System

Abstract

An object of a system according to an embodiment is to collect information from a plurality of data sources without expert knowledge and visualize data in response to a request from a user.SOLUTION: A system according to an embodiment includes a data collection unit, a prompt analysis unit, and a data visualization unit. The data collection unit collects market data, public information, and in-house closed data. The prompt analyzer analyzes a user's prompt based on the market data, the public information, and the in-house closed data collected by the data collector. The data visualization unit visualizes the data based on the prompt analyzed by the prompt analysis unit.SELECTED DRAWING: Figure 1

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 have the drawback that the process of collecting information from multiple data sources and visualizing the data according to user requests is complex and difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to collect information from multiple data sources without requiring specialized knowledge, and visualize the data in response to a user's request. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a prompt analysis unit, and a data visualization unit. The data collection unit collects market data, public information, and in-house closed data. The prompt analysis unit analyzes user prompts based on the market data, public information, and in-house closed data collected by the data collection unit. The data visualization unit visualizes data based on the prompts analyzed by the prompt analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect information from multiple data sources without requiring specialized knowledge and visualize the data in response to a user's request. [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 the embodiment of the present invention covers market data, public information, and internal closed data, and allows anyone to easily access the information they want by simply entering a prompt, and also visualizes that data. This allows anyone to quickly and easily perform analysis like a data analyst, anytime, anywhere.

[0029] A data analysis system according to an embodiment includes a data collection unit, a prompt analysis unit, and a data visualization unit. The data collection unit collects market data, public information, and internal closed data. For example, the data collection unit collects market data such as stock price information and economic indicators. The data collection unit can also collect public information such as news articles and government statistical data. The data collection unit can also collect internal closed data such as sales data and customer information. The prompt analysis unit analyzes user prompts based on the market data, public information, and internal closed data collected by the data collection unit. For example, the prompt analysis unit analyzes a prompt entered by a user, such as "Tell me the sales data for 2022," and extracts related data. The prompt analysis unit can also analyze a prompt, such as "Show me the latest economic news," and extract related news articles. The data visualization unit visualizes data based on the prompt analyzed by the prompt analysis unit. For example, the data visualization unit displays sales data as a line graph. The data visualization unit can also display customer distribution as a pie chart. The data visualization unit can also display economic indicators as bar graphs. This allows the data analysis system according to the embodiment to easily access necessary information and visualize data simply by the user entering prompts. For example, this makes it easier for managers to quickly grasp sales data and for marketing personnel to analyze customer data.

[0030] The data collection unit can evaluate the reliability of the collected data and automatically filter out unreliable data. For example, in the data collection unit, the generation AI assigns a reliability score to the collected data and automatically excludes unreliable data. For example, the data collection unit evaluates the reliability of news articles and filters out unreliable articles. In addition, in the data collection unit, the generation AI analyzes the source of the data and automatically excludes data from unreliable sources. For example, it excludes data from anonymous blog articles and unverified sources. In addition, in the data collection unit, the generation AI cross-references the collected data and integrates only matching data from multiple reliable sources. For example, it compares government statistical data with reliable news articles. This improves the quality of the data by automatically excluding unreliable data.

[0031] The data collection section can track the origin of the data and rank the importance of the data based on its origin. For example, the generation AI in the data collection section tracks the origin of the data and assigns a higher importance to data from reliable sources. For example, data from government agencies and major media outlets is prioritized. The data collection section also has the generation AI rank the importance of the data based on its origin and prioritizes the integration of highly important data. For example, academic papers and expert reports are highly rated. The data collection section also has the generation AI analyze the origin of the data and determine the importance of the data based on the reliability and relevance of the source. For example, data from industry leading companies is highly rated. This allows important data to be prioritized by ranking its importance based on its origin.

[0032] The data collection unit can collect data from different data sources in real time and integrate the data on the spot. For example, the data collection unit builds a system in which the generation AI collects data from different data sources in real time and integrates it on the spot. For example, it simultaneously collects data from news sites, social media, and government databases. The data collection unit also has the generation AI instantly analyze and integrate the data collected in real time. For example, it integrates stock price information and economic news in real time. The data collection unit also collects data from different data sources in real time, and the generation AI integrates the data while checking its consistency on the spot. For example, it integrates sales data and customer feedback in real time. This allows data from different data sources to be collected in real time and integrated on the spot, providing the most up-to-date information.

[0033] The data collection unit can refer to the user's past search history and prioritize collecting data based on the user's interests. For example, the generation AI analyzes the user's past search history and prioritizes collecting data based on the user's interests. For example, it collects the latest data related to topics previously searched. In addition, the data collection unit determines the priority of data collection based on the user's search history and prioritizes integrating data of interest. For example, it prioritizes data related to a specific industry or theme. In addition, the generation AI refers to the user's past search history and collects data based on the user's interests in real time. For example, it prioritizes collecting news articles related to keywords previously searched. This prioritizes collecting data based on the user's interests, making it possible to provide information that is highly relevant to the user.

[0034] The prompt analysis unit refers to the user's past search history and behavioral patterns to provide more accurate information. For example, the generation AI analyzes the user's past search history and provides highly relevant information based on prompts. For example, it displays the latest information related to topics previously searched. The prompt analysis unit also analyzes the user's behavioral patterns, and the generation AI provides optimal information based on prompts. For example, it prioritizes displaying information related to frequently searched keywords. The prompt analysis unit also takes the user's past search history and behavioral patterns into consideration, and provides highly accurate information based on prompts. For example, it displays related news articles based on the user's past search history. This allows more accurate information to be provided based on the user's past search history and behavioral patterns.

[0035] The prompt analysis unit can provide the latest information by referencing related external data sources. For example, the prompt analysis unit has the generation AI analyze the prompt and refer to related external data sources (e.g., social media or blogs) to provide the latest information. For example, it displays the latest trends and topics. Furthermore, when analyzing the prompt, the prompt analysis unit has the generation AI crawl external data sources to collect the latest related information. For example, it displays the latest news articles and blog posts. Furthermore, the prompt analysis unit builds a system in which the generation AI refers to external data sources based on the prompt to provide the latest information. For example, it displays social media posts and blog articles in real time. This makes it possible to provide the latest information by referencing related external data sources.

[0036] The prompt analysis unit can display related additional information and suggestions to the user in real time when the prompt is entered. The prompt analysis unit builds a system in which, for example, the generation AI displays related additional information and suggestions in real time when the prompt is entered. For example, related news articles and datasets are displayed. The prompt analysis unit also enables the generation AI to provide related additional information in real time based on the prompt, allowing the user to quickly access the information they need. For example, related statistical data and reports are displayed. The prompt analysis unit also enables the generation AI to display related suggestions to the user in real time when the prompt is entered. For example, related topics and keywords are suggested. In this way, by displaying related additional information and suggestions in real time when the prompt is entered, the user can quickly access the information they need.

[0037] The prompt analysis unit automatically performs information searches in different languages ​​and can provide information from an international perspective. For example, the prompt analysis unit constructs a system in which a generation AI analyzes prompts and automatically performs information searches in different languages. For example, information is searched in multiple languages, such as English, French, and Chinese. Furthermore, the prompt analysis unit, when analyzing prompts, performs information searches in different languages ​​and provides information from an international perspective. For example, it displays news articles and reports in different languages. Furthermore, the prompt analysis unit develops a system in which a generation AI automatically performs information searches in different languages ​​based on prompts and provides information from an international perspective. For example, it displays data sets and statistical information in different languages. In this way, information searches in different languages ​​can be automatically performed to provide information from an international perspective.

[0038] The data visualization unit can refer to the user's past visualization history and display data in a format that is easiest for the user to understand. For example, the generation AI in the data visualization unit analyzes the user's past visualization history and displays data in the format that is easiest for the user to understand. For example, it prioritizes displaying graph formats that have been used in the past. The data visualization unit also selects the optimal visualization format based on the user's visualization history and displays the data. For example, it prioritizes chart formats that have been preferred in the past. The data visualization unit also builds a system in which the generation AI refers to the user's past visualization history and displays data in a format that is easiest for the user to understand. For example, it learns past visualization patterns and suggests the optimal format. In this way, by referring to the user's past visualization history, data can be displayed in a format that is easiest for the user to understand.

[0039] The data visualization unit can automatically analyze data correlations and trends and provide insights to users. For example, the data visualization unit uses a generation AI to automatically analyze data correlations and provide insights to users when visualizing the data. For example, it shows the correlation between sales data and marketing activities. The data visualization unit also uses a generation AI to automatically analyze trends when visualizing the data and provide insights to users. For example, it shows seasonal sales trends. The data visualization unit also builds a system in which the generation AI analyzes data correlations and trends and provides insights to users when visualizing the data. For example, it shows the factors that cause data fluctuations. In this way, useful insights can be provided to users by automatically analyzing data correlations and trends.

[0040] The data visualization unit can display data in a format optimized for different devices. For example, when the generation AI visualizes data, the data visualization unit displays the data in a format optimized for smartphones and tablets. For example, a responsive design is adopted. Furthermore, to display data in a format optimized for different devices, the generation AI selects a visualization format taking into account the characteristics of the device. For example, a graph format appropriate for the screen size is used. Furthermore, the data visualization unit builds a system in which, when the generation AI visualizes data, the data is displayed in a format optimized for different devices. For example, a simplified graph for smartphones is displayed. In this way, by displaying data in a format optimized for different devices, users can easily view the data on any device.

[0041] The data visualization unit enables interactive operation, allowing users to freely manipulate data. For example, the data visualization unit enables interactive operation when the generating AI visualizes data, allowing users to freely manipulate the data. For example, it provides graph zoom and filtering functions. The data visualization unit also provides interactive visualization tools so that users can freely manipulate the data. For example, it adds a function to manipulate data by drag and drop. The data visualization unit also builds a system where the generating AI enables interactive operation when visualizing data, allowing users to freely manipulate the data. For example, it provides a function to update data in real time. This enables interactive operation, allowing users to freely manipulate the data and gain deeper insights.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data collection unit can collect data from different data sources in real time and integrate the data on the fly. For example, it can simultaneously collect data from news sites, social media, and government databases. The data collection unit also immediately analyzes and integrates the data collected in real time. For example, it can integrate stock price information and economic news in real time. The data collection unit also collects data from different data sources in real time and integrates the data on the fly while checking its consistency. For example, it can integrate sales data and customer feedback in real time. This allows data from different data sources to be collected in real time and integrated on the fly, providing the most up-to-date information.

[0044] The data collection unit can refer to the user's past search history and prioritize collecting data based on the user's interests. For example, it collects the latest data related to topics searched in the past. The data collection unit also determines the priority of data collection based on the user's search history and prioritizes integrating data of interest. For example, it prioritizes data related to a specific industry or theme. The data collection unit also refers to the user's past search history and collects data based on the user's interests in real time. For example, it prioritizes collecting news articles related to keywords searched in the past. In this way, by prioritizing the collection of data based on the user's interests, it is possible to provide information that is highly relevant to the user.

[0045] The prompt analysis unit can provide more accurate information by referring to the user's past search history and behavioral patterns. For example, it can display the latest information related to topics previously searched. The prompt analysis unit also analyzes the user's behavioral patterns and provides optimal information based on prompts. For example, it can preferentially display information related to frequently searched keywords. The prompt analysis unit also takes into account the user's past search history and behavioral patterns and provides more accurate information based on prompts. For example, it can display related news articles based on the user's past search history. This makes it possible to provide more accurate information based on the user's past search history and behavioral patterns.

[0046] The prompt analysis unit can provide the latest information by referencing related external data sources. For example, it analyzes a prompt and provides the latest information by referencing related external data sources (e.g., social media or blogs). For example, it displays the latest trends and topics. The prompt analysis unit also crawls external data sources when analyzing a prompt to collect the latest related information. For example, it displays the latest news articles and blog posts. The prompt analysis unit also builds a system that references external data sources based on the prompt to provide the latest information. For example, it displays social media posts and blog articles in real time. This makes it possible to provide the latest information by referencing related external data sources.

[0047] The prompt analysis unit can display related additional information and suggestions to the user in real time when the user enters a prompt. For example, a system is constructed that displays related additional information and suggestions in real time when the user enters a prompt. For example, related news articles and data sets are displayed. The prompt analysis unit also provides related additional information in real time based on the prompt, allowing the user to quickly access the information they need. For example, related statistical data and reports are displayed. The prompt analysis unit also displays related suggestions to the user in real time when the user enters a prompt. For example, related topics and keywords are suggested. In this way, by displaying related additional information and suggestions in real time when the user enters a prompt, the user can quickly access the information they need.

[0048] The prompt analysis unit can automatically perform information searches in different languages ​​and provide information from an international perspective. For example, a system is constructed that analyzes prompts and automatically performs information searches in different languages. For example, information is searched in multiple languages, such as English, French, and Chinese. The prompt analysis unit also performs information searches in different languages ​​when analyzing prompts and provides information from an international perspective. For example, news articles and reports in different languages ​​are displayed. The prompt analysis unit also develops a system that automatically performs information searches in different languages ​​based on prompts and provides information from an international perspective. For example, data sets and statistical information in different languages ​​are displayed. In this way, information searches in different languages ​​can be automatically performed to provide information from an international perspective.

[0049] The data visualization unit can refer to the user's past visualization history and display data in a format that is easiest for the user to understand. For example, it can analyze the user's past visualization history and display data in the format that is easiest for the user to understand. For example, it can prioritize displaying graph formats that have been used in the past. The data visualization unit can also select the optimal visualization format based on the user's visualization history and display the data. For example, it can prioritize chart formats that have been preferred in the past. The data visualization unit can also refer to the user's past visualization history and build a system that displays data in a format that is easiest for the user to understand. For example, it can learn past visualization patterns and suggest an optimal format. In this way, by referring to the user's past visualization history, it can display data in a format that is easiest for the user to understand.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection department collects market data, public information, and internal closed data. For example, the data collection department collects market data such as stock price information and economic indicators. It also collects public information such as news articles and government statistical data, as well as internal closed data such as sales data and customer information. Step 2: The prompt analysis unit analyzes the user's prompt based on the market data, public information, and internal closed data collected by the data collection unit. For example, the prompt analysis unit analyzes the user's prompt, "What are the sales figures for 2022?" and extracts relevant data. It can also analyze the prompt, "Show me the latest economic news," and extract relevant news articles. Step 3: The data visualization unit visualizes the data based on the prompts analyzed by the prompt analysis unit. For example, sales data can be displayed as a line graph, customer distribution as a pie chart, and economic indicators as a bar graph.

[0052] (Example 2) The data analysis system according to the embodiment of the present invention covers market data, public information, and internal closed data, and allows anyone to easily access the information they want by simply entering a prompt, and also visualizes that data. This allows anyone to quickly and easily perform analysis like a data analyst, anytime, anywhere.

[0053] A data analysis system according to an embodiment includes a data collection unit, a prompt analysis unit, and a data visualization unit. The data collection unit collects market data, public information, and internal closed data. For example, the data collection unit collects market data such as stock price information and economic indicators. The data collection unit can also collect public information such as news articles and government statistical data. The data collection unit can also collect internal closed data such as sales data and customer information. The prompt analysis unit analyzes user prompts based on the market data, public information, and internal closed data collected by the data collection unit. For example, the prompt analysis unit analyzes a prompt entered by a user, such as "Tell me the sales data for 2022," and extracts related data. The prompt analysis unit can also analyze a prompt, such as "Show me the latest economic news," and extract related news articles. The data visualization unit visualizes data based on the prompt analyzed by the prompt analysis unit. For example, the data visualization unit displays sales data as a line graph. The data visualization unit can also display customer distribution as a pie chart. The data visualization unit can also display economic indicators as bar graphs. This allows the data analysis system according to the embodiment to easily access necessary information and visualize data simply by the user entering prompts. For example, this makes it easier for managers to quickly grasp sales data and for marketing personnel to analyze customer data.

[0054] The data collection unit can evaluate the reliability of the collected data and automatically filter out unreliable data. For example, in the data collection unit, the generation AI assigns a reliability score to the collected data and automatically excludes unreliable data. For example, the data collection unit evaluates the reliability of news articles and filters out unreliable articles. In addition, in the data collection unit, the generation AI analyzes the source of the data and automatically excludes data from unreliable sources. For example, it excludes data from anonymous blog articles and unverified sources. In addition, in the data collection unit, the generation AI cross-references the collected data and integrates only matching data from multiple reliable sources. For example, it compares government statistical data with reliable news articles. This improves the quality of the data by automatically excluding unreliable data.

[0055] The data collection section can track the origin of the data and rank the importance of the data based on its origin. For example, the generation AI in the data collection section tracks the origin of the data and assigns a higher importance to data from reliable sources. For example, data from government agencies and major media outlets is prioritized. The data collection section also has the generation AI rank the importance of the data based on its origin and prioritizes the integration of highly important data. For example, academic papers and expert reports are highly rated. The data collection section also has the generation AI analyze the origin of the data and determine the importance of the data based on the reliability and relevance of the source. For example, data from industry leading companies is highly rated. This allows important data to be prioritized by ranking its importance based on its origin.

[0056] The data collection unit can use the emotion estimation function to analyze emotional elements contained in the collected data and prioritize integration of emotionally positive data. For example, the data collection unit performs emotion analysis on the collected data and prioritizes integration of data with positive emotions. For example, it prioritizes positive news articles and favorable customer feedback. The data collection unit also uses the emotion estimation function to calculate an emotion score for the data and prioritize integration of data with a high positive emotion score. For example, it prioritizes reviews with high customer satisfaction. The data collection unit also uses the generation AI to perform emotion analysis and automatically filter and integrate data with positive emotions. For example, it prioritizes social media posts that show positive trends. This allows the preferential integration of emotionally positive data to provide useful information to users.

[0057] The data collection unit can collect data from different data sources in real time and integrate the data on the spot. For example, the data collection unit builds a system in which the generation AI collects data from different data sources in real time and integrates it on the spot. For example, it simultaneously collects data from news sites, social media, and government databases. The data collection unit also has the generation AI instantly analyze and integrate the data collected in real time. For example, it integrates stock price information and economic news in real time. The data collection unit also collects data from different data sources in real time, and the generation AI integrates the data while checking its consistency on the spot. For example, it integrates sales data and customer feedback in real time. This allows data from different data sources to be collected in real time and integrated on the spot, providing the most up-to-date information.

[0058] The data collection unit can refer to the user's past search history and prioritize collecting data based on the user's interests. For example, the generation AI analyzes the user's past search history and prioritizes collecting data based on the user's interests. For example, it collects the latest data related to topics previously searched. In addition, the data collection unit determines the priority of data collection based on the user's search history and prioritizes integrating data of interest. For example, it prioritizes data related to a specific industry or theme. In addition, the generation AI refers to the user's past search history and collects data based on the user's interests in real time. For example, it prioritizes collecting news articles related to keywords previously searched. This prioritizes collecting data based on the user's interests, making it possible to provide information that is highly relevant to the user.

[0059] The data collection unit can use the emotion estimation function to analyze the user's emotional state when collecting data and prioritize collecting data that the user is most interested in. For example, the data collection unit can use the emotion estimation function to analyze the user's emotional state when collecting data and prioritize collecting data that the user is interested in. For example, data in which the user shows positive emotions is prioritized. The data collection unit also analyzes the user's emotional state in real time, and the generation AI determines the priority of data collection based on the results. For example, data related to when the user is excited is prioritized. The data collection unit also uses the emotion estimation function to analyze the user's emotional state and collect data that the user is most interested in in real time. For example, data related to topics in which the user is interested is prioritized. This makes it possible to provide information that is highly relevant to the user by prioritized collection of data that the user is interested in based on the user's emotional state.

[0060] The prompt analysis unit refers to the user's past search history and behavioral patterns to provide more accurate information. For example, the generation AI analyzes the user's past search history and provides highly relevant information based on prompts. For example, it displays the latest information related to topics previously searched. The prompt analysis unit also analyzes the user's behavioral patterns, and the generation AI provides optimal information based on prompts. For example, it prioritizes displaying information related to frequently searched keywords. The prompt analysis unit also takes the user's past search history and behavioral patterns into consideration, and provides highly accurate information based on prompts. For example, it displays related news articles based on the user's past search history. This allows more accurate information to be provided based on the user's past search history and behavioral patterns.

[0061] The prompt analysis unit can provide the latest information by referencing related external data sources. For example, the prompt analysis unit has the generation AI analyze the prompt and refer to related external data sources (e.g., social media or blogs) to provide the latest information. For example, it displays the latest trends and topics. Furthermore, when analyzing the prompt, the prompt analysis unit has the generation AI crawl external data sources to collect the latest related information. For example, it displays the latest news articles and blog posts. Furthermore, the prompt analysis unit builds a system in which the generation AI refers to external data sources based on the prompt to provide the latest information. For example, it displays social media posts and blog articles in real time. This makes it possible to provide the latest information by referencing related external data sources.

[0062] The prompt analysis unit can use the emotion estimation function to analyze the emotion contained in the user's prompt and provide emotionally positive information preferentially. The prompt analysis unit, for example, uses the emotion estimation function to analyze the emotion contained in the user's prompt and provide positive information preferentially. For example, it displays positive news articles and success stories. The prompt analysis unit also constructs a system in which the generation AI analyzes the emotion contained in the prompt and provides emotionally positive information preferentially. For example, it gives priority to information in which the user expresses joy or excitement. The prompt analysis unit also uses the emotion estimation function to analyze the emotion contained in the user's prompt and provide positive information. For example, it gives priority to information related to topics in which the user expresses positive emotions. In this way, it is possible to provide useful information to the user by analyzing the emotion contained in the user's prompt and providing emotionally positive information preferentially.

[0063] The prompt analysis unit can display related additional information and suggestions to the user in real time when the prompt is entered. The prompt analysis unit builds a system in which, for example, the generation AI displays related additional information and suggestions in real time when the prompt is entered. For example, related news articles and datasets are displayed. The prompt analysis unit also enables the generation AI to provide related additional information in real time based on the prompt, allowing the user to quickly access the information they need. For example, related statistical data and reports are displayed. The prompt analysis unit also enables the generation AI to display related suggestions to the user in real time when the prompt is entered. For example, related topics and keywords are suggested. In this way, by displaying related additional information and suggestions in real time when the prompt is entered, the user can quickly access the information they need.

[0064] The prompt analysis unit automatically performs information searches in different languages ​​and can provide information from an international perspective. For example, the prompt analysis unit constructs a system in which a generation AI analyzes prompts and automatically performs information searches in different languages. For example, information is searched in multiple languages, such as English, French, and Chinese. Furthermore, the prompt analysis unit, when analyzing prompts, performs information searches in different languages ​​and provides information from an international perspective. For example, it displays news articles and reports in different languages. Furthermore, the prompt analysis unit develops a system in which a generation AI automatically performs information searches in different languages ​​based on prompts and provides information from an international perspective. For example, it displays data sets and statistical information in different languages. In this way, information searches in different languages ​​can be automatically performed to provide information from an international perspective.

[0065] The prompt analysis unit uses the emotion estimation function to analyze the user's emotional state when entering a prompt, and can prioritize providing information that the user is most interested in. For example, the prompt analysis unit uses the emotion estimation function to analyze the user's emotional state when entering a prompt, and prioritize providing information that the user is interested in. For example, it prioritizes information that is relevant when the user is excited. In addition, the prompt analysis unit uses the generation AI to analyze the user's emotional state in real time, and provides optimal information based on the prompt. For example, it prioritizes information that indicates a positive emotion. In addition, the prompt analysis unit uses the emotion estimation function to analyze the user's emotional state when entering a prompt, and provides information that the user is most interested in in real time. For example, it prioritizes information related to topics that the user is interested in. In this way, by analyzing the user's emotional state when entering a prompt and priority-providing information that the user is most interested in, it is possible to provide information that is highly relevant to the user.

[0066] The data visualization unit can refer to the user's past visualization history and display data in a format that is easiest for the user to understand. For example, the generation AI in the data visualization unit analyzes the user's past visualization history and displays data in the format that is easiest for the user to understand. For example, it prioritizes displaying graph formats that have been used in the past. The data visualization unit also selects the optimal visualization format based on the user's visualization history and displays the data. For example, it prioritizes chart formats that have been preferred in the past. The data visualization unit also builds a system in which the generation AI refers to the user's past visualization history and displays data in a format that is easiest for the user to understand. For example, it learns past visualization patterns and suggests the optimal format. In this way, by referring to the user's past visualization history, data can be displayed in a format that is easiest for the user to understand.

[0067] The data visualization unit can automatically analyze data correlations and trends and provide insights to users. For example, the data visualization unit uses a generation AI to automatically analyze data correlations and provide insights to users when visualizing the data. For example, it shows the correlation between sales data and marketing activities. The data visualization unit also uses a generation AI to automatically analyze trends when visualizing the data and provide insights to users. For example, it shows seasonal sales trends. The data visualization unit also builds a system in which the generation AI analyzes data correlations and trends and provides insights to users when visualizing the data. For example, it shows the factors that cause data fluctuations. In this way, useful insights can be provided to users by automatically analyzing data correlations and trends.

[0068] The data visualization unit can use the emotion estimation function to select a visualization format that evokes the most positive emotion in the user and display the data. For example, the data visualization unit uses the emotion estimation function to select a visualization format that evokes the most positive emotion in the user and display the data. For example, it uses the user's preferred colors and design. The data visualization unit also uses a generation AI to analyze the user's emotional state and select a visualization format that elicits the most positive emotion. For example, it uses a graph format that the user prefers. The data visualization unit also uses the emotion estimation function to build a system that selects a visualization format that evokes the most positive emotion in the user and displays the data. For example, it adjusts the visualization format based on the user's emotional response. As a result, by selecting a visualization format that evokes the most positive emotion in the user, it becomes possible to display data in a way that is easy for the user to understand.

[0069] The data visualization unit can display data in a format optimized for different devices. For example, when the generation AI visualizes data, the data visualization unit displays the data in a format optimized for smartphones and tablets. For example, a responsive design is adopted. Furthermore, to display data in a format optimized for different devices, the generation AI selects a visualization format taking into account the characteristics of the device. For example, a graph format appropriate for the screen size is used. Furthermore, the data visualization unit builds a system in which, when the generation AI visualizes data, the data is displayed in a format optimized for different devices. For example, a simplified graph for smartphones is displayed. In this way, by displaying data in a format optimized for different devices, users can easily view the data on any device.

[0070] The data visualization unit enables interactive operation, allowing users to freely manipulate data. For example, the data visualization unit enables interactive operation when the generating AI visualizes data, allowing users to freely manipulate the data. For example, it provides graph zoom and filtering functions. The data visualization unit also provides interactive visualization tools so that users can freely manipulate the data. For example, it adds a function to manipulate data by drag and drop. The data visualization unit also builds a system where the generating AI enables interactive operation when visualizing data, allowing users to freely manipulate the data. For example, it provides a function to update data in real time. This enables interactive operation, allowing users to freely manipulate the data and gain deeper insights.

[0071] The data visualization unit can use the emotion estimation function to analyze the user's emotional state and highlight the data that the user is most interested in. For example, the data visualization unit uses the emotion estimation function to analyze the user's emotional state and highlight the data that the user is most interested in. For example, it highlights data points that the user is excited about. The data visualization unit also builds a system in which the generative AI analyzes the user's emotional state in real time and highlights the data that the user is most interested in. For example, it prominently displays data that the user is interested in. The data visualization unit also uses the emotion estimation function to analyze the user's emotional state and highlight the data that the user is most interested in. For example, it preferentially displays data that the user expresses positive emotions in. In this way, by analyzing the user's emotional state and highlighting the data that the user is most interested in, it is possible to make important information for the user stand out.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The data collection unit can collect data from different data sources in real time and integrate the data on the fly. For example, it can simultaneously collect data from news sites, social media, and government databases. The data collection unit also immediately analyzes and integrates the data collected in real time. For example, it can integrate stock price information and economic news in real time. The data collection unit also collects data from different data sources in real time and integrates the data on the fly while checking its consistency. For example, it can integrate sales data and customer feedback in real time. This allows data from different data sources to be collected in real time and integrated on the fly, providing the most up-to-date information.

[0074] The data collection unit can refer to the user's past search history and prioritize collecting data based on the user's interests. For example, it collects the latest data related to topics searched in the past. The data collection unit also determines the priority of data collection based on the user's search history and prioritizes integrating data of interest. For example, it prioritizes data related to a specific industry or theme. The data collection unit also refers to the user's past search history and collects data based on the user's interests in real time. For example, it prioritizes collecting news articles related to keywords searched in the past. In this way, by prioritizing the collection of data based on the user's interests, it is possible to provide information that is highly relevant to the user.

[0075] The data collection unit can use the emotion estimation function to analyze the user's emotional state during data collection and prioritize collection of data that the user is most interested in. For example, data in which the user expresses positive emotions is prioritized. The data collection unit also analyzes the user's emotional state in real time and determines the priority of data collection based on the results. For example, data related to when the user is excited is prioritized. The data collection unit also uses the emotion estimation function to analyze the user's emotional state and collect data that the user is most interested in in real time. For example, data related to topics in which the user is interested is prioritized. This allows the user to be provided with highly relevant information by preferentially collecting data that the user is interested in based on their emotional state.

[0076] The prompt analysis unit can provide more accurate information by referring to the user's past search history and behavioral patterns. For example, it can display the latest information related to topics previously searched. The prompt analysis unit also analyzes the user's behavioral patterns and provides optimal information based on prompts. For example, it can preferentially display information related to frequently searched keywords. The prompt analysis unit also takes into account the user's past search history and behavioral patterns and provides more accurate information based on prompts. For example, it can display related news articles based on the user's past search history. This makes it possible to provide more accurate information based on the user's past search history and behavioral patterns.

[0077] The prompt analysis unit can provide the latest information by referencing related external data sources. For example, it analyzes a prompt and provides the latest information by referencing related external data sources (e.g., social media or blogs). For example, it displays the latest trends and topics. The prompt analysis unit also crawls external data sources when analyzing a prompt to collect the latest related information. For example, it displays the latest news articles and blog posts. The prompt analysis unit also builds a system that references external data sources based on the prompt to provide the latest information. For example, it displays social media posts and blog articles in real time. This makes it possible to provide the latest information by referencing related external data sources.

[0078] The prompt analysis unit can use the emotion estimation function to analyze the emotion contained in the user's prompt and provide emotionally positive information preferentially. For example, the emotion contained in the user's prompt can be analyzed and positive information can be provided preferentially. For example, positive news articles and success stories can be displayed. The prompt analysis unit also analyzes the emotion contained in the prompt and builds a system that provides emotionally positive information preferentially. For example, it can provide information that indicates joy or excitement to the user. The prompt analysis unit also analyzes the emotion contained in the user's prompt and provides positive information. For example, it can provide information related to topics that indicate positive emotions to the user. In this way, by analyzing the emotion contained in the user's prompt and providing emotionally positive information preferentially, it is possible to provide useful information to the user.

[0079] The prompt analysis unit can display related additional information and suggestions to the user in real time when the user enters a prompt. For example, a system is constructed that displays related additional information and suggestions in real time when the user enters a prompt. For example, related news articles and data sets are displayed. The prompt analysis unit also provides related additional information in real time based on the prompt, allowing the user to quickly access the information they need. For example, related statistical data and reports are displayed. The prompt analysis unit also displays related suggestions to the user in real time when the user enters a prompt. For example, related topics and keywords are suggested. In this way, by displaying related additional information and suggestions in real time when the user enters a prompt, the user can quickly access the information they need.

[0080] The prompt analysis unit can automatically perform information searches in different languages ​​and provide information from an international perspective. For example, a system is constructed that analyzes prompts and automatically performs information searches in different languages. For example, information is searched in multiple languages, such as English, French, and Chinese. The prompt analysis unit also performs information searches in different languages ​​when analyzing prompts and provides information from an international perspective. For example, news articles and reports in different languages ​​are displayed. The prompt analysis unit also develops a system that automatically performs information searches in different languages ​​based on prompts and provides information from an international perspective. For example, data sets and statistical information in different languages ​​are displayed. In this way, information searches in different languages ​​can be automatically performed to provide information from an international perspective.

[0081] The data visualization unit can refer to the user's past visualization history and display data in a format that is easiest for the user to understand. For example, it can analyze the user's past visualization history and display data in the format that is easiest for the user to understand. For example, it can prioritize displaying graph formats that have been used in the past. The data visualization unit can also select the optimal visualization format based on the user's visualization history and display the data. For example, it can prioritize chart formats that have been preferred in the past. The data visualization unit can also refer to the user's past visualization history and build a system that displays data in a format that is easiest for the user to understand. For example, it can learn past visualization patterns and suggest an optimal format. In this way, by referring to the user's past visualization history, it can display data in a format that is easiest for the user to understand.

[0082] The data visualization unit can use the emotion estimation function to select a visualization format that evokes the most positive emotion in the user and display the data. For example, the emotion estimation function can be used to select a visualization format that evokes the most positive emotion in the user and display the data. For example, the emotion estimation function can be used to use a color or design that the user prefers. The data visualization unit can also analyze the user's emotional state and select a visualization format that elicits the most positive emotion. For example, the data visualization unit can use a graph format that the user prefers. The data visualization unit can also use the emotion estimation function to select a visualization format that evokes the most positive emotion in the user and build a system to display the data. For example, the visualization format can be adjusted based on the user's emotional response. By selecting a visualization format that evokes the most positive emotion in the user, it is possible to display data in a way that is easy for the user to understand.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The data collection department collects market data, public information, and internal closed data. For example, the data collection department collects market data such as stock price information and economic indicators. It also collects public information such as news articles and government statistical data, as well as internal closed data such as sales data and customer information. Step 2: The prompt analysis unit analyzes the user's prompt based on the market data, public information, and internal closed data collected by the data collection unit. For example, the prompt analysis unit analyzes the user's prompt, "What are the sales figures for 2022?" and extracts relevant data. It can also analyze the prompt, "Show me the latest economic news," and extract relevant news articles. Step 3: The data visualization unit visualizes the data based on the prompts analyzed by the prompt analysis unit. For example, sales data can be displayed as a line graph, customer distribution as a pie chart, and economic indicators as a bar graph.

[0085] 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.

[0086] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 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 data collection department that collects market data, public information, and internal closed data; a prompt analysis unit that analyzes a user's prompt based on the market data, the public information, and the in-house closed data collected by the data collection unit; a data visualization unit that visualizes data based on the prompt analyzed by the prompt analysis unit. A system characterized by:

2. The data collection unit Evaluate the reliability of collected data and automatically filter out unreliable data 2. The system of claim 1.

3. The data collection unit Tracking the origin of data and ranking the importance of said data based on said origin 2. The system of claim 1.

4. The data collection unit Analyze the emotional elements contained in the collected data and prioritize integration of emotionally positive data 2. The system of claim 1.

5. The data collection unit Collect data from different sources in real time and integrate said data on the fly 2. The system of claim 1.

Images