system

Abstract

The system according to this embodiment aims to efficiently support everything from defining data analysis requirements to generating reports. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives requirements from the user. The analysis unit analyzes the requirements received by the reception unit and identifies the necessary data sets. The collection unit collects and organizes data based on the data sets identified by the analysis unit. The analysis unit analyzes the data collected by the collection unit and creates a report. The provision unit provides the report created by the analysis unit.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the requirement definition, design, construction, and operation of data analysis are complicated and difficult to execute when the skill set is insufficient.

[0005] The system according to the embodiment aims to efficiently support from the requirement definition of data analysis to report creation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives requirements from users. The analysis unit analyzes the requirements received by the reception unit and identifies the necessary data sets. The collection unit collects and organizes data based on the data sets identified by the analysis unit. The analysis unit analyzes the data collected by the collection unit and creates a report. The provision unit provides the report created by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently support everything from defining data analysis requirements to generating reports. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The data analysis support system according to an embodiment of the present invention is an AI agent for supporting data-driven sales and management activities. This data analysis support system provides an AI agent that supports implementation and reporting in each phase of data analysis, from requirements definition, design and construction to operation. This enables data-driven sales and strategy formulation even in departments or small and medium-sized enterprises that lack sufficient data analysis skills. For example, a user inputs data analysis requirements. For example, a user inputs a requirement such as, "I want to analyze sales data and forecast sales for the next quarter." This requirement is input into the generating AI. Next, the generating AI analyzes the input requirements and identifies the necessary data sets. The generating AI proposes which data should be collected, methods for data cleansing and preparation, and the selection of appropriate analysis methods and tools. For example, it proposes methods for collecting sales data, customer data, marketing data, etc., and for performing data cleansing and preparation. Based on the proposed data sets, the generating AI collects and prepares the data. For example, it collects sales data, performs data cleansing, and uses the prepared data for analysis. Next, the generating AI analyzes the data and creates a report of the analysis results. For example, sales data can be analyzed to forecast sales for the next quarter, and the results compiled into a report. Finally, the report created by the generative AI is provided to the user. Based on the report created by the generative AI, the user can conduct data-driven sales and strategy formulation. This system enables data-driven sales and strategy formulation even in departments or small and medium-sized enterprises that lack data analysis skill sets. For example, by having the generative AI support implementation and reporting in each phase of data analysis—from requirements definition, design and construction to operation—the analysis necessary for data-driven sales and strategy formulation, and the creation of reports for management, etc., can be accelerated. As a result, the data analysis support system can enable data-driven sales and strategy formulation to be conducted quickly and efficiently.

[0029] The data analysis support system according to this embodiment comprises a reception unit, an analysis unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives requirements from users. For example, a user can input a requirement such as, "I want to analyze sales data and make a sales forecast for the next quarter." After receiving the requirements, the reception unit transmits them to the analysis unit. The analysis unit analyzes the requirements received by the reception unit and identifies the necessary data sets. For example, the analysis unit identifies sales data, customer data, marketing data, etc. The analysis unit proposes which data should be collected, methods for data cleansing and preparation, and the selection of appropriate analysis methods and tools. For example, the analysis unit can propose methods for cleansing and preparing sales data. The collection unit collects and prepares data based on the data sets identified by the analysis unit. For example, the collection unit collects sales data, cleanses the data, and uses the prepared data for analysis. After collecting and preparing the data, the collection unit transmits the data to the analysis unit. The analysis unit analyzes the data collected by the collection unit and creates a report of the analysis results. For example, the analysis department analyzes sales data, makes sales forecasts for the next quarter, and compiles the results into a report. The service department provides the report created by the analysis department to the user. For example, the service department provides the user with a report created by a generation AI. In this way, the data analysis support system according to the embodiment can collect, analyze, organize, analyze, and provide data based on user requirements, enabling data-driven sales and strategy formulation.

[0030] The reception department receives requirements from users. For example, a user can input a requirement such as, "I want to analyze sales data and forecast sales for the next quarter." The reception department receives requirements through a user interface. The user interface is provided via a web browser or mobile application and is designed to be intuitive for users. When inputting requirements, users can specify details such as the type of data, the purpose of the analysis, and the desired output format. For example, in addition to analyzing sales data, it is also possible to input requirements to evaluate sales trends by customer segment or the effectiveness of marketing campaigns. The reception department uses natural language processing technology to automatically analyze the input requirements and extract the necessary information. This ensures that even if the user's input requirements are vague, the system can perform an appropriate analysis and identify the precise requirements. After receiving the requirements, the reception department transmits them to the analysis department. The transmission of requirements is done using a secure communication protocol, ensuring the confidentiality and integrity of the data. This ensures that the user's requirements are accurately and securely transmitted to the analysis department.

[0031] The analysis department analyzes the requirements received by the reception department and identifies the necessary data sets. For example, the analysis department identifies sales data, customer data, and marketing data. The analysis department utilizes natural language processing technology and machine learning algorithms to analyze the requirements. This allows it to accurately understand the user's requirements and identify the necessary data. Based on the identified data sets, the analysis department proposes which data should be collected, methods for data cleansing and preparation, and the selection of appropriate analytical methods and tools. For example, the analysis department can propose methods for cleansing and preparing sales data. Specifically, it may propose methods for imputing missing values ​​in sales data, detecting outliers, and methods for data normalization and standardization. The analysis department also proposes data sources and collection methods. For example, sales data can be collected from the company's ERP system and POS system, and customer data can be collected from CRM systems and marketing automation tools. Based on these proposals, the analysis department instructs the collection department to efficiently collect and prepare the data.

[0032] The data collection unit collects and prepares data based on the data sets identified by the analysis unit. For example, the data collection unit collects sales data, cleanses the data, and uses the prepared data for analysis. The data collection unit accesses various data sources to collect and prepare data. For example, sales data can be collected from a company's ERP and POS systems, and customer data can be collected from CRM systems and marketing automation tools. The data collection unit uses APIs to retrieve data from these data sources and performs data cleansing and preparation. Data cleansing includes techniques such as imputing missing values, detecting outliers, and normalizing and standardizing the data. For example, to imputate missing values ​​in sales data, estimates can be calculated using historical data and other relevant data. Also, for detecting outliers, statistical methods and machine learning algorithms are used to analyze the distribution and patterns of the data and identify anomalous data points. After cleansing and preparing the data, the data collection unit sends the prepared data to the analysis unit. Data transmission is performed using secure communication protocols to ensure data confidentiality and integrity. This allows the data collection unit to collect and prepare data efficiently and accurately.

[0033] The analysis department analyzes data collected by the data collection department and creates reports based on the analysis results. For example, the analysis department analyzes sales data to forecast sales for the next quarter and compiles the results into a report. The analysis department uses various statistical methods and machine learning algorithms in its data analysis. For example, it can use time series analysis to analyze trends in sales data and forecast sales for the next quarter. It can also use regression analysis to identify factors influencing sales and build sales forecasting models. Furthermore, it can use clustering techniques to segment customer data and analyze sales trends for each segment. Based on these analysis results, the analysis department creates reports that provide useful information to users. The reports include an overview of the analysis results, detailed data, and visual information such as graphs and charts. For example, they may include graphs showing sales forecast results and charts showing sales trends for each customer segment. The analysis department can also utilize generative AI in creating reports. Generative AI uses natural language generation technology to automatically generate easy-to-understand text explaining the analysis results. This allows users to intuitively understand the analysis results.

[0034] The service department provides users with reports created by the analysis department. For example, the service department provides users with reports created by generative AI. The service department can use various methods to provide reports. For example, reports can be made available for download via a web portal, sent via email, or made available for viewing through a dedicated mobile application. The service department selects the most suitable method considering user convenience. For example, reports can be provided via a mobile application so that users can check them while on the go. The service department also takes measures to ensure security when providing reports. For example, user authentication is required for downloading and viewing reports, and data is encrypted to prevent the leakage of confidential information. Furthermore, the service department can collect feedback from users and use it to improve the content and delivery method of reports. For example, if a user requests additional analysis or information regarding the content of a report, the service department can pass on the request to the reception department, which can then perform further analysis and report creation. This allows the service department to provide users with high-quality reports and support data-driven sales and strategy development.

[0035] The analysis unit can propose which data should be collected, methods for data cleansing and preparation, and the selection of specific analysis techniques and tools. For example, the analysis unit can identify sales data, customer data, marketing data, etc., and propose methods for cleansing and preparing this data. The analysis unit can propose cleansing methods such as imputing missing values ​​and removing outliers. It can also propose preparation methods such as data normalization and standardization of data formats. Furthermore, the analysis unit can propose specific analysis techniques such as regression analysis, clustering, and classification. For example, the analysis unit can propose applying regression analysis to sales data. The analysis unit can also propose the selection of specific analysis tools such as Python libraries or R packages. As a result, the accuracy of data preparation and analysis is improved by the analysis unit proposing appropriate data collection methods and analysis techniques. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input user requirements into generative AI, which can analyze the requirements and propose data collection methods and analysis techniques.

[0036] The data collection unit can collect and organize data based on the proposed data set. For example, the data collection unit can collect sales data, cleanse the data, and use the organized data for analysis. The data collection unit can, for example, read sales data with a scanner and save it as image data. Then, the data collection unit can convert the image data into text data using OCR technology. Alternatively, the data collection unit can take a picture of the sales data using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The data collection unit can also write sales data with a dedicated digital pen, and the digital pen can convert it into digital data in real time. For example, the movement of the pen can be detected by a sensor and saved as character data. This improves the quality of the data by having the data collection unit collect and organize data based on the proposed data set. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the proposed data set into a generative AI, and the generative AI can collect and organize the data.

[0037] The analytics department can analyze collected data and create reports based on the analysis results. For example, the analytics department can analyze sales data to forecast sales for the next quarter and compile the results into a report. The analytics department can analyze data by applying statistical analysis or machine learning algorithms, for example. For example, the analytics department can apply regression analysis to sales data to forecast sales. The analytics department can also apply clustering to customer data to identify customer segments. Furthermore, the analytics department can apply classification algorithms to marketing data to evaluate the effectiveness of marketing campaigns. The analytics department can create reports based on the analysis results and present them visually using graphs and charts. For example, the analytics department can display sales forecast results as a line graph and customer segment results as a pie chart. This enables data-driven decision-making by allowing the analytics department to analyze collected data and create reports. Some or all of the above processes in the analytics department may be performed using generative AI, or not. For example, the analytics department can input collected data into a generative AI, which can then analyze the data and create a report.

[0038] The service provider can provide users with reports generated by the generation AI. For example, the service provider can provide users with a sales forecast report generated by the generation AI. The service provider can, for example, send the report by email. The service provider can also publish the report on a website. Furthermore, the service provider can provide the report in paper form. For example, the service provider can generate a sales forecast report in PDF format and send it by email. The service provider can also upload the report to a website so that users can download it. Furthermore, the service provider can print the report on a printer and provide it in paper form. This allows users to conduct data-driven sales and strategy development by providing them with reports from the service provider. Some or all of the above processes in the service provider may be performed using the generation AI or not. For example, the service provider can build a system that automatically sends reports generated by the generation AI.

[0039] The reception department can analyze a user's past requirement submission history and select the most suitable reception method. For example, the reception department can prioritize suggesting reception methods that the user has frequently used in the past. Furthermore, the reception department can select the most efficient reception method based on the user's past requirement submission history. In addition, the reception department can analyze the user's past requirement submission history and suggest the most suitable reception method for a specific time period. This allows for efficient requirement reception by selecting the optimal method through analysis of the user's past requirement submission history. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the user's past requirement submission history data into an AI, which can then select the most suitable reception method.

[0040] The reception desk can filter requirements based on the user's current projects and areas of interest when receiving them. For example, the reception desk can prioritize requirements related to projects the user is currently working on. It can also filter and accept relevant requirements based on the user's areas of interest. Furthermore, the reception desk can accept the most relevant requirements based on the progress of the user's current projects. This allows for the priority acceptance of highly relevant requirements by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's project data and area of ​​interest data into an AI, which can then filter the requirements.

[0041] The reception desk can prioritize receiving requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize requests related to that region. Furthermore, the reception desk can suggest the most relevant requests based on the user's geographical location. Additionally, if the user is on the move, the reception desk can also prioritize requests based on their current location. This allows for priority processing of requests based on their relevance by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into an AI, which can then select the most relevant requests.

[0042] The reception desk can analyze the user's social media activity when receiving requirements and accept relevant requirements. For example, the reception desk can prioritize requirements related to topics of interest based on the user's social media activity. It can also analyze the user's social media posts and suggest relevant requirements. Furthermore, the reception desk can accept relevant requirements by referring to the activities of the user's social media followers and friends. This allows for the prioritization of relevant requirements by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then select relevant requirements.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the requirements during the analysis. For example, the analysis unit can perform a detailed analysis for high-importance requirements and a simplified analysis for low-importance requirements. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the requirements. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the requirements. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input requirement importance data into a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of requirements during analysis. For example, the analysis unit can apply a time series analysis algorithm to sales data. It can also apply a clustering algorithm to customer data. Furthermore, it can apply a regression analysis algorithm to marketing data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of requirements. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the category data of requirements into a generative AI, which can then select an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the submission date of the requirements. For example, the analysis unit will prioritize the analysis of requirements submitted earlier. It can also postpone the analysis of requirements submitted later. Furthermore, the analysis unit can adjust the priority of the analysis in stages based on the submission date. This enables efficient analysis by determining the priority of the analysis based on the submission date of the requirements. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input requirement submission date data into a generation AI, and the generation AI can determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the requirements during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant requirements. It can also postpone the analysis of less relevant requirements. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the requirements. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the requirements. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input requirement relevance data into a generation AI, and the generation AI can adjust the order of analysis.

[0047] The data collection unit can analyze the user's past data collection history to select the optimal collection method during data collection. For example, the collection unit can prioritize suggesting data collection methods previously used by the user. Furthermore, the collection unit can select the most efficient collection method based on the user's past data collection history. In addition, the collection unit can analyze the user's past data collection history and suggest the optimal collection method for a specific time period. This allows for efficient data collection by analyzing the user's past data collection history and selecting the most suitable method. Some or all of the above-described processes in the collection unit may be performed using AI, or they may not. For example, the collection unit can input the user's past data collection history data into an AI, which can then select the optimal collection method.

[0048] The data collection unit can filter data based on the user's current lifestyle 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. It can also filter and collect relevant data based on the user's areas of interest. Furthermore, the data collection unit can collect the most relevant data according to the user's current lifestyle. This allows for the priority collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest. 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 lifestyle data and areas of interest data into an AI, which can then filter the data.

[0049] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also suggest 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 also collect highly relevant data based on their current location. In this way, by collecting data while considering the user's geographical location information, highly relevant data can be prioritized. 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 the user's geographical location information data into AI, which can then select highly relevant data.

[0050] 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 prioritize collecting data related to topics of interest from the user's social media activity. It can also analyze the content of the user's social media posts and suggest relevant data. Furthermore, the data collection unit can collect relevant data by referring to the activities of the user's social media followers and friends. This allows for the priority collection of relevant data by analyzing the user's social media activity. 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 the user's social media data into an AI, which can then select relevant data.

[0051] 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 can perform a detailed analysis on highly important data, and a concise analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data importance data into a generative AI, and the generative AI can adjust the level of detail of the analysis.

[0052] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a time series analysis algorithm to sales data. It can also apply a clustering algorithm to customer data. Furthermore, it can apply a regression analysis algorithm to marketing data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input data category information into a generative AI, which can then select an appropriate analysis algorithm.

[0053] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department can prioritize analyzing data submitted earlier. It can also postpone analyzing data submitted later. Furthermore, the analysis department can adjust the priority of analysis in stages based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the analysis department can input data submission date data into a generative AI, and the generative AI can determine the priority of analysis.

[0054] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data relevance data into a generative AI, and the generative AI can adjust the order of analysis.

[0055] The delivery unit can select the optimal delivery method by referring to the user's past report viewing history when providing reports. For example, the delivery unit can prioritize providing reports in the format the user has previously viewed. Furthermore, the delivery unit can select the most efficient delivery method based on the user's past report viewing history. In addition, the delivery unit can analyze the user's past report viewing history and suggest the optimal delivery method for specific time periods. This enables efficient report delivery by selecting the optimal delivery method based on the user's past report viewing history. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's past report viewing history data into AI, which can then select the optimal delivery method.

[0056] The service provider can select the optimal delivery method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a report adapted to the screen size. If the user is using a tablet, the service provider can provide a report optimized for a larger screen. Furthermore, if the user is using a desktop computer, the service provider can provide a detailed report. By providing reports while considering the user's device information, the service provider can deliver reports in the most optimal format for the user. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user device information data into AI, which can then select the optimal delivery method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception department can analyze a user's past requirement submission history and select the most suitable reception method. For example, the reception department will prioritize suggesting reception methods that the user has frequently used in the past. Furthermore, the reception department can select the most efficient reception method based on the user's past requirement submission history. In addition, the reception department can analyze the user's past requirement submission history and suggest the most suitable reception method for specific time periods. This allows for efficient requirement reception by selecting the optimal method through analysis of the user's past requirement submission history.

[0059] The reception desk can filter requirements based on the user's current projects and areas of interest. For example, it can prioritize requirements related to projects the user is currently working on. It can also filter and accept relevant requirements based on the user's areas of interest. Furthermore, it can accept the most relevant requirements based on the progress of the user's current projects. This allows for the prioritization of highly relevant requirements by filtering them based on the user's current projects and areas of interest.

[0060] The data collection unit can analyze the user's past data collection history to select the optimal collection method during data collection. For example, the collection unit prioritizes suggesting data collection methods the user has used in the past. Furthermore, the collection unit can select the most efficient collection method based on the user's past data collection history. In addition, the collection unit can analyze the user's past data collection history and suggest the most suitable collection method for a specific time period. This allows for efficient data collection by analyzing the user's past data collection history and selecting the optimal method.

[0061] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the data collection process. For example, the unit prioritizes collecting data related to projects the user is currently working on. It can also filter and collect relevant data based on the user's areas of interest. Furthermore, the unit can collect the most relevant data according to the user's current lifestyle. This allows for the priority collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest.

[0062] The service provider can select the optimal delivery method by referring to the user's past report viewing history when providing reports. For example, the service provider can prioritize providing reports in the format the user has previously viewed. Furthermore, the service provider can select the most efficient delivery method based on the user's past report viewing history. In addition, the service provider can analyze the user's past report viewing history and suggest the most suitable delivery method for specific time periods. This allows for efficient report delivery by selecting the optimal method based on the user's past report viewing history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception department receives requirements from users. For example, a user can enter a requirement such as, "I want to analyze sales data and make a sales forecast for the next quarter." After receiving the requirement, the reception department sends it to the analysis department. Step 2: The analysis department analyzes the requirements received by the reception department and identifies the necessary data sets. For example, the analysis department identifies sales data, customer data, marketing data, etc. The analysis department proposes which data should be collected, how to cleanse and prepare the data, and the selection of appropriate analytical methods and tools. Step 3: The data collection unit collects and prepares data based on the data sets identified by the analysis unit. For example, the data collection unit collects sales data, cleanses the data, and uses the prepared data for analysis. After collecting and preparing the data, the data collection unit sends the data to the analysis unit. Step 4: The analysis department analyzes the data collected by the data collection department and creates a report based on the analysis results. For example, the analysis department analyzes sales data to forecast sales for the next quarter and compiles the results into a report. Step 5: The service department provides the user with the report created by the analysis department. For example, the service department provides the user with a report created by the generation AI.

[0065] (Example of form 2) The data analysis support system according to an embodiment of the present invention is an AI agent for supporting data-driven sales and management activities. This data analysis support system provides an AI agent that supports implementation and reporting in each phase of data analysis, from requirements definition, design and construction to operation. This enables data-driven sales and strategy formulation even in departments or small and medium-sized enterprises that lack sufficient data analysis skills. For example, a user inputs data analysis requirements. For example, a user inputs a requirement such as, "I want to analyze sales data and forecast sales for the next quarter." This requirement is input into the generating AI. Next, the generating AI analyzes the input requirements and identifies the necessary data sets. The generating AI proposes which data should be collected, methods for data cleansing and preparation, and the selection of appropriate analysis methods and tools. For example, it proposes methods for collecting sales data, customer data, marketing data, etc., and for performing data cleansing and preparation. Based on the proposed data sets, the generating AI collects and prepares the data. For example, it collects sales data, performs data cleansing, and uses the prepared data for analysis. Next, the generating AI analyzes the data and creates a report of the analysis results. For example, sales data can be analyzed to forecast sales for the next quarter, and the results compiled into a report. Finally, the report created by the generative AI is provided to the user. Based on the report created by the generative AI, the user can conduct data-driven sales and strategy formulation. This system enables data-driven sales and strategy formulation even in departments or small and medium-sized enterprises that lack data analysis skill sets. For example, by having the generative AI support implementation and reporting in each phase of data analysis—from requirements definition, design and construction to operation—the analysis necessary for data-driven sales and strategy formulation, and the creation of reports for management, etc., can be accelerated. As a result, the data analysis support system can enable data-driven sales and strategy formulation to be conducted quickly and efficiently.

[0066] The data analysis support system according to this embodiment comprises a reception unit, an analysis unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives requirements from users. For example, a user can input a requirement such as, "I want to analyze sales data and make a sales forecast for the next quarter." After receiving the requirements, the reception unit transmits them to the analysis unit. The analysis unit analyzes the requirements received by the reception unit and identifies the necessary data sets. For example, the analysis unit identifies sales data, customer data, marketing data, etc. The analysis unit proposes which data should be collected, methods for data cleansing and preparation, and the selection of appropriate analysis methods and tools. For example, the analysis unit can propose methods for cleansing and preparing sales data. The collection unit collects and prepares data based on the data sets identified by the analysis unit. For example, the collection unit collects sales data, cleanses the data, and uses the prepared data for analysis. After collecting and preparing the data, the collection unit transmits the data to the analysis unit. The analysis unit analyzes the data collected by the collection unit and creates a report of the analysis results. For example, the analysis department analyzes sales data, makes sales forecasts for the next quarter, and compiles the results into a report. The service department provides the report created by the analysis department to the user. For example, the service department provides the user with a report created by a generation AI. In this way, the data analysis support system according to the embodiment can collect, analyze, organize, analyze, and provide data based on user requirements, enabling data-driven sales and strategy formulation.

[0067] The reception department receives requirements from users. For example, a user can input a requirement such as, "I want to analyze sales data and forecast sales for the next quarter." The reception department receives requirements through a user interface. The user interface is provided via a web browser or mobile application and is designed to be intuitive for users. When inputting requirements, users can specify details such as the type of data, the purpose of the analysis, and the desired output format. For example, in addition to analyzing sales data, it is also possible to input requirements to evaluate sales trends by customer segment or the effectiveness of marketing campaigns. The reception department uses natural language processing technology to automatically analyze the input requirements and extract the necessary information. This ensures that even if the user's input requirements are vague, the system can perform an appropriate analysis and identify the precise requirements. After receiving the requirements, the reception department transmits them to the analysis department. The transmission of requirements is done using a secure communication protocol, ensuring the confidentiality and integrity of the data. This ensures that the user's requirements are accurately and securely transmitted to the analysis department.

[0068] The analysis department analyzes the requirements received by the reception department and identifies the necessary data sets. For example, the analysis department identifies sales data, customer data, and marketing data. The analysis department utilizes natural language processing technology and machine learning algorithms to analyze the requirements. This allows it to accurately understand the user's requirements and identify the necessary data. Based on the identified data sets, the analysis department proposes which data should be collected, methods for data cleansing and preparation, and the selection of appropriate analytical methods and tools. For example, the analysis department can propose methods for cleansing and preparing sales data. Specifically, it may propose methods for imputing missing values ​​in sales data, detecting outliers, and methods for data normalization and standardization. The analysis department also proposes data sources and collection methods. For example, sales data can be collected from the company's ERP system and POS system, and customer data can be collected from CRM systems and marketing automation tools. Based on these proposals, the analysis department instructs the collection department to efficiently collect and prepare the data.

[0069] The data collection unit collects and prepares data based on the data sets identified by the analysis unit. For example, the data collection unit collects sales data, cleanses the data, and uses the prepared data for analysis. The data collection unit accesses various data sources to collect and prepare data. For example, sales data can be collected from a company's ERP and POS systems, and customer data can be collected from CRM systems and marketing automation tools. The data collection unit uses APIs to retrieve data from these data sources and performs data cleansing and preparation. Data cleansing includes techniques such as imputing missing values, detecting outliers, and normalizing and standardizing the data. For example, to imputate missing values ​​in sales data, estimates can be calculated using historical data and other relevant data. Also, for detecting outliers, statistical methods and machine learning algorithms are used to analyze the distribution and patterns of the data and identify anomalous data points. After cleansing and preparing the data, the data collection unit sends the prepared data to the analysis unit. Data transmission is performed using secure communication protocols to ensure data confidentiality and integrity. This allows the data collection unit to collect and prepare data efficiently and accurately.

[0070] The analysis department analyzes data collected by the data collection department and creates reports based on the analysis results. For example, the analysis department analyzes sales data to forecast sales for the next quarter and compiles the results into a report. The analysis department uses various statistical methods and machine learning algorithms in its data analysis. For example, it can use time series analysis to analyze trends in sales data and forecast sales for the next quarter. It can also use regression analysis to identify factors influencing sales and build sales forecasting models. Furthermore, it can use clustering techniques to segment customer data and analyze sales trends for each segment. Based on these analysis results, the analysis department creates reports that provide useful information to users. The reports include an overview of the analysis results, detailed data, and visual information such as graphs and charts. For example, they may include graphs showing sales forecast results and charts showing sales trends for each customer segment. The analysis department can also utilize generative AI in creating reports. Generative AI uses natural language generation technology to automatically generate easy-to-understand text explaining the analysis results. This allows users to intuitively understand the analysis results.

[0071] The service department provides users with reports created by the analysis department. For example, the service department provides users with reports created by generative AI. The service department can use various methods to provide reports. For example, reports can be made available for download via a web portal, sent via email, or made available for viewing through a dedicated mobile application. The service department selects the most suitable method considering user convenience. For example, reports can be provided via a mobile application so that users can check them while on the go. The service department also takes measures to ensure security when providing reports. For example, user authentication is required for downloading and viewing reports, and data is encrypted to prevent the leakage of confidential information. Furthermore, the service department can collect feedback from users and use it to improve the content and delivery method of reports. For example, if a user requests additional analysis or information regarding the content of a report, the service department can pass on the request to the reception department, which can then perform further analysis and report creation. This allows the service department to provide users with high-quality reports and support data-driven sales and strategy development.

[0072] The analysis unit can propose which data should be collected, methods for data cleansing and preparation, and the selection of specific analysis techniques and tools. For example, the analysis unit can identify sales data, customer data, marketing data, etc., and propose methods for cleansing and preparing this data. The analysis unit can propose cleansing methods such as imputing missing values ​​and removing outliers. It can also propose preparation methods such as data normalization and standardization of data formats. Furthermore, the analysis unit can propose specific analysis techniques such as regression analysis, clustering, and classification. For example, the analysis unit can propose applying regression analysis to sales data. The analysis unit can also propose the selection of specific analysis tools such as Python libraries or R packages. As a result, the accuracy of data preparation and analysis is improved by the analysis unit proposing appropriate data collection methods and analysis techniques. Some or all of the above processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input user requirements into generative AI, which can analyze the requirements and propose data collection methods and analysis techniques.

[0073] The data collection unit can collect and organize data based on the proposed data set. For example, the data collection unit can collect sales data, cleanse the data, and use the organized data for analysis. The data collection unit can, for example, read sales data with a scanner and save it as image data. Then, the data collection unit can convert the image data into text data using OCR technology. Alternatively, the data collection unit can take a picture of the sales data using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The data collection unit can also write sales data with a dedicated digital pen, and the digital pen can convert it into digital data in real time. For example, the movement of the pen can be detected by a sensor and saved as character data. This improves the quality of the data by having the data collection unit collect and organize data based on the proposed data set. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the proposed data set into a generative AI, and the generative AI can collect and organize the data.

[0074] The analytics department can analyze collected data and create reports based on the analysis results. For example, the analytics department can analyze sales data to forecast sales for the next quarter and compile the results into a report. The analytics department can analyze data by applying statistical analysis or machine learning algorithms, for example. For example, the analytics department can apply regression analysis to sales data to forecast sales. The analytics department can also apply clustering to customer data to identify customer segments. Furthermore, the analytics department can apply classification algorithms to marketing data to evaluate the effectiveness of marketing campaigns. The analytics department can create reports based on the analysis results and present them visually using graphs and charts. For example, the analytics department can display sales forecast results as a line graph and customer segment results as a pie chart. This enables data-driven decision-making by allowing the analytics department to analyze collected data and create reports. Some or all of the above processes in the analytics department may be performed using generative AI, or not. For example, the analytics department can input collected data into a generative AI, which can then analyze the data and create a report.

[0075] The service provider can provide users with reports generated by the generation AI. For example, the service provider can provide users with a sales forecast report generated by the generation AI. The service provider can, for example, send the report by email. The service provider can also publish the report on a website. Furthermore, the service provider can provide the report in paper form. For example, the service provider can generate a sales forecast report in PDF format and send it by email. The service provider can also upload the report to a website so that users can download it. Furthermore, the service provider can print the report on a printer and provide it in paper form. This allows users to conduct data-driven sales and strategy development by providing them with reports from the service provider. Some or all of the above processes in the service provider may be performed using the generation AI or not. For example, the service provider can build a system that automatically sends reports generated by the generation AI.

[0076] The reception desk can estimate the user's emotions and adjust the timing of request acceptance based on the estimated emotions. For example, if the reception desk is stressed, it can delay the acceptance time to give the user time to relax. Conversely, if the reception desk is relaxed, it can immediately accept the request and begin processing quickly. Furthermore, if the reception desk is in a hurry, it can prioritize the acceptance of the request and process it quickly. In this way, by adjusting the timing of request acceptance according to the user's emotions, it is possible to reduce user stress and enable efficient request acceptance. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can estimate emotions and adjust the acceptance timing.

[0077] The reception department can analyze a user's past requirement submission history and select the most suitable reception method. For example, the reception department can prioritize suggesting reception methods that the user has frequently used in the past. Furthermore, the reception department can select the most efficient reception method based on the user's past requirement submission history. In addition, the reception department can analyze the user's past requirement submission history and suggest the most suitable reception method for a specific time period. This allows for efficient requirement reception by selecting the optimal method through analysis of the user's past requirement submission history. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the user's past requirement submission history data into an AI, which can then select the most suitable reception method.

[0078] The reception desk can filter requirements based on the user's current projects and areas of interest when receiving them. For example, the reception desk can prioritize requirements related to projects the user is currently working on. It can also filter and accept relevant requirements based on the user's areas of interest. Furthermore, the reception desk can accept the most relevant requirements based on the progress of the user's current projects. This allows for the priority acceptance of highly relevant requirements by filtering them based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's project data and area of ​​interest data into an AI, which can then filter the requirements.

[0079] The reception desk can estimate the user's emotions and prioritize the requirements to be received based on those emotions. For example, if the user is stressed, the reception desk will postpone less important requirements. Conversely, if the user is relaxed, the reception desk can prioritize high-importance requirements. Furthermore, if the user is in a hurry, the reception desk can prioritize urgent requirements. This enables efficient requirement reception by prioritizing requirements 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 reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI, which can then estimate emotions and determine the priority of requirements.

[0080] The reception desk can prioritize receiving requests based on their relevance, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize requests related to that region. Furthermore, the reception desk can suggest the most relevant requests based on the user's geographical location. Additionally, if the user is on the move, the reception desk can also prioritize requests based on their current location. This allows for priority processing of requests based on their relevance by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into an AI, which can then select the most relevant requests.

[0081] The reception desk can analyze the user's social media activity when receiving requirements and accept relevant requirements. For example, the reception desk can prioritize requirements related to topics of interest based on the user's social media activity. It can also analyze the user's social media posts and suggest relevant requirements. Furthermore, the reception desk can accept relevant requirements by referring to the activities of the user's social media followers and friends. This allows for the prioritization of relevant requirements by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which can then select relevant requirements.

[0082] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can provide analysis results using visually easy-to-understand graphs and charts. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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 analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's facial expression data into the generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the requirements during the analysis. For example, the analysis unit can perform a detailed analysis for high-importance requirements and a simplified analysis for low-importance requirements. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the requirements. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the requirements. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input requirement importance data into a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of requirements during analysis. For example, the analysis unit can apply a time series analysis algorithm to sales data. It can also apply a clustering algorithm to customer data. Furthermore, it can apply a regression analysis algorithm to marketing data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of requirements. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the category data of requirements into a generative AI, which can then select an appropriate analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is stressed, the analysis unit can perform a visually easy-to-understand analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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-described processes in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's facial expression data into the generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0086] The analysis unit can determine the priority of the analysis based on the submission date of the requirements. For example, the analysis unit will prioritize the analysis of requirements submitted earlier. It can also postpone the analysis of requirements submitted later. Furthermore, the analysis unit can adjust the priority of the analysis in stages based on the submission date. This enables efficient analysis by determining the priority of the analysis based on the submission date of the requirements. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input requirement submission date data into a generation AI, and the generation AI can determine the priority of the analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the requirements during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant requirements. It can also postpone the analysis of less relevant requirements. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the requirements. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the requirements. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input requirement relevance data into a generation AI, and the generation AI can adjust the order of analysis.

[0088] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect detailed data. If the user is in a hurry, the data collection unit can collect only the minimum necessary data. Furthermore, if the user is stressed, the data collection unit can collect data simply and quickly. This allows for efficient data collection by adjusting the data collection method 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. For example, the data collection unit can input user facial expression data into a generative AI, which can then estimate emotions and adjust the data collection method.

[0089] The data collection unit can analyze the user's past data collection history to select the optimal collection method during data collection. For example, the collection unit can prioritize suggesting data collection methods previously used by the user. Furthermore, the collection unit can select the most efficient collection method based on the user's past data collection history. In addition, the collection unit can analyze the user's past data collection history and suggest the optimal collection method for a specific time period. This allows for efficient data collection by analyzing the user's past data collection history and selecting the most suitable method. Some or all of the above-described processes in the collection unit may be performed using AI, or they may not. For example, the collection unit can input the user's past data collection history data into an AI, which can then select the optimal collection method.

[0090] The data collection unit can filter data based on the user's current lifestyle 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. It can also filter and collect relevant data based on the user's areas of interest. Furthermore, the data collection unit can collect the most relevant data according to the user's current lifestyle. This allows for the priority collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest. 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 lifestyle data and areas of interest data into an AI, which can then filter the data.

[0091] 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 will postpone collecting less important data. If the user is relaxed, the data collection unit can prioritize collecting more important data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting urgent data. This enables efficient data collection by prioritizing 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. For example, the data collection unit can input user facial expression data into a generative AI, which can then estimate emotions and determine data priority.

[0092] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also suggest 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 also collect highly relevant data based on their current location. In this way, by collecting data while considering the user's geographical location information, highly relevant data can be prioritized. 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 the user's geographical location information data into AI, which can then select highly relevant data.

[0093] 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 prioritize collecting data related to topics of interest from the user's social media activity. It can also analyze the content of the user's social media posts and suggest relevant data. Furthermore, the data collection unit can collect relevant data by referring to the activities of the user's social media followers and friends. This allows for the priority collection of relevant data by analyzing the user's social media activity. 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 the user's social media data into an AI, which can then select relevant data.

[0094] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can provide analysis results using visually easy-to-understand graphs and charts. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without generative AI. For example, the analysis unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0095] 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 can perform a detailed analysis on highly important data, and a concise analysis on less important data. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data importance data into a generative AI, and the generative AI can adjust the level of detail of the analysis.

[0096] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a time series analysis algorithm to sales data. It can also apply a clustering algorithm to customer data. Furthermore, it can apply a regression analysis algorithm to marketing data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input data category information into a generative AI, which can then select an appropriate analysis algorithm.

[0097] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is stressed, the analysis unit can perform a visually easy-to-understand analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. 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-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user facial expression data into a generative AI, which can then estimate emotions and adjust the length of the analysis.

[0098] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department can prioritize analyzing data submitted earlier. It can also postpone analyzing data submitted later. Furthermore, the analysis department can adjust the priority of analysis in stages based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the analysis department can input data submission date data into a generative AI, and the generative AI can determine the priority of analysis.

[0099] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis step by step based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input data relevance data into a generative AI, and the generative AI can adjust the order of analysis.

[0100] The service provider can estimate the user's emotions and adjust the report delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed report. If the user is in a hurry, the service provider can provide a concise report that gets straight to the point. Furthermore, if the user is stressed, the service provider can provide a report using visually easy-to-understand graphs and charts. In this way, by adjusting the report delivery method according to the user's emotions, a report that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI, which can estimate emotions and adjust the report delivery method.

[0101] The delivery unit can select the optimal delivery method by referring to the user's past report viewing history when providing reports. For example, the delivery unit can prioritize providing reports in the format the user has previously viewed. Furthermore, the delivery unit can select the most efficient delivery method based on the user's past report viewing history. In addition, the delivery unit can analyze the user's past report viewing history and suggest the optimal delivery method for specific time periods. This enables efficient report delivery by selecting the optimal delivery method based on the user's past report viewing history. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's past report viewing history data into AI, which can then select the optimal delivery method.

[0102] The service provider can estimate the user's emotions and prioritize reports based on those emotions. For example, if the user is stressed, the service provider will postpone less important reports. Conversely, if the user is relaxed, the service provider can prioritize providing more important reports. Furthermore, if the user is in a hurry, the service provider can prioritize providing urgent reports. This enables efficient report delivery by prioritizing reports 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 service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI, which can then estimate emotions and determine report priorities.

[0103] The service provider can select the optimal delivery method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a report adapted to the screen size. If the user is using a tablet, the service provider can provide a report optimized for a larger screen. Furthermore, if the user is using a desktop computer, the service provider can provide a detailed report. By providing reports while considering the user's device information, the service provider can deliver reports in the most optimal format for the user. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user device information data into AI, which can then select the optimal delivery method.

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

[0105] The reception desk can estimate the user's emotions and adjust the way requirements are received based on those estimates. For example, if the user is stressed, the reception desk can simplify the requirement input process to help the user relax. If the user is relaxed, the reception desk can encourage more detailed requirement input to gather more accurate information. Furthermore, if the user is in a hurry, the reception desk can speed up the process by allowing for quick requirement input. In this way, by adjusting the requirement reception process according to the user's emotions, user stress can be reduced and requirements can be received efficiently. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0106] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results that are easy for the user to understand. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can provide analysis results using visually easy-to-understand graphs and charts. In this way, by adjusting the presentation of the analysis results according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0107] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect detailed data. If the user is in a hurry, the data collection unit will collect only the minimum necessary data. Furthermore, if the user is stressed, the data collection unit can perform simple and rapid data collection. This allows for efficient data collection by adjusting the data collection method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0108] The service provider can estimate the user's emotions and adjust the report delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider will provide a detailed report. If the user is in a hurry, the service provider can provide a concise report that gets straight to the point. Furthermore, if the user is stressed, the service provider can provide a report using visually easy-to-understand graphs and charts. In this way, by adjusting the report delivery method according to the user's emotions, the service provider can provide reports that are easy for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0109] The service provider can estimate the user's emotions and prioritize reports based on those emotions. For example, if the user is stressed, the service provider will postpone less important reports. Conversely, if the user is relaxed, the service provider can prioritize highly important reports. Furthermore, if the user is in a hurry, the service provider can prioritize urgent reports. This enables efficient report delivery by prioritizing reports according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0110] The reception department can analyze a user's past requirement submission history and select the most suitable reception method. For example, the reception department will prioritize suggesting reception methods that the user has frequently used in the past. Furthermore, the reception department can select the most efficient reception method based on the user's past requirement submission history. In addition, the reception department can analyze the user's past requirement submission history and suggest the most suitable reception method for specific time periods. This allows for efficient requirement reception by selecting the optimal method through analysis of the user's past requirement submission history.

[0111] The reception desk can filter requirements based on the user's current projects and areas of interest. For example, it can prioritize requirements related to projects the user is currently working on. It can also filter and accept relevant requirements based on the user's areas of interest. Furthermore, it can accept the most relevant requirements based on the progress of the user's current projects. This allows for the prioritization of highly relevant requirements by filtering them based on the user's current projects and areas of interest.

[0112] The data collection unit can analyze the user's past data collection history to select the optimal collection method during data collection. For example, the collection unit prioritizes suggesting data collection methods the user has used in the past. Furthermore, the collection unit can select the most efficient collection method based on the user's past data collection history. In addition, the collection unit can analyze the user's past data collection history and suggest the most suitable collection method for a specific time period. This allows for efficient data collection by analyzing the user's past data collection history and selecting the optimal method.

[0113] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the data collection process. For example, the unit prioritizes collecting data related to projects the user is currently working on. It can also filter and collect relevant data based on the user's areas of interest. Furthermore, the unit can collect the most relevant data according to the user's current lifestyle. This allows for the priority collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest.

[0114] The service provider can select the optimal delivery method by referring to the user's past report viewing history when providing reports. For example, the service provider can prioritize providing reports in the format the user has previously viewed. Furthermore, the service provider can select the most efficient delivery method based on the user's past report viewing history. In addition, the service provider can analyze the user's past report viewing history and suggest the most suitable delivery method for specific time periods. This allows for efficient report delivery by selecting the optimal method based on the user's past report viewing history.

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

[0116] Step 1: The reception department receives requirements from users. For example, a user can enter a requirement such as, "I want to analyze sales data and make a sales forecast for the next quarter." After receiving the requirement, the reception department sends it to the analysis department. Step 2: The analysis department analyzes the requirements received by the reception department and identifies the necessary data sets. For example, the analysis department identifies sales data, customer data, marketing data, etc. The analysis department proposes which data should be collected, how to cleanse and prepare the data, and the selection of appropriate analytical methods and tools. Step 3: The data collection unit collects and prepares data based on the data sets identified by the analysis unit. For example, the data collection unit collects sales data, cleanses the data, and uses the prepared data for analysis. After collecting and preparing the data, the data collection unit sends the data to the analysis unit. Step 4: The analysis department analyzes the data collected by the data collection department and creates a report based on the analysis results. For example, the analysis department analyzes sales data to forecast sales for the next quarter and compiles the results into a report. Step 5: The service department provides the user with the report created by the analysis department. For example, the service department provides the user with a report created by the generation AI.

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

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

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

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives requirements from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the requirements transmitted from the reception unit and identifies the necessary data set. The collection unit is implemented by the control unit 46A of the smart device 14 and collects and organizes the data set identified by the analysis unit. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected by the collection unit and creates a report of the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the report created by the analysis unit to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

[0125] The microphone 238 receives voice 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.

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

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

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

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, data analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives requirements from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the requirements transmitted from the reception unit and identifies the necessary data set. The collection unit is implemented by the control unit 46A of the smart glasses 214 and collects and organizes the data set identified by the analysis unit. The data analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected by the collection unit and creates a report of the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with the report created by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

[0141] The microphone 238 receives voice 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.

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

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

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

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, data analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives requirements from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the requirements transmitted from the reception unit and identifies the necessary data set. The collection unit is implemented by the control unit 46A of the headset terminal 314 and collects and organizes the data set identified by the analysis unit. The data analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected by the collection unit and creates a report of the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with the report created by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

[0157] The microphone 238 receives voice 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.

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

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

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

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

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, collection unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives requirements from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the requirements transmitted from the reception unit to identify the necessary data sets. The collection unit is implemented by the control unit 46A of the robot 414 and collects and organizes the data sets identified by the analysis unit. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected by the collection unit and creates a report of the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the user with the report created by the analysis unit. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

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

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0188] (Note 1) A reception desk that receives requirements from users, An analysis unit analyzes the requirements received by the reception unit and identifies the necessary data sets, A data collection unit collects and organizes data based on the data set identified by the analysis unit, An analysis unit analyzes the data collected by the aforementioned collection unit and creates a report, The system comprises a provisioning unit that provides reports created by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We propose which data should be collected, methods for data cleansing and preparation, and the selection of specific analysis methods and tools. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Data collection and organization will be carried out based on the proposed dataset. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Analyze the collected data and create a report based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide users with reports generated by our AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of request acceptance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past requirement submission history and select a specific method for receiving them. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving requirements, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the requirements to be accepted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving requirements, the system prioritizes accepting highly relevant requirements by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requirements, the system analyzes the user's social media activity and accepts relevant requirements. 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 the analysis, adjust the level of detail based on the importance of the requirements. 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 category of requirements. 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 the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the requirements were submitted. 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 requirements. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is We estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is When collecting data, the user's past data collection history is analyzed to select a specific collection method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is 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 26) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is 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 30) The aforementioned supply unit is, We estimate user sentiment and adjust how reports are delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing reports, the specific method of delivery is selected by referring to the user's past report viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing reports, the optimal delivery method will be selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A reception desk that receives requirements from users, An analysis unit analyzes the requirements received by the reception unit and identifies the necessary data sets, A data collection unit collects and organizes data based on the data set identified by the analysis unit, An analysis unit analyzes the data collected by the aforementioned collection unit and creates a report, The system comprises a provisioning unit that provides reports created by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, We propose which data should be collected, methods for data cleansing and preparation, and the selection of specific analysis methods and tools. The system according to feature 1.

3. The aforementioned collection unit is Data collection and organization will be carried out based on the proposed dataset. The system according to feature 1.

4. The aforementioned analysis unit is Analyze the collected data and create a report based on the analysis results. The system according to feature 1.

5. The aforementioned supply unit is, The AI ​​generates reports and provides them to the user. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of request acceptance based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past requirement submission history and select a specific method for receiving them. The system according to feature 1.

8. The aforementioned reception unit is When receiving requirements, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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