit evaluates the importance of the client's work and adjusts the content of the proposal based on the importance. In this way, by adjusting the level of detail of the proposal based on the importance of the client's work, more appropriate proposals can be made.

[0056] During installation support, the installation support unit can analyze the client's past installation history and select an appropriate support method. For example, the installation support unit analyzes the client's past installation history and applies a successful method again. For example, the installation support unit analyzes the client's past installation history and applies a successful method again. The installation support unit can also avoid unsuccessful methods based on the client's past installation history. For example, the installation support unit analyzes the client's past installation history and applies a failed method again. Furthermore, the installation support unit can also select an optimal support method by referring to the client's past installation history. For example, the installation support unit analyzes the client's past installation history and selects an optimal support method. In this way, a more appropriate support method can be selected by analyzing the client's past installation history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The interview department listens to the client's business operations and needs. Specifically, they gather information on the client's business flow, current issues, and expected results. For example, they gain a detailed understanding of the business operations, such as manufacturing, sales, and service. Step 2: The proposal department proposes specific ways to utilize generative AI based on the information collected by the hearing department. For example, for manufacturing clients, they propose production line optimization and quality control automation, and for service industry clients, they propose customer service automation and marketing optimization. Step 3: The Implementation Support Department will support the implementation of the generative AI based on the proposals made by the Proposal Department. Specifically, this involves selecting the generative AI, formulating an implementation plan, and building and configuring the system. For example, the implementation AI will be selected taking into consideration performance evaluation, cost evaluation, and scope of application, and an implementation plan will be formulated that includes schedules, resource allocation, and risk management. Step 4: The Operational Support Department provides operational support for the Generative AI introduced by the Implementation Support Department, including explaining how to operate the Generative AI, providing practical exercises, providing training materials, responding to inquiries, troubleshooting, and providing technical support.

[0059] (Example 2) A generative AI implementation support system according to an embodiment of the present invention provides consulting services to companies and individuals who wish to implement generative AI but are unsure of specific ways to utilize it. This system interviews clients about their business operations and needs and evaluates the feasibility of implementing generative AI. Next, it proposes specific ways to utilize generative AI based on the evaluation results. Furthermore, it provides support for implementing generative AI based on the proposals and also provides operational support after implementation. This mechanism facilitates the implementation of generative AI, potentially improving business efficiency and productivity. For example, the system interviews clients about their business operations and needs. During this interview, it gains a detailed understanding of the client's operations and the challenges they face. For example, a manufacturing client may have issues with production line efficiency and quality control. This allows the feasibility of implementing generative AI to be evaluated. Next, it proposes specific ways to utilize generative AI based on the evaluation results. For example, for manufacturing clients, it can propose optimizing production lines and automating quality control using generative AI. For service industry clients, it can propose automating customer service and marketing using generative AI. This is expected to improve business efficiency and productivity. Furthermore, we provide support for implementing generative AI based on the proposal. Specifically, we select the generative AI, develop an implementation plan, and build and configure the necessary systems. We also provide operational support after implementation to help clients effectively utilize generative AI. This includes training on generative AI operation and providing a support desk. This facilitates the implementation of generative AI and is expected to improve business efficiency and productivity. Clients can learn specific application methods tailored to their profession, enabling them to effectively implement and operate generative AI. For example, manufacturing clients can expect improved productivity and cost reductions through improved production line efficiency and automated quality control. Similarly, service industry clients can expect improved customer satisfaction and increased sales through automated customer service and optimized marketing.As a result, the generative AI implementation support system can support the implementation and operation of generative AI based on the client's business operations and needs, which is expected to improve business efficiency and productivity.

[0060] A generative AI introduction support system according to an embodiment includes a hearing unit, a proposal unit, an introduction support unit, and an operation support unit. The hearing unit hears about the client's business operations and needs. The client's business operations include, but are not limited to, manufacturing operations, sales operations, and service operations. The hearing unit collects information, such as the client's business flow, current challenges, and expected results. For example, the hearing unit obtains a detailed understanding of the client's operations and challenges. The proposal unit proposes specific ways to utilize generative AI based on the information collected by the hearing unit. For example, for a client in the manufacturing industry, the proposal unit can propose production line optimization and quality control automation using generative AI. For a client in the service industry, the proposal unit can also propose customer response automation and marketing optimization using generative AI. The introduction support unit provides support for the introduction of generative AI based on the content proposed by the proposal unit. For example, the introduction support unit selects a generative AI, develops an introduction plan, and builds and configures the system. For example, the implementation support department clarifies the selection criteria and method for the generative AI and makes the selection taking into consideration performance evaluation, cost evaluation, scope of application, etc. The implementation support department also clarifies the specific content and formulation method of the implementation plan and formulates the plan including the schedule, resource allocation, risk management, etc. The implementation support department also clarifies the system construction method and procedures and performs hardware configuration, software installation, network construction, etc. The operation support department provides operation support for the generative AI implemented by the implementation support department. For example, the operation support department provides training and a support desk for the operation of the generative AI. For example, the operation support department provides explanations on how to operate the generative AI, practical exercises, and training materials. The operation support department also provides inquiry response, troubleshooting, technical support, etc. As a result, the generative AI implementation support system according to the embodiment can be expected to improve business efficiency and productivity by supporting the implementation and operation of generative AI based on the client's business content and needs.

[0061] The interview department can collect information on the client's business flow, current issues, and expected results. For example, the interview department can understand the client's business flow in detail. For example, the interview department can collect details of the client's business procedures and processes. The interview department can also understand the client's current issues. For example, the interview department can collect issues such as decreased efficiency, increased costs, and decreased quality. Furthermore, the interview department can also understand the client's expected results. For example, the interview department can collect results such as improved efficiency, reduced costs, and improved quality. This makes it easier to evaluate the feasibility of introducing generative AI by understanding the client's business flow, issues, and expected results in detail.

[0062] The proposal department can propose specific ways to utilize generative AI based on the collected information. For example, the proposal department can propose to a manufacturing client how to optimize production lines and automate quality control using generative AI. For example, the proposal department can propose ways to maximize production line efficiency and automate quality control processes using generative AI. The proposal department can also propose to a service industry client how to automate customer service and marketing using generative AI. For example, the proposal department can propose ways to use generative AI to automate customer service processes and maximize the effectiveness of marketing campaigns. This allows the proposal department to maximize the effectiveness of implementation by proposing specific ways to utilize generative AI based on the client's business operations and needs.

[0063] The Implementation Support Department may select a generative AI, develop an implementation plan, and build and configure the system. For example, the Implementation Support Department may clarify the selection criteria and method for the generative AI, and make the selection taking into consideration performance evaluation, cost evaluation, and scope of application. For example, the Implementation Support Department may evaluate the performance of the generative AI and select the AI ​​that best meets the client's needs. The Implementation Support Department may also clarify the specific content and formulation method of the implementation plan, and develop a plan that includes schedules, resource allocation, and risk management. For example, the Implementation Support Department may develop an implementation schedule, allocate necessary resources, and develop a risk management plan. Furthermore, the Implementation Support Department may clarify the system construction method and procedures, and perform tasks such as configuring the hardware, installing software, and building the network. For example, the Implementation Support Department may configure the hardware, install the necessary software, and build the network. This allows clients to smoothly implement generative AI by selecting a generative AI, developing an implementation plan, and building and configuring the system.

[0064] The Operations Support Department may provide training and a support desk for the operation of the generative AI. For example, the Operations Support Department may explain how to operate the generative AI, provide practical exercises, and provide training materials. For example, the Operations Support Department may provide detailed explanations of how to operate the generative AI and help clients become familiar with its operation through practical exercises. The Operations Support Department may also provide inquiry support, troubleshooting, technical support, and other services. For example, the Operations Support Department may respond quickly to inquiries from clients and provide appropriate troubleshooting when problems arise. Furthermore, the Operations Support Department may provide regular maintenance and updates. For example, the Operations Support Department may perform regular maintenance on the generative AI and provide updates as needed. This allows clients to effectively utilize the generative AI by providing training and a support desk for the operation of the generative AI.

[0065] The hearing unit can estimate the client's emotions and adjust the timing of the hearing based on the estimated emotions. For example, if the client is feeling stressed, the hearing unit can conduct the hearing at a time when the client is able to relax. For example, the hearing unit can analyze the client's facial expressions and voice to determine whether the client is feeling stressed. Furthermore, if the client is busy, the hearing unit can conduct the hearing efficiently in a short amount of time. For example, the hearing unit can take the client's schedule into consideration and collect necessary information in a short amount of time. Furthermore, if the client is relaxed, the hearing unit can conduct a detailed hearing. For example, the hearing unit can ask detailed questions and collect more information when the client is relaxed. This allows for more effective hearing by adjusting the timing of the hearing according to the client's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The hearing department can analyze the client's past work history and select the optimal hearing method. The hearing department, for example, analyzes the client's past project history and prepares related questions. For example, the hearing department understands the details of the client's past projects and prepares related questions. The hearing department can also analyze the client's work patterns and select the optimal hearing time. For example, the hearing department analyzes the client's work patterns and selects the optimal hearing time. Furthermore, the hearing department can also adjust the way the hearing proceeds by referring to the client's past feedback. For example, the hearing department analyzes the client's past feedback and adjusts the way the hearing proceeds. In this way, a more appropriate hearing method can be selected by analyzing the client's past work history.

[0067] During the interview, the interview department can customize the questions based on the client's current projects and areas of interest. For example, the interview department asks questions related to the client's current ongoing projects. For example, the interview department grasps details of the client's current ongoing projects and asks related questions. The interview department can also ask questions that incorporate specific examples based on the client's areas of interest. For example, the interview department asks questions that incorporate specific examples based on the client's areas of interest. Furthermore, the interview department can also ask questions about future prospects based on trends in the client's industry. For example, the interview department grasps trends in the client's industry and asks questions about future prospects. In this way, by customizing the questions based on the client's current projects and areas of interest, more specific information can be collected.

[0068] The hearing unit can estimate the client's emotions and determine the priority of the hearing based on the estimated emotions. For example, if the client is feeling anxious, the hearing unit can quickly conduct the hearing. For example, the hearing unit can analyze the client's facial expressions and voice to determine whether the client is feeling anxious and quickly conduct the hearing. Furthermore, if the client is relaxed, the hearing unit can prioritize other important clients. For example, the hearing unit can grasp the client's emotional state and, if the client is relaxed, prioritize other important clients. Furthermore, if the client is in a hurry, the hearing unit can quickly conduct the hearing. For example, the hearing unit can grasp the client's emotional state and, if the client is in a hurry, quickly conduct the hearing. This enables more effective hearing by determining the priority of the hearing based on the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] During the interview, the interview department can prioritize collecting highly relevant information by taking into account the client's geographical location information. The interview department, for example, asks questions about issues specific to the region based on the characteristics of the region where the client is located. For example, the interview department grasps the characteristics of the client's location and asks questions about issues specific to the region. The interview department can also ask questions by taking into account market trends in the region where the client's business is conducted. For example, the interview department grasps market trends in the region where the client's business is conducted and asks questions based on that. Furthermore, the interview department can also select the optimal interview method based on the client's geographical location. For example, the interview department selects the optimal interview method by taking into account the client's geographical location information. In this way, more relevant information can be collected by taking into account the client's geographical location information.

[0070] The interview department can analyze the client's social media activity and collect relevant information during the interview. For example, the interview department analyzes the client's social media comments and asks questions based on topics of interest. For example, the interview department analyzes the client's social media comments and asks questions based on topics of interest. The interview department can also collect relevant information by referring to the client's social media activity history. For example, the interview department analyzes the client's social media activity history and collects relevant information. Furthermore, the interview department can analyze the reactions of the client's followers on social media and adjust the content of the interview. For example, the interview department analyzes the reactions of the client's followers on social media and adjust the content of the interview. In this way, more relevant information can be collected by analyzing the client's social media activity.

[0071] The suggestion unit can estimate the client's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, if the client is feeling anxious, the suggestion unit uses an expression that provides a sense of security. For example, the suggestion unit analyzes the client's facial expressions and voice to determine whether the client is feeling anxious and uses an expression that provides a sense of security. The suggestion unit can also provide a suggestion that includes specific examples if the client is excited. For example, the suggestion unit grasps the client's emotional state and, if the client is excited, provides a suggestion that includes specific examples. The suggestion unit can also provide a suggestion that includes detailed explanations if the client is relaxed. For example, the suggestion unit grasps the client's emotional state and, if the client is relaxed, provides a suggestion that includes detailed explanations. This enables more effective suggestions by adjusting the way the suggestion is expressed based on the client's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the client's work. For example, if the client's work is important, the proposal unit makes a detailed proposal. For example, the proposal unit evaluates the importance of the client's work and makes a detailed proposal for the important work. The proposal unit can also make a concise proposal if the client's work is routine. For example, the proposal unit evaluates the importance of the client's work and makes a concise proposal for the routine work. Furthermore, the proposal unit can adjust the content of the proposal according to the importance of the client's work. For example, the proposal unit evaluates the importance of the client's work and adjusts the content of the proposal according to the importance. In this way, by adjusting the level of detail of the proposal based on the importance of the client's work, more appropriate proposals can be made.

[0073] When making a proposal, the proposal unit can apply different proposal algorithms depending on the industry of the client. For example, the proposal unit makes proposals regarding optimization of production lines to a client in the manufacturing industry. For example, the proposal unit makes proposals to maximize the efficiency of production lines to a client in the manufacturing industry. The proposal unit can also make proposals regarding automation of customer support to a client in the service industry. For example, the proposal unit makes proposals to automate customer support processes to a client in the service industry. Furthermore, the proposal unit can also make proposals regarding improving the efficiency of inventory management to a client in the retail industry. For example, the proposal unit makes proposals to improve the efficiency of inventory management processes to a client in the retail industry. This makes it possible to make more effective proposals by applying different proposal algorithms depending on the industry of the client.

[0074] The suggestion unit can estimate the client's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the client is in a hurry, the suggestion unit can provide a short, concise suggestion. For example, the suggestion unit can analyze the client's facial expressions and voice to determine whether the client is in a hurry and provide a short, concise suggestion. Furthermore, if the client is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. For example, the suggestion unit can grasp the client's emotional state and, if the client is relaxed, provide a longer suggestion with detailed explanations. Furthermore, if the client is excited, the suggestion unit can provide a suggestion with visually stimulating effects. For example, the suggestion unit can grasp the client's emotional state and, if the client is excited, provide a suggestion with visually stimulating effects. This allows for more effective suggestions by adjusting the length of the suggestion based on the client's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of the client's work submission. For example, the proposal unit prioritizes proposals when the client's work submission deadline is approaching. For example, the proposal unit grasps the client's work submission deadline and prioritizes proposals when the submission deadline is approaching. Furthermore, the proposal unit can also prioritize other clients when there is ample time in the client's work submission deadline. For example, the proposal unit grasps the client's work submission deadline and prioritizes other clients when there is ample time in the submission deadline. Furthermore, the proposal unit can also adjust the proposal schedule based on the client's work submission timing. For example, the proposal unit adjusts the proposal schedule taking into account the client's work submission timing. This enables more appropriate proposals to be made by determining the priority of proposals based on the client's work submission timing.

[0076] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the client's business. For example, the proposal unit prioritizes proposals that are directly related to the client's business. For example, the proposal unit evaluates the relevance of the client's business and prioritizes directly related proposals. The proposal unit can also postpone proposals that are indirectly related to the client's business. For example, the proposal unit evaluates the relevance of the client's business and postpones indirectly related proposals. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of the client's business. For example, the proposal unit evaluates the relevance of the client's business and prioritizes highly related business to make proposals. This enables more effective proposals by adjusting the order of proposals based on the relevance of the client's business.

[0077] The introduction support unit can estimate the client's emotions and adjust the introduction support method based on the estimated emotions. For example, if the client is feeling anxious, the introduction support unit uses a support method that provides a sense of security. For example, the introduction support unit analyzes the client's facial expressions and voice to determine whether the client is feeling anxious and uses a support method that provides a sense of security. Furthermore, if the client is excited, the introduction support unit can provide support that includes specific examples. For example, the introduction support unit grasps the client's emotional state and, if the client is excited, provides support that includes specific examples. Furthermore, if the client is relaxed, the introduction support unit can provide support that includes detailed explanations. For example, the introduction support unit grasps the client's emotional state and, if the client is relaxed, provides support that includes detailed explanations. This enables more effective support by adjusting the introduction support method according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] During installation support, the installation support unit can analyze the client's past installation history and select an appropriate support method. For example, the installation support unit analyzes the client's past installation history and applies a successful method again. For example, the installation support unit analyzes the client's past installation history and applies a successful method again. The installation support unit can also avoid unsuccessful methods based on the client's past installation history. For example, the installation support unit analyzes the client's past installation history and applies a failed method again. Furthermore, the installation support unit can also select an optimal support method by referring to the client's past installation history. For example, the installation support unit analyzes the client's past installation history and selects an optimal support method. In this way, a more appropriate support method can be selected by analyzing the client's past installation history.

[0079] During installation support, the installation support department can customize the installation plan based on the client's current system environment. For example, the installation support department analyzes the client's current system environment and formulates an optimal installation plan. For example, the installation support department analyzes the client's current system environment and formulates an optimal installation plan. The installation support department can also perform settings necessary for installation based on the client's system environment. For example, the installation support department analyzes the client's system environment and performs settings necessary for installation. Furthermore, the installation support department can customize the installation plan to suit the client's system environment. For example, the installation support department customizes the installation plan taking the client's system environment into consideration. In this way, customizing the installation plan based on the client's current system environment enables more effective installation.

[0080] The introduction support unit can estimate the client's emotions and determine the priority of introduction support based on the estimated emotions. For example, if the client is feeling anxious, the introduction support unit can provide introduction support promptly. For example, the introduction support unit can analyze the client's facial expressions and voice to determine whether the client is feeling anxious and provide introduction support promptly. Furthermore, if the client is relaxed, the introduction support unit can prioritize other important clients. For example, the introduction support unit can grasp the client's emotional state and, if the client is relaxed, prioritize other important clients. Furthermore, if the client is in a hurry, the introduction support unit can provide introduction support promptly. For example, the introduction support unit can grasp the client's emotional state and, if the client is in a hurry, provide introduction support promptly. This enables more effective support by determining the priority of introduction support according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During onboarding support, the onboarding support department can select an appropriate support method by taking into account the client's geographical location information. The onboarding support department, for example, provides support regarding issues specific to the region based on the characteristics of the region where the client is located. For example, the onboarding support department understands the characteristics of the client's location and provides support regarding issues specific to the region. The onboarding support department can also provide support by taking into account market trends in the region where the client's business is conducted. For example, the onboarding support department understands market trends in the region where the client's business is conducted and provides support based on that. Furthermore, the onboarding support department can also select an optimal support method based on the client's geographical location. For example, the onboarding support department selects an optimal support method by taking into account the client's geographical location information. In this way, a more appropriate support method can be selected by taking into account the client's geographical location information.

[0082] During onboarding support, the onboarding support department can analyze the client's social media activity and suggest means of support. For example, the onboarding support department analyzes the client's social media posts and provides support based on topics of interest. For example, the onboarding support department analyzes the client's social media posts and provides support based on topics of interest. The onboarding support department can also provide relevant support by referring to the client's social media activity history. For example, the onboarding support department analyzes the client's social media activity history and provides relevant support. Furthermore, the onboarding support department can analyze the reactions of the client's followers on social media and adjust the support content. For example, the onboarding support department analyzes the reactions of the client's followers on social media and adjust the support content. In this way, by analyzing the client's social media activity, more appropriate means of support can be suggested.

[0083] The operational support unit can estimate the client's emotions and adjust the operational support method based on the estimated emotions. For example, if the client is feeling anxious, the operational support unit uses a support method that provides a sense of security. For example, the operational support unit analyzes the client's facial expressions and voice to determine whether the client is feeling anxious and uses a support method that provides a sense of security. Furthermore, if the client is excited, the operational support unit can provide support that includes specific examples. For example, the operational support unit grasps the client's emotional state and, if the client is excited, provides support that includes specific examples. Furthermore, if the client is relaxed, the operational support unit can provide support that includes detailed explanations. For example, the operational support unit grasps the client's emotional state and, if the client is relaxed, provides support that includes detailed explanations. This enables more effective support by adjusting the operational support method according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When providing operational support, the operations support department can analyze the client's past operational history and select the optimal support method. For example, the operations support department analyzes the client's past operational history and applies a successful method again. For example, the operations support department analyzes the client's past operational history and applies a successful method again. The operations support department can also avoid unsuccessful methods based on the client's past operational history. For example, the operations support department analyzes the client's past operational history and avoids unsuccessful methods. Furthermore, the operations support department can also select the optimal support method by referring to the client's past operational history. For example, the operations support department analyzes the client's past operational history and selects the optimal support method. In this way, a more appropriate support method can be selected by analyzing the client's past operational history.

[0085] During operational support, the operational support department can customize the support content based on the client's current system environment. For example, the operational support department analyzes the client's current system environment and provides the optimal support content. For example, the operational support department analyzes the client's current system environment and provides the optimal support content. The operational support department can also make settings necessary for support based on the client's system environment. For example, the operational support department analyzes the client's system environment and makes settings necessary for support. Furthermore, the operational support department can customize the support content to suit the client's system environment. For example, the operational support department customizes the support content taking the client's system environment into consideration. This enables more effective support by customizing the support content based on the client's current system environment.

[0086] The operational support unit can estimate the client's emotions and determine the priority of operational support based on the estimated emotions. For example, if the client is feeling anxious, the operational support unit can provide operational support promptly. For example, the operational support unit can analyze the client's facial expressions and voice to determine whether the client is feeling anxious and provide operational support promptly. In addition, if the client is relaxed, the operational support unit can prioritize other important clients. For example, the operational support unit can grasp the client's emotional state and, if the client is relaxed, prioritize other important clients. Furthermore, if the client is in a hurry, the operational support unit can provide operational support promptly. For example, the operational support unit can grasp the client's emotional state and, if the client is in a hurry, provide operational support promptly. This enables more effective support by determining the priority of operational support according to the client's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When providing operational support, the operations support department can select an appropriate support method by taking into account the client's geographical location information. The operations support department, for example, provides support regarding issues specific to the region based on the characteristics of the region where the client is located. For example, the operations support department understands the characteristics of the client's location and provides support regarding issues specific to the region. The operations support department can also provide support by taking into account market trends in the region where the client's business is conducted. For example, the operations support department understands market trends in the region where the client's business is conducted and provides support based on that. Furthermore, the operations support department can also select the optimal support method based on the client's geographical location. For example, the operations support department selects the optimal support method by taking into account the client's geographical location information. In this way, a more appropriate support method can be selected by taking into account the client's geographical location information.

[0088] When providing operational support, the operations support department can analyze the client's social media activity and suggest support methods. For example, the operations support department analyzes the client's social media comments and provides support based on topics of interest. For example, the operations support department analyzes the client's social media comments and provides support based on topics of interest. The operations support department can also provide related support by referring to the client's social media activity history. For example, the operations support department analyzes the client's social media activity history and provides related support. Furthermore, the operations support department can analyze the reactions of the client's followers on social media and adjust the support content. For example, the operations support department analyzes the reactions of the client's followers on social media and adjust the support content. In this way, by analyzing the client's social media activity, more appropriate support methods can be suggested. === Hard Collateral 1-1 === Each of the multiple elements, including the hearing unit, proposal unit, introduction support unit, and operation support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the hearing unit collects information about the client's business operations and needs using the microphone 38B and touch panel 38A of the smart device 14, and processes the information using the control unit 46A. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes specific ways to utilize the generation AI based on the collected information. The introduction support unit, realized, for example, by the specific processing unit 290 of the data processing device 12, selects the generation AI, formulates an introduction plan, and builds and configures the system. The operation support unit, realized, for example, by the control unit 46A of the smart device 14, provides training and a support desk for the operation of the generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the hearing unit, proposal unit, implementation support unit, and operation support 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 hearing unit collects information about the client's business operations and needs using the microphone 238 of the smart glasses 214, and processes the information using the control unit 46A. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes specific ways to utilize the generation AI based on the collected information. The implementation support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects the generation AI, formulates an implementation plan, and builds and configures the system. The operation support unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides training and a support desk for operating the generation AI. === Hard Collateral 1-3 === Each of the multiple elements, including the hearing unit, proposal unit, introduction support unit, and operation support unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the hearing unit collects information about the client's business operations and needs using the microphone 238 of the headset terminal 314, and processes the information using the control unit 46A. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes specific ways to utilize the generation AI based on the collected information. The introduction support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects the generation AI, formulates an introduction plan, and builds and configures the system. The operation support unit is realized, for example, by the control unit 46A of the headset terminal 314, and provides training and a support desk for operating the generation AI. === Hard Collateral 1-4 === Each of the multiple elements, including the hearing unit, proposal unit, implementation support unit, and operation support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the hearing unit collects information about the client's business operations and needs using the microphone 238 of the robot 414, and processes the information using the control unit 46A. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes specific ways to utilize the generative AI based on the collected information. The implementation support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects the generative AI, formulates an implementation plan, and builds and configures the system. The operation support unit is realized, for example, by the control unit 46A of the robot 414, and provides training and a support desk for operating the generative AI.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] When interviewing clients about their business operations and needs, the Interview Department can collect and analyze external data related to the client's operations. For example, the Interview Department can collect market trend data related to the client's industry and analyze it in relation to the client's business operations. The Interview Department can also research the trends of the client's competitors and provide information that will help improve the client's operations. Furthermore, the Interview Department can research laws, regulations, and guidelines related to the client's operations and identify the matters the client must comply with. This allows for a deeper understanding of the client's business operations and needs, enabling the department to make appropriate proposals.

[0091] When proposing specific ways to utilize generative AI based on collected information, the proposal department can simulate the client's business processes and create optimal AI implementation scenarios. For example, for a manufacturing client, the proposal department can simulate a production line to predict the efficiency benefits of introducing AI. For a service industry client, the proposal department can also simulate customer service to predict the improvement in customer satisfaction that will result from introducing AI. Furthermore, for a retail client, the proposal department can simulate inventory management to predict the effects of inventory optimization through the introduction of AI. This allows the client to see specific effects and understand the benefits of introducing AI.

[0092] When selecting generative AI, formulating an implementation plan, and building and configuring the system, the Implementation Support Department can evaluate the current state of the client's IT infrastructure and propose the optimal implementation method. For example, the Implementation Support Department evaluates the performance of the client's server and network to clarify the hardware and software requirements for AI implementation. The Implementation Support Department can also consider the client's security policy, evaluate the security risks associated with AI implementation, and propose countermeasures. Furthermore, the Implementation Support Department can consider how to integrate with the client's existing systems and clarify any changes to business processes that will result from AI implementation. This enables the Implementation Support Department to help clients smoothly implement AI and utilize it effectively.

[0093] When providing training and support desk services for the operation of generative AI, the Operations Support Department can create training programs tailored to each client's business. For example, the Operations Support Department can provide a training program on optimizing production lines to a manufacturing client. The Operations Support Department can also provide a training program on automating customer support to a service client. Furthermore, the Operations Support Department can provide a training program on improving inventory management efficiency to a retail client. This enables clients to effectively utilize generative AI and achieve improved business efficiency and productivity.

[0094] The hearing unit can estimate the client's emotions and adjust the timing of the hearing based on the estimated emotions. For example, if the client is feeling stressed, the hearing unit can conduct the hearing at a time when the client is able to relax. For example, the hearing unit can analyze the client's facial expressions and voice to determine whether the client is feeling stressed. Furthermore, if the client is busy, the hearing unit can conduct the hearing efficiently in a short amount of time. For example, the hearing unit can take the client's schedule into consideration and collect necessary information in a short amount of time. Furthermore, if the client is relaxed, the hearing unit can conduct a detailed hearing. For example, the hearing unit can ask detailed questions and collect more information when the client is relaxed. This allows the timing of the hearing to be adjusted according to the client's emotions, enabling more effective hearing.

[0095] The suggestion unit can estimate the client's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, if the client is feeling anxious, the suggestion unit uses an expression that gives a sense of security. For example, the suggestion unit analyzes the client's facial expressions and voice to determine whether the client is feeling anxious and uses an expression that gives a sense of security. Furthermore, if the client is excited, the suggestion unit can make a suggestion that includes specific examples. For example, the suggestion unit grasps the client's emotional state, and if the client is excited, makes a suggestion that includes specific examples. Furthermore, if the client is relaxed, the suggestion unit can make a suggestion that includes detailed explanations. For example, the suggestion unit grasps the client's emotional state, and if the client is relaxed, makes a suggestion that includes detailed explanations. This enables more effective suggestions to be made by adjusting the way the suggestion is expressed based on the client's emotions.

[0096] The introduction support unit can estimate the client's emotions and adjust the introduction support method based on the estimated emotions. For example, if the client is feeling anxious, the introduction support unit uses a support method that provides a sense of security. For example, the introduction support unit analyzes the client's facial expressions and voice to determine whether the client is feeling anxious and uses a support method that provides a sense of security. Furthermore, if the client is excited, the introduction support unit can provide support that includes specific examples. For example, the introduction support unit grasps the client's emotional state, and if the client is excited, provides support that includes specific examples. Furthermore, if the client is relaxed, the introduction support unit can provide support that includes detailed explanations. For example, the introduction support unit grasps the client's emotional state, and if the client is relaxed, provides support that includes detailed explanations. This enables more effective support by adjusting the introduction support method according to the client's emotions.

[0097] The operational support unit can estimate the client's emotions and adjust the operational support method based on the estimated emotions. For example, if the client is feeling anxious, the operational support unit uses a support method that provides a sense of security. For example, the operational support unit analyzes the client's facial expressions and voice to determine whether the client is feeling anxious and uses a support method that provides a sense of security. Furthermore, if the client is excited, the operational support unit can provide support that includes specific examples. For example, the operational support unit grasps the client's emotional state, and if the client is excited, provides support that includes specific examples. Furthermore, if the client is relaxed, the operational support unit can provide support that includes detailed explanations. For example, the operational support unit grasps the client's emotional state, and if the client is relaxed, provides support that includes detailed explanations. This allows for more effective support by adjusting the operational support method according to the client's emotions.

[0098] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the client's work. For example, if the client's work is important, the proposal unit makes a detailed proposal. For example, the proposal unit evaluates the importance of the client's work and makes a detailed proposal for the important work. The proposal unit can also make a concise proposal if the client's work is routine. For example, the proposal unit evaluates the importance of the client's work and makes a concise proposal for the routine work. Furthermore, the proposal unit can adjust the content of the proposal based on the importance of the client's work. For example, the proposal unit evaluates the importance of the client's work and adjusts the content of the proposal based on the importance. In this way, by adjusting the level of detail of the proposal based on the importance of the client's work, more appropriate proposals can be made.

[0099] During installation support, the installation support unit can analyze the client's past installation history and select an appropriate support method. For example, the installation support unit analyzes the client's past installation history and applies a successful method again. For example, the installation support unit analyzes the client's past installation history and applies a successful method again. The installation support unit can also avoid unsuccessful methods based on the client's past installation history. For example, the installation support unit analyzes the client's past installation history and applies a failed method again. Furthermore, the installation support unit can also select an optimal support method by referring to the client's past installation history. For example, the installation support unit analyzes the client's past installation history and selects an optimal support method. In this way, a more appropriate support method can be selected by analyzing the client's past installation history.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The interview department listens to the client's business operations and needs. Specifically, they gather information on the client's business flow, current issues, and expected results. For example, they gain a detailed understanding of the business operations, such as manufacturing, sales, and service. Step 2: The proposal department proposes specific ways to utilize generative AI based on the information collected by the hearing department. For example, for manufacturing clients, they propose production line optimization and quality control automation, and for service industry clients, they propose customer service automation and marketing optimization. Step 3: The Implementation Support Department will support the implementation of the generative AI based on the proposals made by the Proposal Department. Specifically, this involves selecting the generative AI, formulating an implementation plan, and building and configuring the system. For example, the implementation AI will be selected taking into consideration performance evaluation, cost evaluation, and scope of application, and an implementation plan will be formulated that includes schedules, resource allocation, and risk management. Step 4: The Operational Support Department provides operational support for the Generative AI introduced by the Implementation Support Department, including explaining how to operate the Generative AI, providing practical exercises, providing training materials, responding to inquiries, troubleshooting, and providing technical support.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The hearing department listens to the client's business details and needs, A proposal unit that proposes a specific utilization method of the generation AI based on the information collected by the hearing unit; an introduction support unit that supports the introduction of the generation AI based on the content proposed by the proposal unit; An operation support unit that provides operation support for the generated AI introduced by the introduction support unit; Equipped with A system characterized by:

2. The hearing section Collect information on the client's business flow, current issues, and desired results 2. The system of claim 1.

3. The proposal unit Propose specific ways to utilize generative AI based on collected information 2. The system of claim 1.

4. The introduction support unit Selecting generative AI, formulating implementation plans, and building and configuring the system 2. The system of claim 1.

5. The operation support department Provide training and support for the operation of generative AI 2. The system of claim 1.

6. The hearing section Estimate the client's emotions and adjust the timing of the interview based on the estimated emotions.

2. The system of claim 1.

7. The hearing section Analyze the client's past business history and select the appropriate interview method 2. The system of claim 1.

8. The hearing section During interviews, tailor questions based on the client's current projects and areas of interest 2. The system of claim 1.

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