system

Abstract

The system according to this embodiment aims to efficiently collect, integrate, and analyze a wide variety of data to support business decision-making. [Solution] The system according to the embodiment comprises a collection unit, an integration unit, an analysis unit, and a support unit. The collection unit collects data. The integration unit integrates the data collected by the collection unit. The analysis unit analyzes the data integrated by the integration unit. The support unit supports decision-making based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently collect, integrate, and analyze various types of data and support business decision-making.

[0005] The system according to the embodiment aims to efficiently collect, integrate, and analyze various types of data and support business decision-making.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an integration unit, an analysis unit, and a support unit. The data collection unit collects data. The integration unit integrates the data collected by the data collection unit. The analysis unit analyzes the data integrated by the integration unit. The support unit supports decision-making based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect, integrate, and analyze a wide variety of data to support business decision-making. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The next-generation AI agent system according to an embodiment of the present invention is a system that autonomously collects, integrates, and analyzes a wide variety of data from within and outside a company, comprehensively supporting business decision-making. This system utilizes a multimodal, large-scale language model that analyzes all data sources, including text, images, audio, and sensor data. Equipped with advanced security features and compliance measures, it securely integrates and analyzes data while adhering to each company's security policy. This eliminates concerns about data governance and security, allowing for the confident promotion of data utilization. Furthermore, it has a function that explains data insights in an easy-to-understand manner, tailored to the data literacy level of management and employees, supporting data literacy education. Through natural language dialogue, everyone can intuitively understand the value of data and promote data-driven decision-making. It also has a function that promotes interdepartmental collaboration, integrating and visualizing information from each department as a data sharing hub. This resolves communication gaps between organizations and supports joint projects and company-wide data utilization. Distinguishing itself from conventional BI tools and analytical tools, the AI ​​autonomously discovers and proposes problems and even simulates solutions. It also flexibly responds to the addition of new data sources, expanding the scope of data utilization. For example, next-generation AI agent systems collect internal business data and external market data. Next, they integrate the collected data. Equipped with advanced security features and compliance measures, they securely integrate data while adhering to each company's security policies, thereby addressing data governance and security concerns. They then analyze the integrated data, utilizing multimodal, large-scale language models to analyze all data sources, including text, images, audio, and sensor data. This maximizes the value of the data. Based on the analysis results, they support decision-making. They have features that explain data insights in an easy-to-understand way, tailored to the data literacy levels of management and employees, supporting data literacy education. Through natural language dialogue, everyone can intuitively understand the value of data and promote data-driven decision-making.Furthermore, it features functions that promote interdepartmental collaboration, integrating and visualizing information from each department as a data sharing hub. This resolves communication gaps between organizations and supports joint projects and company-wide data utilization. Distinguishing itself from conventional BI and analytical tools, the AI ​​autonomously identifies and proposes problems and even simulates solutions. It also flexibly responds to the addition of new data sources, expanding the scope of data utilization. As a result, the next-generation AI agent system can autonomously collect, integrate, and analyze a wide variety of data from both inside and outside the company, comprehensively supporting business decision-making.

[0029] The next-generation AI agent system according to this embodiment comprises a collection unit, an integration unit, an analysis unit, and a support unit. The collection unit collects data. The collection unit can collect, for example, internal business data or external market data. The collection unit targets any data source, such as text data, image data, voice data, and sensor data. The collection unit can collect, for example, internal business data and external market data. The collection unit can also collect, for example, sensor data and acquire data in real time. The integration unit integrates the data collected by the collection unit. The integration unit can securely integrate data while adhering to each company's security policy, for example, by providing advanced security functions and compliance support. The integration unit can perform, for example, data format conversion to unify data in different formats. The integration unit can integrate data from multiple data sources using, for example, a data merging method. The integration unit can detect and automatically correct data duplication and inconsistencies to maintain data consistency. The analysis unit analyzes the data integrated by the integration unit. The analytics department can analyze any data source, such as text, images, audio, and sensor data, by leveraging multimodal, large-scale language models. For example, the analytics department can analyze text data and extract important information. It can analyze image data and understand its content. It can analyze audio data and understand its content. It can analyze sensor data and understand information from sensors. The support department assists decision-making based on the analysis results obtained by the analytics department. For example, the support department has the function of explaining data insights in an easy-to-understand way, tailored to the data literacy level of management and employees, and can support data literacy education. For example, the support department can enable everyone to intuitively understand the value of data and promote data-driven decision-making through natural language dialogue. For example, the support department has the function of promoting interdepartmental collaboration and can integrate and visualize information from each department as a data sharing hub.The support unit can, for example, enable AI to autonomously identify and propose problems and even simulate solutions. The support unit can also flexibly respond to the addition of new data sources, expanding the scope of data utilization. As a result, the next-generation AI agent system according to this embodiment can consistently perform data collection, integration, analysis, and decision support.

[0030] The data collection unit collects data. For example, the data collection unit can collect internal business data and external market data. Specifically, internal business data includes sales data, customer data, inventory data, and financial data. This data is automatically collected from various departments within the company and stored in a central database. External market data includes competitor activity, industry trends, economic indicators, and consumer behavior data. This data is obtained from publicly available data on the internet or from paid data vendors. The data collection unit targets all data sources, such as text data, image data, audio data, and sensor data. Text data is collected from emails, chat logs, reports, and news articles. Image data is collected from surveillance camera footage, product photos, and marketing materials. Audio data is collected from call center call recordings, meeting recordings, and voice memos. Sensor data is collected from IoT devices, environmental sensor data, and machine operation data. The data collection unit can also collect internal business data and external market data. This allows companies to integrate internal and external data for more comprehensive analysis. The data collection unit can, for example, collect sensor data and obtain data in real time. This enables companies to understand the situation in real time and make quick decisions. The data collection unit allows for flexible configuration of data collection frequency and methods, enabling data collection tailored to specific needs and situations. For example, when a significant event occurs, the data collection frequency can be increased to obtain more detailed information. The data collection unit also has functions to validate and clean data to ensure data quality. This guarantees that the collected data is accurate and reliable.

[0031] The Integration Department integrates the data collected by the Collection Department. The Integration Department, for example, provides advanced security features and compliance measures, enabling secure data integration while adhering to each company's security policies. Specifically, it provides security features such as data encryption, access control, and audit log management to protect data confidentiality and integrity. The Integration Department can, for example, perform data format conversion to unify data in different formats. This allows for consistent handling of data in different formats, such as text data, image data, audio data, and sensor data. The Integration Department can integrate data from multiple data sources using data merging methods. This enables centralized management of information from different data sources and comprehensive analysis. The Integration Department can, for example, detect and automatically correct data duplication and inconsistencies to maintain data consistency. This improves data quality and leads to more reliable analytical results. The Integration Department can automate the data integration process for efficient data integration. For example, by setting a schedule for regularly collecting and integrating data, manual intervention can be minimized. The Integration Department can monitor the data integration status in real time and respond immediately if problems occur. This allows the data integration process to proceed smoothly and improves the overall system performance.

[0032] The Analysis Department analyzes data integrated by the Integration Department. The Analysis Department can analyze any data source, including text, images, audio, and sensor data, by leveraging, for example, multimodal, large-scale language models. Specifically, it uses natural language processing techniques to analyze text data and extract important information. For example, it can analyze customer feedback and market trends to aid in corporate strategy. For image data analysis, it can use image recognition technology to perform product quality inspections and analyze surveillance camera footage. For audio data analysis, it can use speech recognition technology to analyze call center conversations and improve customer service. For sensor data analysis, it can use machine learning algorithms to detect machine anomalies and monitor environmental changes. The Analysis Department can, for example, analyze text data and extract important information. This allows companies to efficiently obtain useful information from large amounts of text data. The Analysis Department can, for example, analyze image data and understand its content. This automates product quality inspections and surveillance camera footage analysis, improving efficiency. The Analysis Department can, for example, analyze audio data and understand its content. This allows for the analysis of call center conversations and the use of that analysis to improve customer service. The analytics department can, for example, analyze sensor data and understand the information it provides. This enables the detection of machine anomalies and the monitoring of environmental changes. The analytics department can utilize AI to detect data patterns and trends and perform predictive analytics. This allows companies to anticipate future risks and opportunities and take appropriate measures. The analytics department provides data visualization capabilities, displaying analysis results clearly in graphs and charts. This allows management and employees to intuitively understand data insights and use them to inform decision-making.

[0033] The support department assists decision-making based on the analysis results obtained by the analysis department. For example, the support department has the function of explaining data insights in an easy-to-understand manner, tailored to the data literacy level of management and employees, and can support data literacy education. Specifically, it uses data visualization tools to create graphs and charts, visually showing data trends and patterns. This allows even users unfamiliar with data to intuitively understand the meaning of the data. For example, the support department can facilitate data-driven decision-making by enabling everyone to intuitively understand the value of data through natural language dialogue. Specifically, it utilizes chatbots and voice assistants to provide data-based answers when users ask questions. This allows users to make data-driven decisions even without specialized knowledge. For example, the support department has the function of promoting interdepartmental collaboration and can integrate and visualize information from each department as a data sharing hub. Specifically, it utilizes project management tools and collaboration platforms so that each department can share data and conduct joint analysis and decision-making. This strengthens interdepartmental collaboration and improves the efficiency of the entire organization. The support department can, for example, use AI to autonomously identify and propose problems and even simulate solutions. Specifically, the AI ​​analyzes data, identifies potential problems and areas for improvement, simulates multiple solutions, and proposes the optimal solution. This allows companies to solve problems quickly and effectively. The support department can also flexibly respond to the addition of new data sources, expanding the scope of data utilization. Specifically, even when new data sources are added, they can be integrated into existing systems, allowing for the continuous process of data collection, integration, analysis, and support. This ensures that companies can always make decisions based on the latest information. The support department can collect user feedback and use it to improve the system. For example, based on user feedback, the system's functions and interface can be improved to enhance usability. This allows the support department to provide more valuable support to users and promote data-driven decision-making.

[0034] The data collection unit can handle any data source, including text, images, audio, and sensor data. For example, the data collection unit can collect text data to obtain internal business data. For example, the data collection unit can collect image data to obtain external market data. For example, the data collection unit can collect audio data to obtain customer feedback. For example, the data collection unit can collect sensor data to obtain data in real time. This allows data to be collected from a variety of data sources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, when collecting text data, the data collection unit can input prompts to a generating AI, which can then analyze and collect the text data.

[0035] The integration unit features advanced security functions and compliance capabilities, enabling secure data integration while adhering to each company's security policies. For example, the integration unit can perform data format conversion to unify data in different formats. It can also integrate data from multiple data sources using data merging methods. Furthermore, it can detect and automatically correct data duplication and inconsistencies to maintain data consistency, thereby addressing data governance and security concerns. Some or all of the above processes in the integration unit may be performed using AI, or not. For example, when performing data format conversion, the integration unit can prompt a generating AI, which can then analyze and unify the data format.

[0036] The analysis unit can analyze any data source, including text, images, audio, and sensor data, by utilizing a multimodal, large-scale language model. For example, the analysis unit can analyze text data and extract important information. For example, the analysis unit can analyze image data and understand the content of the images. For example, the analysis unit can analyze audio data and understand the content of the audio. For example, the analysis unit can analyze sensor data and understand the information from the sensors. This allows for maximizing the value of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, when analyzing text data, the analysis unit can input prompts to the generative AI, which can then analyze the text data and extract important information.

[0037] The support unit has the function of explaining data insights in an easy-to-understand manner, tailored to the data literacy level of management and employees, and can support data literacy education. For example, the support unit can visually display data insights so that management and employees can understand them intuitively. For example, the support unit can provide training programs for data literacy education to improve employees' data literacy. For example, the support unit can perform simulations based on data insights to help management and employees make data-driven decisions. This can support data literacy education. Some or all of the above processes in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, when generating data insights, the support unit can input prompts to the generative AI, which can then generate and visually display the data insights.

[0038] The support system enables everyone to intuitively understand the value of data and facilitate data-driven decision-making through natural language dialogue. For example, the support system can use a chatbot to allow users to input questions in natural language and receive data-driven answers. Alternatively, it can use a voice assistant to allow users to input questions by voice and receive data-driven answers. Furthermore, it can use natural language processing technology to analyze user questions and provide relevant data insights, thereby facilitating data-driven decision-making. Some or all of the above-described processes in the support system may be performed using, for example, generative AI, or without generative AI. For example, when engaging in natural language dialogue, the support system can input prompts to generative AI, which can then generate answers to user questions.

[0039] The support department has functions to promote inter-departmental collaboration and can integrate and visualize information from each department as a data sharing hub. For example, the support department can centrally manage data from each department and share information in real time. For example, the support department can use project management tools to efficiently advance joint projects between departments. For example, the support department can use data visualization tools to visually display data from each department and promote information sharing. This promotes inter-departmental collaboration. Some or all of the above processes in the support department may be performed using, for example, generative AI, or not using generative AI. For example, when visualizing data, the support department can input prompts to the generative AI, which can then analyze the data and display it visually.

[0040] The support unit can autonomously discover and propose problems and even simulate solutions using AI. For example, the support unit can automatically discover problems from data using machine learning algorithms. For example, the support unit can propose solutions to problems and simulate those solutions. For example, the support unit can select the optimal solution based on the simulation results. This allows the AI ​​to autonomously discover and propose problems and even simulate solutions. Some or all of the above processes in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, when discovering problems, the support unit can input prompts to the generative AI, which can then analyze the data and discover problems.

[0041] The data collection unit can flexibly respond to the addition of new data sources, thereby expanding the scope of data utilization. For example, the data collection unit can add a new sensor device and collect its data. For example, the data collection unit can add an external database and collect its data. For example, the data collection unit can support a new data format and collect that data. This expands the scope of data utilization. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when adding a new data source, the data collection unit can input a prompt to the generative AI, which can then analyze and collect the new data source.

[0042] The data collection unit can dynamically change the types of data it collects based on the user's work content and interests. For example, if the user belongs to the marketing department, the data collection unit can prioritize collecting marketing-related data. For example, if the user belongs to the technology department, the data collection unit can prioritize collecting technology-related data. For example, if the user's interests change, the data collection unit can dynamically change the types of data it collects according to those interests. This allows for the collection of more relevant data by changing the types of data collected according to the user's work content and interests. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, when changing the types of data to collect, the data collection unit can input a prompt to the generative AI, which can then analyze the user's work content and interests to change the types of data.

[0043] The data collection unit can evaluate the reliability of the data during data collection and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of the data source and prioritize the collection of data from highly reliable data sources. For example, the data collection unit can evaluate the consistency of the data and prioritize the collection of consistent data. For example, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. This improves data quality by prioritizing the collection of highly reliable data. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, when evaluating the reliability of the data, the data collection unit can input a prompt to the generative AI, which can then analyze and evaluate the reliability of the data source.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit can prioritize the collection of data related to their destination. For example, if the user is participating in a specific event, the data collection unit can prioritize the collection of data related to that event. This allows for the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, when considering the user's geographical location information, the data collection unit can input a prompt to the generative AI, which can then analyze the geographical location information and collect highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can collect relevant data based on information about accounts followed by the user on social media. For example, the data collection unit can collect relevant data based on information about groups the user participates in on social media. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, when analyzing a user's social media activity, the data collection unit can input prompts to the generative AI, which can then analyze the social media activity and collect relevant data.

[0046] The integration unit can add a function to detect and automatically correct data duplication and inconsistencies during data integration. For example, the integration unit can detect and automatically delete data duplication. For example, the integration unit can detect and automatically correct data inconsistencies. For example, the integration unit can automatically adjust inconsistent data to maintain data consistency. This ensures data consistency by automatically correcting data duplication and inconsistencies. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, when detecting data duplication or inconsistencies, the integration unit can prompt the generative AI, which can then analyze the data to detect and correct the duplication or inconsistencies.

[0047] The integration unit can add a function to automatically standardize the format and structure of data during data integration. For example, the integration unit can automatically unify data of different formats. For example, the integration unit can automatically standardize data of different structures. For example, the integration unit can improve the accuracy of integration by unifying the format and structure of data. This improves the accuracy of integration by standardizing the format and structure of data. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, when standardizing the format and structure of data, the integration unit can input prompts to the generative AI, which can then analyze and standardize the data.

[0048] The integration unit can perform data integration while considering the geographical distribution of the data. For example, the integration unit can perform integration on a regional basis, taking into account the geographical distribution of the data. For example, the integration unit can prioritize the integration of geographically related data. For example, the integration unit can adjust the data integration method based on the geographical distribution. This makes it possible to integrate data on a regional basis by considering the geographical distribution of the data. Some or all of the above-described processes in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when considering the geographical distribution of the data, the integration unit can input prompts to the generative AI, which can then analyze the geographical distribution and perform the integration.

[0049] The integration unit can improve the accuracy of data integration by referring to relevant literature during data integration. For example, the integration unit can improve the accuracy of data integration by referring to relevant literature. For example, the integration unit can adjust the data integration method based on relevant literature. For example, the integration unit can maintain data consistency by referring to relevant literature. This improves the accuracy of data integration by referring to relevant literature. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the integration unit refers to relevant literature for data, it can input a prompt to the generative AI, which can then analyze the relevant literature to improve the accuracy of integration.

[0050] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can prioritize the analysis of highly relevant data by considering the interrelationships between data. For example, the analysis unit can adjust its analysis method based on the interrelationships between data. For example, the analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. This improves the accuracy of the analysis by considering the interrelationships between data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, when considering the interrelationships between data, the analysis unit can input prompts to the generative AI, which can then analyze the interrelationships between data to improve the accuracy of the analysis.

[0051] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can adjust the analysis method based on the attribute information of the data submitter. For example, the analysis unit can prioritize the analysis of highly relevant data by considering the attribute information of the submitter. For example, the analysis unit can interpret the analysis results based on the attribute information of the submitter. This makes it possible to perform more appropriate analysis by considering the attribute information of the data submitter. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, when considering the attribute information of the data submitter, the analysis unit can input a prompt to the generative AI, which can then analyze the attribute information and perform the analysis.

[0052] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can perform analysis by region, taking into account the geographical distribution of the data. For example, the analysis unit can prioritize the analysis of geographically relevant data. For example, the analysis unit can adjust the analysis method based on the geographical distribution. This makes it possible to perform region-specific analysis by considering the geographical distribution of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when considering the geographical distribution of the data, the analysis unit can input prompts to the generative AI, which can then analyze the geographical distribution and perform the analysis.

[0053] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data. For example, the analysis unit can adjust its analysis method based on relevant literature. For example, the analysis unit can maintain data consistency by referring to relevant literature. This improves the accuracy of the analysis by referring to relevant literature on the data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the analysis unit refers to relevant literature on the data, it can input prompts to the generative AI, which can then analyze the relevant literature to improve the accuracy of the analysis.

[0054] The support unit can select the optimal support method by referring to the user's past decision-making history when providing decision support. For example, the support unit can analyze patterns of decisions the user has made in the past and propose the optimal support method for similar situations. For example, the support unit can extract the factors behind successful decisions based on the user's past decision-making history and provide similar support methods. For example, the support unit can refer to the user's past decision-making history and propose support methods to avoid failed decisions. In this way, the optimal support method can be provided by referring to the user's past decision-making history. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the support unit refers to the user's past decision-making history, it can input a prompt to the generative AI, which can then analyze the past decision-making history and select the optimal support method.

[0055] The support unit can customize the means of support based on the user's current work situation when providing decision support. For example, the support unit can grasp the user's current work situation in real time and provide the optimal support method. For example, the support unit can dynamically change the means of support according to the user's work situation. For example, the support unit can prioritize providing necessary information based on the user's work situation. This makes it possible to provide more appropriate support by customizing the means of support based on the user's current work situation. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the support unit grasps the user's current work situation, it can input a prompt to the generative AI, which can then analyze the work situation and customize the means of support.

[0056] The support unit can select the optimal support method when providing decision support, taking into account the user's geographical location information. For example, if the user is in a specific region, the support unit can prioritize providing information related to that region. For example, if the user is on the move, the support unit can prioritize providing information related to their destination. For example, if the user is participating in a specific event, the support unit can prioritize providing information related to that event. In this way, the optimal support method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, when considering the user's geographical location information, the support unit can input a prompt to the generative AI, which can then analyze the geographical location information and select the optimal support method.

[0057] The support unit can analyze a user's social media activity and propose support measures when providing decision support. For example, the support unit can propose relevant support methods based on information shared by the user on social media. For example, the support unit can propose relevant support methods based on information about accounts followed by the user on social media. For example, the support unit can propose relevant support methods based on information about groups the user participates in on social media. This allows the support unit to provide highly relevant support methods by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using, for example, generative AI, or without generative AI. For example, when analyzing a user's social media activity, the support unit can input prompts to the generative AI, which can then analyze the social media activity and propose support measures.

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

[0059] Next-generation AI agent systems can also be equipped with a prediction unit. This prediction unit can forecast future trends and risks based on data obtained from the data collection, integration, and analysis units. For example, the prediction unit can analyze a company's sales data to predict future sales trends. It can also analyze external market data to predict future market trends. Furthermore, it can analyze sensor data to predict equipment failure risks. This allows companies to anticipate future risks and opportunities and take appropriate measures.

[0060] Next-generation AI agent systems can also be equipped with a feedback unit. This feedback unit can collect user feedback and use it to improve the system. For example, the feedback unit can provide an interface where users can input their opinions on the system's usability. It can also have a function that allows users to provide feedback on the system's analysis results. Furthermore, it can provide an interface where users can input their opinions on system suggestions. This allows the system to reflect user feedback, making it more user-friendly and effective.

[0061] Next-generation AI agent systems can also be equipped with a notification unit. This notification unit can notify users of important information and alerts. For example, it can notify management of important business insights in real time. It can notify employees of important work-related information. It can also notify administrators of system anomalies or errors. This allows users to receive important information in a timely manner, enabling rapid response.

[0062] Next-generation AI agent systems can also include a customization section. This customization section allows users to customize the system's functions and interface according to their needs and preferences. For example, the customization section can provide a function that allows users to customize the system's dashboard to their liking. It can also provide a function that allows users to adjust the system's notification settings to their needs. Furthermore, it can provide a function that allows users to customize the format of the system's analysis reports to their liking. This allows users to optimize the system to their specific needs.

[0063] Next-generation AI agent systems can also be equipped with a learning unit. This learning unit allows the system to learn from user behavior and feedback, thereby improving its performance. For example, the learning unit can analyze user operation history and learn user preferences and patterns. It can also analyze user feedback and identify areas for system improvement. Furthermore, it can analyze system usage and suggest optimal system settings. This allows the system to evolve to meet user needs and function more effectively.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit collects data. The collection unit targets all data sources, including internal business data, external market data, text data, image data, audio data, and sensor data. For example, it can collect internal business data and external market data, and acquire sensor data in real time. Step 2: The Integration Unit integrates the data collected by the Collection Unit. The Integration Unit securely integrates the data while adhering to each company's security policy, with advanced security features and compliance measures. It performs data format conversion, unifies data in different formats, and integrates data from multiple data sources using data merging methods. To maintain data consistency, it can also detect and automatically correct data duplication and inconsistencies. Step 3: The analysis unit analyzes the data integrated by the integration unit. The analysis unit utilizes a multimodal, large-scale language model to analyze all data sources, including text, images, audio, and sensor data. For example, it can analyze text data to extract important information, analyze image data to understand the content of images, analyze audio data to understand the content of audio, and analyze sensor data to understand information from sensors. Step 4: The support department assists decision-making based on the analysis results obtained by the analysis department. The support department explains data insights in an easy-to-understand manner, tailored to the data literacy level of management and employees, and supports data literacy education. Through natural language dialogue, everyone can intuitively understand the value of data and promote data-driven decision-making. It has functions to promote interdepartmental collaboration and integrates and visualizes information from each department as a data sharing hub. AI can autonomously identify and propose problems and even simulate solutions. It can flexibly respond to the addition of new data sources and expand the scope of data utilization.

[0066] (Example of form 2) The next-generation AI agent system according to an embodiment of the present invention is a system that autonomously collects, integrates, and analyzes a wide variety of data from within and outside a company, comprehensively supporting business decision-making. This system utilizes a multimodal, large-scale language model that analyzes all data sources, including text, images, audio, and sensor data. Equipped with advanced security features and compliance measures, it securely integrates and analyzes data while adhering to each company's security policy. This eliminates concerns about data governance and security, allowing for the confident promotion of data utilization. Furthermore, it has a function that explains data insights in an easy-to-understand manner, tailored to the data literacy level of management and employees, supporting data literacy education. Through natural language dialogue, everyone can intuitively understand the value of data and promote data-driven decision-making. It also has a function that promotes interdepartmental collaboration, integrating and visualizing information from each department as a data sharing hub. This resolves communication gaps between organizations and supports joint projects and company-wide data utilization. Distinguishing itself from conventional BI tools and analytical tools, the AI ​​autonomously discovers and proposes problems and even simulates solutions. It also flexibly responds to the addition of new data sources, expanding the scope of data utilization. For example, next-generation AI agent systems collect internal business data and external market data. Next, they integrate the collected data. Equipped with advanced security features and compliance measures, they securely integrate data while adhering to each company's security policies, thereby addressing data governance and security concerns. They then analyze the integrated data, utilizing multimodal, large-scale language models to analyze all data sources, including text, images, audio, and sensor data. This maximizes the value of the data. Based on the analysis results, they support decision-making. They have features that explain data insights in an easy-to-understand way, tailored to the data literacy levels of management and employees, supporting data literacy education. Through natural language dialogue, everyone can intuitively understand the value of data and promote data-driven decision-making.Furthermore, it features functions that promote interdepartmental collaboration, integrating and visualizing information from each department as a data sharing hub. This resolves communication gaps between organizations and supports joint projects and company-wide data utilization. Distinguishing itself from conventional BI and analytical tools, the AI ​​autonomously identifies and proposes problems and even simulates solutions. It also flexibly responds to the addition of new data sources, expanding the scope of data utilization. As a result, the next-generation AI agent system can autonomously collect, integrate, and analyze a wide variety of data from both inside and outside the company, comprehensively supporting business decision-making.

[0067] The next-generation AI agent system according to this embodiment comprises a collection unit, an integration unit, an analysis unit, and a support unit. The collection unit collects data. The collection unit can collect, for example, internal business data or external market data. The collection unit targets any data source, such as text data, image data, voice data, and sensor data. The collection unit can collect, for example, internal business data and external market data. The collection unit can also collect, for example, sensor data and acquire data in real time. The integration unit integrates the data collected by the collection unit. The integration unit can securely integrate data while adhering to each company's security policy, for example, by providing advanced security functions and compliance support. The integration unit can perform, for example, data format conversion to unify data in different formats. The integration unit can integrate data from multiple data sources using, for example, a data merging method. The integration unit can detect and automatically correct data duplication and inconsistencies to maintain data consistency. The analysis unit analyzes the data integrated by the integration unit. The analytics department can analyze any data source, such as text, images, audio, and sensor data, by leveraging multimodal, large-scale language models. For example, the analytics department can analyze text data and extract important information. It can analyze image data and understand its content. It can analyze audio data and understand its content. It can analyze sensor data and understand information from sensors. The support department assists decision-making based on the analysis results obtained by the analytics department. For example, the support department has the function of explaining data insights in an easy-to-understand way, tailored to the data literacy level of management and employees, and can support data literacy education. For example, the support department can enable everyone to intuitively understand the value of data and promote data-driven decision-making through natural language dialogue. For example, the support department has the function of promoting interdepartmental collaboration and can integrate and visualize information from each department as a data sharing hub.The support unit can, for example, enable AI to autonomously identify and propose problems and even simulate solutions. The support unit can also flexibly respond to the addition of new data sources, expanding the scope of data utilization. As a result, the next-generation AI agent system according to this embodiment can consistently perform data collection, integration, analysis, and decision support.

[0068] The data collection unit collects data. For example, the data collection unit can collect internal business data and external market data. Specifically, internal business data includes sales data, customer data, inventory data, and financial data. This data is automatically collected from various departments within the company and stored in a central database. External market data includes competitor activity, industry trends, economic indicators, and consumer behavior data. This data is obtained from publicly available data on the internet or from paid data vendors. The data collection unit targets all data sources, such as text data, image data, audio data, and sensor data. Text data is collected from emails, chat logs, reports, and news articles. Image data is collected from surveillance camera footage, product photos, and marketing materials. Audio data is collected from call center call recordings, meeting recordings, and voice memos. Sensor data is collected from IoT devices, environmental sensor data, and machine operation data. The data collection unit can also collect internal business data and external market data. This allows companies to integrate internal and external data for more comprehensive analysis. The data collection unit can, for example, collect sensor data and obtain data in real time. This enables companies to understand the situation in real time and make quick decisions. The data collection unit allows for flexible configuration of data collection frequency and methods, enabling data collection tailored to specific needs and situations. For example, when a significant event occurs, the data collection frequency can be increased to obtain more detailed information. The data collection unit also has functions to validate and clean data to ensure data quality. This guarantees that the collected data is accurate and reliable.

[0069] The Integration Department integrates the data collected by the Collection Department. The Integration Department, for example, provides advanced security features and compliance measures, enabling secure data integration while adhering to each company's security policies. Specifically, it provides security features such as data encryption, access control, and audit log management to protect data confidentiality and integrity. The Integration Department can, for example, perform data format conversion to unify data in different formats. This allows for consistent handling of data in different formats, such as text data, image data, audio data, and sensor data. The Integration Department can integrate data from multiple data sources using data merging methods. This enables centralized management of information from different data sources and comprehensive analysis. The Integration Department can, for example, detect and automatically correct data duplication and inconsistencies to maintain data consistency. This improves data quality and leads to more reliable analytical results. The Integration Department can automate the data integration process for efficient data integration. For example, by setting a schedule for regularly collecting and integrating data, manual intervention can be minimized. The Integration Department can monitor the data integration status in real time and respond immediately if problems occur. This allows the data integration process to proceed smoothly and improves the overall system performance.

[0070] The Analysis Department analyzes data integrated by the Integration Department. The Analysis Department can analyze any data source, including text, images, audio, and sensor data, by leveraging, for example, multimodal, large-scale language models. Specifically, it uses natural language processing techniques to analyze text data and extract important information. For example, it can analyze customer feedback and market trends to aid in corporate strategy. For image data analysis, it can use image recognition technology to perform product quality inspections and analyze surveillance camera footage. For audio data analysis, it can use speech recognition technology to analyze call center conversations and improve customer service. For sensor data analysis, it can use machine learning algorithms to detect machine anomalies and monitor environmental changes. The Analysis Department can, for example, analyze text data and extract important information. This allows companies to efficiently obtain useful information from large amounts of text data. The Analysis Department can, for example, analyze image data and understand its content. This automates product quality inspections and surveillance camera footage analysis, improving efficiency. The Analysis Department can, for example, analyze audio data and understand its content. This allows for the analysis of call center conversations and the use of that analysis to improve customer service. The analytics department can, for example, analyze sensor data and understand the information it provides. This enables the detection of machine anomalies and the monitoring of environmental changes. The analytics department can utilize AI to detect data patterns and trends and perform predictive analytics. This allows companies to anticipate future risks and opportunities and take appropriate measures. The analytics department provides data visualization capabilities, displaying analysis results clearly in graphs and charts. This allows management and employees to intuitively understand data insights and use them to inform decision-making.

[0071] The support department assists decision-making based on the analysis results obtained by the analysis department. For example, the support department has the function of explaining data insights in an easy-to-understand manner, tailored to the data literacy level of management and employees, and can support data literacy education. Specifically, it uses data visualization tools to create graphs and charts, visually showing data trends and patterns. This allows even users unfamiliar with data to intuitively understand the meaning of the data. For example, the support department can facilitate data-driven decision-making by enabling everyone to intuitively understand the value of data through natural language dialogue. Specifically, it utilizes chatbots and voice assistants to provide data-based answers when users ask questions. This allows users to make data-driven decisions even without specialized knowledge. For example, the support department has the function of promoting interdepartmental collaboration and can integrate and visualize information from each department as a data sharing hub. Specifically, it utilizes project management tools and collaboration platforms so that each department can share data and conduct joint analysis and decision-making. This strengthens interdepartmental collaboration and improves the efficiency of the entire organization. The support department can, for example, use AI to autonomously identify and propose problems and even simulate solutions. Specifically, the AI ​​analyzes data, identifies potential problems and areas for improvement, simulates multiple solutions, and proposes the optimal solution. This allows companies to solve problems quickly and effectively. The support department can also flexibly respond to the addition of new data sources, expanding the scope of data utilization. Specifically, even when new data sources are added, they can be integrated into existing systems, allowing for the continuous process of data collection, integration, analysis, and support. This ensures that companies can always make decisions based on the latest information. The support department can collect user feedback and use it to improve the system. For example, based on user feedback, the system's functions and interface can be improved to enhance usability. This allows the support department to provide more valuable support to users and promote data-driven decision-making.

[0072] The data collection unit can handle any data source, including text, images, audio, and sensor data. For example, the data collection unit can collect text data to obtain internal business data. For example, the data collection unit can collect image data to obtain external market data. For example, the data collection unit can collect audio data to obtain customer feedback. For example, the data collection unit can collect sensor data to obtain data in real time. This allows data to be collected from a variety of data sources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, when collecting text data, the data collection unit can input prompts to a generating AI, which can then analyze and collect the text data.

[0073] The integration unit features advanced security functions and compliance capabilities, enabling secure data integration while adhering to each company's security policies. For example, the integration unit can perform data format conversion to unify data in different formats. It can also integrate data from multiple data sources using data merging methods. Furthermore, it can detect and automatically correct data duplication and inconsistencies to maintain data consistency, thereby addressing data governance and security concerns. Some or all of the above processes in the integration unit may be performed using AI, or not. For example, when performing data format conversion, the integration unit can prompt a generating AI, which can then analyze and unify the data format.

[0074] The analysis unit can analyze any data source, including text, images, audio, and sensor data, by utilizing a multimodal, large-scale language model. For example, the analysis unit can analyze text data and extract important information. For example, the analysis unit can analyze image data and understand the content of the images. For example, the analysis unit can analyze audio data and understand the content of the audio. For example, the analysis unit can analyze sensor data and understand the information from the sensors. This allows for maximizing the value of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, when analyzing text data, the analysis unit can input prompts to the generative AI, which can then analyze the text data and extract important information.

[0075] The support unit has the function of explaining data insights in an easy-to-understand manner, tailored to the data literacy level of management and employees, and can support data literacy education. For example, the support unit can visually display data insights so that management and employees can understand them intuitively. For example, the support unit can provide training programs for data literacy education to improve employees' data literacy. For example, the support unit can perform simulations based on data insights to help management and employees make data-driven decisions. This can support data literacy education. Some or all of the above processes in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, when generating data insights, the support unit can input prompts to the generative AI, which can then generate and visually display the data insights.

[0076] The support system enables everyone to intuitively understand the value of data and facilitate data-driven decision-making through natural language dialogue. For example, the support system can use a chatbot to allow users to input questions in natural language and receive data-driven answers. Alternatively, it can use a voice assistant to allow users to input questions by voice and receive data-driven answers. Furthermore, it can use natural language processing technology to analyze user questions and provide relevant data insights, thereby facilitating data-driven decision-making. Some or all of the above-described processes in the support system may be performed using, for example, generative AI, or without generative AI. For example, when engaging in natural language dialogue, the support system can input prompts to generative AI, which can then generate answers to user questions.

[0077] The support department has functions to promote inter-departmental collaboration and can integrate and visualize information from each department as a data sharing hub. For example, the support department can centrally manage data from each department and share information in real time. For example, the support department can use project management tools to efficiently advance joint projects between departments. For example, the support department can use data visualization tools to visually display data from each department and promote information sharing. This promotes inter-departmental collaboration. Some or all of the above processes in the support department may be performed using, for example, generative AI, or not using generative AI. For example, when visualizing data, the support department can input prompts to the generative AI, which can then analyze the data and display it visually.

[0078] The support unit can autonomously discover and propose problems and even simulate solutions using AI. For example, the support unit can automatically discover problems from data using machine learning algorithms. For example, the support unit can propose solutions to problems and simulate those solutions. For example, the support unit can select the optimal solution based on the simulation results. This allows the AI ​​to autonomously discover and propose problems and even simulate solutions. Some or all of the above processes in the support unit may be performed using, for example, generative AI, or not using generative AI. For example, when discovering problems, the support unit can input prompts to the generative AI, which can then analyze the data and discover problems.

[0079] The data collection unit can flexibly respond to the addition of new data sources, thereby expanding the scope of data utilization. For example, the data collection unit can add a new sensor device and collect its data. For example, the data collection unit can add an external database and collect its data. For example, the data collection unit can support a new data format and collect that data. This expands the scope of data utilization. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when adding a new data source, the data collection unit can input a prompt to the generative AI, which can then analyze and collect the new data source.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize the collection of only important data and process it quickly. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the data collection unit can input prompts to the generative AI, which can analyze and estimate the user's emotions.

[0081] The data collection unit can dynamically change the types of data it collects based on the user's work content and interests. For example, if the user belongs to the marketing department, the data collection unit can prioritize collecting marketing-related data. For example, if the user belongs to the technology department, the data collection unit can prioritize collecting technology-related data. For example, if the user's interests change, the data collection unit can dynamically change the types of data it collects according to those interests. This allows for the collection of more relevant data by changing the types of data collected according to the user's work content and interests. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, when changing the types of data to collect, the data collection unit can input a prompt to the generative AI, which can then analyze the user's work content and interests to change the types of data.

[0082] The data collection unit can evaluate the reliability of the data during data collection and prioritize the collection of highly reliable data. For example, the data collection unit can evaluate the reliability of the data source and prioritize the collection of data from highly reliable data sources. For example, the data collection unit can evaluate the consistency of the data and prioritize the collection of consistent data. For example, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. This improves data quality by prioritizing the collection of highly reliable data. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, when evaluating the reliability of the data, the data collection unit can input a prompt to the generative AI, which can then analyze and evaluate the reliability of the data source.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting data that can be processed quickly. This allows for the collection of more appropriate data by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not using AI. For example, when estimating the user's emotions, the data collection unit can input prompts to the generative AI, which can analyze and estimate the user's emotions.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit can prioritize the collection of data related to their destination. For example, if the user is participating in a specific event, the data collection unit can prioritize the collection of data related to that event. This allows for the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, when considering the user's geographical location information, the data collection unit can input a prompt to the generative AI, which can then analyze the geographical location information and collect highly relevant data.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can collect relevant data based on information about accounts followed by the user on social media. For example, the data collection unit can collect relevant data based on information about groups the user participates in on social media. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, when analyzing a user's social media activity, the data collection unit can input prompts to the generative AI, which can then analyze the social media activity and collect relevant data.

[0086] The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated user emotions. For example, if the user is stressed, the integration unit can provide a simple integration method to reduce the burden. For example, if the user is relaxed, the integration unit can provide a detailed integration method to improve accuracy. For example, if the user is in a hurry, the integration unit can provide a method that allows for rapid integration. This reduces the burden on the user by adjusting the data integration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the integration unit can input prompts to the generative AI, which can analyze and estimate the user's emotions.

[0087] The integration unit can add a function to detect and automatically correct data duplication and inconsistencies during data integration. For example, the integration unit can detect and automatically delete data duplication. For example, the integration unit can detect and automatically correct data inconsistencies. For example, the integration unit can automatically adjust inconsistent data to maintain data consistency. This ensures data consistency by automatically correcting data duplication and inconsistencies. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, when detecting data duplication or inconsistencies, the integration unit can prompt the generative AI, which can then analyze the data to detect and correct the duplication or inconsistencies.

[0088] The integration unit can add a function to automatically standardize the format and structure of data during data integration. For example, the integration unit can automatically unify data of different formats. For example, the integration unit can automatically standardize data of different structures. For example, the integration unit can improve the accuracy of integration by unifying the format and structure of data. This improves the accuracy of integration by standardizing the format and structure of data. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, when standardizing the format and structure of data, the integration unit can input prompts to the generative AI, which can then analyze and standardize the data.

[0089] The integration unit can estimate the user's emotions and determine the priority of data to integrate based on the estimated user emotions. For example, if the user is stressed, the integration unit can prioritize integrating only important data. For example, if the user is relaxed, the integration unit can prioritize integrating detailed data. For example, if the user is in a hurry, the integration unit can prioritize integrating data that can be quickly integrated. This allows for more appropriate data integration by prioritizing the data to integrate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or not using AI. For example, when estimating the user's emotions, the integration unit can input prompts to the generative AI, which can analyze and estimate the user's emotions.

[0090] The integration unit can perform data integration while considering the geographical distribution of the data. For example, the integration unit can perform integration on a regional basis, taking into account the geographical distribution of the data. For example, the integration unit can prioritize the integration of geographically related data. For example, the integration unit can adjust the data integration method based on the geographical distribution. This makes it possible to integrate data on a regional basis by considering the geographical distribution of the data. Some or all of the above-described processes in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when considering the geographical distribution of the data, the integration unit can input prompts to the generative AI, which can then analyze the geographical distribution and perform the integration.

[0091] The integration unit can improve the accuracy of data integration by referring to relevant literature during data integration. For example, the integration unit can improve the accuracy of data integration by referring to relevant literature. For example, the integration unit can adjust the data integration method based on relevant literature. For example, the integration unit can maintain data consistency by referring to relevant literature. This improves the accuracy of data integration by referring to relevant literature. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the integration unit refers to relevant literature for data, it can input a prompt to the generative AI, which can then analyze the relevant literature to improve the accuracy of integration.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simpler analysis method to reduce the burden. For example, if the user is relaxed, the analysis unit can provide a more detailed analysis method to improve accuracy. For example, if the user is in a hurry, the analysis unit can provide a method that allows for rapid analysis. This reduces the burden on the user by adjusting the analysis method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the analysis unit can input prompts to the generative AI, which can then analyze and estimate the user's emotions.

[0093] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can prioritize the analysis of highly relevant data by considering the interrelationships between data. For example, the analysis unit can adjust its analysis method based on the interrelationships between data. For example, the analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. This improves the accuracy of the analysis by considering the interrelationships between data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, when considering the interrelationships between data, the analysis unit can input prompts to the generative AI, which can then analyze the interrelationships between data to improve the accuracy of the analysis.

[0094] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can adjust the analysis method based on the attribute information of the data submitter. For example, the analysis unit can prioritize the analysis of highly relevant data by considering the attribute information of the submitter. For example, the analysis unit can interpret the analysis results based on the attribute information of the submitter. This makes it possible to perform more appropriate analysis by considering the attribute information of the data submitter. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, when considering the attribute information of the data submitter, the analysis unit can input a prompt to the generative AI, which can then analyze the attribute information and perform the analysis.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the burden on the user can be reduced by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the analysis unit can input prompts to the generative AI, which can analyze and estimate the user's emotions.

[0096] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can perform analysis by region, taking into account the geographical distribution of the data. For example, the analysis unit can prioritize the analysis of geographically relevant data. For example, the analysis unit can adjust the analysis method based on the geographical distribution. This makes it possible to perform region-specific analysis by considering the geographical distribution of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, when considering the geographical distribution of the data, the analysis unit can input prompts to the generative AI, which can then analyze the geographical distribution and perform the analysis.

[0097] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data. For example, the analysis unit can adjust its analysis method based on relevant literature. For example, the analysis unit can maintain data consistency by referring to relevant literature. This improves the accuracy of the analysis by referring to relevant literature on the data. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the analysis unit refers to relevant literature on the data, it can input prompts to the generative AI, which can then analyze the relevant literature to improve the accuracy of the analysis.

[0098] The support unit can estimate the user's emotions and adjust the decision support method based on the estimated user emotions. For example, if the user is stressed, the support unit can provide a simple decision support method to reduce the burden. For example, if the user is relaxed, the support unit can provide a detailed decision support method to improve accuracy. For example, if the user is in a hurry, the support unit can provide a method that allows for quick decision-making. In this way, the burden on the user can be reduced by adjusting the decision support method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, when estimating the user's emotions, the support unit can input prompts to the generative AI, which can analyze and estimate the user's emotions.

[0099] The support unit can select the optimal support method by referring to the user's past decision-making history when providing decision support. For example, the support unit can analyze patterns of decisions the user has made in the past and propose the optimal support method for similar situations. For example, the support unit can extract the factors behind successful decisions based on the user's past decision-making history and provide similar support methods. For example, the support unit can refer to the user's past decision-making history and propose support methods to avoid failed decisions. In this way, the optimal support method can be provided by referring to the user's past decision-making history. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the support unit refers to the user's past decision-making history, it can input a prompt to the generative AI, which can then analyze the past decision-making history and select the optimal support method.

[0100] The support unit can customize the means of support based on the user's current work situation when providing decision support. For example, the support unit can grasp the user's current work situation in real time and provide the optimal support method. For example, the support unit can dynamically change the means of support according to the user's work situation. For example, the support unit can prioritize providing necessary information based on the user's work situation. This makes it possible to provide more appropriate support by customizing the means of support based on the user's current work situation. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, when the support unit grasps the user's current work situation, it can input a prompt to the generative AI, which can then analyze the work situation and customize the means of support.

[0101] The support unit can estimate the user's emotions and determine the priority of decision support based on the estimated emotions. For example, if the user is stressed, the support unit can prioritize support for important decisions. For example, if the user is relaxed, the support unit can provide detailed decision support. For example, if the user is in a hurry, the support unit can provide support methods that enable quick decision-making. This allows for more appropriate support by prioritizing decision support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, when estimating the user's emotions, the support unit can input prompts to the generative AI, which can analyze and estimate the user's emotions.

[0102] The support unit can select the optimal support method when providing decision support, taking into account the user's geographical location information. For example, if the user is in a specific region, the support unit can prioritize providing information related to that region. For example, if the user is on the move, the support unit can prioritize providing information related to their destination. For example, if the user is participating in a specific event, the support unit can prioritize providing information related to that event. In this way, the optimal support method can be provided by taking into account the user's geographical location information. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, when considering the user's geographical location information, the support unit can input a prompt to the generative AI, which can then analyze the geographical location information and select the optimal support method.

[0103] The support unit can analyze a user's social media activity and propose support measures when providing decision support. For example, the support unit can propose relevant support methods based on information shared by the user on social media. For example, the support unit can propose relevant support methods based on information about accounts followed by the user on social media. For example, the support unit can propose relevant support methods based on information about groups the user participates in on social media. This allows the support unit to provide highly relevant support methods by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using, for example, generative AI, or without generative AI. For example, when analyzing a user's social media activity, the support unit can input prompts to the generative AI, which can then analyze the social media activity and propose support measures.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] Next-generation AI agent systems can also be equipped with a prediction unit. This prediction unit can forecast future trends and risks based on data obtained from the data collection, integration, and analysis units. For example, the prediction unit can analyze a company's sales data to predict future sales trends. It can also analyze external market data to predict future market trends. Furthermore, it can analyze sensor data to predict equipment failure risks. This allows companies to anticipate future risks and opportunities and take appropriate measures.

[0106] Next-generation AI agent systems can also be equipped with a feedback unit. This feedback unit can collect user feedback and use it to improve the system. For example, the feedback unit can provide an interface where users can input their opinions on the system's usability. It can also have a function that allows users to provide feedback on the system's analysis results. Furthermore, it can provide an interface where users can input their opinions on system suggestions. This allows the system to reflect user feedback, making it more user-friendly and effective.

[0107] Next-generation AI agent systems can also be equipped with a notification unit. This notification unit can notify users of important information and alerts. For example, it can notify management of important business insights in real time. It can notify employees of important work-related information. It can also notify administrators of system anomalies or errors. This allows users to receive important information in a timely manner, enabling rapid response.

[0108] Next-generation AI agent systems can also include a customization section. This customization section allows users to customize the system's functions and interface according to their needs and preferences. For example, the customization section can provide a function that allows users to customize the system's dashboard to their liking. It can also provide a function that allows users to adjust the system's notification settings to their needs. Furthermore, it can provide a function that allows users to customize the format of the system's analysis reports to their liking. This allows users to optimize the system to their specific needs.

[0109] Next-generation AI agent systems can also be equipped with a learning unit. This learning unit allows the system to learn from user behavior and feedback, thereby improving its performance. For example, the learning unit can analyze user operation history and learn user preferences and patterns. It can also analyze user feedback and identify areas for system improvement. Furthermore, it can analyze system usage and suggest optimal system settings. This allows the system to evolve to meet user needs and function more effectively.

[0110] Next-generation AI agent systems can also be equipped with an emotion estimation unit. This unit can estimate the user's emotions and adjust the system's responses and suggestions accordingly. For example, if the user is stressed, the emotion estimation unit can soften the system's responses, reducing the user's burden. If the user is relaxed, the emotion estimation unit can provide detailed information, deepening the user's understanding. If the user is in a hurry, the emotion estimation unit can provide a quick response, saving the user's time. This allows the system to respond flexibly to the user's emotions, providing a better user experience.

[0111] Next-generation AI agent systems can also be equipped with an emotional feedback unit. This unit can evaluate system performance based on user emotions and identify areas for improvement. For example, it can record how users felt about system responses and use this information to improve the system. It can also record how users felt about system suggestions and improve the quality of those suggestions. Furthermore, it can record how users felt about the system's interface and improve its usability. This allows the system to continuously improve based on user emotions, providing a better user experience.

[0112] Next-generation AI agent systems can also be equipped with an emotion analysis unit. This unit can analyze the user's emotions in detail and optimize the system's responses and suggestions. For example, it can analyze emotions from the user's text input and provide appropriate responses. It can also analyze emotions from the user's voice input and provide appropriate suggestions. Furthermore, it can analyze emotions from the user's facial expressions and adjust the system's interface accordingly. This allows the system to provide optimal responses and suggestions based on the user's emotions, thereby improving user satisfaction.

[0113] Next-generation AI agent systems can also be equipped with an emotion monitoring unit. This unit can monitor the user's emotions in real time and dynamically adjust the system's responses and suggestions. For example, if the user is stressed, the emotion monitoring unit can soften the system's responses, reducing the user's burden. If the user is relaxed, the emotion monitoring unit can provide detailed information, deepening the user's understanding. If the user is in a hurry, the emotion monitoring unit can provide a quick response, saving the user's time. This allows the system to respond flexibly to the user's emotions, providing a better user experience.

[0114] Next-generation AI agent systems can also be equipped with an emotion adaptation unit. This unit can adapt the system's behavior based on the user's emotions. For example, if the user is stressed, the emotion adaptation unit can simplify the system's operation, reducing the user's burden. If the user is relaxed, the emotion adaptation unit can make the system's operation more detailed, deepening the user's understanding. If the user is in a hurry, the emotion adaptation unit can speed up the system's operation, saving the user's time. This allows the system to respond flexibly to the user's emotions, providing a better user experience.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The collection unit collects data. The collection unit targets all data sources, including internal business data, external market data, text data, image data, audio data, and sensor data. For example, it can collect internal business data and external market data, and acquire sensor data in real time. Step 2: The Integration Unit integrates the data collected by the Collection Unit. The Integration Unit securely integrates the data while adhering to each company's security policy, with advanced security features and compliance measures. It performs data format conversion, unifies data in different formats, and integrates data from multiple data sources using data merging methods. To maintain data consistency, it can also detect and automatically correct data duplication and inconsistencies. Step 3: The analysis unit analyzes the data integrated by the integration unit. The analysis unit utilizes a multimodal, large-scale language model to analyze all data sources, including text, images, audio, and sensor data. For example, it can analyze text data to extract important information, analyze image data to understand the content of images, analyze audio data to understand the content of audio, and analyze sensor data to understand information from sensors. Step 4: The support department assists decision-making based on the analysis results obtained by the analysis department. The support department explains data insights in an easy-to-understand manner, tailored to the data literacy level of management and employees, and supports data literacy education. Through natural language dialogue, everyone can intuitively understand the value of data and promote data-driven decision-making. It has functions to promote interdepartmental collaboration and integrates and visualizes information from each department as a data sharing hub. AI can autonomously identify and propose problems and even simulate solutions. It can flexibly respond to the addition of new data sources and expand the scope of data utilization.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the data collection unit, integration unit, analysis unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects data from both inside and outside the company. The integration unit is implemented in the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the integrated data. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports decision-making based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the data collection unit, integration unit, analysis unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects data from both inside and outside the company. The integration unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and integrates the collected data. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the integrated data. The support unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and supports decision-making based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, integration unit, analysis unit, and support unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects data from both inside and outside the company. The integration unit is implemented in the specific processing unit 290 of the data processing unit 12 and integrates the collected data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the integrated data. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports decision-making based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the collection unit, integration unit, analysis unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects data from both inside and outside the company. The integration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and integrates the collected data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the integrated data. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and supports decision-making based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A data collection unit that collects data, An integration unit that integrates the data collected by the aforementioned collection unit, An analysis unit analyzes the data integrated by the aforementioned integration unit, A support unit that assists in decision-making based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is It covers all data sources, including text, images, audio, and sensor data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned integration unit is Equipped with advanced security features and compliance measures, it securely integrates data while adhering to each company's security policies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Leveraging multimodal, large-scale language models, we analyze all data sources, including text, images, audio, and sensor data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, It has a function that explains data insights in an easy-to-understand way, tailored to the data literacy level of management and employees, and supports data literacy education. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, Through natural language dialogue, everyone can intuitively understand the value of data and facilitate data-driven decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned support unit, It features functions that promote collaboration between departments and integrates and visualizes information from each department as a data sharing hub. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned support unit, The AI ​​autonomously identifies and proposes problems, and even simulates solutions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We can flexibly accommodate the addition of new data sources, expanding the scope of data utilization. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The types of data collected are dynamically changed based on the user's work content and interests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, evaluate the reliability of the data and prioritize collecting reliable data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned integration unit is We estimate user sentiment and adjust the data integration method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned integration unit is During data integration, add a feature to automatically detect and correct data duplication and inconsistencies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned integration unit is Add a feature that automatically standardizes data format and structure during data integration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned integration unit is It estimates user sentiment and determines the priority of data to integrate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned integration unit is When integrating data, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned integration unit is When integrating data, referencing relevant literature improves the accuracy of the integration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When performing analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is When performing the analysis, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is When performing analysis, consider the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, It estimates the user's emotions and adjusts the decision support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, When providing decision support, the system selects the optimal support method by referring to the user's past decision-making history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit, When providing decision support, customize the support methods based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit, It estimates the user's emotions and prioritizes decision support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit, When providing decision support, the optimal support method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit, When providing decision support, we analyze the user's social media activity and propose ways to support them. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An integration unit that integrates the data collected by the aforementioned collection unit, An analysis unit analyzes the data integrated by the aforementioned integration unit, A support unit that assists in decision-making based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is It covers all data sources, including text, images, audio, and sensor data. The system according to feature 1.

3. The aforementioned integration unit is Equipped with advanced security features and compliance measures, it securely integrates data while adhering to each company's security policies. The system according to feature 1.

4. The aforementioned analysis unit is Leveraging multimodal, large-scale language models, we analyze all data sources, including text, images, audio, and sensor data. The system according to feature 1.

5. The aforementioned support unit, It has a function that explains data insights in an easy-to-understand way, tailored to the data literacy level of management and employees, and supports data literacy education. The system according to feature 1.

6. The aforementioned support unit, Through natural language dialogue, everyone can intuitively understand the value of data and facilitate data-driven decision-making. The system according to feature 1.

7. The aforementioned support unit, It features functions that promote collaboration between departments and acts as a data sharing hub, integrating and visualizing information from each department. The system according to feature 1.

8. The aforementioned support unit, AI autonomously identifies and proposes problems, and even simulates solutions. The system according to feature 1.

Images