system

Abstract

The system according to the embodiment aims to enable small and medium-sized enterprises to effectively advance IT and DX. [Solution] The system according to the embodiment includes a reception unit, a proposal unit, a provision unit, and a support unit. The reception unit accepts questions from companies regarding IT / DX. The proposal unit analyzes the questions accepted by the reception unit and proposes appropriate IT tools and methods for proceeding with DX. The provision unit provides information on the IT tools and methods for proceeding with DX proposed by the proposal unit. The support unit provides post-implementation support based on the information provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, small and medium-sized enterprises faced the challenge of not knowing what to do when advancing IT and digital transformation.

[0005] The system according to the embodiment aims to enable small and medium-sized enterprises to effectively advance IT and DX. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a provision unit, and a support unit. The reception unit accepts questions from companies regarding IT / DX. The proposal unit analyzes the questions accepted by the reception unit and proposes appropriate IT tools and methods for proceeding with DX. The provision unit provides information on the IT tools and methods for proceeding with DX proposed by the proposal unit. The support unit provides post-implementation support based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment enables small and medium-sized enterprises to effectively advance IT and DX. [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) The IT / DX support system according to an embodiment of the present invention provides solutions to help small and medium-sized enterprises advance their IT / DX initiatives. In this system, companies send questions and inquiries about IT / DX via LINE (registered trademark). A LINE chatbot accepts the questions and analyzes them using AI. The AI ​​proposes optimal IT tools and digital transformation methods based on information such as the company's industry, size, and current IT environment. Furthermore, detailed information and implementation procedures for the proposed IT tools and digital transformation methods are provided via LINE. Post-implementation support is also provided via LINE. This solution allows small and medium-sized enterprises to easily advance their IT / DX initiatives, potentially improving business efficiency and strengthening competitiveness. For example, the AI ​​analysis algorithm uses machine learning to propose optimal IT tools and digital transformation methods based on information such as the company's industry, size, and current IT environment. This algorithm learns from past successes and failures and makes optimal proposals for each company. Appropriate measures are taken to protect privacy and data security when handling data provided by companies. Information will be provided in a format that is easy for companies to understand, such as videos, manuals, and webinars. Furthermore, for post-implementation support, FAQs and troubleshooting guides will be enhanced to enable LINE's chatbot to respond quickly, and expert support will also be provided as needed. This allows the IT / DX support system to enable small and medium-sized enterprises to easily advance their IT / DX initiatives, improving business efficiency and strengthening their competitiveness.

[0029] An IT / DX support system according to an embodiment includes a reception unit, a proposal unit, a provision unit, and a support unit. The reception unit accepts questions about IT / DX from companies. Questions from companies include, but are not limited to, technical questions and business questions. The reception unit can accept questions from companies using, for example, a LINE chatbot. The reception unit can also automatically categorize questions from companies and assign them to the appropriate department. The proposal unit analyzes the questions accepted by the reception unit and proposes optimal IT tools and methods for advancing DX. The proposal unit proposes optimal IT tools and methods for advancing DX based on information such as the company's industry, size, and current IT environment. The proposal unit uses an analytical algorithm based on machine learning. For example, the proposal unit learns from past success stories and failure stories and makes optimal proposals for each company. The provision unit provides information on the IT tools and methods for advancing DX proposed by the proposal unit. The provision unit provides information in the form of, for example, videos, manuals, webinars, etc. The provision unit lowers the barrier to implementation by providing information in a format that is easy for companies to understand. The support unit provides post-implementation support based on the information provided by the provision unit. The support unit provides, for example, FAQs and troubleshooting guides, and provides expert support as needed. By providing comprehensive post-implementation support, the support unit reduces companies' anxieties and supports smooth operation. As a result, the IT / DX support system according to the embodiment can support the IT / DX adoption of small and medium-sized enterprises by accepting and analyzing questions from companies, making optimal proposals, and providing information and support.

[0030] The proposal department can propose appropriate IT tools and methods for proceeding with DX based on information such as the company's industry, size, and current IT environment. The proposal department can propose optimal IT tools and methods for proceeding with DX based on information such as the company's industry, size, and current IT environment. The proposal department achieves effective IT / DX by making optimal proposals tailored to the characteristics of each company. For example, the proposal department can make proposals to improve the efficiency of manufacturing processes to manufacturing companies. The proposal department can also make proposals to service industry companies regarding the introduction of customer management systems. Furthermore, the proposal department can make proposals to retail companies regarding the optimization of inventory management systems. In this way, the proposal department can achieve effective IT / DX by making optimal proposals tailored to the characteristics of each company.

[0031] The proposal unit can use an analysis algorithm that uses machine learning. The proposal unit uses, for example, an analysis algorithm that uses machine learning. By using machine learning, the proposal unit can improve the accuracy of the proposal. For example, the proposal unit can use an analysis algorithm that uses deep learning. The proposal unit can also use an analysis algorithm that uses a support vector machine. Furthermore, the proposal unit can use an analysis algorithm that uses a random forest. In this way, the proposal unit can improve the accuracy of the proposal by using machine learning.

[0032] The information providing department can provide information in the form of videos, manuals, and webinars. For example, the information providing department can provide information in the form of videos, manuals, and webinars. The information providing department can lower the barrier to implementation by providing information in a format that is easy for companies to understand. For example, the information providing department can provide information in the form of videos. The videos are, for example, about 5 to 10 minutes long and contain content that is easy to understand visually. The information providing department can also provide information in the form of manuals. The manuals are, for example, provided in PDF format and include detailed procedures and illustrations. Furthermore, the information providing department can provide information in the form of webinars. Webinars are, for example, held once a month, and include explanations by experts and question-and-answer sessions. In this way, the information providing department can lower the barrier to implementation by providing information in a format that is easy for companies to understand.

[0033] The support department can provide FAQs and troubleshooting guides and provide support from experts. The support department can provide, for example, FAQs and troubleshooting guides. The FAQ is, for example, a list of frequently asked questions that contains answers to problems that companies may face. The troubleshooting guide is, for example, a detailed explanation of problem-solving procedures, helping companies to solve problems on their own. The support department can also provide support from experts as needed. Expert support includes, for example, telephone support and on-site support. In this way, the support department can alleviate companies' anxiety and support smooth operation by providing comprehensive support after implementation.

[0034] The proposal unit can learn from past success stories and failure stories and make appropriate proposals for each company. The proposal unit, for example, learns from past success stories and failure stories and makes optimal proposals for each company. By learning from past cases, the proposal unit can make more accurate proposals. For example, the proposal unit can build a database of success stories and make optimal proposals for each company. The proposal unit can also build a database of failure stories and make proposals to avoid similar failures. Furthermore, the proposal unit can set evaluation criteria for success stories and failure stories and improve the accuracy of proposals. In this way, the proposal unit can make more accurate proposals by learning from past cases.

[0035] The providing unit can take measures to protect privacy and ensure data security when handling data provided by companies. For example, the providing unit takes measures to protect privacy and ensure data security when handling data provided by companies. The providing unit ensures the privacy protection and data security of companies when handling data. For example, the providing unit uses data encryption technology to protect data provided by companies. The providing unit can also introduce access control to prevent unauthorized access to data. Furthermore, the providing unit can regularly back up data to prevent data loss or damage. This allows the providing unit to ensure the privacy protection and data security of companies when handling data.

[0036] The reception unit can analyze the company's past question history and select an appropriate reception method. For example, the reception unit analyzes the company's past question history and selects the optimal reception method. By analyzing the past question history, the reception unit can receive questions more effectively. For example, the reception unit prioritizes receiving questions that the company has frequently asked in the past. Furthermore, if the reception unit determines from the company's past question history that there are many questions during a specific time period, it can strengthen reception during that time period. Furthermore, the reception unit can analyze the company's past question history and assign questions to specific staff members. In this way, the reception unit can receive questions more effectively by analyzing the past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the company's past question history into AI and have the AI ​​select the optimal reception method.

[0037] The reception unit can perform filtering based on the company's current projects and areas of interest when receiving a question. For example, the reception unit filters based on the company's current projects and areas of interest when receiving a question. The reception unit can receive questions according to the company's current situation. For example, the reception unit prioritizes receiving questions related to projects currently underway by the company. The reception unit can also filter related questions based on the company's areas of interest and assign them to an appropriate person. Furthermore, the reception unit can adjust the reception of questions taking into account the company's current project status. This allows the reception unit to receive questions according to the company's current situation. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input information about the company's current projects and areas of interest into AI and have the AI ​​perform the filtering.

[0038] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the geographical location information of the company. For example, when receiving a question, the reception unit prioritizes receiving highly relevant questions by taking into account the geographical location information of the company. By taking into account the geographical location information, the reception unit is able to respond appropriately to issues specific to the region. For example, the reception unit prioritizes receiving questions regarding issues specific to the region based on the location of the company. The reception unit can also prioritize receiving questions regarding collaboration with neighboring companies by taking into account the geographical location information of the company. Furthermore, the reception unit can prioritize receiving questions regarding local events and regulations based on the geographical location information of the company. This allows the reception unit to respond appropriately to issues specific to the region by taking into account the geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the company into AI and have the AI ​​select highly relevant questions.

[0039] The reception unit can analyze the company's social media activity when receiving a question and receive related questions. For example, the reception unit analyzes the company's social media activity when receiving a question and receives related questions. By analyzing the social media activity, the reception unit can receive questions that are in line with company trends. For example, the reception unit analyzes the company's social media activity and prioritizes receiving questions related to trends. The reception unit can also receive questions based on customer feedback from the company's social media activity. Furthermore, the reception unit can prioritize receiving questions about marketing strategies based on the company's social media activity. In this way, the reception unit can analyze the social media activity and receive questions that are in line with company trends. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the company's social media activity into AI and have the AI ​​select related questions.

[0040] The proposal unit can adjust the level of detail of the proposal taking into account the importance of the company when making a proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the company when making a proposal. The proposal unit adjusts the level of detail of the proposal according to the importance of the company, thereby enabling a more appropriate proposal. For example, the proposal unit makes a detailed proposal to a company with a high importance. The proposal unit can also make a concise proposal to a company with a low importance. Furthermore, the proposal unit can gradually adjust the level of detail of the proposal according to the importance of the company. In this way, the proposal unit can adjust the level of detail of the proposal according to the importance of the company, thereby enabling a more appropriate proposal. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input information on the importance of the company into AI and cause the AI ​​to adjust the level of detail of the proposal.

[0041] The suggestion unit can apply different suggestion algorithms taking into account the company's category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the company's category when making a suggestion. By applying a suggestion algorithm depending on the company's category, the suggestion unit can make more effective suggestions. For example, the suggestion unit can make suggestions regarding improving the efficiency of the manufacturing process to a manufacturing company. The suggestion unit can also make suggestions regarding the introduction of a customer management system to a service company. Furthermore, the suggestion unit can make suggestions regarding the optimization of an inventory management system to a retail company. In this way, the suggestion unit can make more effective suggestions by applying a suggestion algorithm depending on the company's category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information about the company's category into AI and cause the AI ​​to apply the suggestion algorithm.

[0042] The proposal unit can determine the priority of proposals taking into account the submission time of the companies when making proposals. For example, the proposal unit determines the priority of proposals based on the submission time of the companies when making proposals. By determining the priority of proposals based on the submission time, the proposal unit can make more effective proposals. For example, the proposal unit can give priority to proposals for companies whose deadlines are approaching. The proposal unit can also give proposals with normal priority to companies whose submission time is further away. Furthermore, the proposal unit can gradually adjust the priority of proposals based on the submission time. This allows the proposal unit to determine the priority of proposals based on the submission time, making more effective proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input information about the submission time of companies into AI and have the AI ​​determine the priority of proposals.

[0043] The proposal unit can adjust the order of proposals taking into account the relevance of the companies when making proposals. For example, the proposal unit adjusts the order of proposals based on the relevance of the companies when making proposals. By adjusting the order of proposals based on the relevance of the companies, more effective proposals can be made. For example, the proposal unit prioritizes highly relevant proposals based on the industry and size of the companies. The proposal unit can also order optimal proposals based on the company's current IT environment. Furthermore, the proposal unit can adjust the order of proposals by referring to past successes and failures of the companies. In this way, the proposal unit can adjust the order of proposals based on the relevance of the companies to make more effective proposals. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input information on the relevance of the companies into AI and cause the AI ​​to adjust the order of proposals.

[0044] When providing information, the providing unit can select an appropriate information provision method based on the company's past implementation history. For example, when providing information, the providing unit selects the optimal information provision method by referring to the company's past implementation history. By referring to the past implementation history, the providing unit can provide more appropriate information. For example, the providing unit provides related information based on IT tools previously implemented by the company. The providing unit can also provide information based on success stories from the company's past implementation history. Furthermore, the providing unit can analyze the company's past implementation history and select the optimal information provision method. This allows the providing unit to provide more appropriate information by referring to the past implementation history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information about the company's past implementation history into AI and have the AI ​​select the information provision method.

[0045] The providing unit can customize the means of providing information based on the company's current IT environment when providing information. For example, the providing unit adjusts the means of providing information based on the company's current IT environment when providing information. The providing unit can provide information according to the company's IT environment. For example, the providing unit selects the optimal means of providing information based on the company's current IT environment. The providing unit can also provide information customized according to the company's IT environment. Furthermore, the providing unit can propose an appropriate means of providing information taking into account the company's IT environment. This enables the providing unit to provide information according to the company's IT environment. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information about the company's current IT environment into AI and have the AI ​​customize the means of providing information.

[0046] The providing unit can select an appropriate information provision method based on the geographical location information of the company when providing information. For example, the providing unit selects the optimal information provision method by taking into account the geographical location information of the company when providing information. By taking into account the geographical location information, the providing unit is able to provide appropriate information for issues specific to the region. For example, the providing unit prioritizes providing information about issues specific to the region based on the location of the company. The providing unit can also provide information about collaboration with neighboring companies by taking into account the geographical location information of the company. Furthermore, the providing unit can provide information about local events and regulations based on the geographical location information of the company. In this way, the providing unit is able to provide appropriate information for issues specific to the region by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the company into AI and have the AI ​​select the information provision method.

[0047] The provision unit can analyze the company's social media activity and suggest a means of providing the information when providing the information. For example, the provision unit can analyze the company's social media activity and suggest a means of providing the information when providing the information. By analyzing the social media activity, the provision unit can provide information that is in line with company trends. For example, the provision unit can analyze the company's social media activity and provide information related to the trend. The provision unit can also provide information based on customer feedback from the company's social media activity. Furthermore, the provision unit can provide information on marketing strategies based on the company's social media activity. In this way, the provision unit can provide information that is in line with company trends by analyzing the social media activity. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data on the company's social media activity into AI and have the AI ​​suggest a means of providing the information.

[0048] When providing support, the support department can select an appropriate support method based on the company's past support history. For example, when providing support, the support department selects the optimal support method by referring to the company's past support history. By referring to the past support history, the support department can provide more appropriate support. For example, the support department provides relevant support based on the support content the company has received in the past. The support department can also provide support based on success stories from the company's past support history. Furthermore, the support department can analyze the company's past support history and select the optimal support method. In this way, the support department can provide more appropriate support by referring to the past support history. Some or all of the above-mentioned processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input information about the company's past support history into AI and have the AI ​​select a support method.

[0049] The support department can adjust the support method based on the company's current IT environment when providing support. For example, the support department customizes the support method based on the company's current IT environment when providing support. The support department can provide support tailored to the company's IT environment. For example, the support department selects the optimal support method based on the company's current IT environment. The support department can also provide customized support tailored to the company's IT environment. Furthermore, the support department can propose appropriate support methods taking into account the company's IT environment. This enables the support department to provide support tailored to the company's IT environment. Some or all of the above-described processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input information about the company's current IT environment into AI and have the AI ​​customize the support method.

[0050] The support department can select an appropriate support method based on the geographical location information of the company when providing support. For example, the support department selects the optimal support method by taking the geographical location information of the company into consideration when providing support. By taking the geographical location information into consideration, the support department can provide appropriate support for issues specific to the region. For example, the support department can prioritize support for issues specific to the region based on the company's location. The support department can also provide support for collaboration with nearby companies by taking the geographical location information of the company into consideration. Furthermore, the support department can provide support for local events and regulations based on the geographical location information of the company. In this way, by taking the geographical location information into consideration, the support department can provide appropriate support for issues specific to the region. Some or all of the above-described processing in the support department can be performed using, for example, AI, or can be performed without using AI. For example, the support department can input the geographical location information of the company into AI and have the AI ​​select a support method.

[0051] The support department can analyze the company's social media activity and propose support measures when providing support. For example, the support department can analyze the company's social media activity and propose support measures when providing support. By analyzing social media activity, the support department can provide support that is in line with company trends. For example, the support department can analyze the company's social media activity and provide support related to trends. The support department can also provide support based on customer feedback from the company's social media activity. Furthermore, the support department can provide support regarding marketing strategies based on the company's social media activity. In this way, the support department can provide support that is in line with company trends by analyzing social media activity. Some or all of the above-mentioned processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input data on the company's social media activity into AI and have the AI ​​suggest support measures.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The Proposal Department can also make proposals that take into account the company's industry, size, and current IT environment, as well as the company's culture and employee skill levels. For example, if a company has an open culture that values ​​innovation, they can propose the latest IT tools and methods for advancing DX. Also, if employees have a high skill level, they can make proposals using more advanced technologies. Furthermore, if a company has a conservative culture, they can propose a gradual approach to IT and DX. In this way, the Proposal Department can make proposals that take into account the company's culture and employee skill levels, thereby achieving more effective IT / DX adoption.

[0054] The provision department can also provide customized training programs according to the type and size of the company. For example, a training program on improving the efficiency of manufacturing processes can be provided to a manufacturing company. A training program on introducing a customer management system can also be provided to a service company. Furthermore, a training program on optimizing an inventory management system can also be provided to a retail company. In this way, the provision department can lower the barrier to implementation by providing customized training programs according to the type and size of the company.

[0055] The support department can also monitor a company's progress in IT / DX in real time and provide support as needed. For example, if a company is experiencing delays in the adoption of IT tools, the support department can respond quickly and offer advice to resolve the issue. Furthermore, when a company reports its progress in DX, the support department can analyze the data and make suggestions for the next step. Furthermore, a dashboard can be provided for companies to share their progress in IT / DX, helping them understand the situation in real time. This allows the support department to monitor a company's progress in IT / DX in real time and provide support as needed, helping to ensure smooth operations.

[0056] The Proposal Department can also make proposals taking into account the company's market competitive situation, in addition to the company's industry, size, and current IT environment. For example, for companies in highly competitive markets, they can propose rapid IT and DX adoption. For companies in less competitive markets, they can propose gradual IT and DX adoption. Furthermore, they can analyze the company's market competitive situation and make proposals to increase competitive advantage. In this way, the Proposal Department can make proposals that take into account the company's market competitive situation, thereby achieving more effective IT and DX adoption.

[0057] The provision department can also provide customized support plans according to the type of industry and size of the company. For example, a support plan related to improving the efficiency of manufacturing processes can be provided to a manufacturing company. A support plan related to the introduction of a customer management system can be provided to a service industry company. Furthermore, a support plan related to the optimization of an inventory management system can be provided to a retail industry company. In this way, the provision department can lower the barrier to implementation by providing customized support plans according to the type of industry and size of the company.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception department accepts questions about IT / DX from companies. Questions from companies include technical questions and business questions. The reception department can accept questions from companies using a LINE chatbot, for example. The reception department can also automatically categorize questions from companies and assign them to the appropriate department. Step 2: The proposal department analyzes the questions received by the reception department and proposes the optimal IT tools and methods for proceeding with DX. The proposal department proposes the optimal IT tools and methods for proceeding with DX based on information such as the company's industry, size, and current IT environment. The proposal department uses an analytical algorithm based on machine learning to learn from past successes and failures and make optimal proposals for each company. Step 3: The provision department provides information on the IT tools and digital transformation methods proposed by the proposal department. The provision department provides information in the form of videos, manuals, webinars, etc., in a format that is easy for companies to understand, thereby lowering the barrier to implementation. Step 4: The Support Department provides post-implementation support based on the information provided by the Service Delivery Department. The Support Department provides FAQs and troubleshooting guides, and provides expert support as needed. By providing comprehensive post-implementation support, the Support Department helps reduce the company's concerns and supports smooth operation.

[0060] (Example 2) The IT / DX support system according to an embodiment of the present invention provides solutions to help small and medium-sized enterprises advance their IT / DX initiatives. In this system, companies send questions and inquiries about IT / DX via LINE. A LINE chatbot accepts the questions and analyzes them using AI. The AI ​​proposes optimal IT tools and digital transformation methods based on information such as the company's industry, size, and current IT environment. Furthermore, detailed information and implementation procedures for the proposed IT tools and digital transformation methods are provided via LINE. Post-implementation support is also provided via LINE. This solution allows small and medium-sized enterprises to easily advance their IT / DX initiatives, potentially improving business efficiency and strengthening their competitiveness. For example, the AI ​​analysis algorithm uses machine learning to propose optimal IT tools and digital transformation methods based on information such as the company's industry, size, and current IT environment. This algorithm learns from past successes and failures and makes optimal proposals for each company. Appropriate measures are taken to protect privacy and data security when handling data provided by companies. Information will be provided in a format that is easy for companies to understand, such as videos, manuals, and webinars. Furthermore, for post-implementation support, FAQs and troubleshooting guides will be enhanced to enable LINE's chatbot to respond quickly, and expert support will also be provided as needed. This allows the IT / DX support system to enable small and medium-sized enterprises to easily advance their IT / DX initiatives, improving business efficiency and strengthening their competitiveness.

[0061] An IT / DX support system according to an embodiment includes a reception unit, a proposal unit, a provision unit, and a support unit. The reception unit accepts questions about IT / DX from companies. Questions from companies include, but are not limited to, technical questions and business questions. The reception unit can accept questions from companies using, for example, a LINE chatbot. The reception unit can also automatically categorize questions from companies and assign them to the appropriate department. The proposal unit analyzes the questions accepted by the reception unit and proposes optimal IT tools and methods for advancing DX. The proposal unit proposes optimal IT tools and methods for advancing DX based on information such as the company's industry, size, and current IT environment. The proposal unit uses an analytical algorithm based on machine learning. For example, the proposal unit learns from past success stories and failure stories and makes optimal proposals for each company. The provision unit provides information on the IT tools and methods for advancing DX proposed by the proposal unit. The provision unit provides information in the form of, for example, videos, manuals, webinars, etc. The provision unit lowers the barrier to implementation by providing information in a format that is easy for companies to understand. The support unit provides post-implementation support based on the information provided by the provision unit. The support unit provides, for example, FAQs and troubleshooting guides, and provides expert support as needed. By providing comprehensive post-implementation support, the support unit reduces companies' anxieties and supports smooth operation. As a result, the IT / DX support system according to the embodiment can support the IT / DX adoption of small and medium-sized enterprises by accepting and analyzing questions from companies, making optimal proposals, and providing information and support.

[0062] The proposal department can propose appropriate IT tools and methods for proceeding with DX based on information such as the company's industry, size, and current IT environment. The proposal department can propose optimal IT tools and methods for proceeding with DX based on information such as the company's industry, size, and current IT environment. The proposal department achieves effective IT / DX by making optimal proposals tailored to the characteristics of each company. For example, the proposal department can make proposals to improve the efficiency of manufacturing processes to manufacturing companies. The proposal department can also make proposals to service industry companies regarding the introduction of customer management systems. Furthermore, the proposal department can make proposals to retail companies regarding the optimization of inventory management systems. In this way, the proposal department can achieve effective IT / DX by making optimal proposals tailored to the characteristics of each company.

[0063] The proposal unit can use an analysis algorithm that uses machine learning. The proposal unit uses, for example, an analysis algorithm that uses machine learning. By using machine learning, the proposal unit can improve the accuracy of the proposal. For example, the proposal unit can use an analysis algorithm that uses deep learning. The proposal unit can also use an analysis algorithm that uses a support vector machine. Furthermore, the proposal unit can use an analysis algorithm that uses a random forest. In this way, the proposal unit can improve the accuracy of the proposal by using machine learning.

[0064] The information providing department can provide information in the form of videos, manuals, and webinars. For example, the information providing department can provide information in the form of videos, manuals, and webinars. The information providing department can lower the barrier to implementation by providing information in a format that is easy for companies to understand. For example, the information providing department can provide information in the form of videos. The videos are, for example, about 5 to 10 minutes long and contain content that is easy to understand visually. The information providing department can also provide information in the form of manuals. The manuals are, for example, provided in PDF format and include detailed procedures and illustrations. Furthermore, the information providing department can provide information in the form of webinars. Webinars are, for example, held once a month, and include explanations by experts and question-and-answer sessions. In this way, the information providing department can lower the barrier to implementation by providing information in a format that is easy for companies to understand.

[0065] The support department can provide FAQs and troubleshooting guides and provide support from experts. The support department can provide, for example, FAQs and troubleshooting guides. The FAQ is, for example, a list of frequently asked questions that contains answers to problems that companies may face. The troubleshooting guide is, for example, a detailed explanation of problem-solving procedures, helping companies to solve problems on their own. The support department can also provide support from experts as needed. Expert support includes, for example, telephone support and on-site support. In this way, the support department can alleviate companies' anxiety and support smooth operation by providing comprehensive support after implementation.

[0066] The proposal unit can learn from past success stories and failure stories and make appropriate proposals for each company. The proposal unit, for example, learns from past success stories and failure stories and makes optimal proposals for each company. By learning from past cases, the proposal unit can make more accurate proposals. For example, the proposal unit can build a database of success stories and make optimal proposals for each company. The proposal unit can also build a database of failure stories and make proposals to avoid similar failures. Furthermore, the proposal unit can set evaluation criteria for success stories and failure stories and improve the accuracy of proposals. In this way, the proposal unit can make more accurate proposals by learning from past cases.

[0067] The providing unit can take measures to protect privacy and ensure data security when handling data provided by companies. For example, the providing unit takes measures to protect privacy and ensure data security when handling data provided by companies. The providing unit ensures the privacy protection and data security of companies when handling data. For example, the providing unit uses data encryption technology to protect data provided by companies. The providing unit can also introduce access control to prevent unauthorized access to data. Furthermore, the providing unit can regularly back up data to prevent data loss or damage. This allows the providing unit to ensure the privacy protection and data security of companies when handling data.

[0068] The reception unit can estimate the user's emotions and adjust the timing of question acceptance based on the estimated user emotions. For example, the reception unit estimates the user's emotions and adjusts the timing of question acceptance based on the estimated user emotions. By adjusting the timing of question acceptance according to the user's emotions, the reception unit can provide a more appropriate response. For example, if the user is stressed, the reception unit delays the timing of question acceptance and accepts the question in a relaxed state. Furthermore, if the user is in a hurry, the reception unit can immediately accept the question and provide a prompt response. Furthermore, if the user is excited, the reception unit can temporarily suspend acceptance of the question until the user calms down. This allows the reception unit to adjust the timing of question acceptance according to the user's emotions and provide a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The reception unit can analyze the company's past question history and select an appropriate reception method. For example, the reception unit analyzes the company's past question history and selects the optimal reception method. By analyzing the past question history, the reception unit can receive questions more effectively. For example, the reception unit prioritizes receiving questions that the company has frequently asked in the past. Furthermore, if the reception unit determines from the company's past question history that there are many questions during a specific time period, it can strengthen reception during that time period. Furthermore, the reception unit can analyze the company's past question history and assign questions to specific staff members. In this way, the reception unit can receive questions more effectively by analyzing the past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the company's past question history into AI and have the AI ​​select the optimal reception method.

[0070] The reception unit can perform filtering based on the company's current projects and areas of interest when receiving a question. For example, the reception unit filters based on the company's current projects and areas of interest when receiving a question. The reception unit can receive questions according to the company's current situation. For example, the reception unit prioritizes receiving questions related to projects currently underway by the company. The reception unit can also filter related questions based on the company's areas of interest and assign them to an appropriate person. Furthermore, the reception unit can adjust the reception of questions taking into account the company's current project status. This allows the reception unit to receive questions according to the company's current situation. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input information about the company's current projects and areas of interest into AI and have the AI ​​perform the filtering.

[0071] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, the reception unit estimates the user's emotions and determines the priority of questions to be received based on the estimated user emotions. The reception unit determines the priority of questions based on the user's emotions, thereby enabling a more appropriate response. For example, when the user is stressed, the reception unit prioritizes questions of high importance. Furthermore, when the user is relaxed, the reception unit can also prioritize questions with a normal priority. Furthermore, when the user is in a hurry, the reception unit can prioritize questions with a high urgency. This allows the reception unit to determine the priority of questions based on the user's emotions, enabling a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the geographical location information of the company. For example, when receiving a question, the reception unit prioritizes receiving highly relevant questions by taking into account the geographical location information of the company. By taking into account the geographical location information, the reception unit is able to respond appropriately to issues specific to the region. For example, the reception unit prioritizes receiving questions regarding issues specific to the region based on the location of the company. The reception unit can also prioritize receiving questions regarding collaboration with neighboring companies by taking into account the geographical location information of the company. Furthermore, the reception unit can prioritize receiving questions regarding local events and regulations based on the geographical location information of the company. This allows the reception unit to respond appropriately to issues specific to the region by taking into account the geographical location information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographical location information of the company into AI and have the AI ​​select highly relevant questions.

[0073] The reception unit can analyze the company's social media activity when receiving a question and receive related questions. For example, the reception unit analyzes the company's social media activity when receiving a question and receives related questions. By analyzing the social media activity, the reception unit can receive questions that are in line with company trends. For example, the reception unit analyzes the company's social media activity and prioritizes receiving questions related to trends. The reception unit can also receive questions based on customer feedback from the company's social media activity. Furthermore, the reception unit can prioritize receiving questions about marketing strategies based on the company's social media activity. In this way, the reception unit can analyze the social media activity and receive questions that are in line with company trends. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the company's social media activity into AI and have the AI ​​select related questions.

[0074] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit adjusts the way the suggestion is expressed based on the user's emotion, thereby enabling more effective suggestions. For example, if the user is feeling stressed, the suggestion unit can make simple and easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make short suggestions that focus on the main points. This allows the suggestion unit to adjust the way the suggestion is expressed based on the user's emotion, thereby enabling more effective suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The proposal unit can adjust the level of detail of the proposal taking into account the importance of the company when making a proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the company when making a proposal. The proposal unit adjusts the level of detail of the proposal according to the importance of the company, thereby enabling a more appropriate proposal. For example, the proposal unit makes a detailed proposal to a company with a high importance. The proposal unit can also make a concise proposal to a company with a low importance. Furthermore, the proposal unit can gradually adjust the level of detail of the proposal according to the importance of the company. In this way, the proposal unit can adjust the level of detail of the proposal according to the importance of the company, thereby enabling a more appropriate proposal. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input information on the importance of the company into AI and cause the AI ​​to adjust the level of detail of the proposal.

[0076] The suggestion unit can apply different suggestion algorithms taking into account the company's category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the company's category when making a suggestion. By applying a suggestion algorithm depending on the company's category, the suggestion unit can make more effective suggestions. For example, the suggestion unit can make suggestions regarding improving the efficiency of the manufacturing process to a manufacturing company. The suggestion unit can also make suggestions regarding the introduction of a customer management system to a service company. Furthermore, the suggestion unit can make suggestions regarding the optimization of an inventory management system to a retail company. In this way, the suggestion unit can make more effective suggestions by applying a suggestion algorithm depending on the company's category. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information about the company's category into AI and cause the AI ​​to apply the suggestion algorithm.

[0077] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. The suggestion unit adjusts the length of the suggestion according to the user's emotion, thereby enabling more appropriate suggestions. For example, if the user is stressed, the suggestion unit can provide short, to-the-point suggestions. Also, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can provide short suggestions that can be quickly understood. This allows the suggestion unit to adjust the length of the suggestion according to the user's emotion, enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The proposal unit can determine the priority of proposals taking into account the submission time of the companies when making proposals. For example, the proposal unit determines the priority of proposals based on the submission time of the companies when making proposals. By determining the priority of proposals based on the submission time, the proposal unit can make more effective proposals. For example, the proposal unit can give priority to proposals for companies whose deadlines are approaching. The proposal unit can also give proposals with normal priority to companies whose submission time is further away. Furthermore, the proposal unit can gradually adjust the priority of proposals based on the submission time. This allows the proposal unit to determine the priority of proposals based on the submission time, making more effective proposals. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input information about the submission time of companies into AI and have the AI ​​determine the priority of proposals.

[0079] The proposal unit can adjust the order of proposals taking into account the relevance of the companies when making proposals. For example, the proposal unit adjusts the order of proposals based on the relevance of the companies when making proposals. By adjusting the order of proposals based on the relevance of the companies, more effective proposals can be made. For example, the proposal unit prioritizes highly relevant proposals based on the industry and size of the companies. The proposal unit can also order optimal proposals based on the company's current IT environment. Furthermore, the proposal unit can adjust the order of proposals by referring to past successes and failures of the companies. In this way, the proposal unit can adjust the order of proposals based on the relevance of the companies to make more effective proposals. Some or all of the above-described processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input information on the relevance of the companies into AI and cause the AI ​​to adjust the order of proposals.

[0080] The providing unit can estimate the user's emotion and adjust the information provision method based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the information provision method based on the estimated user's emotion. By adjusting the information provision method according to the user's emotion, the providing unit can provide more effective information. For example, when the user is stressed, the providing unit can provide simple and easy-to-understand information. Furthermore, when the user is relaxed, the providing unit can provide detailed information. Furthermore, when the user is in a hurry, the providing unit can provide short information that focuses on the main points. This allows the providing unit to adjust the information provision method according to the user's emotion, thereby providing more effective information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When providing information, the providing unit can select an appropriate information provision method based on the company's past implementation history. For example, when providing information, the providing unit selects the optimal information provision method by referring to the company's past implementation history. By referring to the past implementation history, the providing unit can provide more appropriate information. For example, the providing unit provides related information based on IT tools previously implemented by the company. The providing unit can also provide information based on success stories from the company's past implementation history. Furthermore, the providing unit can analyze the company's past implementation history and select the optimal information provision method. This allows the providing unit to provide more appropriate information by referring to the past implementation history. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information about the company's past implementation history into AI and have the AI ​​select the information provision method.

[0082] The providing unit can customize the means of providing information based on the company's current IT environment when providing information. For example, the providing unit adjusts the means of providing information based on the company's current IT environment when providing information. The providing unit can provide information according to the company's IT environment. For example, the providing unit selects the optimal means of providing information based on the company's current IT environment. The providing unit can also provide information customized according to the company's IT environment. Furthermore, the providing unit can propose an appropriate means of providing information taking into account the company's IT environment. This enables the providing unit to provide information according to the company's IT environment. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information about the company's current IT environment into AI and have the AI ​​customize the means of providing information.

[0083] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. By determining the priority of information provision according to the user's emotions, the providing unit can provide more appropriate information. For example, when the user is feeling stressed, the providing unit can provide information with a higher priority level. Furthermore, when the user is relaxed, the providing unit can provide information with a normal priority level. Furthermore, when the user is in a hurry, the providing unit can provide information with a higher priority level. In this way, by determining the priority of information provision according to the user's emotions, the providing unit can provide more appropriate information. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The providing unit can select an appropriate information provision method based on the geographical location information of the company when providing information. For example, the providing unit selects the optimal information provision method by taking into account the geographical location information of the company when providing information. By taking into account the geographical location information, the providing unit is able to provide appropriate information for issues specific to the region. For example, the providing unit prioritizes providing information about issues specific to the region based on the location of the company. The providing unit can also provide information about collaboration with neighboring companies by taking into account the geographical location information of the company. Furthermore, the providing unit can provide information about local events and regulations based on the geographical location information of the company. In this way, the providing unit is able to provide appropriate information for issues specific to the region by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the company into AI and have the AI ​​select the information provision method.

[0085] The provision unit can analyze the company's social media activity and suggest a means of providing the information when providing the information. For example, the provision unit can analyze the company's social media activity and suggest a means of providing the information when providing the information. By analyzing the social media activity, the provision unit can provide information that is in line with company trends. For example, the provision unit can analyze the company's social media activity and provide information related to the trend. The provision unit can also provide information based on customer feedback from the company's social media activity. Furthermore, the provision unit can provide information on marketing strategies based on the company's social media activity. In this way, the provision unit can provide information that is in line with company trends by analyzing the social media activity. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data on the company's social media activity into AI and have the AI ​​suggest a means of providing the information.

[0086] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, the support unit estimates the user's emotions and adjusts the support method based on the estimated user's emotions. By adjusting the support method according to the user's emotions, the support unit can provide more effective support. For example, if the user is feeling stressed, the support unit can provide simple and easy-to-understand support. Furthermore, if the user is relaxed, the support unit can provide support including detailed information. Furthermore, if the user is in a hurry, the support unit can provide short support that focuses on the main points. This allows the support unit to adjust the support method according to the user's emotions, thereby providing more effective support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When providing support, the support department can select an appropriate support method based on the company's past support history. For example, when providing support, the support department selects the optimal support method by referring to the company's past support history. By referring to the past support history, the support department can provide more appropriate support. For example, the support department provides relevant support based on the support content the company has received in the past. The support department can also provide support based on success stories from the company's past support history. Furthermore, the support department can analyze the company's past support history and select the optimal support method. In this way, the support department can provide more appropriate support by referring to the past support history. Some or all of the above-mentioned processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input information about the company's past support history into AI and have the AI ​​select a support method.

[0088] The support department can adjust the support method based on the company's current IT environment when providing support. For example, the support department customizes the support method based on the company's current IT environment when providing support. The support department can provide support tailored to the company's IT environment. For example, the support department selects the optimal support method based on the company's current IT environment. The support department can also provide customized support tailored to the company's IT environment. Furthermore, the support department can propose appropriate support methods taking into account the company's IT environment. This enables the support department to provide support tailored to the company's IT environment. Some or all of the above-described processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input information about the company's current IT environment into AI and have the AI ​​customize the support method.

[0089] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. For example, the support unit estimates the user's emotions and determines the priority of support based on the estimated user emotions. By determining the priority of support according to the user's emotions, the support unit can provide more appropriate support. For example, if the user is feeling stressed, the support unit can provide support with a higher priority. Furthermore, if the user is relaxed, the support unit can provide support with a normal priority. Furthermore, if the user is in a hurry, the support unit can provide support with a higher priority. In this way, the support unit can determine the priority of support according to the user's emotions and provide more appropriate support. Emotion estimation is realized using an emotion estimation function using, 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.

[0090] The support department can select an appropriate support method based on the geographical location information of the company when providing support. For example, the support department selects the optimal support method by taking the geographical location information of the company into consideration when providing support. By taking the geographical location information into consideration, the support department can provide appropriate support for issues specific to the region. For example, the support department can prioritize support for issues specific to the region based on the company's location. The support department can also provide support for collaboration with nearby companies by taking the geographical location information of the company into consideration. Furthermore, the support department can provide support for local events and regulations based on the geographical location information of the company. In this way, by taking the geographical location information into consideration, the support department can provide appropriate support for issues specific to the region. Some or all of the above-described processing in the support department can be performed using, for example, AI, or can be performed without using AI. For example, the support department can input the geographical location information of the company into AI and have the AI ​​select a support method.

[0091] The support department can analyze the company's social media activity and propose support measures when providing support. For example, the support department can analyze the company's social media activity and propose support measures when providing support. By analyzing social media activity, the support department can provide support that is in line with company trends. For example, the support department can analyze the company's social media activity and provide support related to trends. The support department can also provide support based on customer feedback from the company's social media activity. Furthermore, the support department can provide support regarding marketing strategies based on the company's social media activity. In this way, the support department can provide support that is in line with company trends by analyzing social media activity. Some or all of the above-mentioned processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input data on the company's social media activity into AI and have the AI ​​suggest support measures. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, proposal unit, provision unit, and support unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives questions from companies via a LINE chatbot. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal IT tools and methods for digital transformation based on information such as the company's industry, size, and current IT environment. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides information in the form of videos, manuals, webinars, etc. The support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides FAQs and troubleshooting guides and provides expert support as needed. The reception unit, for example, has a function of estimating a user's emotions and adjusting the timing of receiving questions based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, proposal unit, provision unit, and support unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts questions from companies via a LINE chatbot. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal IT tools and methods for advancing digital transformation based on information such as the company's industry, size, and current IT environment. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides information in the form of videos, manuals, webinars, etc. The support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides FAQs and troubleshooting guides, and provides expert support as needed. The reception unit, for example, has a function of estimating a user's emotions and adjusting the timing of accepting questions based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, proposal unit, provision unit, and support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives questions from companies via a LINE chatbot. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal IT tools and methods for digital transformation based on information such as the company's industry, size, and current IT environment. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides information in the form of videos, manuals, webinars, etc. The support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides FAQs and troubleshooting guides, and provides expert support as needed. The reception unit, for example, has a function of estimating a user's emotions and adjusting the timing of receiving questions based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, proposal unit, provision unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and accepts questions from companies via a LINE chatbot. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal IT tools and methods for digital transformation based on information such as the company's industry, size, and current IT environment. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides information in the form of videos, manuals, webinars, etc. The support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides FAQs and troubleshooting guides, and provides expert support as needed. The reception unit, for example, has a function of estimating a user's emotions and adjusting the timing of question acceptance based on the estimated user emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The Proposal Department can also make proposals that take into account the company's industry, size, and current IT environment, as well as the company's culture and employee skill levels. For example, if a company has an open culture that values ​​innovation, they can propose the latest IT tools and methods for advancing DX. Also, if employees have a high skill level, they can make proposals using more advanced technologies. Furthermore, if a company has a conservative culture, they can propose a gradual approach to IT and DX. In this way, the Proposal Department can make proposals that take into account the company's culture and employee skill levels, thereby achieving more effective IT / DX adoption.

[0094] The provision department can also provide customized training programs according to the type and size of the company. For example, a training program on improving the efficiency of manufacturing processes can be provided to a manufacturing company. A training program on introducing a customer management system can also be provided to a service company. Furthermore, a training program on optimizing an inventory management system can also be provided to a retail company. In this way, the provision department can lower the barrier to implementation by providing customized training programs according to the type and size of the company.

[0095] The support department can also monitor a company's progress in IT / DX in real time and provide support as needed. For example, if a company is experiencing delays in the adoption of IT tools, the support department can respond quickly and offer advice to resolve the issue. Furthermore, when a company reports its progress in DX, the support department can analyze the data and make suggestions for the next step. Furthermore, a dashboard can be provided for companies to share their progress in IT / DX, helping them understand the situation in real time. This allows the support department to monitor a company's progress in IT / DX in real time and provide support as needed, helping to ensure smooth operations.

[0096] The Proposal Department can also make proposals taking into account the company's market competitive situation, in addition to the company's industry, size, and current IT environment. For example, for companies in highly competitive markets, they can propose rapid IT and DX adoption. For companies in less competitive markets, they can propose gradual IT and DX adoption. Furthermore, they can analyze the company's market competitive situation and make proposals to increase competitive advantage. In this way, the Proposal Department can make proposals that take into account the company's market competitive situation, thereby achieving more effective IT and DX adoption.

[0097] The provision department can also provide customized support plans according to the type of industry and size of the company. For example, a support plan related to improving the efficiency of manufacturing processes can be provided to a manufacturing company. A support plan related to the introduction of a customer management system can be provided to a service industry company. Furthermore, a support plan related to the optimization of an inventory management system can be provided to a retail industry company. In this way, the provision department can lower the barrier to implementation by providing customized support plans according to the type of industry and size of the company.

[0098] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can delay the timing of suggestions and make suggestions in a relaxed state. Also, if the user is in a hurry, the suggestion unit can make suggestions immediately and respond quickly. Furthermore, if the user is excited, the suggestion unit can temporarily suspend suggestions until the user calms down. This allows the suggestion unit to adjust the timing of suggestions according to the user's emotions, enabling more appropriate responses.

[0099] The providing unit can estimate the user's emotions and adjust the frequency of information provision based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of information provision can be reduced to reduce the burden. Also, if the user is relaxed, the frequency of information provision can be increased to provide more detailed information. Furthermore, if the user is in a hurry, the frequency of information provision can be adjusted to quickly provide necessary information. In this way, the providing unit can provide information more effectively by adjusting the frequency of information provision according to the user's emotions.

[0100] The support unit can estimate the user's emotions and adjust the content of support based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide simple and easy-to-understand support. If the user is relaxed, it can provide support that includes detailed information. Furthermore, if the user is in a hurry, it can provide short support that focuses on the main points. This allows the support unit to adjust the content of support according to the user's emotions, enabling more effective support.

[0101] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make short suggestions that get to the point. This allows the suggestion unit to adjust the content of the suggestions according to the user's emotions, enabling more effective suggestions.

[0102] The providing unit can estimate the user's emotions and adjust the format of information provision based on the estimated user's emotions. For example, if the user is feeling stressed, information can be provided in a simple, visually easy-to-understand format. If the user is relaxed, information can be provided in a format including detailed information. Furthermore, if the user is in a hurry, information can be provided in a short format that focuses on the main points. In this way, the providing unit can provide information more effectively by adjusting the format of information provision according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception department accepts questions about IT / DX from companies. Questions from companies include technical questions and business questions. The reception department can accept questions from companies using a LINE chatbot, for example. The reception department can also automatically categorize questions from companies and assign them to the appropriate department. Step 2: The proposal department analyzes the questions received by the reception department and proposes the optimal IT tools and methods for proceeding with DX. The proposal department proposes the optimal IT tools and methods for proceeding with DX based on information such as the company's industry, size, and current IT environment. The proposal department uses an analytical algorithm based on machine learning to learn from past successes and failures and make optimal proposals for each company. Step 3: The provision department provides information on the IT tools and digital transformation methods proposed by the proposal department. The provision department provides information in the form of videos, manuals, webinars, etc., in a format that is easy for companies to understand, thereby lowering the barrier to implementation. Step 4: The Support Department provides post-implementation support based on the information provided by the Service Delivery Department. The Support Department provides FAQs and troubleshooting guides, and provides expert support as needed. By providing comprehensive post-implementation support, the Support Department helps reduce the company's concerns and supports smooth operation.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

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

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the 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.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] The data processing system 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.

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

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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, in order to avoid confusion and to 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts questions from companies regarding IT / DX, a proposal section that analyzes the questions received by the reception section and proposes appropriate IT tools and methods for proceeding with DX; a provision department that provides information on IT tools and methods of proceeding with DX proposed by the proposal department; a support unit that provides post-implementation support based on the information provided by the providing unit. A system characterized by:

2. The proposal unit We propose appropriate IT tools and methods for digital transformation based on information such as the company's industry, size, and current IT environment. The system of claim 1 .

3. The proposal unit Use machine learning-based analysis algorithms The system of claim 1 .

4. The providing unit Providing information in the form of videos, manuals, and webinars The system of claim 1 .

5. The support portion is Providing FAQs, troubleshooting guides, and expert support The system of claim 1 .

6. The proposal unit Study past successes and failures to make appropriate proposals for each company The system of claim 1 .

7. The providing unit Implement privacy and data security measures for handling data provided by companies The system of claim 1 .

8. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions. The system of claim 1 .

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