system

Abstract

The system according to the embodiment aims to enable data analysis, graph visualization, and policy planning without requiring specialized knowledge. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a graph generation unit, and a policy proposal unit. The reception unit receives input of data. The analysis unit analyzes the data received by the reception unit. The graph generation unit generates a graph based on the data analyzed by the analysis unit. The policy proposal unit proposes a policy based on the graph generated by the graph generation unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies required specialized knowledge to analyze data, visualize graphs, and develop policies, making it difficult for customers to use them effectively.

[0005] The system according to the embodiment aims to enable data analysis, graph visualization, and policy planning without requiring specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a graph generation unit, and a policy proposal unit. The reception unit receives input of data. The analysis unit analyzes the data received by the reception unit. The graph generation unit generates a graph based on the data analyzed by the analysis unit. The policy proposal unit proposes a policy based on the graph generated by the graph generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to analyze data, visualize graphs, and develop policies without specialized knowledge. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The data analysis system according to an embodiment of the present invention requires expertise to analyze nationwide pedestrian flow data, often leaving customers feeling unsure about their ability to use it. To address this issue, we propose a system that uses generative AI to perform data analysis, graph visualization, and policy planning. This system aims to provide customers with an easy-to-use and flexible data analysis experience. First, customers input the data they want to analyze into the system. Next, the generative AI analyzes the input data and generates appropriate graphs and charts. The generated graphs and charts are displayed in a format that is intuitively easy for customers to understand. Furthermore, the generative AI proposes specific policies based on the analysis results. For example, marketing strategies based on changes in pedestrian flow in a specific area or optimizing the timing of an event. This system allows customers to easily analyze data and plan effective policies without specialized expertise. This lowers the barrier to data analysis for customers and increases the likelihood of winning orders. For example, when customers input the data they want to analyze into the system, the system can accept numerical data, text data, image data, and other data types, regardless of their format or type. Next, the Generative AI analyzes the input data and extracts patterns and trends using statistical analysis and machine learning algorithms. For example, the Generative AI analyzes data correlations and identifies important data points. Furthermore, the Generative AI generates appropriate graphs and charts, such as bar graphs, line graphs, and heat maps, based on the analysis results. These graphs and charts are displayed in a format that is intuitively easy for customers to understand. For example, the Generative AI creates graphs using colors and shapes to visually show data trends. Furthermore, the Generative AI proposes specific measures based on the analysis results. For example, this could include a marketing strategy based on increases or decreases in foot traffic in a specific area or optimizing the timing of an event. This allows customers to easily analyze data and develop effective measures, even without specialized know-how. This lowers the barrier to data analysis for customers and makes it easier to secure orders.This allows the data analysis system to easily analyze data and develop effective measures, even if customers do not have specialized know-how.

[0029] A data analysis system according to an embodiment includes a reception unit, an analysis unit, a graph generation unit, and a policy proposal unit. The reception unit receives data that a customer wishes to analyze. The data that the customer wishes to analyze includes, but is not limited to, numerical data, text data, and image data. The reception unit can receive, for example, numerical data in a spreadsheet format. The reception unit can also analyze text data using natural language processing technology. The reception unit can also analyze image data using image recognition technology. For example, the reception unit receives numerical data in a spreadsheet format and transmits it to the analysis unit. The text data is analyzed using natural language processing technology and transmitted to the analysis unit. The image data is analyzed using image recognition technology and transmitted to the analysis unit. The analysis unit uses a generation AI to analyze the data received by the reception unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit extracts patterns and trends in the data using statistical analysis. The analysis unit can also analyze correlations in the data using a machine learning algorithm. The analysis unit can also identify important points in the data using the generation AI. For example, the analysis unit extracts patterns and trends in the data using statistical analysis and sends them to the graph generation unit. It uses a machine learning algorithm to analyze correlations in the data and sends them to the graph generation unit. It uses generation AI to identify important points in the data and sends them to the graph generation unit. The graph generation unit generates a graph based on the data analyzed by the analysis unit. The graph may be generated in the form of, for example, a bar graph, a line graph, a heat map, or the like, but is not limited to these examples. For example, the graph generation unit generates a bar graph to visually show the trend of the data. The graph generation unit can also generate a line graph to visually show the fluctuations of the data. Furthermore, the graph generation unit can generate a heat map to visually show the distribution of the data. For example, the graph generation unit generates a bar graph to visually show the trend of the data and sends it to the policy proposal unit. It generates a line graph to visually show the fluctuations of the data and sends it to the policy proposal unit. It generates a heat map to visually show the distribution of the data and sends it to the policy proposal unit.The policy proposal unit proposes a policy based on the graph generated by the graph generation unit. The policy may be proposed in the form of, for example, a marketing policy or an operational policy, but is not limited to these examples. For example, the policy proposal unit proposes a marketing strategy in response to an increase or decrease in foot traffic in a specific area. The policy proposal unit can also propose optimal event timing. Furthermore, the policy proposal unit can propose new business opportunities based on data trends. For example, the policy proposal unit proposes a marketing strategy in response to an increase or decrease in foot traffic in a specific area and notifies customers. The policy proposal unit proposes optimal event timing and notifies customers. The policy proposal unit proposes new business opportunities based on data trends and notifies customers. This allows the data analysis system according to the embodiment to easily analyze data and develop effective policies even if the customer does not have specialized know-how. Some or all of the above-described processing in the policy proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the policy proposal unit may propose policies using an AI model that inputs the graph generated by the graph generation unit and outputs policies.

[0030] The reception unit can analyze the user's past data input history and select the optimal input method. The reception unit analyzes the user's past data input history and selects the optimal input method. For example, the reception unit prioritizes and suggests input methods (such as voice input and text input) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. The reception unit can also analyze the user's past input history and select the most efficient input method. For example, the reception unit prioritizes and suggests input methods that the user has frequently used in the past, improving data input efficiency. The reception unit suggests the optimal input method for a specific time period, improving the user's work efficiency. The reception unit selects the most efficient input method and reduces the user's burden. In this way, the optimal input method can be provided by analyzing the user's past data input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past input history data to the generation AI and have the generation AI select the optimal input method. The reception unit can also accumulate the user's input history data and analyze long-term input patterns. For example, the reception unit analyzes the user's input history data in chronological order to identify changes in input patterns, thereby obtaining a detailed understanding of the user's input history and providing an optimal input method.

[0031] The reception unit can filter data based on the user's current project and areas of interest when inputting data. The reception unit can filter data based on the user's current project and areas of interest when inputting data. For example, the reception unit can prioritize inputting only data related to the user's current project. The reception unit can also filter and input highly relevant data based on the user's areas of interest. The reception unit can also automatically select and input necessary data according to the progress of the user's project. For example, the reception unit can prioritize inputting data related to the user's current project to support the progress of the project. The reception unit can filter and input highly relevant data based on the user's areas of interest to provide data that attracts the user's attention. The reception unit can automatically select and input necessary data according to the progress of the project, thereby achieving efficient data input. By filtering data based on the user's current project and areas of interest, highly relevant data can be prioritized and input. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's project data and area of ​​interest data to a generation AI and have the generation AI perform filtering. Furthermore, the reception unit can accumulate the user's project data and area of ​​interest data and analyze long-term project progress and changes in the area of ​​interest. For example, the reception unit can analyze the user's project data in chronological order to identify changes in the project progress. It can also analyze the user's area of ​​interest data in chronological order to identify changes in the area of ​​interest. This allows the user's projects and areas of interest to be understood in detail and appropriate filtering to be performed when entering data.

[0032] The reception unit can prioritize inputting highly relevant data based on the user's geographical location information when inputting data. The reception unit prioritizes inputting highly relevant data based on the user's geographical location information when inputting data. For example, if the user is in a specific area, the reception unit prioritizes inputting data related to that area. The reception unit can also prioritize inputting data related to locations close to the user's current location. The reception unit can also select and input the most relevant data based on the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting data related to that area and provides information specific to the area. Prioritizing inputting data related to locations close to the user's current location improves user convenience. Selecting and inputting the most relevant data achieves efficient data input. This allows highly relevant data to be input preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant data. Furthermore, the reception unit can accumulate the user's geographical location information data and analyze long-term geographical trends. For example, the reception unit can analyze the user's geographical location information data over time to identify changes in geographical trends. This allows the user's geographical location information to be grasped in detail and appropriate filtering to be performed when entering data.

[0033] The reception unit can analyze the user's social media activity and input relevant data when data is input. The reception unit can analyze the user's social media activity and input relevant data when data is input. For example, the reception unit inputs relevant data based on information shared by the user on social media. The reception unit can also preferentially input data related to topics of interest from the user's social media activity. The reception unit can also analyze the user's social media activity history and select and input the most relevant data. For example, the reception unit inputs relevant data based on information shared by the user on social media and provides data that attracts the user's interest. The reception unit can preferentially input data related to topics of interest from the user's social media activity and provide data that attracts the user's interest. The reception unit analyzes the user's social media activity history and selects and inputs the most relevant data, thereby achieving efficient data input. In this way, highly relevant data can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to a generation AI and have the generation AI select relevant data. Furthermore, the reception unit can accumulate the user's social media activity data and analyze long-term social media activity trends. For example, the reception unit can analyze the user's social media activity data over time to identify changes in activity trends. This allows for a detailed understanding of the user's social media activity and appropriate filtering when entering data.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. On the other hand, the analysis unit can perform a concise analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance to identify important points of the data. On the other hand, the analysis unit can perform a concise analysis on data with low importance to achieve efficient data processing. The analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the data to provide optimal analysis results. As a result, adjusting the level of detail of the analysis based on the importance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that evaluates the importance of the data and adjust the level of detail of the analysis. Furthermore, the analysis unit can evaluate the importance of the data in real time and instantly adjust the level of detail of the analysis. For example, the analysis unit evaluates the importance of data in real time and dynamically adjusts the level of detail of the analysis. The analysis unit can also accumulate the importance of data and analyze long-term changes in importance. For example, the analysis unit analyzes the importance of data over time and identifies patterns of change in importance. This allows the importance of data to be grasped in detail and the level of detail of the analysis to be adjusted.

[0035] The analysis unit can apply different analysis algorithms depending on the data category during analysis. The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can perform analysis using an economic model on economic data. Furthermore, the analysis unit can apply a sociological analysis algorithm to social data. Furthermore, the analysis unit can perform analysis using an environmental model on environmental data. For example, the analysis unit can analyze economic data using an economic model to identify economic trends. The analysis unit can apply a sociological analysis algorithm to social data to analyze social trends. The analysis unit can analyze environmental data using an environmental model to evaluate environmental changes. By applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can have a generation AI execute an algorithm that identifies the data category and select an appropriate analysis algorithm. Furthermore, the analysis unit can identify the data category in real time and instantly apply the analysis algorithm. For example, the analysis unit can identify the data category in real time and dynamically apply the appropriate analysis algorithm. The analysis unit can also accumulate data categories and analyze long-term changes in those categories. For example, the analysis unit can analyze data categories over time to identify patterns of change in those categories. This allows for a detailed understanding of the data categories and the application of appropriate analysis algorithms during analysis.

[0036] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit can determine the analysis priority based on the time of data submission during analysis. For example, the latest data can be analyzed preferentially. Furthermore, data submitted earlier can be postponed. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. For example, the analysis unit can prioritize analysis of the latest data to provide the latest information. Data submitted earlier can be postponed, thereby achieving efficient data processing. The analysis priority can be dynamically adjusted based on the time of submission to provide optimal analysis results. Thus, determining the analysis priority based on the time of data submission enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have the generation AI execute an algorithm that evaluates the time of data submission to determine the analysis priority. Furthermore, the analysis unit can evaluate the time of data submission in real time and immediately adjust the analysis priority. For example, the analysis unit evaluates the time of data submission in real time and dynamically adjusts the analysis priority. The analysis unit can also accumulate data submission dates and analyze long-term changes in the submission dates. For example, the analysis unit can analyze data submission dates over time and identify patterns of change in the submission dates. This allows for a detailed understanding of data submission dates and allows for appropriate prioritization during analysis.

[0037] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, highly relevant data can be analyzed preferentially. Also, less relevant data can be postponed. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data to identify important points of the data. Less relevant data can be postponed to achieve efficient data processing. The analysis unit dynamically adjusts the order of analysis based on the relevance of the data to provide optimal analysis results. As a result, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause a generation AI to execute an algorithm that evaluates the relevance of the data and adjust the order of analysis. Furthermore, the analysis unit can evaluate the relevance of the data in real time and instantly adjust the order of analysis. For example, the analysis unit evaluates the relevance of the data in real time and dynamically adjusts the order of analysis. The analysis unit can also accumulate data relevance and analyze long-term changes in relevance. For example, the analysis unit can analyze data relevance over time and identify patterns of change in relevance. This allows for a detailed understanding of data relevance and allows for appropriate adjustment of the order during analysis.

[0038] The graph generation unit can improve the accuracy of the graph based on the interrelationships of data when generating the graph. The graph generation unit improves the accuracy of the graph based on the interrelationships of data when generating the graph. For example, the graph generation unit analyzes the interrelationships of data and generates a graph that emphasizes highly correlated data. The graph generation unit can also generate a graph with minimized error by taking the interrelationships of data into consideration. Furthermore, the graph generation unit can select and generate the most appropriate graph format based on the interrelationships of data. For example, the graph generation unit analyzes the interrelationships of data and generates a graph that emphasizes highly correlated data to visually show important points of the data. The graph generation unit generates a graph with minimized error by taking the interrelationships of data into consideration and provides accurate information. The graph generation unit selects and generates the most appropriate graph format based on the interrelationships of data to deepen the user's understanding. In this way, a highly accurate graph can be generated by taking the interrelationships of data into consideration. Some or all of the above-described processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit can have a generation AI execute an algorithm that evaluates the interrelationships of data to improve the accuracy of the graph. Furthermore, the graph generation unit can evaluate data interrelationships in real time and instantly improve the accuracy of the graph. For example, the graph generation unit evaluates data interrelationships in real time and dynamically improves the accuracy of the graph. The graph generation unit can also accumulate data interrelationships and analyze long-term changes in the interrelationships. For example, the graph generation unit analyzes data interrelationships over time and identifies patterns of change in the interrelationships. This allows for a detailed understanding of data interrelationships and appropriate improvement of accuracy when generating a graph.

[0039] The graph generation unit can generate a graph based on the attribute information of the data submitter when generating a graph. The graph generation unit generates a graph based on the attribute information of the data submitter when generating a graph. For example, if the data submitter is an expert, the graph generation unit generates a detailed graph. Furthermore, if the data submitter is a general user, the graph generation unit can generate a concise and easy-to-understand graph. Furthermore, the graph generation unit can select and generate the most appropriate graph format based on the attribute information of the data submitter. For example, if the data submitter is an expert, the graph generation unit generates a detailed graph to provide specialized information. If the data submitter is a general user, the graph generation unit generates a concise and easy-to-understand graph to deepen the user's understanding. Based on the attribute information of the data submitter, the graph generation unit selects and generates the most appropriate graph format to provide information according to the user's needs. This allows for the generation of a more appropriate graph by taking the attribute information of the data submitter into consideration. Some or all of the above-described processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit can generate a graph by having a generation AI execute an algorithm that evaluates the attribute information of the data submitter. Furthermore, the graph generation unit can evaluate the attribute information of the data submitter in real time and instantly generate a graph. For example, the graph generation unit evaluates the attribute information of the data submitter in real time and dynamically generates a graph. The graph generation unit can also accumulate the attribute information of the data submitter and analyze long-term changes in the attribute information. For example, the graph generation unit analyzes the attribute information of the data submitter over time and identifies patterns of change in the attribute information. This allows for a detailed understanding of the attribute information of the data submitter and provides appropriate information when generating a graph.

[0040] The graph generation unit can generate a graph based on the geographical distribution of the data when generating the graph. The graph generation unit generates a graph based on the geographical distribution of the data when generating the graph. For example, the graph generation unit analyzes the geographical distribution of the data and generates a graph for each region. The graph generation unit can also select and generate the most appropriate graph format based on the geographical distribution. Furthermore, the graph generation unit can generate a graph with minimal error by taking the geographical distribution into consideration. For example, the graph generation unit analyzes the geographical distribution of the data and generates a graph for each region to provide region-specific information. The graph generation unit selects and generates the most appropriate graph format based on the geographical distribution to deepen the user's understanding. The graph generation unit generates a graph with minimal error by taking the geographical distribution into consideration to provide accurate information. This allows for the generation of a more appropriate graph by taking the geographical distribution of the data into consideration. Some or all of the above-described processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit can cause a generation AI to execute an algorithm that evaluates the geographical distribution of the data to generate a graph. Furthermore, the graph generation unit can evaluate the geographical distribution of the data in real time and instantly generate a graph. For example, the graph generation unit evaluates the geographic distribution of data in real time and dynamically generates graphs. The graph generation unit can also accumulate the geographic distribution of data and analyze long-term changes in the geographic distribution. For example, the graph generation unit analyzes the geographic distribution of data over time and identifies patterns of change in the geographic distribution. This allows for a detailed understanding of the geographic distribution of data and provides appropriate information when generating graphs.

[0041] The graph generation unit can improve the accuracy of the graph by referring to literature related to the data when generating the graph. The graph generation unit can improve the accuracy of the graph by referring to literature related to the data when generating the graph. For example, the graph generation unit can refer to literature related to the data and select and generate the most appropriate graph format. The graph generation unit can also generate a graph with minimized error based on data from the related literature. The graph generation unit can also generate a graph that highlights highly correlated data by referring to the related literature. For example, the graph generation unit can refer to literature related to the data and select and generate the most appropriate graph format to deepen a user's understanding. The graph generation unit can generate a graph with minimized error based on data from the related literature to provide accurate information. The graph generation unit can generate a graph that highlights highly correlated data by referring to the related literature to visually show important points of the data. In this way, by referring to literature related to the data, a highly accurate graph can be generated. Some or all of the above-mentioned processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit can have a generation AI execute an algorithm that evaluates literature related to the data to improve the accuracy of the graph. Furthermore, the graph generation unit can evaluate literature related to the data in real time and instantly improve the accuracy of the graph. For example, the graph generation unit can evaluate literature related to the data in real time and dynamically improve the accuracy of the graph. The graph generation unit can also accumulate literature related to the data and analyze long-term changes in the literature. For example, the graph generation unit can analyze literature related to the data in chronological order and identify patterns of change in the literature. This allows for a detailed understanding of literature related to the data and appropriate improvement of accuracy when generating the graph.

[0042] The policy proposal unit can predict a current policy by referring to past policy data when proposing a policy. The policy proposal unit can predict a current policy by referring to past policy data when proposing a policy. For example, the policy proposal unit proposes the most effective policy based on past policy data. The policy proposal unit can also predict an optimal policy for a current situation by referring to past policy data. The policy proposal unit can also analyze past policy data and propose a policy with a high success rate. For example, the policy proposal unit proposes the most effective policy based on past policy data and notifies the user. The policy proposal unit predicts an optimal policy for a current situation by referring to past policy data and notifies the user. The policy proposal unit analyzes past policy data, proposes a policy with a high success rate, and notifies the user. In this way, the optimal policy for a current situation can be proposed by referring to past policy data. Some or all of the above-described processing in the policy proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the policy proposal unit can cause a generation AI to execute an algorithm that evaluates past policy data to predict a current policy. The policy proposal unit can evaluate past policy data in real time and instantly predict a current policy. For example, the policy proposal unit evaluates past policy data in real time and dynamically predicts current policies. The policy proposal unit can also accumulate past policy data and analyze long-term policy changes. For example, the policy proposal unit analyzes past policy data in chronological order and identifies policy change patterns. This allows for a detailed understanding of past policy data and makes appropriate predictions when proposing policies.

[0043] The policy proposal unit can apply different policy proposal methods to each data category when proposing a policy. The policy proposal unit can apply different policy proposal methods to each data category when proposing a policy. For example, when proposing a policy based on economic data, the policy proposal unit can apply a method using an economic model. Furthermore, when proposing a policy based on social data, the policy proposal unit can apply a sociological method. Furthermore, when proposing a policy based on environmental data, the policy proposal unit can apply a method using an environmental model. For example, when proposing a policy based on economic data, the policy proposal unit can apply a method using an economic model to identify economic trends. When proposing a policy based on social data, the policy proposal unit can apply a sociological method to analyze social trends. When proposing a policy based on environmental data, the policy proposal unit can apply a method using an environmental model to evaluate environmental changes. This enables more effective policy proposals by applying different policy proposal methods depending on the data category. Some or all of the above-mentioned processing in the policy proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the policy proposal unit can have a generation AI execute an algorithm that identifies data categories to select an appropriate policy proposal method. Furthermore, the policy proposal unit can identify data categories in real time and immediately apply policy proposal techniques. For example, the policy proposal unit can identify data categories in real time and dynamically apply appropriate policy proposal techniques. The policy proposal unit can also accumulate data categories and analyze long-term changes in categories. For example, the policy proposal unit can analyze data categories over time and identify patterns of category change. This allows for a detailed understanding of data categories and the application of appropriate techniques when proposing measures.

[0044] The policy proposal unit can analyze changes in policies based on the timing of data submission when proposing policies. The policy proposal unit can analyze changes in policies based on the timing of data submission when proposing policies. For example, based on the latest data, the policy proposal unit proposes policies that are optimal for the current situation. Furthermore, data submitted earlier can be postponed and the latest data can be analyzed preferentially. Furthermore, the policy proposal unit can dynamically analyze changes in policies based on the timing of submission and propose optimal policies. For example, the policy proposal unit proposes policies that are optimal for the current situation based on the latest data and notifies the user. Data submitted earlier can be postponed, achieving efficient data processing. Based on the timing of submission, changes in policies are dynamically analyzed, optimal policies are proposed, and the user is notified. This enables more appropriate policy proposals by analyzing changes in policies based on the timing of data submission. Some or all of the above-described processing in the policy proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the policy proposal unit can have a generation AI execute an algorithm that evaluates the timing of data submission to analyze changes in policies. Furthermore, the policy proposal unit can evaluate the timing of data submission in real time and immediately analyze changes in policies. For example, the policy proposal unit evaluates the timing of data submission in real time and dynamically analyzes changes in policies. The policy proposal unit can also accumulate data submission times and analyze changes in submission times over the long term. For example, the policy proposal unit can analyze data submission times over time and identify patterns of change in submission times. This allows for a detailed understanding of data submission times and appropriate change analysis when proposing policies.

[0045] The measure proposal unit can analyze the measure by referring to market data related to the data when proposing the measure. The measure proposal unit analyzes the measure by referring to market data related to the data when proposing the measure. For example, the measure proposal unit proposes the most effective measure based on the related market data. The measure proposal unit can also analyze the measure that is optimal for the current situation by referring to the related market data. Furthermore, the measure proposal unit can analyze the relevant market data and propose a measure with a high success rate. For example, the measure proposal unit proposes the most effective measure based on the relevant market data and notifies the user. The measure proposal unit analyzes the measure that is optimal for the current situation by referring to the relevant market data and notifies the user. The relevant market data is analyzed, proposes a measure with a high success rate, and notifies the user. This enables more effective measure proposals by referring to the relevant market data of the data. Some or all of the above-mentioned processing in the measure proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the measure proposal unit can cause a generation AI to execute an algorithm that evaluates the relevant market data and analyze the measure. Furthermore, the measure proposal unit can evaluate the relevant market data in real time and instantly analyze the measure. For example, the policy proposal department evaluates related market data in real time and dynamically analyzes policies. The policy proposal department can also accumulate related market data and analyze long-term changes in the market data. For example, the policy proposal department analyzes related market data in chronological order and identifies patterns of change in the market data. This allows for a detailed understanding of the related market data and appropriate analysis when proposing policies.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data analysis system can also customize the way the analysis results are displayed, taking into account the user's past data analysis history. For example, it can prioritize the use of graph formats and color patterns that the user has previously preferred. It can also highlight data points that the user has previously shown particular interest in. It can also adjust the level of detail of the analysis results based on the user's past feedback. This allows the user to receive analysis results that suit their preferences, improving the efficiency of data analysis.

[0048] The reception unit can analyze the user's past data input history and select the optimal input method. For example, it can preferentially suggest input methods (such as voice input or text input) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and select the most efficient input method. In this way, the optimal input method can be provided by analyzing the user's past data input history.

[0049] The reception unit can filter data based on the user's current project or area of ​​interest when inputting the data. For example, the reception unit can preferentially input only data related to the user's ongoing project. The reception unit can also filter and input highly relevant data based on the user's area of ​​interest. Furthermore, the reception unit can automatically select and input necessary data according to the progress of the user's project. In this way, by filtering data based on the user's current project or area of ​​interest, highly relevant data can be preferentially input.

[0050] When inputting data, the reception unit can prioritize inputting highly relevant data based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting data related to that area. The reception unit can also prioritize inputting data related to locations close to the user's current location. Furthermore, the reception unit can select and input the most relevant data based on the user's geographical location information. This allows highly relevant data to be input preferentially by taking the user's geographical location information into consideration.

[0051] When data is input, the reception unit can analyze the user's social media activity and input relevant data. For example, the reception unit inputs relevant data based on information shared by the user on social media. The reception unit can also preferentially input data related to topics of interest from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity history and select and input the most relevant data. In this way, highly relevant data can be input by analyzing the user's social media activity.

[0052] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. On the other hand, the analysis unit can perform a concise analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0053] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, for economic data, the analysis unit performs analysis using an economic model. For social data, the analysis unit can apply a sociological analysis algorithm. Furthermore, for environmental data, the analysis unit can also perform analysis using an environmental model. This allows for more accurate analysis by applying different analysis algorithms depending on the data category.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception department inputs the data that the customer wants to analyze. The data that the customer wants to analyze can include numerical data, text data, image data, etc. For example, numerical data is input in spreadsheet format, text data is analyzed using natural language processing technology, and image data is analyzed using image recognition technology. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis is performed using statistical analysis, machine learning algorithms, and generative AI. For example, statistical analysis is used to extract patterns and trends in the data, machine learning algorithms are used to analyze correlations in the data, and generative AI is used to identify important points in the data. Step 3: The graph generator generates a graph based on the data analyzed by the analyzer. The graph may be generated in the form of a bar graph, a line graph, a heat map, or the like. For example, a bar graph visually shows the trend of the data, a line graph visually shows the fluctuation of the data, and a heat map visually shows the distribution of the data. Step 4: The policy proposal unit proposes policies based on the graph generated by the graph generation unit. The policies are proposed in the form of marketing policies, operational policies, etc. Examples include marketing strategies in response to increases or decreases in foot traffic in a specific area, optimizing the timing of events, and proposing new business opportunities.

[0056] (Example 2) The data analysis system according to an embodiment of the present invention requires expertise to analyze nationwide pedestrian flow data, often leaving customers feeling unsure about their ability to use it. To address this issue, we propose a system that uses generative AI to perform data analysis, graph visualization, and policy planning. This system aims to provide customers with an easy-to-use and flexible data analysis experience. First, customers input the data they want to analyze into the system. Next, the generative AI analyzes the input data and generates appropriate graphs and charts. The generated graphs and charts are displayed in a format that is intuitively easy for customers to understand. Furthermore, the generative AI proposes specific policies based on the analysis results. For example, marketing strategies based on changes in pedestrian flow in a specific area or optimizing the timing of an event. This system allows customers to easily analyze data and plan effective policies without specialized expertise. This lowers the barrier to data analysis for customers and increases the likelihood of winning orders. For example, when customers input the data they want to analyze into the system, the system can accept numerical data, text data, image data, and other data types, regardless of their format or type. Next, the Generative AI analyzes the input data and extracts patterns and trends using statistical analysis and machine learning algorithms. For example, the Generative AI analyzes data correlations and identifies important data points. Furthermore, the Generative AI generates appropriate graphs and charts, such as bar graphs, line graphs, and heat maps, based on the analysis results. These graphs and charts are displayed in a format that is intuitively easy for customers to understand. For example, the Generative AI creates graphs using colors and shapes to visually show data trends. Furthermore, the Generative AI proposes specific measures based on the analysis results. For example, this could include a marketing strategy based on increases or decreases in foot traffic in a specific area or optimizing the timing of an event. This allows customers to easily analyze data and develop effective measures, even without specialized know-how. This lowers the barrier to data analysis for customers and makes it easier to secure orders.This allows the data analysis system to easily analyze data and develop effective measures, even if customers do not have specialized know-how.

[0057] A data analysis system according to an embodiment includes a reception unit, an analysis unit, a graph generation unit, and a policy proposal unit. The reception unit receives data that a customer wishes to analyze. The data that the customer wishes to analyze includes, but is not limited to, numerical data, text data, and image data. The reception unit can receive, for example, numerical data in a spreadsheet format. The reception unit can also analyze text data using natural language processing technology. The reception unit can also analyze image data using image recognition technology. For example, the reception unit receives numerical data in a spreadsheet format and transmits it to the analysis unit. The text data is analyzed using natural language processing technology and transmitted to the analysis unit. The image data is analyzed using image recognition technology and transmitted to the analysis unit. The analysis unit uses a generation AI to analyze the data received by the reception unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit extracts patterns and trends in the data using statistical analysis. The analysis unit can also analyze correlations in the data using a machine learning algorithm. The analysis unit can also identify important points in the data using the generation AI. For example, the analysis unit extracts patterns and trends in the data using statistical analysis and sends them to the graph generation unit. It uses a machine learning algorithm to analyze correlations in the data and sends them to the graph generation unit. It uses generation AI to identify important points in the data and sends them to the graph generation unit. The graph generation unit generates a graph based on the data analyzed by the analysis unit. The graph may be generated in the form of, for example, a bar graph, a line graph, a heat map, or the like, but is not limited to these examples. For example, the graph generation unit generates a bar graph to visually show the trend of the data. The graph generation unit can also generate a line graph to visually show the fluctuations of the data. Furthermore, the graph generation unit can generate a heat map to visually show the distribution of the data. For example, the graph generation unit generates a bar graph to visually show the trend of the data and sends it to the policy proposal unit. It generates a line graph to visually show the fluctuations of the data and sends it to the policy proposal unit. It generates a heat map to visually show the distribution of the data and sends it to the policy proposal unit.The policy proposal unit proposes a policy based on the graph generated by the graph generation unit. The policy may be proposed in the form of, for example, a marketing policy or an operational policy, but is not limited to these examples. For example, the policy proposal unit proposes a marketing strategy in response to an increase or decrease in foot traffic in a specific area. The policy proposal unit can also propose optimal event timing. Furthermore, the policy proposal unit can propose new business opportunities based on data trends. For example, the policy proposal unit proposes a marketing strategy in response to an increase or decrease in foot traffic in a specific area and notifies customers. The policy proposal unit proposes optimal event timing and notifies customers. The policy proposal unit proposes new business opportunities based on data trends and notifies customers. This allows the data analysis system according to the embodiment to easily analyze data and develop effective policies even if the customer does not have specialized know-how. Some or all of the above-described processing in the policy proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the policy proposal unit may propose policies using an AI model that inputs the graph generated by the graph generation unit and outputs policies.

[0058] The data analysis system includes a reception unit that estimates a user's emotion and adjusts the timing of data input based on the estimated user emotion. The reception unit estimates the user's emotion and adjusts the timing of data input based on the estimated user emotion. For example, if the user is stressed, the reception unit delays the timing of data input to provide a relaxing environment. Furthermore, if the user is relaxed, the reception unit accelerates the timing of data input, allowing for efficient data input. Furthermore, if the user is in a hurry, the reception unit optimizes the timing of data input to quickly complete input. This allows for more appropriate data input by adjusting the timing of data input according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation. Furthermore, the reception unit can analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the reception unit can monitor the user's facial expressions in real time and immediately detect changes in emotions. The reception unit can also accumulate the user's emotional data and analyze long-term emotional trends. For example, the reception unit can analyze the user's emotional data in chronological order and identify patterns of emotional changes. This allows the user's emotions to be grasped in detail and the timing of data input to be adjusted.

[0059] The reception unit can analyze the user's past data input history and select the optimal input method. The reception unit analyzes the user's past data input history and selects the optimal input method. For example, the reception unit prioritizes and suggests input methods (such as voice input and text input) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. The reception unit can also analyze the user's past input history and select the most efficient input method. For example, the reception unit prioritizes and suggests input methods that the user has frequently used in the past, improving data input efficiency. The reception unit suggests the optimal input method for a specific time period, improving the user's work efficiency. The reception unit selects the most efficient input method and reduces the user's burden. In this way, the optimal input method can be provided by analyzing the user's past data input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's past input history data to the generation AI and have the generation AI select the optimal input method. The reception unit can also accumulate the user's input history data and analyze long-term input patterns. For example, the reception unit analyzes the user's input history data in chronological order to identify changes in input patterns, thereby obtaining a detailed understanding of the user's input history and providing an optimal input method.

[0060] The reception unit can filter data based on the user's current project and areas of interest when inputting data. The reception unit can filter data based on the user's current project and areas of interest when inputting data. For example, the reception unit can prioritize inputting only data related to the user's current project. The reception unit can also filter and input highly relevant data based on the user's areas of interest. The reception unit can also automatically select and input necessary data according to the progress of the user's project. For example, the reception unit can prioritize inputting data related to the user's current project to support the progress of the project. The reception unit can filter and input highly relevant data based on the user's areas of interest to provide data that attracts the user's attention. The reception unit can automatically select and input necessary data according to the progress of the project, thereby achieving efficient data input. By filtering data based on the user's current project and areas of interest, highly relevant data can be prioritized and input. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's project data and area of ​​interest data to a generation AI and have the generation AI perform filtering. Furthermore, the reception unit can accumulate the user's project data and area of ​​interest data and analyze long-term project progress and changes in the area of ​​interest. For example, the reception unit can analyze the user's project data in chronological order to identify changes in the project progress. It can also analyze the user's area of ​​interest data in chronological order to identify changes in the area of ​​interest. This allows the user's projects and areas of interest to be understood in detail and appropriate filtering to be performed when entering data.

[0061] The reception unit can estimate the user's emotions and determine the priority of data to be input based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of data to be input based on the estimated user emotions. For example, when the user is stressed, the reception unit postpones less important data and prioritizes input of more important data. Furthermore, when the user is relaxed, the reception unit can input all data evenly. Furthermore, when the user is in a hurry, the reception unit can prioritize input of the most important data. For example, when the user is stressed, the reception unit postpones less important data and prioritizes input of more important data, thereby reducing the user's burden. When the user is relaxed, all data is input evenly, enabling efficient data input. When the user is in a hurry, the reception unit prioritizes input of the most important data, enabling faster data input. Thus, by prioritizing data according to the user's emotions, more efficient data input is possible. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI. For example, the reception unit may input a user's facial expression data to the generation AI and cause the generation AI to estimate emotions. Furthermore, the reception unit may analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the reception unit may monitor the user's facial expressions in real time and immediately detect changes in emotions. The reception unit may also accumulate the user's emotional data and analyze long-term emotional trends. For example, the reception unit may analyze the user's emotional data over time to identify patterns of emotional changes. This allows a detailed understanding of the user's emotions and allows data prioritization.

[0062] The reception unit can prioritize inputting highly relevant data based on the user's geographical location information when inputting data. The reception unit prioritizes inputting highly relevant data based on the user's geographical location information when inputting data. For example, if the user is in a specific area, the reception unit prioritizes inputting data related to that area. The reception unit can also prioritize inputting data related to locations close to the user's current location. The reception unit can also select and input the most relevant data based on the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting data related to that area and provides information specific to the area. Prioritizing inputting data related to locations close to the user's current location improves user convenience. Selecting and inputting the most relevant data achieves efficient data input. This allows highly relevant data to be input preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant data. Furthermore, the reception unit can accumulate the user's geographical location information data and analyze long-term geographical trends. For example, the reception unit can analyze the user's geographical location information data over time to identify changes in geographical trends. This allows the user's geographical location information to be grasped in detail and appropriate filtering to be performed when entering data.

[0063] The reception unit can analyze the user's social media activity and input relevant data when data is input. The reception unit can analyze the user's social media activity and input relevant data when data is input. For example, the reception unit inputs relevant data based on information shared by the user on social media. The reception unit can also preferentially input data related to topics of interest from the user's social media activity. The reception unit can also analyze the user's social media activity history and select and input the most relevant data. For example, the reception unit inputs relevant data based on information shared by the user on social media and provides data that attracts the user's interest. The reception unit can preferentially input data related to topics of interest from the user's social media activity and provide data that attracts the user's interest. The reception unit analyzes the user's social media activity history and selects and inputs the most relevant data, thereby achieving efficient data input. In this way, highly relevant data can be input by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to a generation AI and have the generation AI select relevant data. Furthermore, the reception unit can accumulate the user's social media activity data and analyze long-term social media activity trends. For example, the reception unit can analyze the user's social media activity data over time to identify changes in activity trends. This allows for a detailed understanding of the user's social media activity and appropriate filtering when entering data.

[0064] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the key points. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results to deepen the user's understanding. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the key points to support quick decision-making. If the user is excited, the analysis unit can provide visually stimulating analysis results to attract the user's attention. This allows the presentation of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input a user's facial expression data into a generation AI and cause the generation AI to estimate emotions. Furthermore, the analysis unit may analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the analysis unit may monitor the user's facial expressions in real time and immediately detect changes in emotions. The analysis unit may also accumulate the user's emotional data and analyze long-term emotional trends. For example, the analysis unit may analyze the user's emotional data over time to identify patterns of emotional change. This allows the user's emotions to be understood in detail and the method of expressing the analysis to be adjusted.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. On the other hand, the analysis unit can perform a concise analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance to identify important points of the data. On the other hand, the analysis unit can perform a concise analysis on data with low importance to achieve efficient data processing. The analysis unit can dynamically adjust the level of detail of the analysis based on the importance of the data to provide optimal analysis results. As a result, adjusting the level of detail of the analysis based on the importance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have a generation AI execute an algorithm that evaluates the importance of the data and adjust the level of detail of the analysis. Furthermore, the analysis unit can evaluate the importance of the data in real time and instantly adjust the level of detail of the analysis. For example, the analysis unit evaluates the importance of data in real time and dynamically adjusts the level of detail of the analysis. The analysis unit can also accumulate the importance of data and analyze long-term changes in importance. For example, the analysis unit analyzes the importance of data over time and identifies patterns of change in importance. This allows the importance of data to be grasped in detail and the level of detail of the analysis to be adjusted.

[0066] The analysis unit can apply different analysis algorithms depending on the data category during analysis. The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can perform analysis using an economic model on economic data. Furthermore, the analysis unit can apply a sociological analysis algorithm to social data. Furthermore, the analysis unit can perform analysis using an environmental model on environmental data. For example, the analysis unit can analyze economic data using an economic model to identify economic trends. The analysis unit can apply a sociological analysis algorithm to social data to analyze social trends. The analysis unit can analyze environmental data using an environmental model to evaluate environmental changes. By applying different analysis algorithms depending on the data category, more accurate analysis is possible. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can have a generation AI execute an algorithm that identifies the data category and select an appropriate analysis algorithm. Furthermore, the analysis unit can identify the data category in real time and instantly apply the analysis algorithm. For example, the analysis unit can identify the data category in real time and dynamically apply the appropriate analysis algorithm. The analysis unit can also accumulate data categories and analyze long-term changes in those categories. For example, the analysis unit can analyze data categories over time to identify patterns of change in those categories. This allows for a detailed understanding of the data categories and the application of appropriate analysis algorithms during analysis.

[0067] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis. Also, if the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis to support quick decision-making. If the user is relaxed, the analysis unit can provide a detailed analysis to deepen the user's understanding. If the user is excited, the analysis unit can provide a visually stimulating analysis to attract the user's attention. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions. Furthermore, the analysis unit can analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the analysis unit can monitor the user's facial expressions in real time and immediately detect changes in emotions. The analysis unit can also accumulate the user's emotional data and analyze long-term emotional trends. For example, the analysis unit can analyze the user's emotional data over time and identify patterns of emotional changes. This allows for a detailed understanding of the user's emotions and allows for the length of the analysis to be adjusted.

[0068] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit can determine the analysis priority based on the time of data submission during analysis. For example, the latest data can be analyzed preferentially. Furthermore, data submitted earlier can be postponed. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the time of submission. For example, the analysis unit can prioritize analysis of the latest data to provide the latest information. Data submitted earlier can be postponed, thereby achieving efficient data processing. The analysis priority can be dynamically adjusted based on the time of submission to provide optimal analysis results. Thus, determining the analysis priority based on the time of data submission enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have the generation AI execute an algorithm that evaluates the time of data submission to determine the analysis priority. Furthermore, the analysis unit can evaluate the time of data submission in real time and immediately adjust the analysis priority. For example, the analysis unit evaluates the time of data submission in real time and dynamically adjusts the analysis priority. The analysis unit can also accumulate data submission dates and analyze long-term changes in the submission dates. For example, the analysis unit can analyze data submission dates over time and identify patterns of change in the submission dates. This allows for a detailed understanding of data submission dates and allows for appropriate prioritization during analysis.

[0069] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, highly relevant data can be analyzed preferentially. Also, less relevant data can be postponed. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data to identify important points of the data. Less relevant data can be postponed to achieve efficient data processing. The analysis unit dynamically adjusts the order of analysis based on the relevance of the data to provide optimal analysis results. As a result, adjusting the order of analysis based on the relevance of the data enables efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause a generation AI to execute an algorithm that evaluates the relevance of the data and adjust the order of analysis. Furthermore, the analysis unit can evaluate the relevance of the data in real time and instantly adjust the order of analysis. For example, the analysis unit evaluates the relevance of the data in real time and dynamically adjusts the order of analysis. The analysis unit can also accumulate data relevance and analyze long-term changes in relevance. For example, the analysis unit can analyze data relevance over time and identify patterns of change in relevance. This allows for a detailed understanding of data relevance and allows for appropriate adjustment of the order during analysis.

[0070] The graph generation unit can estimate the user's emotions and adjust the graph display method based on the estimated user emotions. The graph generation unit estimates the user's emotions and adjusts the graph display method based on the estimated user emotions. For example, if the user is relaxed, the graph generation unit displays a detailed graph. On the other hand, if the user is in a hurry, the graph generation unit can display a concise graph that focuses on the main points. Furthermore, if the user is excited, the graph generation unit can display a visually stimulating graph. For example, if the user is relaxed, the graph generation unit displays a detailed graph to deepen the user's understanding. If the user is in a hurry, the graph generation unit displays a concise graph that focuses on the main points to support quick decision-making. If the user is excited, the graph generation unit displays a visually stimulating graph to attract the user's interest. This allows for more appropriate graph display by adjusting the graph display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit may input a user's facial expression data into a generation AI and cause the generation AI to estimate emotions. Furthermore, the graph generation unit may analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the graph generation unit may monitor the user's facial expressions in real time and immediately detect changes in emotions. The graph generation unit may also accumulate the user's emotional data and analyze long-term emotional trends. For example, the graph generation unit may analyze the user's emotional data over time and identify patterns of emotional changes. This may allow a detailed understanding of the user's emotions and adjust the way the graph is displayed.

[0071] The graph generation unit can improve the accuracy of the graph based on the interrelationships of data when generating the graph. The graph generation unit improves the accuracy of the graph based on the interrelationships of data when generating the graph. For example, the graph generation unit analyzes the interrelationships of data and generates a graph that emphasizes highly correlated data. The graph generation unit can also generate a graph with minimized error by taking the interrelationships of data into consideration. Furthermore, the graph generation unit can select and generate the most appropriate graph format based on the interrelationships of data. For example, the graph generation unit analyzes the interrelationships of data and generates a graph that emphasizes highly correlated data to visually show important points of the data. The graph generation unit generates a graph with minimized error by taking the interrelationships of data into consideration and provides accurate information. The graph generation unit selects and generates the most appropriate graph format based on the interrelationships of data to deepen the user's understanding. In this way, a highly accurate graph can be generated by taking the interrelationships of data into consideration. Some or all of the above-described processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit can have a generation AI execute an algorithm that evaluates the interrelationships of data to improve the accuracy of the graph. Furthermore, the graph generation unit can evaluate data interrelationships in real time and instantly improve the accuracy of the graph. For example, the graph generation unit evaluates data interrelationships in real time and dynamically improves the accuracy of the graph. The graph generation unit can also accumulate data interrelationships and analyze long-term changes in the interrelationships. For example, the graph generation unit analyzes data interrelationships over time and identifies patterns of change in the interrelationships. This allows for a detailed understanding of data interrelationships and appropriate improvement of accuracy when generating a graph.

[0072] The graph generation unit can generate a graph based on the attribute information of the data submitter when generating a graph. The graph generation unit generates a graph based on the attribute information of the data submitter when generating a graph. For example, if the data submitter is an expert, the graph generation unit generates a detailed graph. Furthermore, if the data submitter is a general user, the graph generation unit can generate a concise and easy-to-understand graph. Furthermore, the graph generation unit can select and generate the most appropriate graph format based on the attribute information of the data submitter. For example, if the data submitter is an expert, the graph generation unit generates a detailed graph to provide specialized information. If the data submitter is a general user, the graph generation unit generates a concise and easy-to-understand graph to deepen the user's understanding. Based on the attribute information of the data submitter, the graph generation unit selects and generates the most appropriate graph format to provide information according to the user's needs. This allows for the generation of a more appropriate graph by taking the attribute information of the data submitter into consideration. Some or all of the above-described processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit can generate a graph by having a generation AI execute an algorithm that evaluates the attribute information of the data submitter. Furthermore, the graph generation unit can evaluate the attribute information of the data submitter in real time and instantly generate a graph. For example, the graph generation unit evaluates the attribute information of the data submitter in real time and dynamically generates a graph. The graph generation unit can also accumulate the attribute information of the data submitter and analyze long-term changes in the attribute information. For example, the graph generation unit analyzes the attribute information of the data submitter over time and identifies patterns of change in the attribute information. This allows for a detailed understanding of the attribute information of the data submitter and provides appropriate information when generating a graph.

[0073] The graph generation unit can estimate the user's emotions and adjust the display order of graphs based on the estimated user emotions. The graph generation unit estimates the user's emotions and adjusts the display order of graphs based on the estimated user emotions. For example, if the user is in a hurry, the graph generation unit displays important graphs first. Also, if the user is relaxed, the graph generation unit can display all graphs evenly. Furthermore, if the user is excited, the graph generation unit can display visually stimulating graphs first. For example, if the user is in a hurry, the graph generation unit displays important graphs first to support quick decision-making. If the user is relaxed, the graph generation unit displays all graphs evenly to deepen the user's understanding. If the user is excited, the graph generation unit displays visually stimulating graphs first to attract the user's attention. This allows for more appropriate graph display by adjusting the display order of graphs according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit may input a user's facial expression data into a generation AI and cause the generation AI to estimate emotions. Furthermore, the graph generation unit may analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the graph generation unit may monitor the user's facial expressions in real time and immediately detect changes in emotions. The graph generation unit may also accumulate the user's emotional data and analyze long-term emotional trends. For example, the graph generation unit may analyze the user's emotional data in chronological order and identify patterns of emotional changes. This may allow a detailed understanding of the user's emotions and adjust the display order of the graph.

[0074] The graph generation unit can generate a graph based on the geographical distribution of the data when generating the graph. The graph generation unit generates a graph based on the geographical distribution of the data when generating the graph. For example, the graph generation unit analyzes the geographical distribution of the data and generates a graph for each region. The graph generation unit can also select and generate the most appropriate graph format based on the geographical distribution. Furthermore, the graph generation unit can generate a graph with minimal error by taking the geographical distribution into consideration. For example, the graph generation unit analyzes the geographical distribution of the data and generates a graph for each region to provide region-specific information. The graph generation unit selects and generates the most appropriate graph format based on the geographical distribution to deepen the user's understanding. The graph generation unit generates a graph with minimal error by taking the geographical distribution into consideration to provide accurate information. This allows for the generation of a more appropriate graph by taking the geographical distribution of the data into consideration. Some or all of the above-described processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit can cause a generation AI to execute an algorithm that evaluates the geographical distribution of the data to generate a graph. Furthermore, the graph generation unit can evaluate the geographical distribution of the data in real time and instantly generate a graph. For example, the graph generation unit evaluates the geographic distribution of data in real time and dynamically generates graphs. The graph generation unit can also accumulate the geographic distribution of data and analyze long-term changes in the geographic distribution. For example, the graph generation unit analyzes the geographic distribution of data over time and identifies patterns of change in the geographic distribution. This allows for a detailed understanding of the geographic distribution of data and provides appropriate information when generating graphs.

[0075] The graph generation unit can improve the accuracy of the graph by referring to literature related to the data when generating the graph. The graph generation unit can improve the accuracy of the graph by referring to literature related to the data when generating the graph. For example, the graph generation unit can refer to literature related to the data and select and generate the most appropriate graph format. The graph generation unit can also generate a graph with minimized error based on data from the related literature. The graph generation unit can also generate a graph that highlights highly correlated data by referring to the related literature. For example, the graph generation unit can refer to literature related to the data and select and generate the most appropriate graph format to deepen a user's understanding. The graph generation unit can generate a graph with minimized error based on data from the related literature to provide accurate information. The graph generation unit can generate a graph that highlights highly correlated data by referring to the related literature to visually show important points of the data. In this way, by referring to literature related to the data, a highly accurate graph can be generated. Some or all of the above-mentioned processing in the graph generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the graph generation unit can have a generation AI execute an algorithm that evaluates literature related to the data to improve the accuracy of the graph. Furthermore, the graph generation unit can evaluate literature related to the data in real time and instantly improve the accuracy of the graph. For example, the graph generation unit can evaluate literature related to the data in real time and dynamically improve the accuracy of the graph. The graph generation unit can also accumulate literature related to the data and analyze long-term changes in the literature. For example, the graph generation unit can analyze literature related to the data in chronological order and identify patterns of change in the literature. This allows for a detailed understanding of literature related to the data and appropriate improvement of accuracy when generating the graph.

[0076] The policy proposal unit can estimate the user's emotions and adjust the display method of policies based on the estimated user emotions. The policy proposal unit estimates the user's emotions and adjusts the display method of policies based on the estimated user emotions. For example, if the user is relaxed, the policy proposal unit displays detailed policies. If the user is in a hurry, the policy proposal unit can display concise policies that focus on the main points. Furthermore, if the user is excited, the policy proposal unit can also display visually stimulating policies. For example, if the user is relaxed, the policy proposal unit displays detailed policies to deepen the user's understanding. If the user is in a hurry, the policy proposal unit displays concise policies that focus on the main points to support quick decision-making. If the user is excited, the policy proposal unit displays visually stimulating policies to attract the user's interest. This enables more appropriate policy display by adjusting the display method of policies according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the policy proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the policy proposal unit may input a user's facial expression data into a generation AI and cause the generation AI to estimate emotions. Furthermore, the policy proposal unit may analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the policy proposal unit may monitor the user's facial expressions in real time and immediately detect changes in emotions. The policy proposal unit may also accumulate the user's emotional data and analyze long-term emotional trends. For example, the policy proposal unit may analyze the user's emotional data over time and identify patterns of emotional change. This allows for a detailed understanding of the user's emotions and adjustment of the way policies are displayed.

[0077] The policy proposal unit can predict a current policy by referring to past policy data when proposing a policy. The policy proposal unit can predict a current policy by referring to past policy data when proposing a policy. For example, the policy proposal unit proposes the most effective policy based on past policy data. The policy proposal unit can also predict an optimal policy for a current situation by referring to past policy data. The policy proposal unit can also analyze past policy data and propose a policy with a high success rate. For example, the policy proposal unit proposes the most effective policy based on past policy data and notifies the user. The policy proposal unit predicts an optimal policy for a current situation by referring to past policy data and notifies the user. The policy proposal unit analyzes past policy data, proposes a policy with a high success rate, and notifies the user. In this way, the optimal policy for a current situation can be proposed by referring to past policy data. Some or all of the above-described processing in the policy proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the policy proposal unit can cause a generation AI to execute an algorithm that evaluates past policy data to predict a current policy. The policy proposal unit can evaluate past policy data in real time and instantly predict a current policy. For example, the policy proposal unit evaluates past policy data in real time and dynamically predicts current policies. The policy proposal unit can also accumulate past policy data and analyze long-term policy changes. For example, the policy proposal unit analyzes past policy data in chronological order and identifies policy change patterns. This allows for a detailed understanding of past policy data and makes appropriate predictions when proposing policies.

[0078] The policy proposal unit can apply different policy proposal methods to each data category when proposing a policy. The policy proposal unit can apply different policy proposal methods to each data category when proposing a policy. For example, when proposing a policy based on economic data, the policy proposal unit can apply a method using an economic model. Furthermore, when proposing a policy based on social data, the policy proposal unit can apply a sociological method. Furthermore, when proposing a policy based on environmental data, the policy proposal unit can apply a method using an environmental model. For example, when proposing a policy based on economic data, the policy proposal unit can apply a method using an economic model to identify economic trends. When proposing a policy based on social data, the policy proposal unit can apply a sociological method to analyze social trends. When proposing a policy based on environmental data, the policy proposal unit can apply a method using an environmental model to evaluate environmental changes. This enables more effective policy proposals by applying different policy proposal methods depending on the data category. Some or all of the above-mentioned processing in the policy proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the policy proposal unit can have a generation AI execute an algorithm that identifies data categories to select an appropriate policy proposal method. Furthermore, the policy proposal unit can identify data categories in real time and immediately apply policy proposal techniques. For example, the policy proposal unit can identify data categories in real time and dynamically apply appropriate policy proposal techniques. The policy proposal unit can also accumulate data categories and analyze long-term changes in categories. For example, the policy proposal unit can analyze data categories over time and identify patterns of category change. This allows for a detailed understanding of data categories and the application of appropriate techniques when proposing measures.

[0079] The policy proposal unit can estimate the user's emotions and adjust the importance of policies based on the estimated user emotions. The policy proposal unit can estimate the user's emotions and adjust the importance of policies based on the estimated user emotions. For example, if the user is feeling stressed, the policy proposal unit can postpone less important policies and prioritize more important policies. Also, if the user is relaxed, the policy proposal unit can propose all policies equally. Furthermore, if the user is in a hurry, the policy proposal unit can prioritize the most important policies. For example, if the user is feeling stressed, the policy proposal unit postpones less important policies and prioritizes more important policies, reducing the user's burden. If the user is relaxed, the policy proposal unit can propose all policies equally, achieving efficient policy proposal. If the user is in a hurry, the policy proposal unit can prioritize the most important policies, supporting quick decision-making. This allows for more appropriate policy proposals by adjusting the importance of policies according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the policy proposal unit may be performed using, for example, AI. For example, the policy proposal unit may input a user's facial expression data into the generation AI and cause the generation AI to estimate emotions. Furthermore, the policy proposal unit may analyze the user's emotional data in real time and immediately grasp changes in emotions. For example, the policy proposal unit may monitor the user's facial expressions in real time and immediately detect changes in emotions. The policy proposal unit may also accumulate the user's emotional data and analyze long-term emotional trends. For example, the policy proposal unit may analyze the user's emotional data over time to identify patterns of emotional changes. This allows for a detailed understanding of the user's emotions and adjustment of the importance of measures.

[0080] The policy proposal unit can analyze changes in policies based on the timing of data submission when proposing policies. The policy proposal unit can analyze changes in policies based on the timing of data submission when proposing policies. For example, based on the latest data, the policy proposal unit proposes policies that are optimal for the current situation. Furthermore, data submitted earlier can be postponed and the latest data can be analyzed preferentially. Furthermore, the policy proposal unit can dynamically analyze changes in policies based on the timing of submission and propose optimal policies. For example, the policy proposal unit proposes policies that are optimal for the current situation based on the latest data and notifies the user. Data submitted earlier can be postponed, achieving efficient data processing. Based on the timing of submission, changes in policies are dynamically analyzed, optimal policies are proposed, and the user is notified. This enables more appropriate policy proposals by analyzing changes in policies based on the timing of data submission. Some or all of the above-described processing in the policy proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the policy proposal unit can have a generation AI execute an algorithm that evaluates the timing of data submission to analyze changes in policies. Furthermore, the policy proposal unit can evaluate the timing of data submission in real time and immediately analyze changes in policies. For example, the policy proposal unit evaluates the timing of data submission in real time and dynamically analyzes changes in policies. The policy proposal unit can also accumulate data submission times and analyze changes in submission times over the long term. For example, the policy proposal unit can analyze data submission times over time and identify patterns of change in submission times. This allows for a detailed understanding of data submission times and appropriate change analysis when proposing policies.

[0081] The measure proposal unit can analyze the measure by referring to market data related to the data when proposing the measure. The measure proposal unit analyzes the measure by referring to market data related to the data when proposing the measure. For example, the measure proposal unit proposes the most effective measure based on the related market data. The measure proposal unit can also analyze the measure that is optimal for the current situation by referring to the related market data. Furthermore, the measure proposal unit can analyze the relevant market data and propose a measure with a high success rate. For example, the measure proposal unit proposes the most effective measure based on the relevant market data and notifies the user. The measure proposal unit analyzes the measure that is optimal for the current situation by referring to the relevant market data and notifies the user. The relevant market data is analyzed, proposes a measure with a high success rate, and notifies the user. This enables more effective measure proposals by referring to the relevant market data of the data. Some or all of the above-mentioned processing in the measure proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the measure proposal unit can cause a generation AI to execute an algorithm that evaluates the relevant market data and analyze the measure. Furthermore, the measure proposal unit can evaluate the relevant market data in real time and instantly analyze the measure. For example, the policy proposal department evaluates related market data in real time and dynamically analyzes policies. The policy proposal department can also accumulate related market data and analyze long-term changes in the market data. For example, the policy proposal department analyzes related market data in chronological order and identifies patterns of change in the market data. This allows for a detailed understanding of the related market data and appropriate analysis when proposing policies. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, graph generation unit, measure proposal unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs data that the customer wants to analyze. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data using a generation AI. The graph generation unit is implemented by the control unit 46A of the smart device 14 and generates a graph based on the analyzed data. The measure proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and proposes a measure based on the generated graph. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the timing of data input. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, graph generation unit, policy proposal unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs data that the customer wants to analyze. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the data using a generation AI. The graph generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates a graph based on the analyzed data. The policy proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes a policy based on the generated graph. The emotion estimation function is realized, for example, by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the timing of data input. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, graph generation unit, policy proposal unit, and emotion estimation function, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and inputs data that the customer wants to analyze. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the data using a generation AI. The graph generation unit is realized by the control unit 46A of the headset-type terminal 314 and generates a graph based on the analyzed data. The policy proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a policy based on the generated graph. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the timing of data input. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, graph generation unit, policy proposal unit, and emotion estimation function, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs data that the customer wants to analyze. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the data using a generation AI. The graph generation unit is realized by the control unit 46A of the robot 414 and generates a graph based on the analyzed data. The policy proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a policy based on the generated graph. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the timing of data input.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The data analysis system can also customize the way the analysis results are displayed, taking into account the user's past data analysis history. For example, it can prioritize the use of graph formats and color patterns that the user has previously preferred. It can also highlight data points that the user has previously shown particular interest in. It can also adjust the level of detail of the analysis results based on the user's past feedback. This allows the user to receive analysis results that suit their preferences, improving the efficiency of data analysis.

[0084] The reception unit can estimate the user's emotions and dynamically change the data entry interface based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface to reduce the burden of data entry. If the user is relaxed, the reception unit can provide an interface with detailed options to increase the freedom of data entry. Furthermore, if the user is in a hurry, the reception unit can display only the most important input fields to support quick data entry. This makes it possible to provide an optimal data entry environment according to the user's emotions.

[0085] The reception unit can analyze the user's past data input history and select the optimal input method. For example, it can preferentially suggest input methods (such as voice input or text input) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the reception unit can analyze the user's past input history and select the most efficient input method. In this way, the optimal input method can be provided by analyzing the user's past data input history.

[0086] The reception unit can filter data based on the user's current project or area of ​​interest when inputting the data. For example, the reception unit can preferentially input only data related to the user's ongoing project. The reception unit can also filter and input highly relevant data based on the user's area of ​​interest. Furthermore, the reception unit can automatically select and input necessary data according to the progress of the user's project. In this way, by filtering data based on the user's current project or area of ​​interest, highly relevant data can be preferentially input.

[0087] The reception unit can estimate the user's emotions and determine the priority of data to be input based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit postpones data of low importance and inputs data of high importance first. Also, if the user is relaxed, the reception unit can input all data evenly. Furthermore, if the user is in a hurry, the reception unit can input the most important data first. Thus, by determining the priority of data according to the user's emotions, more efficient data input is possible.

[0088] When inputting data, the reception unit can prioritize inputting highly relevant data based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting data related to that area. The reception unit can also prioritize inputting data related to locations close to the user's current location. Furthermore, the reception unit can select and input the most relevant data based on the user's geographical location information. This allows highly relevant data to be input preferentially by taking the user's geographical location information into consideration.

[0089] When data is input, the reception unit can analyze the user's social media activity and input relevant data. For example, the reception unit inputs relevant data based on information shared by the user on social media. The reception unit can also preferentially input data related to topics of interest from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity history and select and input the most relevant data. In this way, highly relevant data can be input by analyzing the user's social media activity.

[0090] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can also provide visually stimulating analysis results. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0091] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. On the other hand, the analysis unit can perform a concise analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0092] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, for economic data, the analysis unit performs analysis using an economic model. For social data, the analysis unit can apply a sociological analysis algorithm. Furthermore, for environmental data, the analysis unit can also perform analysis using an environmental model. This allows for more accurate analysis by applying different analysis algorithms depending on the data category.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The reception department inputs the data that the customer wants to analyze. The data that the customer wants to analyze can include numerical data, text data, image data, etc. For example, numerical data is input in spreadsheet format, text data is analyzed using natural language processing technology, and image data is analyzed using image recognition technology. Step 2: The analysis unit analyzes the data received by the reception unit. The analysis is performed using statistical analysis, machine learning algorithms, and generative AI. For example, statistical analysis is used to extract patterns and trends in the data, machine learning algorithms are used to analyze correlations in the data, and generative AI is used to identify important points in the data. Step 3: The graph generator generates a graph based on the data analyzed by the analyzer. The graph may be generated in the form of a bar graph, a line graph, a heat map, or the like. For example, a bar graph visually shows the trend of the data, a line graph visually shows the fluctuation of the data, and a heat map visually shows the distribution of the data. Step 4: The policy proposal unit proposes policies based on the graph generated by the graph generation unit. The policies are proposed in the form of marketing policies, operational policies, etc. Examples include marketing strategies in response to increases or decreases in foot traffic in a specific area, optimizing the timing of events, and proposing new business opportunities.

[0095] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of data; an analysis unit that analyzes the data accepted by the acceptance unit; a graph generation unit that generates a graph based on the data analyzed by the analysis unit; a measure proposal unit that proposes measures based on the graph generated by the graph generation unit; Equipped with A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of data input based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past data entry history and select the input method 2. The system of claim 1.

4. The reception unit Filter data entry based on the user's current projects or areas of interest 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the data to be input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Prioritize relevant data based on the user's geographic location during data entry 2. The system of claim 1.

7. The reception unit Analyze users' social media activity and enter relevant data when entering data 2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

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