system

Abstract

The system according to this embodiment aims to improve data utilization skills by organizing data information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides information based on the data analyzed 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 prior art, there is a problem that the organization of data information has not advanced and the skill level regarding data utilization is low.

[0005] The system according to the embodiment aims to organize data information and improve the skills of data utilization.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides information based on the data analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can organize data information and improve skills in data utilization. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 system according to an embodiment of the present invention is a mechanism for improving the data utilization skills of DWH users using a generation AI. This system gathers disparate information about SB systems in one place, trains the generation AI, and centrally manages the architecture, data linkage, structure, hardware, and software of each system. This entrusts the generation AI with the task of organizing information that will lead to system unification, and serves as the foundation for creating a mechanism that leads to unified SB system information management among employees, improved IT skills, and business contributions. For example, it trains the AI ​​on information and logic related to DWH tables, making system data easy to use and providing information with a single click. It also manages information such as generating SQL according to the purpose of use, searching and coordinating with the responsible department from among more than 1000 organizations, and unifying search conditions with other departments. As a result, DWH users will have a clear understanding of how to set search conditions according to their purpose of use, and will be able to understand DWH table definitions, item definitions, and list values. It also resolves the lack of knowledge required to write queries and SQL, and lays the foundation for realizing data-driven management. In this way, the system can serve as the foundation for creating a mechanism that leads to unified SB system information management among employees, improved IT skills, and business contributions.

[0029] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The data collection unit can collect data such as text data, numerical data, and image data. The data collection unit can collect environmental data using sensors, for example. The data collection unit can also collect publicly available data from the internet, for example. Furthermore, the data collection unit can also collect user input data, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can perform statistical analysis, for example. The analysis unit can also analyze data by applying machine learning algorithms, for example. Furthermore, the analysis unit can analyze data using data mining techniques, for example. The data provision unit provides information based on the data analyzed by the analysis unit. The data provision unit can provide information in report format, for example. The data provision unit can also provide information through a dashboard display, for example. Furthermore, the data provision unit can provide information through alert notifications, for example. This enables the system to efficiently collect, analyze, and provide data. Some or all of the above-described processes in the collection unit, analysis unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired from sensors into a generation AI and have the generation AI perform data preprocessing. The analysis unit can input data collected by the collection unit into a generation AI and have the generation AI perform data analysis. The provision unit can input data analyzed by the analysis unit into a generation AI and have the generation AI provide the information.

[0030] The data collection unit collects data. For example, the data collection unit can collect data such as text data, numerical data, and image data. Specifically, text data includes news articles, blog posts, and social media comments. Numerical data includes measurements from sensors, statistical data, and sales data. Image data includes surveillance camera footage, satellite images, and medical images. The data collection unit can also collect environmental data using sensors. Environmental data includes temperature, humidity, atmospheric pressure, wind speed, and light intensity, and this data is collected in real time. Furthermore, the data collection unit can also collect publicly available data from the internet. Publicly available data includes statistical and weather data provided by government agencies, and financial data published by companies. This data is often automatically retrieved via APIs. The data collection unit can also collect user input data. User input data includes survey responses, feedback, and files uploaded by users. This allows the data collection unit to collect a wide range of data from diverse data sources, strengthening the overall data infrastructure of the system. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can perform statistical analysis. Statistical analysis includes calculating basic statistics such as the mean, median, and standard deviation. It can also utilize advanced statistical methods such as regression analysis, correlation analysis, and time series analysis. Furthermore, the analysis unit can analyze data by applying machine learning algorithms. These include classification, regression, clustering, and anomaly detection, which can be used to extract patterns and trends from the data. For example, it can perform object recognition using collected image data and analyze the frequency of occurrence of specific objects. It can also build predictive models using collected numerical data to forecast future trends. Additionally, the analysis unit can analyze data using data mining techniques. These include association rule discovery, pattern recognition, and clustering, which can be used to extract useful knowledge from the data. For example, it can analyze customer purchase history data to discover purchase patterns and related products. This allows the analysis unit to analyze collected data from multiple perspectives and strengthen the knowledge base of the entire system. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions or time periods based on historical data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The service provider provides information based on data analyzed by the analysis unit. For example, the service provider can provide information in report format. Reports include an overview of the analysis results, detailed analysis results, and recommended actions. This allows users to grasp the analysis results at a glance and make appropriate decisions. The service provider can also provide information through dashboard displays. Dashboards visually display real-time data and analysis results, allowing users to intuitively understand the information. Furthermore, the service provider can provide information through alert notifications. Alert notifications include a function to immediately notify users when anomalies are detected or risks occur. This allows users to respond quickly and minimize risks. The service provider can input data analyzed by the analysis unit into a generation AI, which then provides the information. The generation AI uses natural language generation technology to convert analysis results into easy-to-understand text and graphs. For example, the generation AI can automatically generate reports based on the analysis results and provide them to users. The generation AI also automatically optimizes the design and layout of dashboards, enabling users to quickly obtain the necessary information. Furthermore, the generation AI can optimize the content and timing of alert notifications, providing information in the most effective way for the user. This allows the service provider to deliver information quickly and appropriately to users, improving the overall efficiency and effectiveness of the system.

[0033] The data provision unit learns information and processing logic related to DWH tables, making system data easy to use and enabling one-click information provision. For example, the data provision unit learns the structure and data attributes of DWH tables and provides an intuitive interface to the user. The data provision unit also learns processing logic and can automatically perform data preprocessing and transformation. Furthermore, the data provision unit can design a user interface for one-click information provision and simplify the operation flow. This enables quick and easy provision of information related to DWH tables. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without AI. For example, the data provision unit can input information from DWH tables into a generating AI and have the generating AI perform information learning and provision.

[0034] The analysis unit can generate SQL tailored to its intended use. For example, it can generate SQL for data analysis. It can also generate SQL for report creation. Furthermore, it can generate SQL for building predictive models. This enables the generation of SQL tailored to specific uses. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the intended use into a generation AI and have the generation AI execute the SQL generation.

[0035] The data collection unit can search for and coordinate responsible departments from among more than 1000 organizations. The data collection unit can search for organizations such as companies, departments, and teams. It can also identify responsible departments such as project management departments and data management departments. Furthermore, the data collection unit can coordinate responsible departments based on the search results. This allows for efficient searching and coordination of responsible departments. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input organizational information into a generating AI and have the generating AI perform the searching and coordination of responsible departments.

[0036] The information provision department can manage information such as standardizing search criteria with other departments. For example, the information provision department can standardize filtering conditions. It can also standardize search queries. Furthermore, the information provision department can provide an interface to facilitate information sharing with other departments. This makes it possible to standardize search criteria with other departments. Some or all of the above processes in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input search criterion information into a generating AI and have the generating AI perform the standardization process.

[0037] The data collection unit can optimize the types of data to be collected based on the user's past usage history. For example, the data collection unit can prioritize collecting data that the user has frequently used in the past. The data collection unit can also predict and collect data needed at specific time periods based on the user's past usage history. Furthermore, the data collection unit can analyze the user's past usage history and collect the most relevant data. This allows for the optimization of data collection based on the user's past usage history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past usage history into a generating AI and have the generating AI perform the optimization of data collection.

[0038] The data collection unit can filter data based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can grasp the user's work situation in real time and appropriately filter and collect the necessary data. This allows data to be filtered based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's work situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0039] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect the most relevant data based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can collect the necessary data based on their current location. This allows for the optimization of data collection based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0040] The data collection unit can analyze the 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. The data collection unit can also analyze the user's social media activity and collect the most relevant data. Furthermore, the data collection unit can collect data based on topics the user has shown interest in on social media. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply a statistical analysis algorithm to numerical data. Furthermore, it can apply an image analysis algorithm to image data. This allows the optimal analysis algorithm to be applied according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select the analysis algorithm to apply.

[0043] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also postpone the analysis of older data. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows the analysis priority to be determined based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The information provider can select the most suitable information provision method by referring to the user's past usage history when providing information. For example, the information provider can prioritize providing information using methods the user has used in the past. The information provider can also select the most suitable information provision method based on the user's past usage history. Furthermore, the information provider can analyze the user's past usage history and provide the most effective information provision method. This allows the information provider to select the most suitable information provision method based on the user's past usage history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past usage history into a generating AI and have the generating AI select the information provision method.

[0046] The information delivery unit can customize the means of information delivery based on the user's current work situation when providing information. For example, the information delivery unit can prioritize providing information related to the project the user is currently working on. The information delivery unit can also grasp the user's work situation in real time and select the most appropriate means of information delivery. Furthermore, the information delivery unit can dynamically customize the means of information delivery based on the user's work situation. This allows for the customization of the means of information delivery based on the user's work situation. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the user's work situation into a generating AI and have the generating AI perform the customization of the means of information delivery.

[0047] The information delivery unit can select the most appropriate method of information delivery based on the user's geographical location information when providing information. For example, if the user is in a specific region, the information delivery unit will prioritize providing information related to that region. The information delivery unit can also provide the most relevant information based on the user's geographical location information. Furthermore, if the user is on the move, the information delivery unit can provide necessary information based on their current location. This allows the system to select the most appropriate method of information delivery based on the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the method of information delivery.

[0048] The information provider can analyze the user's social media activity and propose methods for providing information when providing information. For example, the information provider can provide relevant information based on information shared by the user on social media. The information provider can also analyze the user's social media activity and provide the most relevant information. Furthermore, the information provider can provide information based on topics the user has shown interest in on social media. This allows the information provider to propose the most suitable method of providing information based on the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input data on the user's social media activity into a generating AI and have the generating AI propose methods of providing information.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0051] The data collection unit can optimize the types of data to be collected based on the user's past usage history. For example, the data collection unit can prioritize collecting data that the user has frequently used in the past. The data collection unit can also predict and collect data needed at specific time periods based on the user's past usage history. Furthermore, the data collection unit can analyze the user's past usage history and collect the most relevant data. This allows for the optimization of data collection based on the user's past usage history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past usage history into a generating AI and have the generating AI perform the optimization of data collection.

[0052] The data collection unit can filter data based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can grasp the user's work situation in real time and appropriately filter and collect the necessary data. This allows data to be filtered based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's work situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0053] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect the most relevant data based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can collect the necessary data based on their current location. This allows for the optimization of data collection based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0054] The information provider can select the most suitable information provision method by referring to the user's past usage history when providing information. For example, the information provider can prioritize providing information using methods the user has used in the past. The information provider can also select the most suitable information provision method based on the user's past usage history. Furthermore, the information provider can analyze the user's past usage history and provide the most effective information provision method. This allows the information provider to select the most suitable information provision method based on the user's past usage history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past usage history into a generating AI and have the generating AI select the information provision method.

[0055] The information provider can analyze the user's social media activity and propose methods for providing information when providing information. For example, the information provider can provide relevant information based on information shared by the user on social media. The information provider can also analyze the user's social media activity and provide the most relevant information. Furthermore, the information provider can provide information based on topics the user has shown interest in on social media. This allows the information provider to propose the most suitable method of providing information based on the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input data on the user's social media activity into a generating AI and have the generating AI propose methods of providing information.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects data. The collection unit can collect data such as text data, numerical data, and image data. The collection unit can collect environmental data using sensors, for example. The collection unit can also collect publicly available data from the internet, for example. Furthermore, the collection unit can also collect user input data, for example. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, perform statistical analysis. It can also analyze the data by applying, for example, machine learning algorithms. Furthermore, the analysis unit can analyze the data using, for example, data mining techniques. Step 3: The service provider provides information based on the data analyzed by the analysis unit. The service provider can provide information in, for example, a report format. Alternatively, the service provider can provide information through, for example, a dashboard display. Furthermore, the service provider can provide information through, for example, alert notifications.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a mechanism for improving the data utilization skills of DWH users using a generation AI. This system gathers disparate information about SB systems in one place, trains the generation AI, and centrally manages the architecture, data linkage, structure, hardware, and software of each system. This entrusts the generation AI with the task of organizing information that will lead to system unification, and serves as the foundation for creating a mechanism that leads to unified SB system information management among employees, improved IT skills, and business contributions. For example, it trains the AI ​​on information and logic related to DWH tables, making system data easy to use and providing information with a single click. It also manages information such as generating SQL according to the purpose of use, searching and coordinating with the responsible department from among more than 1000 organizations, and unifying search conditions with other departments. As a result, DWH users will have a clear understanding of how to set search conditions according to their purpose of use, and will be able to understand DWH table definitions, item definitions, and list values. It also resolves the lack of knowledge required to write queries and SQL, and lays the foundation for realizing data-driven management. In this way, the system can serve as the foundation for creating a mechanism that leads to unified SB system information management among employees, improved IT skills, and business contributions.

[0059] The system according to this embodiment comprises a data collection unit, an analysis unit, and a data provision unit. The data collection unit collects data. The data collection unit can collect data such as text data, numerical data, and image data. The data collection unit can collect environmental data using sensors, for example. The data collection unit can also collect publicly available data from the internet, for example. Furthermore, the data collection unit can also collect user input data, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can perform statistical analysis, for example. The analysis unit can also analyze data by applying machine learning algorithms, for example. Furthermore, the analysis unit can analyze data using data mining techniques, for example. The data provision unit provides information based on the data analyzed by the analysis unit. The data provision unit can provide information in report format, for example. The data provision unit can also provide information through a dashboard display, for example. Furthermore, the data provision unit can provide information through alert notifications, for example. This enables the system to efficiently collect, analyze, and provide data. Some or all of the above-described processes in the collection unit, analysis unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired from sensors into a generation AI and have the generation AI perform data preprocessing. The analysis unit can input data collected by the collection unit into a generation AI and have the generation AI perform data analysis. The provision unit can input data analyzed by the analysis unit into a generation AI and have the generation AI provide the information.

[0060] The data collection unit collects data. For example, the data collection unit can collect data such as text data, numerical data, and image data. Specifically, text data includes news articles, blog posts, and social media comments. Numerical data includes measurements from sensors, statistical data, and sales data. Image data includes surveillance camera footage, satellite images, and medical images. The data collection unit can also collect environmental data using sensors. Environmental data includes temperature, humidity, atmospheric pressure, wind speed, and light intensity, and this data is collected in real time. Furthermore, the data collection unit can also collect publicly available data from the internet. Publicly available data includes statistical and weather data provided by government agencies, and financial data published by companies. This data is often automatically retrieved via APIs. The data collection unit can also collect user input data. User input data includes survey responses, feedback, and files uploaded by users. This allows the data collection unit to collect a wide range of data from diverse data sources, strengthening the overall data infrastructure of the system. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and provisioning units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can perform statistical analysis. Statistical analysis includes calculating basic statistics such as the mean, median, and standard deviation. It can also utilize advanced statistical methods such as regression analysis, correlation analysis, and time series analysis. Furthermore, the analysis unit can analyze data by applying machine learning algorithms. These include classification, regression, clustering, and anomaly detection, which can be used to extract patterns and trends from the data. For example, it can perform object recognition using collected image data and analyze the frequency of occurrence of specific objects. It can also build predictive models using collected numerical data to forecast future trends. Additionally, the analysis unit can analyze data using data mining techniques. These include association rule discovery, pattern recognition, and clustering, which can be used to extract useful knowledge from the data. For example, it can analyze customer purchase history data to discover purchase patterns and related products. This allows the analysis unit to analyze collected data from multiple perspectives and strengthen the knowledge base of the entire system. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions or time periods based on historical data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0062] The service provider provides information based on data analyzed by the analysis unit. For example, the service provider can provide information in report format. Reports include an overview of the analysis results, detailed analysis results, and recommended actions. This allows users to grasp the analysis results at a glance and make appropriate decisions. The service provider can also provide information through dashboard displays. Dashboards visually display real-time data and analysis results, allowing users to intuitively understand the information. Furthermore, the service provider can provide information through alert notifications. Alert notifications include a function to immediately notify users when anomalies are detected or risks occur. This allows users to respond quickly and minimize risks. The service provider can input data analyzed by the analysis unit into a generation AI, which then provides the information. The generation AI uses natural language generation technology to convert analysis results into easy-to-understand text and graphs. For example, the generation AI can automatically generate reports based on the analysis results and provide them to users. The generation AI also automatically optimizes the design and layout of dashboards, enabling users to quickly obtain the necessary information. Furthermore, the generation AI can optimize the content and timing of alert notifications, providing information in the most effective way for the user. This allows the service provider to deliver information quickly and appropriately to users, improving the overall efficiency and effectiveness of the system.

[0063] The data provision unit learns information and processing logic related to DWH tables, making system data easy to use and enabling one-click information provision. For example, the data provision unit learns the structure and data attributes of DWH tables and provides an intuitive interface to the user. The data provision unit also learns processing logic and can automatically perform data preprocessing and transformation. Furthermore, the data provision unit can design a user interface for one-click information provision and simplify the operation flow. This enables quick and easy provision of information related to DWH tables. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without AI. For example, the data provision unit can input information from DWH tables into a generating AI and have the generating AI perform information learning and provision.

[0064] The analysis unit can generate SQL tailored to its intended use. For example, it can generate SQL for data analysis. It can also generate SQL for report creation. Furthermore, it can generate SQL for building predictive models. This enables the generation of SQL tailored to specific uses. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the intended use into a generation AI and have the generation AI execute the SQL generation.

[0065] The data collection unit can search for and coordinate responsible departments from among more than 1000 organizations. The data collection unit can search for organizations such as companies, departments, and teams. It can also identify responsible departments such as project management departments and data management departments. Furthermore, the data collection unit can coordinate responsible departments based on the search results. This allows for efficient searching and coordination of responsible departments. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input organizational information into a generating AI and have the generating AI perform the searching and coordination of responsible departments.

[0066] The information provision department can manage information such as standardizing search criteria with other departments. For example, the information provision department can standardize filtering conditions. It can also standardize search queries. Furthermore, the information provision department can provide an interface to facilitate information sharing with other departments. This makes it possible to standardize search criteria with other departments. Some or all of the above processes in the information provision department may be performed using AI, for example, or not using AI. For example, the information provision department can input search criterion information into a generating AI and have the generating AI perform the standardization process.

[0067] 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. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can shorten the timing of data collection to quickly collect the necessary data. This allows the timing of data collection to be adjusted 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 may be, 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 or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0068] The data collection unit can optimize the types of data to be collected based on the user's past usage history. For example, the data collection unit can prioritize collecting data that the user has frequently used in the past. The data collection unit can also predict and collect data needed at specific time periods based on the user's past usage history. Furthermore, the data collection unit can analyze the user's past usage history and collect the most relevant data. This allows for the optimization of data collection based on the user's past usage history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past usage history into a generating AI and have the generating AI perform the optimization of data collection.

[0069] The data collection unit can filter data based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can grasp the user's work situation in real time and appropriately filter and collect the necessary data. This allows data to be filtered based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's work situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone the collection of less important data. Conversely, if the user is relaxed, the data collection unit may prioritize the collection of detailed data. Furthermore, if the user is in a hurry, the data collection unit may prioritize the collection of the most important data. This allows for the prioritization of data 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 may be, 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, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0071] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect the most relevant data based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can collect the necessary data based on their current location. This allows for the optimization of data collection based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0072] The data collection unit can analyze the 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. The data collection unit can also analyze the user's social media activity and collect the most relevant data. Furthermore, the data collection unit can collect data based on topics the user has shown interest in on social media. This allows for the collection of relevant data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI perform the collection of relevant data.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, 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, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply a statistical analysis algorithm to numerical data. Furthermore, it can apply an image analysis algorithm to image data. This allows the optimal analysis algorithm to be applied according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select the analysis algorithm to apply.

[0076] 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 tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0077] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also postpone the analysis of older data. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows the analysis priority to be determined based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows the order of analysis to be adjusted based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0079] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible method of information delivery. If the user is relaxed, the information provider can also provide a method of information delivery that includes detailed information. Furthermore, if the user is in a hurry, the information provider can provide a concise method of information delivery. This allows the information provider to adjust the method of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the method of information delivery.

[0080] The information provider can select the most suitable information provision method by referring to the user's past usage history when providing information. For example, the information provider can prioritize providing information using methods the user has used in the past. The information provider can also select the most suitable information provision method based on the user's past usage history. Furthermore, the information provider can analyze the user's past usage history and provide the most effective information provision method. This allows the information provider to select the most suitable information provision method based on the user's past usage history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past usage history into a generating AI and have the generating AI select the information provision method.

[0081] The information delivery unit can customize the means of information delivery based on the user's current work situation when providing information. For example, the information delivery unit can prioritize providing information related to the project the user is currently working on. The information delivery unit can also grasp the user's work situation in real time and select the most appropriate means of information delivery. Furthermore, the information delivery unit can dynamically customize the means of information delivery based on the user's work situation. This allows for the customization of the means of information delivery based on the user's work situation. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the user's work situation into a generating AI and have the generating AI perform the customization of the means of information delivery.

[0082] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider may postpone the delivery of less important information. Conversely, if the user is relaxed, the information provider may prioritize the delivery of detailed information. Furthermore, if the user is in a hurry, the information provider may prioritize the delivery of the most important information. This allows the information provider to determine the priority of information delivery 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information delivery.

[0083] The information delivery unit can select the most appropriate method of information delivery based on the user's geographical location information when providing information. For example, if the user is in a specific region, the information delivery unit will prioritize providing information related to that region. The information delivery unit can also provide the most relevant information based on the user's geographical location information. Furthermore, if the user is on the move, the information delivery unit can provide necessary information based on their current location. This allows the system to select the most appropriate method of information delivery based on the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's geographical location information into a generating AI and have the generating AI select the method of information delivery.

[0084] The information provider can analyze the user's social media activity and propose methods for providing information when providing information. For example, the information provider can provide relevant information based on information shared by the user on social media. The information provider can also analyze the user's social media activity and provide the most relevant information. Furthermore, the information provider can provide information based on topics the user has shown interest in on social media. This allows the information provider to propose the most suitable method of providing information based on the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input data on the user's social media activity into a generating AI and have the generating AI propose methods of providing information.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] 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. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can shorten the timing of data collection to quickly collect the necessary data. This allows the timing of data collection to be adjusted 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 may be, 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 or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, 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, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0088] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible method of information delivery. If the user is relaxed, the information provider can also provide a method of information delivery that includes detailed information. Furthermore, if the user is in a hurry, the information provider can provide a concise method of information delivery. This allows the information provider to adjust the method of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the method of information delivery.

[0089] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider may postpone the delivery of less important information. Conversely, if the user is relaxed, the information provider may prioritize the delivery of detailed information. Furthermore, if the user is in a hurry, the information provider may prioritize the delivery of the most important information. This allows the information provider to determine the priority of information delivery 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information delivery.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows the level of detail of the analysis to be adjusted according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0091] The data collection unit can optimize the types of data to be collected based on the user's past usage history. For example, the data collection unit can prioritize collecting data that the user has frequently used in the past. The data collection unit can also predict and collect data needed at specific time periods based on the user's past usage history. Furthermore, the data collection unit can analyze the user's past usage history and collect the most relevant data. This allows for the optimization of data collection based on the user's past usage history. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past usage history into a generating AI and have the generating AI perform the optimization of data collection.

[0092] The data collection unit can filter data based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can grasp the user's work situation in real time and appropriately filter and collect the necessary data. This allows data to be filtered based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information about the user's work situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0093] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect the most relevant data based on the user's geographical location information. Furthermore, if the user is on the move, the data collection unit can collect the necessary data based on their current location. This allows for the optimization of data collection based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0094] The information provider can select the most suitable information provision method by referring to the user's past usage history when providing information. For example, the information provider can prioritize providing information using methods the user has used in the past. The information provider can also select the most suitable information provision method based on the user's past usage history. Furthermore, the information provider can analyze the user's past usage history and provide the most effective information provision method. This allows the information provider to select the most suitable information provision method based on the user's past usage history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's past usage history into a generating AI and have the generating AI select the information provision method.

[0095] The information provider can analyze the user's social media activity and propose methods for providing information when providing information. For example, the information provider can provide relevant information based on information shared by the user on social media. The information provider can also analyze the user's social media activity and provide the most relevant information. Furthermore, the information provider can provide information based on topics the user has shown interest in on social media. This allows the information provider to propose the most suitable method of providing information based on the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input data on the user's social media activity into a generating AI and have the generating AI propose methods of providing information.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The collection unit collects data. The collection unit can collect data such as text data, numerical data, and image data. The collection unit can collect environmental data using sensors, for example. The collection unit can also collect publicly available data from the internet, for example. Furthermore, the collection unit can also collect user input data, for example. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, perform statistical analysis. It can also analyze the data by applying, for example, machine learning algorithms. Furthermore, the analysis unit can analyze the data using, for example, data mining techniques. Step 3: The service provider provides information based on the data analyzed by the analysis unit. The service provider can provide information in, for example, a report format. Alternatively, the service provider can provide information through, for example, a dashboard display. Furthermore, the service provider can provide information through, for example, alert notifications.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision 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 sensors and internet connection of the smart device 14 and manages the collected data with the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The data provision unit is implemented in the control unit 46A of the smart device 14 and provides the analysis results in the form of reports, dashboard displays, alert notifications, etc. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented 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 sensors and internet connection of the smart glasses 214 and manages the collected data with the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The data provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the analysis results in the form of reports, dashboard displays, alert notifications, etc. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision 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 sensors and internet connection of the headset terminal 314 and manages the collected data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The data provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the analysis results in the form of reports, dashboard displays, alert notifications, etc. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the robot 414's sensors and internet connectivity, and manages the collected data using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The data provision unit is implemented in the control unit 46A of the robot 414 and provides the analysis results in the form of reports, dashboard displays, alert notifications, etc. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a providing unit that provides information based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Learns information and logic from DWH tables, makes system data easier to use, and provides information with a single click. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Generate SQL tailored to your specific needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Searching for and coordinating with the relevant department from among more than 1000 organizations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, This involves managing information, such as standardizing search criteria across different departments. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is The types of data collected are optimized based on the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, the system selects the most suitable method of information delivery by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, customize the method of information delivery based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, the most suitable method of information delivery is selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we analyze users' social media activity and propose methods for providing that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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 analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a providing unit that provides information based on the data analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, Learns information and logic from DWH tables, makes system data easier to use, and provides information with a single click. The system according to feature 1.

3. The aforementioned analysis unit, Generate SQL tailored to your specific needs. The system according to feature 1.

4. The aforementioned collection unit is Search for and coordinate with the relevant department from among numerous organizations. The system according to feature 1.

5. The aforementioned supply unit is, This involves managing information, such as standardizing search criteria across different departments. The system according to feature 1.

6. 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 according to feature 1.

7. The aforementioned collection unit is The types of data collected are optimized based on the user's past usage history. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system according to feature 1.

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