system

Abstract

The system according to this embodiment aims to integrate highly reliable information and discover cross-cutting insights. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a ranking determination unit. The data collection unit collects highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. The analysis unit analyzes the data collected by the data collection unit and integrates new and old knowledge. The proposal unit discovers cross-cutting insights based on the knowledge integrated by the analysis unit and proposes solutions. The ranking determination unit determines a ranking according to the difficulty level of the questions and solutions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a lack of information sharing between independently decentralized departments, and there is room for improvement in analysis and decision-making within limited information sources.

[0005] The system according to the embodiment aims to integrate highly reliable information and discover cross-cutting insights.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a ranking determination unit. The data collection unit collects highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. The analysis unit analyzes the data collected by the data collection unit and integrates new and old knowledge. The proposal unit discovers cross-cutting insights based on the knowledge integrated by the analysis unit and proposes solutions. The ranking determination unit determines a ranking according to the difficulty level of the questions and solutions. [Effects of the Invention]

[0007] The system according to this embodiment can integrate highly reliable information and discover cross-cutting insights. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 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 reception 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 reception 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 marketing research AI agent according to an embodiment of the present invention is a system that builds an AI platform that constantly inputs highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office for the entire SB Group, and grasps all new and old knowledge to realize cross-sectional insight discovery and solution proposals. The marketing research AI agent collects highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office, and integrates new and old knowledge by having the AI ​​analyze it. Furthermore, it discovers cross-sectional insights and proposes solutions based on the integrated knowledge. As a result, all employees are expected to have the same level of insight as top marketers, and productivity improvements in analysis, strategy, and customer service are expected. In addition, the marketing research AI agent is equipped with a ranking judgment program according to the difficulty level of questions and answers, and usage rights can be sold. For example, a higher price will be set for high-difficulty questions, and a lower price for low-difficulty questions. In this way, a marketing research AI agent that evolves day by day will be provided. As a result, the marketing research AI agent will enable all employees to have the same level of insight as top marketers, and productivity improvements in analysis, strategy, and customer service will be realized.

[0029] The marketing research AI agent according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a ranking determination unit. The data collection unit collects reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. The data collection unit can collect customer understanding data such as survey results, purchase history, and website browsing history. The data collection unit can also collect behavioral logs such as clickstream data and app usage history. Furthermore, the data collection unit can also collect publicly available data from the National Statistical Office, such as demographic data and economic indicator data. Some or all of the above-described processing in the data collection unit may be performed using AI or not. The analysis unit analyzes the data collected by the data collection unit and integrates new and old insights. The analysis unit can analyze the data using, for example, data mining techniques or statistical analysis techniques. Furthermore, the analysis unit can integrate past research results and new research results. Furthermore, the analysis unit can discover commonalities and trends between different data sources. Some or all of the above-described processing in the analysis unit may be performed using AI or not. The proposal unit discovers cross-cutting insights based on the knowledge integrated by the analysis unit and proposes solutions. For example, the proposal unit can make specific action plans or strategic proposals. The proposal unit can also make proposals based on commonalities and trends across different data sources. Furthermore, the proposal unit can make proposals based on customer needs and market trends. Some or all of the above processes in the proposal unit may be performed using AI or not. The ranking unit determines rankings according to the difficulty of questions and solutions and sells usage rights. For example, the ranking unit can set higher prices for difficult questions and lower prices for easy questions. The ranking unit can also determine rankings using ranking algorithms. Furthermore, the ranking unit can adjust the ranking criteria and evaluation methods. Some or all of the above processes in the ranking unit may be performed using AI or not.As a result, the marketing research AI agent according to this embodiment enables all employees to have the same level of insight as top marketers, thereby improving productivity in analysis, strategy, and customer service.

[0030] The data collection unit collects reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. Specifically, it can collect customer understanding data such as survey results, purchase history, and website browsing history. Survey results are data that directly reflect customer preferences and opinions, and purchase history is data that shows customers' actual behavior. Website browsing history is data that shows which pages customers visited and which content they were interested in. This data is important for understanding customer behavior and preferences in detail. The data collection unit can also collect behavioral logs such as clickstream data and app usage history. Clickstream data is data that shows which links customers clicked on a website, and app usage history is data that shows which apps customers used and how. This data is important for tracking customers' online behavior in detail. Furthermore, the data collection unit can also collect publicly available data from the National Statistical Office, such as demographic data and economic indicator data. Demographic data is data that shows the population composition and age distribution by region, and economic indicator data is data that shows the economic situation and consumption trends by region. This data is important for understanding customer background and market conditions. Some or all of the processing described above in the data collection unit may be performed using AI or not. When using AI, natural language processing techniques and machine learning algorithms can be used to efficiently collect large amounts of data and extract important information. For example, natural language processing techniques can be used to automatically extract customer opinions and emotions from survey results. Machine learning algorithms can also be used to automatically analyze customer behavior patterns from purchase history and behavioral logs. As a result, the data collection unit can collect data efficiently and accurately, deepening its understanding of customers.

[0031] The analysis unit analyzes the data collected by the collection unit and integrates new and old insights. Specifically, it can analyze data using data mining techniques and statistical analysis techniques. Data mining techniques are methods for discovering useful patterns and relationships from large amounts of data, while statistical analysis techniques are methods for analyzing the distribution and trends of data. By using these techniques, new insights into customer behavior and preferences can be obtained. The analysis unit can also integrate past research results and new research findings. Past research results are data that demonstrates already obtained knowledge and theories, while new research findings are data that demonstrates the latest data and information. By integrating this data, more comprehensive and reliable insights can be obtained. Furthermore, the analysis unit can also discover commonalities and trends between different data sources. For example, by comparing a customer's purchase history with their website browsing history, it is possible to clarify what products customers are interested in and what actions they take. Some or all of the above processing in the analysis unit may be performed using AI or not. When using AI, machine learning algorithms and deep learning techniques can be used to automate data analysis and obtain more accurate results. For example, machine learning algorithms can be used to automatically classify customer behavior patterns, and deep learning techniques can be used to discover complex relationships and trends. This allows the analysis unit to efficiently and accurately analyze data and integrate new and old insights.

[0032] The proposal department discovers cross-cutting insights and proposes solutions based on the knowledge integrated by the analysis department. Specifically, it can provide concrete action plans and strategic proposals. Action plans outline specific actions and measures, while strategic proposals outline long-term goals and policies. These proposals are based on customer needs and market trends. The proposal department can also make proposals based on commonalities and trends across different data sources. For example, based on customer purchase history and website browsing history, it can identify what products customers are interested in and what actions they take, and propose marketing strategies based on this. Furthermore, the proposal department can also make proposals based on customer needs and market trends. For example, based on customer survey results and behavioral logs, it can identify what products and services customers are looking for, and propose the development of new products or improvements to existing services based on this. Some or all of the above processes in the proposal department may be performed using AI or not. When using AI, natural language processing technology and machine learning algorithms can be used to automate data analysis and provide more accurate proposals. For example, natural language processing technology can be used to extract important opinions and emotions from customer survey results, and machine learning algorithms can be used to analyze customer behavior patterns and make optimal suggestions based on that analysis. This allows the proposal department to efficiently and accurately discover insights and propose solutions.

[0033] The ranking unit determines a ranking based on the difficulty of the questions and solutions, and sells usage rights. Specifically, it can set higher prices for difficult questions and lower prices for easy questions. The ranking unit can also determine rankings using a ranking algorithm. The ranking algorithm evaluates the difficulty of the questions and the quality of the solutions, and determines the ranking based on these. For example, the difficulty of a question is evaluated based on the content of the question, the complexity of the solution, and the time required to answer it. The quality of the solution is evaluated based on the accuracy and reliability of the solution and the consistency of the answer. Using these evaluation criteria, the ranking unit can determine a ranking based on the difficulty of the questions and solutions. The ranking unit can also adjust the ranking criteria and evaluation methods. For example, the ranking criteria and evaluation methods can be changed according to specific periods or conditions. This allows the ranking unit to flexibly and appropriately determine rankings and sell usage rights. Some or all of the above processing in the ranking unit may be performed using AI, or not. When using AI, machine learning algorithms and deep learning techniques can be used to automate ranking determination and obtain more accurate results. For example, machine learning algorithms can be used to automatically evaluate the difficulty of questions and solutions, and deep learning techniques can be used to discover complex relationships and patterns. As a result, the ranking determination unit can efficiently and accurately determine rankings and sell usage rights.

[0034] The data collection unit can collect highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. For example, the data collection unit can collect customer understanding data such as survey results, purchase history, and website browsing history. The data collection unit can also collect behavioral logs such as clickstream data and app usage history. The data collection unit can also collect publicly available data from the National Statistical Office, such as demographic data and economic indicator data. By collecting highly reliable information, the accuracy of analysis and recommendations is improved. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer understanding data into a generating AI, which can then analyze the data and extract highly reliable information.

[0035] The analysis unit can analyze the collected data and integrate new and old knowledge. For example, the analysis unit can analyze the data using data mining techniques or statistical analysis techniques. The analysis unit can also integrate past research results with new research findings. For example, the analysis unit can discover commonalities and trends across different data sources. This allows for more comprehensive insights by integrating new and old knowledge. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and integrate new and old knowledge.

[0036] The proposal department can discover cross-cutting insights and propose solutions based on integrated knowledge. For example, the proposal department can make specific action plans and strategic proposals. For example, the proposal department can make proposals based on commonalities and trends across different data sources. For example, the proposal department can make proposals based on customer needs and market trends. This allows all employees to have the same level of insight as top marketers by discovering cross-cutting insights and proposing solutions. Some or all of the processes described above in the proposal department may or may not be performed using AI. For example, the proposal department can input integrated knowledge into a generative AI, which can then discover cross-cutting insights and propose solutions.

[0037] The ranking unit can determine a ranking based on the difficulty of the questions and solutions and sell usage rights. For example, the ranking unit can set a higher price for difficult questions and a lower price for easy questions. The ranking unit can also determine the ranking using a ranking algorithm. The ranking unit can also adjust the ranking criteria and evaluation methods. This increases the value of the marketing research AI agent by allowing the ranking unit to determine a ranking based on the difficulty of the questions and solutions and sell usage rights. Some or all of the above processing in the ranking unit may be performed using AI or not. For example, the ranking unit can input question and solution data into a generating AI, which can then determine the ranking and sell usage rights.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past data collection history and reflect this in future data collection. The data collection unit can also analyze past data collection history to find areas for improvement in collection methods and optimize them. For example, the data collection unit can analyze patterns in collection methods based on past data collection history and collect data at the optimal timing. This allows for the selection of the optimal collection method and improvement of data collection efficiency by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data collection history into a generating AI, which can then select the optimal collection method.

[0039] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can collect necessary data at the appropriate time according to the progress of the user's projects. This allows for the efficient collection of highly relevant data by filtering data based on the user's current projects 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 the user's project information into a generating AI, which can then filter and collect relevant data.

[0040] 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, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect region-specific data based on the user's geographical location information. The data collection unit can also collect highly relevant data by considering the user's travel history. This allows for the efficient collection of region-specific data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media posts and collect relevant data. The data collection unit can also collect highly relevant data based on a user's interests on social media. For example, the data collection unit can analyze a user's followers and followed accounts on social media and collect relevant data. This allows for the efficient collection of highly relevant data by analyzing a 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 a user's social media data into a generating AI, which can then collect relevant data.

[0042] 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 data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the data category. The analysis unit can also apply an appropriate analysis algorithm to data of different categories. For example, the analysis unit can customize the analysis algorithm for each data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply the optimal analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of data submitted earlier. The analysis unit can also adjust the analysis schedule based on the data submission date. For example, the analysis unit can perform rapid analysis on data with upcoming submission dates. 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 unit may be performed using AI or not. For example, the analysis unit can input the data submission date into a generating AI, which can then determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. For example, the analysis unit may postpone the analysis of less relevant data to perform analysis more efficiently. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input the relevance of the data into a generating AI, which can then adjust the order of analysis.

[0046] The proposal unit can adjust the level of detail of its proposals based on the importance of the insights it generates. For example, it can provide detailed proposals for high-importance insights, and simplified proposals for low-importance insights. The proposal unit can also prioritize proposals based on the importance of the insights. This allows for efficient proposal generation by adjusting the level of detail based on the importance of the insights. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the importance of the insights into a generating AI, which can then adjust the level of detail of the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the category of the insight when making a proposal. For example, the proposal unit can select the optimal proposal algorithm depending on the category of the insight. The proposal unit can also apply an appropriate proposal algorithm to insights of different categories. The proposal unit can also customize the proposal algorithm for each category of insight. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of insight. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the category of insight into a generating AI, and the generating AI can apply the optimal proposal algorithm.

[0048] The proposal department can prioritize proposals based on the timing of insight submissions. For example, the proposal department will prioritize proposals based on the timing of insight submissions. The proposal department can also adjust the proposal schedule based on the timing of insight submissions. For example, the proposal department can quickly submit proposals to insights whose submission dates are approaching. This enables efficient proposals by prioritizing proposals based on the timing of insight submissions. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the timing of insight submissions into a generating AI, which can then determine the priority of proposals.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the insights during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant insights. The suggestion unit can also determine the order of suggestions based on the relevance of the insights. For example, the suggestion unit may postpone suggesting less relevant insights to make suggestions more efficiently. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the insights. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit may input the relevance of the insights into a generating AI, which can then adjust the order of suggestions.

[0050] The ranking determination unit can improve the accuracy of the ranking by considering the interrelationship between questions and solutions when determining the ranking. For example, the ranking determination unit can analyze the relationship between questions and solutions to improve the accuracy of the ranking. For example, the ranking determination unit can also determine the priority of the ranking by considering the interrelationship between questions and solutions. For example, the ranking determination unit can adjust the order of the ranking based on the relationship between questions and solutions. In this way, the accuracy of the ranking can be improved by considering the interrelationship between questions and solutions. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input question and solution data into a generating AI, and the generating AI can improve the accuracy of the ranking by considering the interrelationship.

[0051] The ranking determination unit can perform ranking by considering the attribute information of the question and solution submitters when determining the ranking. The ranking determination unit can improve the accuracy of the ranking based on, for example, the submitter's expertise and experience. The ranking determination unit can also determine the ranking priority by considering the submitter's attribute information. The ranking determination unit can also adjust the ranking order based on, for example, the submitter's past performance. In this way, the accuracy of the ranking can be improved by considering the submitter's attribute information. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input the submitter's attribute information into a generating AI, and the generating AI can perform the ranking.

[0052] The ranking determination unit can perform ranking while considering the geographical distribution of questions and solutions. For example, the ranking determination unit may prioritize ranking questions and solutions that are geographically relevant. The ranking determination unit can also determine the ranking priority by considering the geographical distribution. The ranking determination unit can also adjust the ranking order based on geographical factors. This improves the accuracy of the ranking by considering the geographical distribution of questions and solutions. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input the geographical distribution of questions and solutions into a generating AI, which can then perform the ranking.

[0053] The ranking determination unit can improve the accuracy of its ranking by referring to related literature for the question and its solution during the ranking determination process. For example, the ranking determination unit can improve the accuracy of its ranking by referring to related literature and evaluating the relevance between the question and its solution. For example, the ranking determination unit can also determine the priority of the ranking based on the related literature. For example, the ranking determination unit can adjust the order of the ranking by considering the content of the related literature. In this way, the accuracy of the ranking can be improved by referring to related literature. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input related literature for the question and its solution into a generating AI, and the generating AI can perform the ranking.

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

[0055] The data collection unit can analyze the user's past behavior history and select the optimal data collection method during data collection. For example, it can identify the most effective data collection method from past data collection history and reflect this in future data collection. It can also analyze past data collection history to find areas for improvement in the collection method and optimize it. Based on past data collection history, it can analyze patterns in the collection method and collect data at the optimal timing. In this way, by analyzing past data collection history, the optimal data collection method can be selected and the efficiency of data collection can be improved. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0056] The analysis unit can determine the priority of analysis based on the importance of the data during the analysis process. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. It can also determine the priority of analysis according to the importance of the data. This allows for efficient analysis by determining the priority of analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI.

[0057] The proposal function can apply different proposal algorithms depending on the category of the insight during the proposal process. For example, it can select the optimal proposal algorithm based on the category of the insight. It can also apply an appropriate proposal algorithm to insights in different categories. It can even customize the proposal algorithm for each category of insight. This improves the accuracy of the proposals by applying the optimal proposal algorithm according to the category of the insight. Some or all of the above processing in the proposal function may be performed using AI or not.

[0058] The ranking determination unit can perform ranking by considering the attribute information of the question and solution submitters. For example, it can improve the accuracy of the ranking based on the submitter's expertise and experience. It can also determine the ranking priority by considering the submitter's attribute information. It can also adjust the ranking order based on the submitter's past performance. In this way, the accuracy of the ranking can be improved by considering the submitter's attribute information. Some or all of the above processing in the ranking determination unit may be performed using AI or not.

[0059] The ranking determination unit can perform ranking while considering the geographical distribution of questions and solutions. For example, it can prioritize ranking questions and solutions that are geographically relevant. It can also determine the ranking priority by considering the geographical distribution. It can also adjust the ranking order based on geographical factors. In this way, the accuracy of the ranking can be improved by considering the geographical distribution of questions and solutions. Some or all of the above processing in the ranking determination unit may be performed using AI or not.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. For example, it can collect customer understanding data such as survey results, purchase history, and website browsing history; behavioral logs such as clickstream data and app usage history; and publicly available data from the National Statistical Office such as demographic data and economic indicator data. The processing in the data collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit and integrates new and old knowledge. For example, it can analyze the data using data mining techniques or statistical analysis techniques and integrate past research results with new research findings. Furthermore, it can discover commonalities and trends between different data sources. The processing in the analysis unit may or may not be performed using AI. Step 3: The proposal department discovers cross-cutting insights based on the knowledge integrated by the analysis department and proposes solutions. For example, it can propose specific action plans and strategic suggestions, and make suggestions based on commonalities and trends across different data sources. Furthermore, it can also make suggestions based on customer needs and market trends. The processing in the proposal department may or may not be performed using AI. Step 4: The ranking determination unit determines the ranking based on the difficulty level of the questions and solutions. For example, higher prices can be set for difficult questions and lower prices for easy questions. The ranking can be determined using a ranking algorithm, and the ranking criteria and evaluation methods can also be adjusted. The processing in the ranking determination unit may be performed using AI or not.

[0062] (Example of form 2) The marketing research AI agent according to an embodiment of the present invention is a system that builds an AI platform that constantly inputs highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office for the entire SB Group, and grasps all new and old knowledge to realize cross-sectional insight discovery and solution proposals. The marketing research AI agent collects highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office, and integrates new and old knowledge by having the AI ​​analyze it. Furthermore, it discovers cross-sectional insights and proposes solutions based on the integrated knowledge. As a result, all employees are expected to have the same level of insight as top marketers, and productivity improvements in analysis, strategy, and customer service are expected. In addition, the marketing research AI agent is equipped with a ranking judgment program according to the difficulty level of questions and answers, and usage rights can be sold. For example, a higher price will be set for high-difficulty questions, and a lower price for low-difficulty questions. In this way, a marketing research AI agent that evolves day by day will be provided. As a result, the marketing research AI agent will enable all employees to have the same level of insight as top marketers, and productivity improvements in analysis, strategy, and customer service will be realized.

[0063] The marketing research AI agent according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a ranking determination unit. The data collection unit collects reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. The data collection unit can collect customer understanding data such as survey results, purchase history, and website browsing history. The data collection unit can also collect behavioral logs such as clickstream data and app usage history. Furthermore, the data collection unit can also collect publicly available data from the National Statistical Office, such as demographic data and economic indicator data. Some or all of the above-described processing in the data collection unit may be performed using AI or not. The analysis unit analyzes the data collected by the data collection unit and integrates new and old insights. The analysis unit can analyze the data using, for example, data mining techniques or statistical analysis techniques. Furthermore, the analysis unit can integrate past research results and new research results. Furthermore, the analysis unit can discover commonalities and trends between different data sources. Some or all of the above-described processing in the analysis unit may be performed using AI or not. The proposal unit discovers cross-cutting insights based on the knowledge integrated by the analysis unit and proposes solutions. For example, the proposal unit can make specific action plans or strategic proposals. The proposal unit can also make proposals based on commonalities and trends across different data sources. Furthermore, the proposal unit can make proposals based on customer needs and market trends. Some or all of the above processes in the proposal unit may be performed using AI or not. The ranking unit determines rankings according to the difficulty of questions and solutions and sells usage rights. For example, the ranking unit can set higher prices for difficult questions and lower prices for easy questions. The ranking unit can also determine rankings using ranking algorithms. Furthermore, the ranking unit can adjust the ranking criteria and evaluation methods. Some or all of the above processes in the ranking unit may be performed using AI or not.As a result, the marketing research AI agent according to this embodiment enables all employees to have the same level of insight as top marketers, thereby improving productivity in analysis, strategy, and customer service.

[0064] The data collection unit collects reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. Specifically, it can collect customer understanding data such as survey results, purchase history, and website browsing history. Survey results are data that directly reflect customer preferences and opinions, and purchase history is data that shows customers' actual behavior. Website browsing history is data that shows which pages customers visited and which content they were interested in. This data is important for understanding customer behavior and preferences in detail. The data collection unit can also collect behavioral logs such as clickstream data and app usage history. Clickstream data is data that shows which links customers clicked on a website, and app usage history is data that shows which apps customers used and how. This data is important for tracking customers' online behavior in detail. Furthermore, the data collection unit can also collect publicly available data from the National Statistical Office, such as demographic data and economic indicator data. Demographic data is data that shows the population composition and age distribution by region, and economic indicator data is data that shows the economic situation and consumption trends by region. This data is important for understanding customer background and market conditions. Some or all of the processing described above in the data collection unit may be performed using AI or not. When using AI, natural language processing techniques and machine learning algorithms can be used to efficiently collect large amounts of data and extract important information. For example, natural language processing techniques can be used to automatically extract customer opinions and emotions from survey results. Machine learning algorithms can also be used to automatically analyze customer behavior patterns from purchase history and behavioral logs. As a result, the data collection unit can collect data efficiently and accurately, deepening its understanding of customers.

[0065] The analysis unit analyzes the data collected by the collection unit and integrates new and old insights. Specifically, it can analyze data using data mining techniques and statistical analysis techniques. Data mining techniques are methods for discovering useful patterns and relationships from large amounts of data, while statistical analysis techniques are methods for analyzing the distribution and trends of data. By using these techniques, new insights into customer behavior and preferences can be obtained. The analysis unit can also integrate past research results and new research findings. Past research results are data that demonstrates already obtained knowledge and theories, while new research findings are data that demonstrates the latest data and information. By integrating this data, more comprehensive and reliable insights can be obtained. Furthermore, the analysis unit can also discover commonalities and trends between different data sources. For example, by comparing a customer's purchase history with their website browsing history, it is possible to clarify what products customers are interested in and what actions they take. Some or all of the above processing in the analysis unit may be performed using AI or not. When using AI, machine learning algorithms and deep learning techniques can be used to automate data analysis and obtain more accurate results. For example, machine learning algorithms can be used to automatically classify customer behavior patterns, and deep learning techniques can be used to discover complex relationships and trends. This allows the analysis unit to efficiently and accurately analyze data and integrate new and old insights.

[0066] The proposal department discovers cross-cutting insights and proposes solutions based on the knowledge integrated by the analysis department. Specifically, it can provide concrete action plans and strategic proposals. Action plans outline specific actions and measures, while strategic proposals outline long-term goals and policies. These proposals are based on customer needs and market trends. The proposal department can also make proposals based on commonalities and trends across different data sources. For example, based on customer purchase history and website browsing history, it can identify what products customers are interested in and what actions they take, and propose marketing strategies based on this. Furthermore, the proposal department can also make proposals based on customer needs and market trends. For example, based on customer survey results and behavioral logs, it can identify what products and services customers are looking for, and propose the development of new products or improvements to existing services based on this. Some or all of the above processes in the proposal department may be performed using AI or not. When using AI, natural language processing technology and machine learning algorithms can be used to automate data analysis and provide more accurate proposals. For example, natural language processing technology can be used to extract important opinions and emotions from customer survey results, and machine learning algorithms can be used to analyze customer behavior patterns and make optimal suggestions based on that analysis. This allows the proposal department to efficiently and accurately discover insights and propose solutions.

[0067] The ranking unit determines a ranking based on the difficulty of the questions and solutions, and sells usage rights. Specifically, it can set higher prices for difficult questions and lower prices for easy questions. The ranking unit can also determine rankings using a ranking algorithm. The ranking algorithm evaluates the difficulty of the questions and the quality of the solutions, and determines the ranking based on these. For example, the difficulty of a question is evaluated based on the content of the question, the complexity of the solution, and the time required to answer it. The quality of the solution is evaluated based on the accuracy and reliability of the solution and the consistency of the answer. Using these evaluation criteria, the ranking unit can determine a ranking based on the difficulty of the questions and solutions. The ranking unit can also adjust the ranking criteria and evaluation methods. For example, the ranking criteria and evaluation methods can be changed according to specific periods or conditions. This allows the ranking unit to flexibly and appropriately determine rankings and sell usage rights. Some or all of the above processing in the ranking unit may be performed using AI, or not. When using AI, machine learning algorithms and deep learning techniques can be used to automate ranking determination and obtain more accurate results. For example, machine learning algorithms can be used to automatically evaluate the difficulty of questions and solutions, and deep learning techniques can be used to discover complex relationships and patterns. As a result, the ranking determination unit can efficiently and accurately determine rankings and sell usage rights.

[0068] The data collection unit can collect highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. For example, the data collection unit can collect customer understanding data such as survey results, purchase history, and website browsing history. The data collection unit can also collect behavioral logs such as clickstream data and app usage history. The data collection unit can also collect publicly available data from the National Statistical Office, such as demographic data and economic indicator data. By collecting highly reliable information, the accuracy of analysis and recommendations is improved. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input customer understanding data into a generating AI, which can then analyze the data and extract highly reliable information.

[0069] The analysis unit can analyze the collected data and integrate new and old knowledge. For example, the analysis unit can analyze the data using data mining techniques or statistical analysis techniques. The analysis unit can also integrate past research results with new research findings. For example, the analysis unit can discover commonalities and trends across different data sources. This allows for more comprehensive insights by integrating new and old knowledge. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI, which can then analyze the data and integrate new and old knowledge.

[0070] The proposal department can discover cross-cutting insights and propose solutions based on integrated knowledge. For example, the proposal department can make specific action plans and strategic proposals. For example, the proposal department can make proposals based on commonalities and trends across different data sources. For example, the proposal department can make proposals based on customer needs and market trends. This allows all employees to have the same level of insight as top marketers by discovering cross-cutting insights and proposing solutions. Some or all of the processes described above in the proposal department may or may not be performed using AI. For example, the proposal department can input integrated knowledge into a generative AI, which can then discover cross-cutting insights and propose solutions.

[0071] The ranking unit can determine a ranking based on the difficulty of the questions and solutions and sell usage rights. For example, the ranking unit can set a higher price for difficult questions and a lower price for easy questions. The ranking unit can also determine the ranking using a ranking algorithm. The ranking unit can also adjust the ranking criteria and evaluation methods. This increases the value of the marketing research AI agent by allowing the ranking unit to determine a ranking based on the difficulty of the questions and solutions and sell usage rights. Some or all of the above processing in the ranking unit may be performed using AI or not. For example, the ranking unit can input question and solution data into a generating AI, which can then determine the ranking and sell usage rights.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. By adjusting the timing of data collection according to the user's emotions, the burden on the user is reduced and efficient data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of data collection.

[0073] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection method from past data collection history and reflect this in future data collection. The data collection unit can also analyze past data collection history to find areas for improvement in collection methods and optimize them. For example, the data collection unit can analyze patterns in collection methods based on past data collection history and collect data at the optimal timing. This allows for the selection of the optimal collection method and improvement of data collection efficiency by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data collection history into a generating AI, which can then select the optimal collection method.

[0074] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can collect necessary data at the appropriate time according to the progress of the user's projects. This allows for the efficient collection of highly relevant data by filtering data based on the user's current projects 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 the user's project information into a generating AI, which can then filter and collect relevant data.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting high-priority data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. If the user is in a hurry, the data collection unit may also prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect 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 emotion data into a generative AI, which can estimate the emotions and determine the priority of data to collect.

[0076] 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, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect region-specific data based on the user's geographical location information. The data collection unit can also collect highly relevant data by considering the user's travel history. This allows for the efficient collection of region-specific data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a user's social media posts and collect relevant data. The data collection unit can also collect highly relevant data based on a user's interests on social media. For example, the data collection unit can analyze a user's followers and followed accounts on social media and collect relevant data. This allows for the efficient collection of highly relevant data by analyzing a 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 a user's social media data into a generating AI, which can then collect relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. 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, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0079] 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 data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the data category. The analysis unit can also apply an appropriate analysis algorithm to data of different categories. For example, the analysis unit can customize the analysis algorithm for each data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply the optimal analysis algorithm.

[0081] 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. If the user is excited, the analysis unit can perform a visually stimulating 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of data submitted earlier. The analysis unit can also adjust the analysis schedule based on the data submission date. For example, the analysis unit can perform rapid analysis on data with upcoming submission dates. 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 unit may be performed using AI or not. For example, the analysis unit can input the data submission date into a generating AI, which can then determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. For example, the analysis unit may postpone the analysis of less relevant data to perform analysis more efficiently. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may input the relevance of the data into a generating AI, which can then adjust the order of analysis.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can present detailed suggestions. If the user is in a hurry, the suggestion unit can present concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the suggestion unit can provide suggestions 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 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the way suggestions are presented.

[0085] The proposal unit can adjust the level of detail of its proposals based on the importance of the insights it generates. For example, it can provide detailed proposals for high-importance insights, and simplified proposals for low-importance insights. The proposal unit can also prioritize proposals based on the importance of the insights. This allows for efficient proposal generation by adjusting the level of detail based on the importance of the insights. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the importance of the insights into a generating AI, which can then adjust the level of detail of the proposals.

[0086] The proposal unit can apply different proposal algorithms depending on the category of the insight when making a proposal. For example, the proposal unit can select the optimal proposal algorithm depending on the category of the insight. The proposal unit can also apply an appropriate proposal algorithm to insights of different categories. The proposal unit can also customize the proposal algorithm for each category of insight. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of insight. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the category of insight into a generating AI, and the generating AI can apply the optimal proposal algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. If the user is excited, for example, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the suggestions.

[0088] The proposal department can prioritize proposals based on the timing of insight submissions. For example, the proposal department will prioritize proposals based on the timing of insight submissions. The proposal department can also adjust the proposal schedule based on the timing of insight submissions. For example, the proposal department can quickly submit proposals to insights whose submission dates are approaching. This enables efficient proposals by prioritizing proposals based on the timing of insight submissions. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input the timing of insight submissions into a generating AI, which can then determine the priority of proposals.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of the insights during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant insights. The suggestion unit can also determine the order of suggestions based on the relevance of the insights. For example, the suggestion unit may postpone suggesting less relevant insights to make suggestions more efficiently. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the insights. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit may input the relevance of the insights into a generating AI, which can then adjust the order of suggestions.

[0090] The ranking determination unit can estimate the user's emotions and adjust the ranking criteria based on the estimated emotions. For example, if the user is nervous, the ranking determination unit can provide simple and easy-to-understand ranking criteria. For example, if the user is relaxed, the ranking determination unit can also provide detailed ranking criteria. For example, if the user is in a hurry, the ranking determination unit can also provide concise ranking criteria. By adjusting the ranking criteria according to the user's emotions, it is possible to provide ranking results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the ranking criteria.

[0091] The ranking determination unit can improve the accuracy of the ranking by considering the interrelationship between questions and solutions when determining the ranking. For example, the ranking determination unit can analyze the relationship between questions and solutions to improve the accuracy of the ranking. For example, the ranking determination unit can also determine the priority of the ranking by considering the interrelationship between questions and solutions. For example, the ranking determination unit can adjust the order of the ranking based on the relationship between questions and solutions. In this way, the accuracy of the ranking can be improved by considering the interrelationship between questions and solutions. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input question and solution data into a generating AI, and the generating AI can improve the accuracy of the ranking by considering the interrelationship.

[0092] The ranking determination unit can perform ranking by considering the attribute information of the question and solution submitters when determining the ranking. The ranking determination unit can improve the accuracy of the ranking based on, for example, the submitter's expertise and experience. The ranking determination unit can also determine the ranking priority by considering the submitter's attribute information. The ranking determination unit can also adjust the ranking order based on, for example, the submitter's past performance. In this way, the accuracy of the ranking can be improved by considering the submitter's attribute information. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input the submitter's attribute information into a generating AI, and the generating AI can perform the ranking.

[0093] The ranking determination unit can estimate the user's emotions and adjust the order in which the ranking results are displayed based on the estimated emotions. For example, if the user is nervous, the ranking determination unit can provide simple and easy-to-read ranking results. For example, if the user is relaxed, the ranking determination unit can also provide detailed ranking results. For example, if the user is in a hurry, the ranking determination unit can also provide concise ranking results. In this way, by adjusting the order in which the ranking results are displayed according to the user's emotions, it is possible to provide ranking results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the ranking determination unit may be performed using AI or not using AI. For example, the ranking determination unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the order in which the ranking results are displayed can be adjusted.

[0094] The ranking determination unit can perform ranking while considering the geographical distribution of questions and solutions. For example, the ranking determination unit may prioritize ranking questions and solutions that are geographically relevant. The ranking determination unit can also determine the ranking priority by considering the geographical distribution. The ranking determination unit can also adjust the ranking order based on geographical factors. This improves the accuracy of the ranking by considering the geographical distribution of questions and solutions. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input the geographical distribution of questions and solutions into a generating AI, which can then perform the ranking.

[0095] The ranking determination unit can improve the accuracy of its ranking by referring to related literature for the question and its solution during the ranking determination process. For example, the ranking determination unit can improve the accuracy of its ranking by referring to related literature and evaluating the relevance between the question and its solution. For example, the ranking determination unit can also determine the priority of the ranking based on the related literature. For example, the ranking determination unit can adjust the order of the ranking by considering the content of the related literature. In this way, the accuracy of the ranking can be improved by referring to related literature. Some or all of the above processing in the ranking determination unit may be performed using AI or not. For example, the ranking determination unit can input related literature for the question and its solution into a generating AI, and the generating AI can perform the ranking.

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

[0097] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of high-priority data. If the user is relaxed, it can prioritize the analysis of detailed data. If the user is in a hurry, it can prioritize the analysis of data that can be analyzed quickly. This enables efficient analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0098] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion timing can be delayed. If the user is relaxed, the suggestion timing can be advanced. If the user is in a hurry, the suggestion can be delivered quickly. By adjusting the timing of suggestions according to the user's emotions, suggestions can be delivered at the optimal time for the user. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not.

[0099] The ranking determination unit can estimate the user's emotions and adjust the ranking display method based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible ranking display can be shown. If the user is relaxed, a detailed ranking display can be shown. If the user is in a hurry, a ranking display that gets straight to the point can be shown. In this way, by adjusting the ranking display method according to the user's emotions, it is possible to provide ranking results that are easy for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the ranking determination unit may be performed using AI or not.

[0100] The data collection unit can estimate the user's emotions and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, simple data can be prioritized. If the user is relaxed, detailed data can be prioritized. If the user is in a hurry, data that can be collected quickly can be prioritized. This allows for efficient data collection by adjusting the type of data collected according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0101] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can also provide a detailed analysis result. If the user is in a hurry, it can also provide a concise analysis result. In this way, by adjusting the level of detail in 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 engine or generative AI, etc. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0102] The data collection unit can analyze the user's past behavior history and select the optimal data collection method during data collection. For example, it can identify the most effective data collection method from past data collection history and reflect this in future data collection. It can also analyze past data collection history to find areas for improvement in the collection method and optimize it. Based on past data collection history, it can analyze patterns in the collection method and collect data at the optimal timing. In this way, by analyzing past data collection history, the optimal data collection method can be selected and the efficiency of data collection can be improved. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0103] The analysis unit can determine the priority of analysis based on the importance of the data during the analysis process. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. It can also determine the priority of analysis according to the importance of the data. This allows for efficient analysis by determining the priority of analysis based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI.

[0104] The proposal function can apply different proposal algorithms depending on the category of the insight during the proposal process. For example, it can select the optimal proposal algorithm based on the category of the insight. It can also apply an appropriate proposal algorithm to insights in different categories. It can even customize the proposal algorithm for each category of insight. This improves the accuracy of the proposals by applying the optimal proposal algorithm according to the category of the insight. Some or all of the above processing in the proposal function may be performed using AI or not.

[0105] The ranking determination unit can perform ranking by considering the attribute information of the question and solution submitters. For example, it can improve the accuracy of the ranking based on the submitter's expertise and experience. It can also determine the ranking priority by considering the submitter's attribute information. It can also adjust the ranking order based on the submitter's past performance. In this way, the accuracy of the ranking can be improved by considering the submitter's attribute information. Some or all of the above processing in the ranking determination unit may be performed using AI or not.

[0106] The ranking determination unit can perform ranking while considering the geographical distribution of questions and solutions. For example, it can prioritize ranking questions and solutions that are geographically relevant. It can also determine the ranking priority by considering the geographical distribution. It can also adjust the ranking order based on geographical factors. In this way, the accuracy of the ranking can be improved by considering the geographical distribution of questions and solutions. Some or all of the above processing in the ranking determination unit may be performed using AI or not.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. For example, it can collect customer understanding data such as survey results, purchase history, and website browsing history; behavioral logs such as clickstream data and app usage history; and publicly available data from the National Statistical Office such as demographic data and economic indicator data. The processing in the data collection unit may or may not be performed using AI. Step 2: The analysis unit analyzes the data collected by the collection unit and integrates new and old knowledge. For example, it can analyze the data using data mining techniques or statistical analysis techniques and integrate past research results with new research findings. Furthermore, it can discover commonalities and trends between different data sources. The processing in the analysis unit may or may not be performed using AI. Step 3: The proposal department discovers cross-cutting insights based on the knowledge integrated by the analysis department and proposes solutions. For example, it can propose specific action plans and strategic suggestions, and make suggestions based on commonalities and trends across different data sources. Furthermore, it can also make suggestions based on customer needs and market trends. The processing in the proposal department may or may not be performed using AI. Step 4: The ranking determination unit determines the ranking based on the difficulty level of the questions and solutions. For example, higher prices can be set for difficult questions and lower prices for easy questions. The ranking can be determined using a ranking algorithm, and the ranking criteria and evaluation methods can also be adjusted. The processing in the ranking determination unit may be performed using AI or not.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and ranking determination unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer understanding data and behavioral logs using the control unit 46A of the smart device 14, and collects publicly available data from the National Statistical Office using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and integrates new and old knowledge. The proposal unit discovers cross-cutting insights using the specific processing unit 290 of the data processing unit 12 and proposes solutions. The ranking determination unit determines rankings according to the difficulty of questions and solutions using the specific processing unit 290 of the data processing unit 12 and sells usage rights. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0126] 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.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and ranking determination unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects customer understanding data and behavioral logs using the control unit 46A of the smart glasses 214, and collects publicly available data from the National Statistical Office using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and integrates new and old knowledge. The proposal unit discovers cross-cutting insights using the specific processing unit 290 of the data processing unit 12 and proposes solutions. The ranking determination unit determines rankings according to the difficulty of questions and solutions using the specific processing unit 290 of the data processing unit 12 and sells usage rights. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and ranking determination unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer understanding data and behavioral logs using the control unit 46A of the headset terminal 314, and collects publicly available data from the National Statistical Office using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and integrates new and old knowledge. The proposal unit discovers cross-cutting insights using the specific processing unit 290 of the data processing unit 12 and proposes solutions. The ranking determination unit determines rankings according to the difficulty of questions and solutions using the specific processing unit 290 of the data processing unit 12 and sells usage rights. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0159] 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.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and ranking determination unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects customer understanding data and behavioral logs by the control unit 46A of the robot 414, and collects publicly available data from the National Statistical Office by the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 and integrates new and old knowledge. The proposal unit discovers cross-cutting insights and proposes solutions by the specific processing unit 290 of the data processing unit 12. The ranking determination unit determines rankings according to the difficulty of questions and solutions by the specific processing unit 290 of the data processing unit 12 and sells usage rights. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The collection department collects highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. The data collected by the aforementioned collection unit is analyzed by an analysis unit that integrates new and old knowledge, Based on the insights integrated by the aforementioned analysis unit, the proposal unit discovers cross-cutting insights and proposes solutions. It comprises a ranking determination unit that determines a ranking according to the difficulty level of the questions and solutions. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data and integrate new and old knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Discover cross-cutting insights and propose solutions based on integrated knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned ranking determination unit, We will determine a ranking based on the difficulty of the questions and solutions, and sell the rights to use them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is 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 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts 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 analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the insights. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the insight. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the insights were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the insights. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned ranking determination unit, We estimate user sentiment and adjust ranking criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned ranking determination unit, When determining the ranking, we consider the relationship between the question and the solution to improve the accuracy of the ranking. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned ranking determination unit, When determining the ranking, the attribute information of the question and the person who submitted the answer will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned ranking determination unit, It estimates user sentiment and adjusts the order in which ranking results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned ranking determination unit, When determining the ranking, the geographical distribution of questions and solutions is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned ranking determination unit, When determining the ranking, we refer to related literature for the question and its solution to improve the accuracy of the ranking. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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. The collection department collects highly reliable information such as customer understanding data, behavioral logs, and publicly available data from the National Statistical Office. The data collected by the aforementioned collection unit is analyzed by an analysis unit that integrates new and old knowledge, Based on the insights integrated by the aforementioned analysis unit, the proposal unit discovers cross-cutting insights and proposes solutions. It comprises a ranking determination unit that determines a ranking according to the difficulty level of the questions and solutions. A system characterized by the following features.

2. The aforementioned ranking determination unit, We will determine a ranking based on the difficulty of the questions and solutions, and sell the rights to use them. The system according to feature 1.

3. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

4. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

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

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

7. 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 according to feature 1.

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