System

Abstract

To provide a system that generates, protects and automates an interactive model specialized for a domain of a company and provides customized solutions.SOLUTION: A system according to an embodiment comprises: a generation unit; a security unit; an automation unit; and a customization unit. The generation unit generates an interactive model specialized for a domain of a company. The security unit protects the interactive model generated by the generation unit. The automation unit automates information using the interactive model generated by the generation unit. The customization unit provides customized solutions according to company requirements on the basis of the information automated by the automation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Existing technologies are lacking in the ability to generate and protect interaction models specific to a company's domain, automate information, and provide customized solutions, leaving room for improvement.

[0005] The system according to the embodiment aims to create, secure and automate interaction models specific to an enterprise's domain and provide customized solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a security unit, an automation unit, and a customization unit. The generation unit generates an interaction model specialized for a company's domain. The security unit protects the interaction model generated by the generation unit. The automation unit automates information using the interaction model generated by the generation unit. The customization unit provides a customized solution according to the company's requirements based on the information automated by the automation unit. Effect of the Invention

[0007] An embodiment of the system can create, secure, and automate interaction models specific to an enterprise's domain, and provide customized solutions. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The custom generation AI system according to the embodiment of the present invention is a system that generates a dialogue model specialized for a company's domain and utilizes it to improve the efficiency of customer support and internal processes. This system aims to improve user experience with an emphasis on security and privacy. It contributes to information automation and strengthening competitiveness, and promotes market growth by providing customized solutions according to company requirements. For example, a company creates a dialogue model specialized for its own domain using a generation AI. This dialogue model is used to improve the efficiency of customer support and internal processes of the company. For example, a chatbot that automatically responds to customer inquiries and a system that automates internal business flows are possible. Next, the generation AI is designed with an emphasis on the security and privacy of the company. For example, data encryption and access control are performed to prevent customer data and internal information from leaking to the outside. In addition, the accuracy and response speed of the dialogue model are optimized to improve the user experience. Furthermore, information automation strengthens the competitiveness of the company. For example, the generation AI automatically collects and analyzes data to provide information useful for management decisions. In addition, by providing customized solutions according to company requirements, an optimal system tailored to the needs of each company is built. Finally, these customized solutions promote market growth. Companies can use generative AI to improve business efficiency and strengthen their competitiveness. For example, companies can improve their productivity by speeding up customer support and automating internal processes. This allows custom generative AI systems to generate dialogue models specific to a company's domain, protect security, automate information, and provide customized solutions, thereby improving the efficiency and competitiveness of the company.

[0029] The custom generation AI system according to the embodiment includes a generation unit, a security unit, an automation unit, and a customization unit. The generation unit generates a dialogue model specialized for a company's domain. The generation unit generates a dialogue model specialized for a specific industry or business of the company using, for example, a generation AI. The generation AI can generate a dialogue model using a text generation AI (for example, LLM). The generation unit can also generate a dialogue model specialized for a customer base of the company using the generation AI. For example, the generation AI learns past dialogue data to generate an optimal response for a specific customer base. The security unit protects the dialogue model generated by the generation unit. The security unit, for example, performs data encryption and access control. Encryption techniques such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for data encryption. The access control includes user authentication and authority management. For example, the security unit performs user authentication to allow only authenticated users to access the dialogue model. The automation unit automates information using the dialogue model generated by the generation unit. The automation unit, for example, collects and analyzes data. The data collection includes sensor data collection and web scraping. The data analysis includes statistical analysis and application of machine learning algorithms. For example, the automation unit collects sensor data and analyzes the data using machine learning algorithms. The customization unit provides customized solutions according to corporate requirements based on information automated by the automation unit. The customization unit, for example, provides a means for transmitting corporate requirements to the generation AI. The corporate requirements include business processes, legal regulations, and customer needs. For example, the customization unit transmits the business processes of the company to the generation AI and provides optimal customized solutions. As a result, the custom generation AI system according to the embodiment can generate dialogue models specialized for the company's domain, protect security, automate information, and provide customized solutions, thereby improving the efficiency and competitiveness of the company.

[0030] The generation unit generates a dialogue model specialized for the company's domain. For example, the generation unit uses a generation AI to generate a dialogue model specialized for a specific industry or business of the company. The generation AI can generate a dialogue model using a text generation AI (for example, LLM). Specifically, the generation unit collects a large amount of data on the company's business content and customer base, and trains the generation AI based on this. For example, the generation AI learns knowledge about a specific industry or business using the company's past customer response history, FAQ database, industry-specific glossary, etc. The generation AI uses natural language processing technology to generate an appropriate response to the input text. Furthermore, the generation unit can also use the generation AI to generate a dialogue model specialized for the company's customer base. For example, the generation AI learns past dialogue data to generate optimal responses for a specific customer base. This allows the generation unit to provide personalized responses to the company's specific customer base. In the training process of the generation AI, the generation unit preprocesses and filters data to use high-quality data with little noise. In addition, the generation unit prepares a test dataset for evaluating the performance of the generation AI, and verifies the accuracy of the generated dialogue model and the naturalness of the responses. This enables the generation unit to provide a high-quality dialogue model that is optimized to meet the needs of the enterprise.

[0031] The security unit protects the dialogue model generated by the generation unit. The security unit performs, for example, data encryption and access control. Encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for data encryption. Specifically, the generated dialogue model and training data are stored using AES-256-bit encryption to protect them from unauthorized access. Access control includes user authentication and authority management. For example, the security unit performs user authentication to allow only authenticated users to access the dialogue model. Advanced authentication methods such as two-factor authentication (2FA) and biometric authentication are used for user authentication. Furthermore, the security unit introduces a monitoring system to monitor access logs and detect unauthorized access and abnormal activities. This allows the security unit to ensure the confidentiality and integrity of the dialogue model and protect the company's data. In addition, the security unit performs regular security evaluations and vulnerability scans to maintain the security level of the system. This allows the security unit to always take the latest security measures and ensure the safety of the dialogue model.

[0032] The automation unit automates information using the dialogue model generated by the generation unit. The automation unit, for example, collects and analyzes data. Data collection includes sensor data collection and web scraping. Specifically, the automation unit uses sensors and APIs to automatically collect data related to the business process of a company. For example, in the manufacturing industry, sensor data can be collected from machines in a factory to detect operating conditions and anomalies. Data analysis includes the application of statistical analysis and machine learning algorithms. For example, the automation unit can use the collected sensor data to apply machine learning algorithms to detect anomalies and perform predictive maintenance. This allows companies to prevent equipment failures and achieve efficient operation. In addition, the automation unit uses the generated dialogue model to automate customer support. For example, in customer support, the generated dialogue model can be used to automatically respond to inquiries from customers. This allows companies to improve the efficiency of customer support and increase customer satisfaction. In addition, the automation unit visualizes data and generates reports to support company decision-making. For example, the collected data is converted into graphs and charts to provide management with visually easy-to-understand information. This will enable the Automation Department to streamline the company's business processes and support data-driven decision-making.

[0033] The customization unit provides customized solutions according to the company's requirements based on the information automated by the automation unit. For example, the customization unit provides a means of communicating the company's requirements to the generation AI. The company's requirements include business processes, laws and regulations, and customer needs. Specifically, the customization unit analyzes the company's business processes in detail and builds a feedback loop to provide the generation AI with the optimal customized solution. For example, in a manufacturing company, a customized solution for improving the efficiency of the production line and quality control can be provided. The customization unit provides the generation AI with information on the company's specific business processes and laws and regulations, and adjusts the generated dialogue model and automation solution to meet the company's requirements. In addition, the customization unit can customize the response content and behavior of the generation AI based on customer needs. For example, the generation AI is adjusted to provide more polite responses and specialized knowledge to a specific customer group. This allows the customization unit to provide solutions optimized to the company's needs and strengthen the company's competitiveness. Furthermore, the customization unit collects feedback from the company and continuously improves the performance and response content of the generation AI. This allows the customization unit to always provide optimal solutions based on the latest information and technology, and support the growth and development of the company.

[0034] The security unit can perform data encryption or access control. For example, encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for data encryption. AES is a symmetric key encryption method that provides fast and secure encryption. RSA is a public key encryption method that uses different keys for data encryption and decryption. For example, access control includes user authentication and authority management. Password authentication and two-factor authentication are used for user authentication. Authority management includes setting different access authority for each user. For example, the security unit performs user authentication so that only authenticated users can access the dialogue model. The security unit can also record access logs and detect unauthorized access. This makes it possible to protect the security and privacy of a company by encrypting data and controlling access. A part or all of the above-mentioned processing in the security unit may be performed using AI, for example, or may be performed without using AI. For example, the security unit can analyze access logs using AI to detect unauthorized access.

[0035] The automation unit can collect and analyze data. Data collection includes, for example, collection of sensor data and web scraping. Temperature sensors, humidity sensors, acceleration sensors, etc. are used to collect sensor data. Web scraping uses a technique for automatically collecting data from a specific website. Data analysis includes, for example, application of statistical analysis and machine learning algorithms. Statistical analysis includes average value, standard deviation, regression analysis, etc. Machine learning algorithms include classification, regression, clustering, etc. For example, the automation unit can collect sensor data and analyze the data using machine learning algorithms. As a result, by collecting and analyzing data, it is possible to realize information automation and enhanced competitiveness of a company. Some or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can analyze sensor data using AI and perform anomaly detection.

[0036] The customization unit can provide a means for communicating the requirements of a company to the generation AI. The requirements of a company include, for example, business processes, legal regulations, and customer needs. The business processes include specific business procedures and flows. The legal regulations include industry-specific regulations and compliance requirements. The customer needs include customer requests and expectations. For example, the customization unit can communicate the business processes of a company to the generation AI and provide an optimal customized solution. The customization unit can also communicate the legal regulations of a company to the generation AI and provide a solution that complies with compliance. Furthermore, the customization unit can communicate the customer needs of a company to the generation AI and provide a solution that improves customer satisfaction. In this way, by providing a means for communicating the requirements of a company to the generation AI, an optimal system tailored to the needs of the company can be constructed. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can analyze the requirements of a company using AI and propose an optimal customized solution.

[0037] The generation unit can generate a chatbot that automatically responds to inquiries from customers. The chatbot includes, for example, natural language processing capabilities, dialogue scenarios, and response speed. The natural language processing capabilities include understanding and generation of text. The dialogue scenarios include response patterns to specific questions. The response speed includes the speed of response to inquiries from users. For example, the generation unit can generate a chatbot that automatically responds to inquiries from customers using a generation AI. The generation AI can generate responses of the chatbot using a text generation AI (for example, LLM). The generation unit can also generate a chatbot based on a specific dialogue scenario using the generation AI. For example, the generation AI learns past dialogue data to generate optimal responses to frequently asked questions from customers. This makes it possible to generate a chatbot that automatically responds to inquiries from customers, thereby improving the efficiency of customer support.

[0038] The automation department can generate a system that automates the internal business flow. Examples of business flow automation include the introduction of a workflow management system and the application of RPA (Robotic Process Automation). The workflow management system includes the automation of business procedures and task assignment. RPA includes the automation of routine tasks and the automation of data entry. For example, the automation department can generate a system that automates the internal business flow using a generation AI. The generation AI can generate an optimal business flow based on a business procedure manual or a process map. The automation department can also use the generation AI to generate an automation system based on a specific business flow. For example, the generation AI can learn a company's business procedure manual and propose an efficient business flow. This can generate a system that automates the internal business flow, thereby realizing the efficiency of internal processes.

[0039] At the time of generation, the generation unit can generate an appropriate dialogue model by referring to the company's past dialogue history. The generation unit, for example, uses the generation AI to refer to the company's past dialogue history. Referencing the dialogue history includes database search, pattern matching, and application of machine learning models. For example, the generation AI analyzes past customer support dialogue history and generates an optimal response to a frequently asked question. The generation AI can also refer to past dialogue history of internal processes to generate a dialogue model that suggests an efficient business flow. Furthermore, the generation AI can analyze past dialogue history of complaint responses and generate a dialogue model including an appropriate response method. In this way, an optimal dialogue model can be generated by referring to the company's past dialogue history.

[0040] The generation unit can customize the dialogue model based on a specific business flow of the company at the time of generation. The generation unit, for example, refers to a specific business flow of the company using the generation AI. The reference to the business flow includes a business procedure manual, a process map, and a business requirement definition. For example, the generation AI can refer to a customer support flow of the company and generate a dialogue model that follows a specific procedure. The generation AI can also generate a dialogue model specialized for sales promotion based on the sales process of the company. Furthermore, the generation AI can refer to the human resources management flow of the company and generate a dialogue model suitable for a job interview. In this way, a dialogue model tailored to the needs of the company can be provided by customizing the dialogue model based on the specific business flow of the company.

[0041] The generation unit can generate a dialogue model based on the geographical characteristics of the company at the time of generation. The generation unit, for example, uses a generation AI to consider the geographical characteristics of the company. Consideration of geographical characteristics includes regional culture, language, and legal regulations. For example, the generation AI generates a dialogue model that reflects the language and culture specific to the region based on the location of the company. The generation AI can also generate a dialogue model that supports multiple languages ​​based on the international bases of the company. Furthermore, the generation AI can also generate a dialogue model that takes into account the regional characteristics of the company and meets the needs of customers in the region. As a result, a dialogue model that meets the needs specific to the region can be provided by taking into account the geographical characteristics of the company.

[0042] At the time of generation, the generation unit can analyze the company's social media activities and generate a related dialogue model. The generation unit, for example, uses the generation AI to analyze the company's social media activities. The analysis of social media activities includes analysis of posted content, engagement measurement, and trend analysis. For example, the generation AI analyzes customer feedback on the company's social media and reflects it in the dialogue model. The generation AI can also generate a dialogue model specialized for promotions based on the content of the company's social media campaign. Furthermore, the generation AI can analyze trends on the company's social media and generate a dialogue model including the latest information. In this way, a related dialogue model can be provided by analyzing the company's social media activities.

[0043] The security department can analyze the company's past security incidents and provide appropriate security measures. The security department, for example, uses AI to analyze the company's past security incidents. The analysis of security incidents includes log analysis, review of incident reports, and utilization of threat intelligence. For example, AI can analyze past data leakage incidents and provide measures to prevent similar incidents. AI can also analyze the history of past unauthorized access and strengthen access control. Furthermore, AI can analyze the history of past virus infections and optimize the settings of antivirus software. In this way, optimal security measures can be provided by analyzing the company's past security incidents. Part or all of the above-mentioned processing in the security department may be performed, for example, using AI, or may be performed without using AI. For example, the security department can analyze incident reports using AI and utilize threat intelligence to propose security measures.

[0044] The security department can customize security measures based on the specific business flow of the company. The security department, for example, refers to the specific business flow of the company using AI. The reference to the business flow includes the application of business procedures, risk assessments, and security policies. For example, the AI ​​can refer to the customer data management flow of the company and strengthen data encryption. The AI ​​can also encrypt communications based on the internal communication flow of the company. Furthermore, the AI ​​can also refer to the payment processing flow of the company and strengthen fraud prevention measures. This makes it possible to provide security measures tailored to the needs of the company by customizing security measures based on the specific business flow of the company. Some or all of the above-mentioned processing in the security department may be performed using AI, for example, or may be performed without using AI. For example, the security department can analyze the business flow using AI and propose optimal security measures.

[0045] The security department can provide security measures based on the geographical characteristics of the company. The security department, for example, uses AI to consider the geographical characteristics of the company. Consideration of geographical characteristics includes local laws and regulations, cultural background, and infrastructure conditions. For example, the AI ​​provides measures to address security risks specific to the region based on the location of the company. The AI ​​can also provide multinational security measures based on the international bases of the company. Furthermore, the AI ​​can also provide measures according to the security needs of the region by considering the regional characteristics of the company. As a result, measures to address security risks specific to the region can be provided by considering the geographical characteristics of the company. Some or all of the above-mentioned processing in the security department may be performed, for example, using AI, or may be performed without using AI. For example, the security department can analyze regional security risks using AI and propose optimal security measures.

[0046] The security department can analyze the company's social media activities and provide relevant security measures. The security department, for example, uses AI to analyze the company's social media activities. The analysis of social media activities includes analysis of posted content, engagement measurement, and trend analysis. For example, AI can analyze customer feedback on the company's social media and identify security risks. In addition, AI can also strengthen security measures based on the content of the company's social media campaigns. Furthermore, AI can also analyze trends on the company's social media and provide measures to address the latest security risks. In this way, by analyzing the company's social media activities, relevant security measures can be provided. Part or all of the above-mentioned processing in the security department may be performed, for example, using AI, or may be performed without using AI. For example, the security department can use AI to analyze social media posted content and identify security risks.

[0047] The automation unit can adjust the automation process based on the specific business flow of the company. The automation unit, for example, refers to the specific business flow of the company using a generation AI. The reference to the business flow includes a business procedure manual, a process map, and a business requirement definition. For example, the generation AI refers to the customer support flow of the company and provides an automation process following a specific procedure. The generation AI can also provide an automation process specialized for sales promotion based on the sales process of the company. Furthermore, the generation AI can also provide an automation process suitable for a job interview by referring to the human resources management flow of the company. In this way, the automation process can be customized based on the specific business flow of the company to provide an automation process tailored to the needs of the company. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can analyze the business flow using AI and propose an optimal automation process.

[0048] The automation unit can collect data based on the geographical characteristics of the company. The automation unit, for example, uses a generation AI to consider the geographical characteristics of the company. Consideration of geographical characteristics includes regional infrastructure conditions, climatic conditions, and legal regulations. For example, the generation AI collects region-specific data based on the location of the company. The generation AI can also collect multinational data based on the international bases of the company. Furthermore, the generation AI can also consider the regional characteristics of the company and collect data according to the needs of the region. In this way, region-specific data can be collected by considering the geographical characteristics of the company. Some or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can analyze regional data using AI and propose an optimal data collection method.

[0049] The automation unit can analyze the company's social media activities and collect related data. The automation unit can analyze the company's social media activities using, for example, a generation AI. The analysis of social media activities includes analysis of posted content, engagement measurement, and trend analysis. For example, the generation AI can analyze customer feedback on the company's social media and collect related data. The generation AI can also collect data related to promotions based on the content of the company's social media campaign. Furthermore, the generation AI can also analyze trends on the company's social media and collect data including the latest information. In this way, the company's social media activities can be analyzed to collect related data. Some or all of the above-mentioned processing in the automation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the automation unit can analyze the content of social media posts using an AI and collect related data.

[0050] The customization unit can analyze the company's past requirement history and provide an appropriate customized solution. The customization unit can, for example, use AI to analyze the company's past requirement history. The analysis of the requirement history includes analysis of past project data, feedback evaluation, and trend analysis. For example, the AI ​​can analyze the past requirement history and provide an optimal solution for common requirements. The AI ​​can also provide a customized solution suitable for a specific business flow based on the past requirement history. Furthermore, the AI ​​can also refer to the past requirement history and provide a customized solution according to the needs of the company. In this way, the optimal customized solution can be provided by analyzing the company's past requirement history. A part or all of the above-mentioned processing in the customization unit can be performed, for example, using AI, or can be performed without using AI. For example, the customization unit can analyze the requirement history using AI and propose an optimal customized solution.

[0051] The customization unit can provide a customized solution based on a specific business flow of a company. The customization unit, for example, refers to a specific business flow of a company using AI. The reference to the business flow includes a business procedure manual, a process map, and a business requirement definition. For example, the AI ​​refers to a customer support flow of a company and provides a customized solution according to a specific procedure. The AI ​​can also provide a customized solution specialized for sales promotion based on the sales process of the company. Furthermore, the AI ​​can also provide a customized solution suitable for a job interview by referring to a human resources management flow of a company. In this way, a customized solution tailored to the needs of a company can be provided by providing a customized solution based on a specific business flow of a company. A part or all of the above-mentioned processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can analyze a business flow using AI and propose an optimal customized solution.

[0052] The customization unit can provide a customization solution based on the geographical characteristics of the company. The customization unit, for example, uses AI to consider the geographical characteristics of the company. Consideration of geographical characteristics includes local culture, language, and legal regulations. For example, the AI ​​provides a customization solution according to local needs based on the location of the company. The AI ​​can also provide a multinational customization solution based on the international bases of the company. Furthermore, the AI ​​can also provide a customization solution according to local needs by considering the local characteristics of the company. As a result, a customization solution according to local needs can be provided by considering the geographical characteristics of the company. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can analyze local needs using AI and propose an optimal customization solution.

[0053] The customization unit can analyze the social media activities of the company and provide a related customization solution. The customization unit can analyze the social media activities of the company using, for example, AI. The analysis of social media activities includes analysis of posted content, measurement of engagement, and trend analysis. For example, the AI ​​can analyze customer feedback on the company's social media and provide a related customization solution. The AI ​​can also provide a customization solution related to a promotion based on the content of the company's social media campaign. Furthermore, the AI ​​can analyze trends on the company's social media and provide a customization solution including the latest information. In this way, the company's social media activities can be analyzed to provide a related customization solution. A part or all of the above-mentioned processing in the customization unit can be performed using, for example, AI, or can be performed without using AI. For example, the customization unit can analyze the content of social media posts using AI and propose a related customization solution.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The generation unit can analyze the user's past behavior history and personalize the response content of the dialogue model. For example, the generation unit can analyze what questions the user has asked in the past and what information the user has sought, and generate a more appropriate response to a similar question. The generation unit can also refer to the user's past purchase history and suggest related products and services. Furthermore, the generation unit can take into account the user's past feedback and generate a response that matches the user's preferences. This can improve the user experience by personalizing the response content of the dialogue model based on the user's past behavior history.

[0056] The security department can detect and respond to threats in real time. For example, the security department can monitor network traffic and detect abnormal activity. The security department can also perform virus scans in real time to prevent malware from entering. Furthermore, the security department can learn user behavior patterns and detect signs of unauthorized access at an early stage. This makes it possible to strengthen the security of a company by detecting and responding to threats in real time.

[0057] The automation unit can automate data preprocessing. For example, the automation unit cleans the data and corrects missing values ​​and outliers. The automation unit can also normalize and standardize the data and convert it into a format suitable for analysis. Furthermore, the automation unit can automatically perform feature engineering of the data to improve the accuracy of the machine learning model. As a result, the efficiency of data analysis can be improved by automating data preprocessing.

[0058] The customization department can analyze the trends of the company's competitors and provide customization solutions to strengthen the company's competitiveness. For example, the customization department can analyze the features of the competitors' products and services and find points of differentiation from the company's products and services. The customization department can also analyze the competitors' marketing strategies and optimize the company's marketing strategies. Furthermore, the customization department can analyze the competitors' customer feedback and propose measures to improve the company's customer satisfaction. In this way, the company's competitiveness can be strengthened by analyzing the trends of competitors.

[0059] The generation unit can generate a dialogue model based on a specific project of the company. For example, the generation unit generates a dialogue model that provides information about a product based on a new product development project of the company. The generation unit can also generate a dialogue model that provides information about a campaign based on a marketing campaign of the company. Furthermore, the generation unit can generate a dialogue model that provides information about support based on a customer support project of the company. In this way, by generating a dialogue model based on a specific project of the company, it is possible to provide a dialogue model tailored to the needs of the company.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The generation unit generates a dialogue model specialized for the company's domain. The generation unit uses a generative AI to generate a dialogue model specialized for a specific industry or business of the company. The generative AI can generate a dialogue model using a text generation AI (e.g., LLM). The generation unit can also use a generative AI to generate a dialogue model specialized for the company's customer base. For example, the generative AI learns from past dialogue data to generate optimal responses for a specific customer base. Step 2: The security unit protects the dialogue model generated by the generation unit. The security unit encrypts data and performs access control. Encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used to encrypt data. Access control includes user authentication and authority management. For example, the security unit performs user authentication to ensure that only authenticated users can access the dialogue model. Step 3: The automation unit automates the information using the dialogue model generated by the generation unit. The automation unit collects and analyzes data. Data collection includes collecting sensor data and web scraping. Data analysis includes applying statistical analysis and machine learning algorithms. For example, the automation unit collects sensor data and analyzes the data using machine learning algorithms. Step 4: The customization department provides customized solutions that meet the company's requirements based on the information automated by the automation department. The customization department provides a means to communicate the company's requirements to the generation AI. The company's requirements include business processes, legal regulations, and customer needs. For example, the customization department communicates the company's business processes to the generation AI and provides the optimal customized solution.

[0062] (Example 2) The custom generation AI system according to the embodiment of the present invention is a system that generates a dialogue model specialized for a company's domain and utilizes it to improve the efficiency of customer support and internal processes. This system aims to improve user experience with an emphasis on security and privacy. It contributes to information automation and strengthening competitiveness, and promotes market growth by providing customized solutions according to company requirements. For example, a company creates a dialogue model specialized for its own domain using a generation AI. This dialogue model is used to improve the efficiency of customer support and internal processes of the company. For example, a chatbot that automatically responds to customer inquiries and a system that automates internal business flows are possible. Next, the generation AI is designed with an emphasis on the security and privacy of the company. For example, data encryption and access control are performed to prevent customer data and internal information from leaking to the outside. In addition, the accuracy and response speed of the dialogue model are optimized to improve the user experience. Furthermore, information automation strengthens the competitiveness of the company. For example, the generation AI automatically collects and analyzes data to provide information useful for management decisions. In addition, by providing customized solutions according to company requirements, an optimal system tailored to the needs of each company is built. Finally, these customized solutions promote market growth. Companies can use generative AI to improve business efficiency and strengthen their competitiveness. For example, companies can improve their productivity by speeding up customer support and automating internal processes. This allows custom generative AI systems to generate dialogue models specific to a company's domain, protect security, automate information, and provide customized solutions, thereby improving the efficiency and competitiveness of the company.

[0063] The custom generation AI system according to the embodiment includes a generation unit, a security unit, an automation unit, and a customization unit. The generation unit generates a dialogue model specialized for a company's domain. The generation unit generates a dialogue model specialized for a specific industry or business of the company using, for example, a generation AI. The generation AI can generate a dialogue model using a text generation AI (for example, LLM). The generation unit can also generate a dialogue model specialized for a customer base of the company using the generation AI. For example, the generation AI learns past dialogue data to generate an optimal response for a specific customer base. The security unit protects the dialogue model generated by the generation unit. The security unit, for example, performs data encryption and access control. Encryption techniques such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for data encryption. The access control includes user authentication and authority management. For example, the security unit performs user authentication to allow only authenticated users to access the dialogue model. The automation unit automates information using the dialogue model generated by the generation unit. The automation unit, for example, collects and analyzes data. The data collection includes sensor data collection and web scraping. The data analysis includes statistical analysis and application of machine learning algorithms. For example, the automation unit collects sensor data and analyzes the data using machine learning algorithms. The customization unit provides customized solutions according to corporate requirements based on information automated by the automation unit. The customization unit, for example, provides a means for transmitting corporate requirements to the generation AI. The corporate requirements include business processes, legal regulations, and customer needs. For example, the customization unit transmits the business processes of the company to the generation AI and provides optimal customized solutions. As a result, the custom generation AI system according to the embodiment can generate dialogue models specialized for the company's domain, protect security, automate information, and provide customized solutions, thereby improving the efficiency and competitiveness of the company.

[0064] The generation unit generates a dialogue model specialized for the company's domain. For example, the generation unit uses a generation AI to generate a dialogue model specialized for a specific industry or business of the company. The generation AI can generate a dialogue model using a text generation AI (for example, LLM). Specifically, the generation unit collects a large amount of data on the company's business content and customer base, and trains the generation AI based on this. For example, the generation AI learns knowledge about a specific industry or business using the company's past customer response history, FAQ database, industry-specific glossary, etc. The generation AI uses natural language processing technology to generate an appropriate response to the input text. Furthermore, the generation unit can also use the generation AI to generate a dialogue model specialized for the company's customer base. For example, the generation AI learns past dialogue data to generate optimal responses for a specific customer base. This allows the generation unit to provide personalized responses to the company's specific customer base. In the training process of the generation AI, the generation unit preprocesses and filters data to use high-quality data with little noise. In addition, the generation unit prepares a test dataset for evaluating the performance of the generation AI, and verifies the accuracy of the generated dialogue model and the naturalness of the responses. This enables the generation unit to provide a high-quality dialogue model that is optimized to meet the needs of the enterprise.

[0065] The security unit protects the dialogue model generated by the generation unit. The security unit performs, for example, data encryption and access control. Encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for data encryption. Specifically, the generated dialogue model and training data are stored using AES-256-bit encryption to protect them from unauthorized access. Access control includes user authentication and authority management. For example, the security unit performs user authentication to allow only authenticated users to access the dialogue model. Advanced authentication methods such as two-factor authentication (2FA) and biometric authentication are used for user authentication. Furthermore, the security unit introduces a monitoring system to monitor access logs and detect unauthorized access and abnormal activities. This allows the security unit to ensure the confidentiality and integrity of the dialogue model and protect the company's data. In addition, the security unit performs regular security evaluations and vulnerability scans to maintain the security level of the system. This allows the security unit to always take the latest security measures and ensure the safety of the dialogue model.

[0066] The automation unit automates information using the dialogue model generated by the generation unit. The automation unit, for example, collects and analyzes data. Data collection includes sensor data collection and web scraping. Specifically, the automation unit uses sensors and APIs to automatically collect data related to the business process of a company. For example, in the manufacturing industry, sensor data can be collected from machines in a factory to detect operating conditions and anomalies. Data analysis includes the application of statistical analysis and machine learning algorithms. For example, the automation unit can use the collected sensor data to apply machine learning algorithms to detect anomalies and perform predictive maintenance. This allows companies to prevent equipment failures and achieve efficient operation. In addition, the automation unit uses the generated dialogue model to automate customer support. For example, in customer support, the generated dialogue model can be used to automatically respond to inquiries from customers. This allows companies to improve the efficiency of customer support and increase customer satisfaction. In addition, the automation unit visualizes data and generates reports to support company decision-making. For example, the collected data is converted into graphs and charts to provide management with visually easy-to-understand information. This will enable the Automation Department to streamline the company's business processes and support data-driven decision-making.

[0067] The customization unit provides customized solutions according to the company's requirements based on the information automated by the automation unit. For example, the customization unit provides a means of communicating the company's requirements to the generation AI. The company's requirements include business processes, laws and regulations, and customer needs. Specifically, the customization unit analyzes the company's business processes in detail and builds a feedback loop to provide the generation AI with the optimal customized solution. For example, in a manufacturing company, a customized solution for improving the efficiency of the production line and quality control can be provided. The customization unit provides the generation AI with information on the company's specific business processes and laws and regulations, and adjusts the generated dialogue model and automation solution to meet the company's requirements. In addition, the customization unit can customize the response content and behavior of the generation AI based on customer needs. For example, the generation AI is adjusted to provide more polite responses and specialized knowledge to a specific customer group. This allows the customization unit to provide solutions optimized to the company's needs and strengthen the company's competitiveness. Furthermore, the customization unit collects feedback from the company and continuously improves the performance and response content of the generation AI. This allows the customization unit to always provide optimal solutions based on the latest information and technology, and support the growth and development of the company.

[0068] The security unit can perform data encryption or access control. For example, encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used for data encryption. AES is a symmetric key encryption method that provides fast and secure encryption. RSA is a public key encryption method that uses different keys for data encryption and decryption. For example, access control includes user authentication and authority management. Password authentication and two-factor authentication are used for user authentication. Authority management includes setting different access authority for each user. For example, the security unit performs user authentication so that only authenticated users can access the dialogue model. The security unit can also record access logs and detect unauthorized access. This makes it possible to protect the security and privacy of a company by encrypting data and controlling access. A part or all of the above-mentioned processing in the security unit may be performed using AI, for example, or may be performed without using AI. For example, the security unit can analyze access logs using AI to detect unauthorized access.

[0069] The automation unit can collect and analyze data. Data collection includes, for example, collection of sensor data and web scraping. Temperature sensors, humidity sensors, acceleration sensors, etc. are used to collect sensor data. Web scraping uses a technique for automatically collecting data from a specific website. Data analysis includes, for example, application of statistical analysis and machine learning algorithms. Statistical analysis includes average value, standard deviation, regression analysis, etc. Machine learning algorithms include classification, regression, clustering, etc. For example, the automation unit can collect sensor data and analyze the data using machine learning algorithms. As a result, by collecting and analyzing data, it is possible to realize information automation and enhanced competitiveness of a company. Some or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can analyze sensor data using AI and perform anomaly detection.

[0070] The customization unit can provide a means for communicating the requirements of a company to the generation AI. The requirements of a company include, for example, business processes, legal regulations, and customer needs. The business processes include specific business procedures and flows. The legal regulations include industry-specific regulations and compliance requirements. The customer needs include customer requests and expectations. For example, the customization unit can communicate the business processes of a company to the generation AI and provide an optimal customized solution. The customization unit can also communicate the legal regulations of a company to the generation AI and provide a solution that complies with compliance. Furthermore, the customization unit can communicate the customer needs of a company to the generation AI and provide a solution that improves customer satisfaction. In this way, by providing a means for communicating the requirements of a company to the generation AI, an optimal system tailored to the needs of the company can be constructed. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can analyze the requirements of a company using AI and propose an optimal customized solution.

[0071] The generation unit can generate a chatbot that automatically responds to inquiries from customers. The chatbot includes, for example, natural language processing capabilities, dialogue scenarios, and response speed. The natural language processing capabilities include understanding and generation of text. The dialogue scenarios include response patterns to specific questions. The response speed includes the speed of response to inquiries from users. For example, the generation unit can generate a chatbot that automatically responds to inquiries from customers using a generation AI. The generation AI can generate responses of the chatbot using a text generation AI (for example, LLM). The generation unit can also generate a chatbot based on a specific dialogue scenario using the generation AI. For example, the generation AI learns past dialogue data to generate optimal responses to frequently asked questions from customers. This makes it possible to generate a chatbot that automatically responds to inquiries from customers, thereby improving the efficiency of customer support.

[0072] The automation department can generate a system that automates the internal business flow. Examples of business flow automation include the introduction of a workflow management system and the application of RPA (Robotic Process Automation). The workflow management system includes the automation of business procedures and task assignment. RPA includes the automation of routine tasks and the automation of data entry. For example, the automation department can generate a system that automates the internal business flow using a generation AI. The generation AI can generate an optimal business flow based on a business procedure manual or a process map. The automation department can also use the generation AI to generate an automation system based on a specific business flow. For example, the generation AI can learn a company's business procedure manual and propose an efficient business flow. This can generate a system that automates the internal business flow, thereby realizing the efficiency of internal processes.

[0073] The generation unit can estimate the user's emotions and adjust the response content of the dialogue model based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, a generation AI. Technologies such as facial expression recognition, voice analysis, and text analysis are used for emotion estimation. For example, the generation AI takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The generation AI can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the generation AI can analyze the user's text message and estimate the emotion using a text analysis technology. Based on the estimated user's emotions, the generation unit adjusts the response content of the dialogue model. For example, if the user is dissatisfied, the generation AI generates a polite response that includes an apology through the dialogue model. Also, if the user is satisfied, the generation AI can generate a response that shows gratitude through the dialogue model. Furthermore, if the user is confused, the generation AI can generate a response that includes a detailed explanation through the dialogue model. This makes it possible to improve the user experience by adjusting the response content of the dialogue model based on the user's emotions. The emotion estimation is realized using, for example, an emotion engine or a generation AI, or the like, using an emotion estimation function. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0074] At the time of generation, the generation unit can generate an appropriate dialogue model by referring to the company's past dialogue history. The generation unit, for example, uses the generation AI to refer to the company's past dialogue history. Referencing the dialogue history includes database search, pattern matching, and application of machine learning models. For example, the generation AI analyzes past customer support dialogue history and generates an optimal response to a frequently asked question. The generation AI can also refer to past dialogue history of internal processes to generate a dialogue model that suggests an efficient business flow. Furthermore, the generation AI can analyze past dialogue history of complaint responses and generate a dialogue model including an appropriate response method. In this way, an optimal dialogue model can be generated by referring to the company's past dialogue history.

[0075] The generation unit can customize the dialogue model based on a specific business flow of the company at the time of generation. The generation unit, for example, refers to a specific business flow of the company using the generation AI. The reference to the business flow includes a business procedure manual, a process map, and a business requirement definition. For example, the generation AI can refer to a customer support flow of the company and generate a dialogue model that follows a specific procedure. The generation AI can also generate a dialogue model specialized for sales promotion based on the sales process of the company. Furthermore, the generation AI can refer to the human resources management flow of the company and generate a dialogue model suitable for a job interview. In this way, a dialogue model tailored to the needs of the company can be provided by customizing the dialogue model based on the specific business flow of the company.

[0076] The generation unit can estimate the user's emotions and adjust the response speed of the dialogue model based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, a generation AI. For emotion estimation, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the generation AI takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The generation AI can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the generation AI can analyze the user's text message and estimate the emotion using a text analysis technology. Based on the estimated user's emotions, the generation unit adjusts the response speed of the dialogue model. For example, if the user is in a hurry, the generation AI can generate a quick response through the dialogue model. Also, if the user is relaxed, the generation AI can generate a response including a detailed explanation through the dialogue model. Furthermore, if the user is feeling anxious, the generation AI can generate a response that gives a sense of security through the dialogue model. This can improve the user experience by adjusting the response speed of the dialogue model based on the user's emotions. The emotion estimation is realized using, for example, an emotion engine or a generation AI, or the like, using an emotion estimation function. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] The generation unit can generate a dialogue model based on the geographical characteristics of the company at the time of generation. The generation unit, for example, uses a generation AI to consider the geographical characteristics of the company. Consideration of geographical characteristics includes regional culture, language, and legal regulations. For example, the generation AI generates a dialogue model that reflects the language and culture specific to the region based on the location of the company. The generation AI can also generate a dialogue model that supports multiple languages ​​based on the international bases of the company. Furthermore, the generation AI can also generate a dialogue model that takes into account the regional characteristics of the company and meets the needs of customers in the region. As a result, a dialogue model that meets the needs specific to the region can be provided by taking into account the geographical characteristics of the company.

[0078] At the time of generation, the generation unit can analyze the company's social media activities and generate a related dialogue model. The generation unit, for example, uses the generation AI to analyze the company's social media activities. The analysis of social media activities includes analysis of posted content, engagement measurement, and trend analysis. For example, the generation AI analyzes customer feedback on the company's social media and reflects it in the dialogue model. The generation AI can also generate a dialogue model specialized for promotions based on the content of the company's social media campaign. Furthermore, the generation AI can analyze trends on the company's social media and generate a dialogue model including the latest information. In this way, a related dialogue model can be provided by analyzing the company's social media activities.

[0079] The security unit can estimate the user's emotions and adjust the security level based on the estimated user's emotions. The security unit estimates the user's emotions using, for example, AI. For emotion estimation, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the AI ​​captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The AI ​​can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the AI ​​can analyze the user's text message and estimate the emotion using text analysis technology. Based on the estimated user's emotion, the security unit adjusts the security level. For example, if the user feels anxious, the security unit applies a high security level. Also, if the user is relaxed, the security unit can apply a standard security level. Furthermore, if the user is in a hurry, the security unit can adjust the security level to allow quick access. This allows the user experience to be improved by adjusting the security level based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] The security department can analyze the company's past security incidents and provide appropriate security measures. The security department, for example, uses AI to analyze the company's past security incidents. The analysis of security incidents includes log analysis, review of incident reports, and utilization of threat intelligence. For example, AI can analyze past data leakage incidents and provide measures to prevent similar incidents. AI can also analyze the history of past unauthorized access and strengthen access control. Furthermore, AI can analyze the history of past virus infections and optimize the settings of antivirus software. In this way, optimal security measures can be provided by analyzing the company's past security incidents. Part or all of the above-mentioned processing in the security department may be performed, for example, using AI, or may be performed without using AI. For example, the security department can analyze incident reports using AI and utilize threat intelligence to propose security measures.

[0081] The security department can customize security measures based on the specific business flow of the company. The security department, for example, refers to the specific business flow of the company using AI. The reference to the business flow includes the application of business procedures, risk assessments, and security policies. For example, the AI ​​can refer to the customer data management flow of the company and strengthen data encryption. The AI ​​can also encrypt communications based on the internal communication flow of the company. Furthermore, the AI ​​can also refer to the payment processing flow of the company and strengthen fraud prevention measures. This makes it possible to provide security measures tailored to the needs of the company by customizing security measures based on the specific business flow of the company. Some or all of the above-mentioned processing in the security department may be performed using AI, for example, or may be performed without using AI. For example, the security department can analyze the business flow using AI and propose optimal security measures.

[0082] The security unit can estimate the user's emotions and determine the priority of access control based on the estimated user's emotions. The security unit estimates the user's emotions using, for example, AI. For emotion estimation, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the AI ​​takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The AI ​​can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the AI ​​can analyze the user's text message and estimate the emotion using text analysis technology. Based on the estimated user's emotions, the security unit determines the priority of access control. For example, if the user feels anxious, the security unit preferentially restricts access to important data. Also, if the user is relaxed, the security unit can apply normal access control. Furthermore, if the user is in a hurry, the security unit can adjust the priority to allow quick access. This makes it possible to improve the user experience by determining the priority of access control based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The security department can provide security measures based on the geographical characteristics of the company. The security department, for example, uses AI to consider the geographical characteristics of the company. Consideration of geographical characteristics includes local laws and regulations, cultural background, and infrastructure conditions. For example, the AI ​​provides measures to address security risks specific to the region based on the location of the company. The AI ​​can also provide multinational security measures based on the international bases of the company. Furthermore, the AI ​​can also provide measures according to the security needs of the region by considering the regional characteristics of the company. As a result, measures to address security risks specific to the region can be provided by considering the geographical characteristics of the company. Some or all of the above-mentioned processing in the security department may be performed, for example, using AI, or may be performed without using AI. For example, the security department can analyze regional security risks using AI and propose optimal security measures.

[0084] The security department can analyze the company's social media activities and provide relevant security measures. The security department, for example, uses AI to analyze the company's social media activities. The analysis of social media activities includes analysis of posted content, engagement measurement, and trend analysis. For example, AI can analyze customer feedback on the company's social media and identify security risks. In addition, AI can also strengthen security measures based on the content of the company's social media campaigns. Furthermore, AI can also analyze trends on the company's social media and provide measures to address the latest security risks. In this way, by analyzing the company's social media activities, relevant security measures can be provided. Part or all of the above-mentioned processing in the security department may be performed, for example, using AI, or may be performed without using AI. For example, the security department can use AI to analyze social media posted content and identify security risks.

[0085] The automation unit can estimate the user's emotions and prioritize the automation process based on the estimated user's emotions. The automation unit estimates the user's emotions using, for example, a generation AI. For emotion estimation, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the generation AI takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The generation AI can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the generation AI can analyze the user's text message and estimate the emotion using a text analysis technology. Based on the estimated user's emotions, the automation unit determines the priority of the automation process. For example, if the user is dissatisfied, the automation unit prioritizes the automation process of customer support. Also, if the user is satisfied, the automation unit can prioritize the automation of internal processes. Furthermore, if the user is confused, the automation unit can prioritize the automation of processes that include explanations. This makes it possible to improve the user experience by determining the priority of the automation process based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The automation unit can adjust the automation process based on the specific business flow of the company. The automation unit, for example, refers to the specific business flow of the company using a generation AI. The reference to the business flow includes a business procedure manual, a process map, and a business requirement definition. For example, the generation AI refers to the customer support flow of the company and provides an automation process following a specific procedure. The generation AI can also provide an automation process specialized for sales promotion based on the sales process of the company. Furthermore, the generation AI can also provide an automation process suitable for a job interview by referring to the human resources management flow of the company. In this way, the automation process can be customized based on the specific business flow of the company to provide an automation process tailored to the needs of the company. Some or all of the above-mentioned processing in the automation unit may be performed using, for example, AI, or may be performed without using AI. For example, the automation unit can analyze the business flow using AI and propose an optimal automation process.

[0087] The automation unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. The automation unit estimates the user's emotions using, for example, a generation AI. For emotion estimation, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the generation AI takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The generation AI can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation AI can analyze the user's text message and estimate the emotion using text analysis technology. Based on the estimated user's emotions, the automation unit adjusts the frequency of data collection. For example, if the user feels anxious, the automation unit increases the frequency of data collection. Also, if the user is relaxed, the automation unit can return the frequency of data collection to normal. Furthermore, if the user is in a hurry, the automation unit can reduce the frequency of data collection and prioritize quick processing. This makes it possible to improve the user experience by adjusting the frequency of data collection based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The automation unit can collect data based on the geographical characteristics of the company. The automation unit, for example, uses a generation AI to consider the geographical characteristics of the company. Consideration of geographical characteristics includes regional infrastructure conditions, climatic conditions, and legal regulations. For example, the generation AI collects region-specific data based on the location of the company. The generation AI can also collect multinational data based on the international bases of the company. Furthermore, the generation AI can also consider the regional characteristics of the company and collect data according to the needs of the region. In this way, region-specific data can be collected by considering the geographical characteristics of the company. Some or all of the above-mentioned processing in the automation unit may be performed, for example, using AI, or may be performed without using AI. For example, the automation unit can analyze regional data using AI and propose an optimal data collection method.

[0089] The automation unit can analyze the company's social media activities and collect related data. The automation unit can analyze the company's social media activities using, for example, a generation AI. The analysis of social media activities includes analysis of posted content, engagement measurement, and trend analysis. For example, the generation AI can analyze customer feedback on the company's social media and collect related data. The generation AI can also collect data related to promotions based on the content of the company's social media campaign. Furthermore, the generation AI can also analyze trends on the company's social media and collect data including the latest information. In this way, the company's social media activities can be analyzed to collect related data. Some or all of the above-mentioned processing in the automation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the automation unit can analyze the content of social media posts using an AI and collect related data.

[0090] The customization unit can estimate the user's emotions and adjust the method of providing the customized solution based on the estimated user's emotions. The customization unit, for example, uses AI to estimate the user's emotions. Technologies such as facial expression recognition, voice analysis, and text analysis are used for emotion estimation. For example, the AI ​​takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The AI ​​can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the AI ​​can analyze the user's text message and estimate the emotion using text analysis technology. Based on the estimated user's emotions, the customization unit adjusts the method of providing the customized solution. For example, if the user is dissatisfied, the customization unit provides prompt and courteous support. Also, if the user is satisfied, the customization unit can provide detailed customization options. Furthermore, if the user is confused, the customization unit can provide support including easy-to-understand explanations. This makes it possible to improve the user experience by adjusting the method of providing the customized solution based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The customization unit can analyze the company's past requirement history and provide an appropriate customized solution. The customization unit can, for example, use AI to analyze the company's past requirement history. The analysis of the requirement history includes analysis of past project data, feedback evaluation, and trend analysis. For example, the AI ​​can analyze the past requirement history and provide an optimal solution for common requirements. The AI ​​can also provide a customized solution suitable for a specific business flow based on the past requirement history. Furthermore, the AI ​​can also refer to the past requirement history and provide a customized solution according to the needs of the company. In this way, the optimal customized solution can be provided by analyzing the company's past requirement history. A part or all of the above-mentioned processing in the customization unit can be performed, for example, using AI, or can be performed without using AI. For example, the customization unit can analyze the requirement history using AI and propose an optimal customized solution.

[0092] The customization unit can provide a customized solution based on a specific business flow of a company. The customization unit, for example, refers to a specific business flow of a company using AI. The reference to the business flow includes a business procedure manual, a process map, and a business requirement definition. For example, the AI ​​refers to a customer support flow of a company and provides a customized solution according to a specific procedure. The AI ​​can also provide a customized solution specialized for sales promotion based on the sales process of the company. Furthermore, the AI ​​can also provide a customized solution suitable for a job interview by referring to a human resources management flow of a company. In this way, a customized solution tailored to the needs of a company can be provided by providing a customized solution based on a specific business flow of a company. A part or all of the above-mentioned processing in the customization unit may be performed using AI, for example, or may be performed without using AI. For example, the customization unit can analyze a business flow using AI and propose an optimal customized solution.

[0093] The customization unit can estimate the user's emotions and determine the priority of customized solutions based on the estimated user's emotions. The customization unit, for example, uses AI to estimate the user's emotions. For emotion estimation, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the AI ​​takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The AI ​​can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the AI ​​can analyze the user's text message and estimate the emotion using text analysis technology. Based on the estimated user's emotions, the customization unit determines the priority of customized solutions. For example, if the user is dissatisfied, the customization unit can preferentially provide solutions for important requirements. Also, if the user is satisfied, the customization unit can provide detailed customization options. Furthermore, if the user is confused, the customization unit can preferentially provide solutions including easy-to-understand explanations. This makes it possible to improve the user experience by determining the priority of customized solutions based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The customization unit can provide a customization solution based on the geographical characteristics of the company. The customization unit, for example, uses AI to consider the geographical characteristics of the company. Consideration of geographical characteristics includes local culture, language, and legal regulations. For example, the AI ​​provides a customization solution according to local needs based on the location of the company. The AI ​​can also provide a multinational customization solution based on the international bases of the company. Furthermore, the AI ​​can also provide a customization solution according to local needs by considering the local characteristics of the company. As a result, a customization solution according to local needs can be provided by considering the geographical characteristics of the company. A part or all of the above-mentioned processing in the customization unit may be performed, for example, using AI, or may be performed without using AI. For example, the customization unit can analyze local needs using AI and propose an optimal customization solution.

[0095] The customization unit can analyze the social media activities of the company and provide a related customization solution. The customization unit can analyze the social media activities of the company using, for example, AI. The analysis of social media activities includes analysis of posted content, measurement of engagement, and trend analysis. For example, the AI ​​can analyze customer feedback on the company's social media and provide a related customization solution. The AI ​​can also provide a customization solution related to a promotion based on the content of the company's social media campaign. Furthermore, the AI ​​can analyze trends on the company's social media and provide a customization solution including the latest information. In this way, the company's social media activities can be analyzed to provide a related customization solution. A part or all of the above-mentioned processing in the customization unit can be performed using, for example, AI, or can be performed without using AI. For example, the customization unit can analyze the content of social media posts using AI and propose a related customization solution.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The generation unit can analyze the user's past behavior history and personalize the response content of the dialogue model. For example, the generation unit can analyze what questions the user has asked in the past and what information the user has sought, and generate a more appropriate response to a similar question. The generation unit can also refer to the user's past purchase history and suggest related products and services. Furthermore, the generation unit can take into account the user's past feedback and generate a response that matches the user's preferences. This can improve the user experience by personalizing the response content of the dialogue model based on the user's past behavior history.

[0098] The security department can detect and respond to threats in real time. For example, the security department can monitor network traffic and detect abnormal activity. The security department can also perform virus scans in real time to prevent malware from entering. Furthermore, the security department can learn user behavior patterns and detect signs of unauthorized access at an early stage. This makes it possible to strengthen the security of a company by detecting and responding to threats in real time.

[0099] The automation unit can automate data preprocessing. For example, the automation unit cleans the data and corrects missing values ​​and outliers. The automation unit can also normalize and standardize the data and convert it into a format suitable for analysis. Furthermore, the automation unit can automatically perform feature engineering of the data to improve the accuracy of the machine learning model. As a result, the efficiency of data analysis can be improved by automating data preprocessing.

[0100] The customization department can analyze the trends of the company's competitors and provide customization solutions to strengthen the company's competitiveness. For example, the customization department can analyze the features of the competitors' products and services and find points of differentiation from the company's products and services. The customization department can also analyze the competitors' marketing strategies and optimize the company's marketing strategies. Furthermore, the customization department can analyze the competitors' customer feedback and propose measures to improve the company's customer satisfaction. In this way, the company's competitiveness can be strengthened by analyzing the trends of competitors.

[0101] The generation unit can estimate the user's emotion and adjust the tone of the dialogue model based on the estimated user's emotion. For example, when the user is angry, the generation unit can cause the dialogue model to respond in a calm and calm tone. When the user is sad, the generation unit can also cause the dialogue model to respond in an empathetic and gentle tone. Furthermore, when the user is happy, the dialogue model can respond in a bright and cheerful tone. In this way, the user experience can be improved by adjusting the tone of the dialogue model based on the user's emotion.

[0102] The automation unit can estimate the user's emotions and adjust the frequency of notifications based on the estimated user's emotions. For example, if the user is feeling stressed, the automation unit can reduce the frequency of notifications. Also, if the user is relaxed, the automation unit can return the frequency of notifications to normal. Furthermore, if the user is in a hurry, the automation unit can prioritize sending only important notifications. This makes it possible to improve the user experience by adjusting the frequency of notifications based on the user's emotions.

[0103] The customization unit can estimate the user's emotions and adjust the content of the customization solution based on the estimated user's emotions. For example, if the user is dissatisfied, the customization unit can provide a quick and specific solution. Also, if the user is satisfied, the customization unit can suggest additional options or services. Furthermore, if the user is confused, the customization unit can provide support including easy-to-understand explanations. In this way, the user experience can be improved by adjusting the content of the customization solution based on the user's emotions.

[0104] The generation unit can estimate the user's emotion and adjust the length of the response of the dialogue model based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit can generate a short and concise response. If the user is relaxed, the generation unit can also generate a response including a detailed explanation. Furthermore, if the user is confused, the generation unit can also generate a response including an easy-to-understand explanation. This makes it possible to improve the user experience by adjusting the length of the response of the dialogue model based on the user's emotion.

[0105] The security unit can estimate the user's emotions and determine the priority of security alerts based on the estimated user's emotions. For example, if the user feels anxious, the security unit will give priority to notifying the user of important security alerts. In addition, if the user is relaxed, the security unit can also notify the user of normal security alerts. Furthermore, if the user is in a hurry, the security unit can also notify only emergency security alerts. In this way, the user experience can be improved by determining the priority of security alerts based on the user's emotions.

[0106] The generation unit can generate a dialogue model based on a specific project of the company. For example, the generation unit generates a dialogue model that provides information about a product based on a new product development project of the company. The generation unit can also generate a dialogue model that provides information about a campaign based on a marketing campaign of the company. Furthermore, the generation unit can generate a dialogue model that provides information about support based on a customer support project of the company. In this way, by generating a dialogue model based on a specific project of the company, it is possible to provide a dialogue model tailored to the needs of the company.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The generation unit generates a dialogue model specialized for the company's domain. The generation unit uses a generative AI to generate a dialogue model specialized for a specific industry or business of the company. The generative AI can generate a dialogue model using a text generation AI (e.g., LLM). The generation unit can also use a generative AI to generate a dialogue model specialized for the company's customer base. For example, the generative AI learns from past dialogue data to generate optimal responses for a specific customer base. Step 2: The security unit protects the dialogue model generated by the generation unit. The security unit encrypts data and performs access control. Encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used to encrypt data. Access control includes user authentication and authority management. For example, the security unit performs user authentication to ensure that only authenticated users can access the dialogue model. Step 3: The automation unit automates the information using the dialogue model generated by the generation unit. The automation unit collects and analyzes data. Data collection includes collecting sensor data and web scraping. Data analysis includes applying statistical analysis and machine learning algorithms. For example, the automation unit collects sensor data and analyzes the data using machine learning algorithms. Step 4: The customization department provides customized solutions that meet the company's requirements based on the information automated by the automation department. The customization department provides a means to communicate the company's requirements to the generation AI. The company's requirements include business processes, legal regulations, and customer needs. For example, the customization department communicates the company's business processes to the generation AI and provides the optimal customized solution.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the above-mentioned generation unit, security unit, automation unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The security unit is realized by the specific processing unit 290 of the data processing device 12, and performs data encryption and access control. The automation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and performs data collection and analysis. The customization unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides a customized solution according to the company requirements. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the above-mentioned generation unit, security unit, automation unit, and customization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The security unit is realized by the specific processing unit 290 of the data processing device 12 and performs data encryption and access control. The automation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and performs data collection and analysis. The customization unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides a customized solution according to the company requirements. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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, the RAM 30, and the 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the above-mentioned generation unit, security unit, automation unit, and customization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The security unit is realized by the specific processing unit 290 of the data processing device 12, and performs data encryption and access control. The automation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and performs data collection and analysis. The customization unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides a customized solution according to the company requirements. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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, the RAM 30, and the 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0161] Each of the multiple elements including the above-mentioned generation unit, security unit, automation unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The security unit is realized by the specific processing unit 290 of the data processing device 12, and performs data encryption and access control. The automation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and performs data collection and analysis. The customization unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides a customized solution according to the company requirements. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A generation unit that generates a dialogue model specific to a company's domain; a security unit for protecting the dialogue model generated by the generation unit; an automation unit that automates information by using the dialogue model generated by the generation unit; A customization unit that provides a customized solution according to a company's requirements based on the information automated by the automation unit. A system characterized by: (Appendix 2) The security unit includes: Encrypting data or controlling access 2. The system of claim 1, (Appendix 3) The automation unit includes: Collect and analyze data 2. The system of claim 1, (Appendix 4) The customization unit includes: Providing a means for companies to communicate their requirements to generative AI 2. The system of claim 1, (Appendix 5) The generation unit is Create a chatbot that automatically responds to customer inquiries 2. The system of claim 1, (Appendix 6) The automation unit includes: Create a system to automate internal business processes 2. The system of claim 1, (Appendix 7) The generation unit is A method for predicting a user's emotions and adjusting the response content of a dialogue model based on the predicted user's emotions 2. The system of claim 1, (Appendix 8) The generation unit is When generating the model, the company's past interaction history is referenced to generate an appropriate interaction model. 2. The system of claim 1, (Appendix 9) The generation unit is Customize interaction models at generation time based on your company's specific business flows 2. The system of claim 1, (Appendix 10) The generation unit is Inferring user emotions and adjusting the response speed of a dialogue model based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The generation unit is At generation time, generate interaction models based on the geographical characteristics of the enterprise 2. The system of claim 1, (Appendix 12) The generation unit is At generation time, the company's social media activity is analyzed and a relevant interaction model is generated. 2. The system of claim 1, (Appendix 13) The security unit includes: Estimate user emotions and adjust security levels based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The security unit includes: Analyze a company's past security incidents and provide appropriate security measures 2. The system of claim 1, (Appendix 15) The security unit includes: Customize security measures based on your company's specific business processes 2. The system of claim 1, (Appendix 16) The security unit includes: To estimate a user's emotion and determine the priority of access control based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The security unit includes: Providing security measures based on the geographic location of the enterprise 2. The system of claim 1, (Appendix 18) The security unit includes: Analyze a company's social media activities and provide relevant security measures 2. The system of claim 1, (Appendix 19) The automation unit includes: Infer user sentiment and prioritize automation processes based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The automation unit includes: Tailor the automation process based on your company's specific business workflow 2. The system of claim 1, (Appendix 21) The automation unit includes: Inferring user sentiment and adjusting the frequency of data collection based on the inferred user sentiment 2. The system of claim 1, (Appendix 22) The automation unit includes: Data collection based on the geographical characteristics of the company 2. The system of claim 1, (Appendix 23) The automation unit includes: Analyze the social media activities of companies and collect relevant data 2. The system of claim 1, (Appendix 24) The customization unit includes: Inferring a user's emotions and adjusting how a customized solution is provided based on the inferred user's emotions 2. The system of claim 1, (Appendix 25) The customization unit includes: Analyzing the company's past requirement history and providing appropriate customized solutions 2. The system of claim 1, (Appendix 26) The customization unit includes: Provide customized solutions based on your company's specific business processes 2. The system of claim 1, (Appendix 27) The customization unit includes: Inferring user sentiment and prioritizing customized solutions based on the inferred user sentiment 2. The system of claim 1, (Appendix 28) The customization unit includes: Offering customized solutions based on the geographical characteristics of companies 2. The system of claim 1, (Appendix 29) The customization unit includes: Analyze your company's social media activities and provide relevant customized solutions 2. The system of claim 1, [Explanation of symbols]

[0181] 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 generation unit that generates a dialogue model specific to a company's domain; a security unit for protecting the dialogue model generated by the generation unit; an automation unit that automates information by using the dialogue model generated by the generation unit; A customization unit that provides a customized solution according to a company's requirements based on the information automated by the automation unit. A system characterized by:

2. The security unit includes: Encrypting data or controlling access 2. The system of claim 1 .

3. The automation unit includes: Collect and analyze data 2. The system of claim 1 .

4. The customization unit includes: Providing a means to communicate enterprise requirements to generative AI 2. The system of claim 1 .

5. The generation unit is Create a chatbot that automatically responds to customer inquiries 2. The system of claim 1 .

6. The automation unit includes: Create a system to automate internal business processes 2. The system of claim 1 .

7. The generation unit is The user's emotions are estimated, and the response content of the dialogue model is adjusted based on the estimated user's emotions.

2. The system of claim 1 .

8. The generation unit is When generating the model, the company's past interaction history is referenced to generate an appropriate interaction model.

2. The system of claim 1 .

9. The generation unit is Customize interaction models at generation time based on your company's specific business flows 2. The system of claim 1 .

10. The generation unit is Inferring user emotions and adjusting the response speed of a dialogue model based on the estimated user emotions 2. The system of claim 1 .

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