system

Abstract

The system according to the embodiment aims to enable anyone to easily collect and visualize data. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a collection unit, and a visualization unit. The reception unit receives a prompt. The analysis unit analyzes the prompt received by the reception unit. The collection unit collects data based on the prompt analyzed by the analysis unit. The analysis unit analyzes the data collected by the collection unit. The visualization unit visualizes the data analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that collecting and visualizing data requires specialized knowledge and is not something that anyone can do easily.

[0005] The system according to the embodiment aims to enable anyone to easily collect and visualize data. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, an analysis unit, a collection unit, and a visualization unit. The reception unit receives a prompt. The analysis unit analyzes the prompt received by the reception unit. The collection unit collects data based on the prompt analyzed by the analysis unit. The analysis unit analyzes the data collected by the collection unit. The visualization unit visualizes the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows anyone to easily collect and visualize data. [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) A data analyst support system according to an embodiment of the present invention covers market data, public information, and internal closed data. It allows anyone to easily access desired information and visualize the data simply by entering a prompt. This data analyst support system provides information in a format that is easy for users to understand by having a user enter a prompt, a generation AI analyze the prompt, and collect, analyze, and visualize related data. For example, if a user enters a prompt such as "Tell me about market trends in 2022," the generation AI analyzes the prompt and collects related data from market data, public information, internal closed data, and the like. The collected data is analyzed by the generation AI and visualized in the form of graphs, charts, and the like. This system allows users to easily access desired information and intuitively understand the data content simply by entering a prompt. This allows anyone to easily handle data like a data analyst, at any time. For example, if a company manager wants to understand market trends, they can simply enter a prompt to collect the latest market data and display it in graphs and charts. Similarly, if a marketing professional wants to know the sales trends of a specific product, they can simply enter a prompt to collect and visualize the relevant data. Thus, the present invention provides a system that allows anyone to easily collect and visualize data simply by entering a prompt. This allows the data analyst support system to collect, analyze, and visualize data simply by the user entering prompts, and provide information in a format that is easy for the user to understand.

[0029] A data analyst support system according to an embodiment includes a reception unit, an analysis unit, a collection unit, and a visualization unit. The reception unit provides an interface for a user to input a prompt. For example, the reception unit can accept prompts in text, audio, image, or other formats. The analysis unit uses a generation AI to analyze the prompt accepted by the reception unit. The analysis unit understands the content of the prompt and identifies relevant data using, for example, natural language processing technology. The collection unit collects the data identified by the analysis unit. The collection unit collects data from, for example, market data, public information, internal closed data, etc. The visualization unit analyzes the data collected by the collection unit and visualizes it in a format that is easy for users to understand. The visualization unit displays the data in a format such as a graph or chart. As a result, the data analyst support system according to an embodiment can collect, analyze, and visualize data and provide information in a format that is easy for users to understand, simply by the user inputting a prompt.

[0030] The collection unit can collect market data, public information, and internal private data. Market data includes, for example, sales data, customer data, and competitor data. The collection unit can collect market data, for example, from public information on the Internet and a company's internal database. Public information includes, for example, news articles, government statistics, and public reports. The collection unit can collect public information using, for example, web scraping technology. Private data includes, for example, internal company data, confidential information, and internal reports. The collection unit can collect private data, for example, from a company's internal network or database. This allows the collection unit to provide comprehensive information by collecting data from a variety of data sources.

[0031] The visualization unit can display the collected data in the form of a graph or chart. Examples of graphs include bar graphs, line graphs, and pie charts. For example, the visualization unit can display the collected data as a bar graph to visually show data fluctuations. Furthermore, the visualization unit can display the collected data as a line graph to visually show data trends. Furthermore, the visualization unit can display the collected data as a pie chart to visually show data proportions. Examples of charts include histograms, scatter plots, and radar charts. For example, the visualization unit can display the collected data as a histogram to visually show data distribution. Furthermore, the visualization unit can display the collected data as a scatter plot to visually show data correlations. Furthermore, the visualization unit can display the collected data as a radar chart to visually show multidimensional characteristics of the data. In this way, the visualization unit can visually display data to make it easier for users to intuitively understand.

[0032] The analyzer can understand the content of the prompt and identify relevant data. The analyzer can analyze the content of the prompt using, for example, natural language processing technology. For example, the analyzer can extract keywords from the prompt and identify relevant data based on the keywords. The analyzer can also understand the context of the prompt using context analysis technology and identify relevant data. Furthermore, the analyzer can understand the meaning of the prompt using semantic analysis technology and identify relevant data. This allows the analyzer to identify appropriate data based on the content of the prompt.

[0033] The visualization unit can display data in a form that allows the user to intuitively understand it. The visualization unit can visually display data using, for example, infographics. For example, the visualization unit can generate infographics that visually show the main points of the data, allowing the user to intuitively understand it. The visualization unit can also display data using interactive visualization. For example, the visualization unit can generate interactive graphs and charts that allow the user to manipulate the data, allowing the user to check the details of the data. In this way, the visualization unit can allow the user to intuitively understand the data.

[0034] The collection unit may include a security unit for ensuring the security of the data. The security unit may, for example, encrypt the data. For example, the security unit may encrypt the collected data to ensure the confidentiality of the data. The security unit may also perform access control. For example, the security unit may limit access to the data to only authorized users to prevent unauthorized access to the data. Furthermore, the security unit may perform data masking. For example, the security unit may mask part of the data to protect confidential information in the data. In this way, the collection unit may ensure the security of the data and prevent information leakage.

[0035] The reception unit may include a guide unit that guides the user to input an appropriate prompt. The guide unit may, for example, guide the user on the format of the prompt. For example, the guide unit may present the user with a prompt format such as text, audio, or image, and guide the user to input the prompt in an appropriate format. The guide unit may also guide the user on the content of the prompt. For example, the guide unit may present the user with examples of prompt content and guide the user to input a prompt with appropriate content. Furthermore, the guide unit may guide the user on the length of the prompt. For example, the guide unit may present the user with an appropriate length of the prompt and guide the user to input a prompt with an appropriate length. In this way, the reception unit can support the user to input an appropriate prompt.

[0036] The reception unit can analyze the user's past prompt input history and suggest an appropriate input method. For example, the reception unit can automatically display prompts that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest prompts to be used in a specific time period based on the user's past input history. This allows the reception unit to improve user convenience by suggesting the optimal input method based on the past input history. Methods for collecting and analyzing the past prompt input history include, for example, analyzing log data and saving history data. This allows the reception unit to analyze the user's past prompt input history and suggest the optimal input method.

[0037] The reception unit can provide input guidance based on the user's current work situation and areas of interest when the user inputs a prompt. For example, when the user inputs a prompt related to a current project, the reception unit can automatically suggest related data sources. The reception unit can also display related keywords and phrases as input guidance based on the user's areas of interest. Furthermore, the reception unit can present examples of appropriate prompts based on the user's work situation and support the input. In this way, the reception unit can support appropriate prompt input by providing guidance based on the user's work situation and areas of interest. Methods and criteria for identifying the work situation include, for example, the progress of work and task priority. Methods and criteria for identifying areas of interest include, for example, past activity history and topics of interest. In this way, the reception unit can provide input guidance based on the user's current work situation and areas of interest.

[0038] The reception unit can provide relevant input guidance based on the user's geographical location information when the user inputs a prompt. For example, if the user is in a specific area, the reception unit can suggest data sources related to that area. The reception unit can also display relevant market data or public information as input guidance based on the user's current location. Furthermore, if the user is moving, the reception unit can present example prompts based on the user's current location. This allows the reception unit to support appropriate prompt input by providing relevant guidance based on the user's geographical location information. Methods for collecting and using geographical location information include, for example, GPS data and location information services. This allows the reception unit to provide relevant input guidance based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity when entering a prompt and provide appropriate input guidance. For example, the reception unit can present examples of relevant prompts based on topics the user has shown interest in on social media. The reception unit can also suggest relevant data sources from the user's social media activity. Furthermore, the reception unit can guide the user to enter an appropriate prompt based on information shared by the user on social media. In this way, the reception unit can support appropriate prompt entry by providing relevant guidance based on the user's social media activity. Methods for collecting and analyzing social media activity include, for example, analyzing post content and followers. In this way, the reception unit can analyze the user's social media activity and provide appropriate input guidance.

[0040] During prompt analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the prompt. For example, the analysis unit can perform a detailed analysis on a prompt with a high level of importance and provide comprehensive data. The analysis unit can also perform a concise analysis on a prompt with a low level of importance and provide data that focuses on the main points. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the prompt and provide data efficiently. This allows the analysis unit to provide data efficiently by adjusting the level of detail of the analysis based on the importance of the prompt. Methods and criteria for evaluating the importance of a prompt include, for example, an impact evaluation and an urgency evaluation. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the prompt.

[0041] When analyzing a prompt, the analysis unit can apply different analysis algorithms depending on the category of the prompt. For example, the analysis unit can apply a specific market analysis algorithm to a prompt related to market data. The analysis unit can also apply a public information analysis algorithm to a prompt related to public information. The analysis unit can also apply a closed data analysis algorithm to a prompt related to internal closed data. This allows the analysis unit to provide appropriate analysis results by applying an analysis algorithm depending on the category of the prompt. Examples of methods and criteria for classifying prompt categories include by topic, by purpose, by format, etc. Examples of types and application methods of analysis algorithms include clustering algorithms and classification algorithms, etc. This allows the analysis unit to apply different analysis algorithms depending on the category of the prompt.

[0042] When analyzing prompts, the analysis unit can determine the priority of analysis based on the submission time of the prompt. For example, the analysis unit can prioritize analysis of prompts that are submitted early. Furthermore, the analysis unit can postpone analysis of prompts that are submitted late. Furthermore, the analysis unit can adjust the analysis schedule according to the submission time and provide data efficiently. In this way, the analysis unit can provide data efficiently by determining the priority of analysis according to the submission time. Methods and criteria for identifying the submission time include, for example, the submission date and time and the submission frequency. In this way, the analysis unit can determine the priority of analysis based on the submission time of the prompt.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the prompts when analyzing the prompts. For example, the analysis unit can prioritize analysis of highly relevant prompts. Furthermore, the analysis unit can postpone analysis of less relevant prompts. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the prompts and provide data efficiently. As a result, the analysis unit can provide data efficiently by adjusting the order of analysis based on the relevance of the prompts. Methods and criteria for evaluating the relevance of prompts include, for example, similarity and correlation of content. As a result, the analysis unit can adjust the order of analysis based on the relevance of the prompts.

[0044] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit can prioritize collection of data sources that the user has frequently collected from in the past. The collection unit can also predict the optimal collection timing from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and suggest an efficient collection method. This allows the collection unit to select the optimal collection method based on the past data collection history, thereby enabling efficient data collection. Methods for collecting and analyzing the data collection history include, for example, analyzing log data and storing history data. This allows the collection unit to analyze the user's past data collection history and select the optimal collection method.

[0045] The collection unit can perform filtering based on the user's current work situation and areas of interest when collecting data. For example, the collection unit can prioritize collecting data related to a project currently underway by the user. The collection unit can also filter related data sources based on the user's areas of interest. Furthermore, the collection unit can collect appropriate data according to the user's work situation. This allows the collection unit to provide more appropriate data by collecting data according to the user's work situation and areas of interest. Filtering methods and criteria include, for example, keyword filtering and attribute filtering. This allows the collection unit to perform filtering based on the user's current work situation and areas of interest when collecting data.

[0046] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting data. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also collect relevant market data and public information based on the user's current location. Furthermore, when the user is moving, the collection unit can prioritize collecting data according to the user's current location. This allows the collection unit to provide appropriate data by collecting highly relevant data based on the user's geographical location information. Methods for collecting and using geographical location information include, for example, GPS data and location information services. This allows the collection unit to prioritize collecting highly relevant data based on the user's geographical location information when collecting data.

[0047] The collection unit can analyze the user's social media activity and collect appropriate data when collecting data. For example, the collection unit can collect relevant data based on topics in which the user has shown interest on social media. The collection unit can also identify relevant data sources from the user's social media activity. Furthermore, the collection unit can collect appropriate data based on information shared by the user on social media. This allows the collection unit to provide appropriate data by collecting relevant data based on the user's social media activity. Methods for collecting and analyzing social media activity include, for example, analyzing posted content and followers. This allows the collection unit to analyze the user's social media activity and collect appropriate data when collecting data.

[0048] The visualization unit can adjust the level of detail of the visualization based on the importance of the data when visualizing the data. For example, the visualization unit can provide detailed graphs and charts for data with high importance. Furthermore, the visualization unit can provide simple graphs and charts for data with low importance. Furthermore, the visualization unit can determine the priority of visualization according to the importance of the data and efficiently display the data. As a result, the visualization unit can efficiently display the data by adjusting the level of detail of the visualization according to the importance of the data. Methods and criteria for evaluating the importance of data include, for example, an impact evaluation and an urgency evaluation. As a result, the visualization unit can adjust the level of detail of the visualization based on the importance of the data.

[0049] The visualization unit can apply different visualization algorithms depending on the data category when visualizing data. For example, the visualization unit can apply a specific market analysis algorithm to data related to market data. The visualization unit can also apply a public information analysis algorithm to data related to public information. The visualization unit can also apply a closed data analysis algorithm to data related to internal closed data. This allows the visualization unit to apply a visualization algorithm depending on the data category, enabling appropriate data display. Examples of data classification methods and criteria include by topic, by use, by format, etc. Types and application methods of visualization algorithms include, for example, clustering algorithms and classification algorithms. This allows the visualization unit to apply different visualization algorithms depending on the data category when visualizing data.

[0050] The visualization unit can determine the priority of visualization based on the time of data submission when visualizing data. For example, the visualization unit can prioritize visualization of data submitted early. Also, the visualization unit can postpone visualization of data submitted late. Furthermore, the visualization unit can adjust the visualization schedule according to the time of submission and efficiently display data. In this way, the visualization unit can efficiently display data by determining the priority of visualization based on the time of submission. Methods and criteria for specifying the time of submission include, for example, the submission date and time and the submission frequency. In this way, the visualization unit can determine the priority of visualization based on the time of data submission when visualizing data.

[0051] The visualization unit can adjust the visualization order based on the relevance of the data when visualizing the data. For example, the visualization unit can prioritize visualization of highly relevant data. Furthermore, the visualization unit can postpone visualization of less relevant data. Furthermore, the visualization unit can adjust the visualization order according to the relevance of the data and efficiently display the data. As a result, the visualization unit can efficiently display the data by adjusting the visualization order according to the relevance of the data. Methods and criteria for evaluating the relevance of the data include, for example, similarity and correlation of content. As a result, the visualization unit can adjust the visualization order based on the relevance of the data when visualizing the data.

[0052] The security unit can strengthen security measures based on the confidentiality of the data when collecting data. For example, the security unit can apply strong encryption to highly confidential data. Furthermore, the security unit can apply standard security measures to less confidential data. Furthermore, the security unit can adjust the level of security measures according to the confidentiality of the data. This allows the security unit to prevent information leaks by strengthening security measures according to the confidentiality of the data. Methods and standards for evaluating the confidentiality of data include, for example, the importance of the data and the extent to which the data is made public. This allows the security unit to strengthen security measures based on the confidentiality of the data when collecting data.

[0053] The security department can adjust security measures taking into account the geographic distribution of data when collecting data. For example, for data related to a specific region, the security department can comply with the security regulations of that region. The security department can also apply appropriate security measures based on the geographic distribution of data. Furthermore, the security department can select a data storage location and strengthen security measures according to the geographic distribution. This allows the security department to prevent information leaks by providing appropriate security measures based on the geographic distribution of data. Methods and criteria for identifying geographic distribution include, for example, data distribution by region or by country. This allows the security department to adjust security measures taking into account the geographic distribution of data when collecting data.

[0054] The guide unit can provide optimal guidance by referring to the user's past input history when entering prompts. The guide unit can provide relevant guidance based on, for example, prompts that the user has frequently entered in the past. The guide unit can also suggest an optimal input method from the user's past input history. Furthermore, the guide unit can analyze the user's past input history and provide efficient input guidance. This allows the guide unit to improve user convenience by providing optimal guidance based on the past input history. Methods for collecting and analyzing the past input history include, for example, analyzing log data and storing history data. This allows the guide unit to provide optimal guidance by referring to the user's past input history when entering prompts.

[0055] The guide unit can provide optimal guidance by taking into account the user's device information when entering prompts. For example, if the user is using a smartphone, the guide unit can provide guidance tailored to the screen size. Furthermore, if the user is using a tablet, the guide unit can provide guidance optimized for a large screen. Furthermore, if the user is using a smartwatch, the guide unit can provide concise and highly visible guidance. This allows the guide unit to support appropriate prompt input by providing optimal guidance based on the user's device information. Methods for collecting and using device information include, for example, the type of device and device setting information. This allows the guide unit to provide optimal guidance by taking into account the user's device information when entering prompts.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The reception unit can analyze the input content in real time when the user enters a prompt and provide an appropriate suggestion function. For example, if the user starts entering "market trends in 2022," the reception unit can automatically suggest related keywords and phrases to support the user in entering more specific prompts. The reception unit can also analyze the user's input speed and typing pattern and present appropriate completion candidates as the user enters information. Furthermore, the reception unit can refer to the history of prompts entered by the user in the past and suggest similar prompts, allowing the user to enter information efficiently. In this way, the reception unit can support the user's input in real time and achieve efficient prompt input.

[0058] The collection unit can adjust the timing of data collection based on the user's work schedule when collecting data. For example, if the user is in a meeting, the collection unit can temporarily delay data collection and resume collection after the meeting ends. The collection unit can also predict the optimal collection timing based on the user's work schedule and collect data efficiently. Furthermore, the collection unit can adjust the priority of data to be collected according to the user's work schedule and collect important data preferentially. As a result, the collection unit can achieve efficient data collection by adjusting the collection timing based on the user's work schedule.

[0059] The visualization unit can display collected data in a format that can be customized according to the user's preferences. For example, if a user prefers a particular color or font, the visualization unit can adjust the design of a graph or chart according to the user's preference. Also, if a user prefers a particular data format (e.g., a bar graph or a pie chart), the visualization unit can display the data in that format. Furthermore, the visualization unit provides an interface that allows the user to adjust the level of detail of the data, and can display detailed data or summarized data as needed. In this way, the visualization unit can realize a customizable data display according to the user's preferences.

[0060] The analysis unit can evaluate the reliability of the related data based on the content of the prompt. For example, the analysis unit can evaluate the source and update frequency of the collected data and prioritize analysis of highly reliable data. The analysis unit can also check the consistency and integrity of the data and exclude less reliable data. Furthermore, the analysis unit can display the reliability of the analysis results based on the reliability of the data, which can be used as a reference when the user judges the analysis results. This allows the analysis unit to evaluate the reliability of the data based on the content of the prompt and provide highly reliable analysis results.

[0061] The visualization unit may provide interactive data exploration capabilities to enable users to gain a deeper understanding of the data. For example, a user may click on a specific data point on a graph or chart to display detailed information related to that data point. The visualization unit may also provide an interface that enables users to filter and sort data, allowing users to narrow down the data based on specific criteria. Furthermore, the visualization unit may provide multiple visualization options that enable users to analyze the data from different perspectives. In this way, the visualization unit may enable users to interactively explore and gain a deeper understanding of the data.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit provides an interface for the user to input a prompt. For example, the reception unit can receive prompts in text format, audio format, image format, and the like. Step 2: The analysis unit uses the generation AI to analyze the prompt received by the reception unit. The analysis unit uses, for example, natural language processing technology to understand the content of the prompt and identify related data. Step 3: The collection unit collects the data identified by the analysis unit. The collection unit collects data from, for example, market data, public information, internal closed data, etc. Step 4: The analysis unit analyzes the data collected by the collection unit. Step 5: The visualization unit analyzes the data collected by the collection unit and visualizes it in a format that is easy for the user to understand. For example, the visualization unit displays the data in the form of a graph or chart.

[0064] (Example 2) A data analyst support system according to an embodiment of the present invention covers market data, public information, and internal closed data. It allows anyone to easily access desired information and visualize the data simply by entering a prompt. This data analyst support system provides information in a format that is easy for users to understand by having a user enter a prompt, a generation AI analyze the prompt, and collect, analyze, and visualize related data. For example, if a user enters a prompt such as "Tell me about market trends in 2022," the generation AI analyzes the prompt and collects related data from market data, public information, internal closed data, and the like. The collected data is analyzed by the generation AI and visualized in the form of graphs, charts, and the like. This system allows users to easily access desired information and intuitively understand the data content simply by entering a prompt. This allows anyone to easily handle data like a data analyst, at any time. For example, if a company manager wants to understand market trends, they can simply enter a prompt to collect the latest market data and display it in graphs and charts. Similarly, if a marketing professional wants to know the sales trends of a specific product, they can simply enter a prompt to collect and visualize the relevant data. Thus, the present invention provides a system that allows anyone to easily collect and visualize data simply by entering a prompt. This allows the data analyst support system to collect, analyze, and visualize data simply by the user entering prompts, and provide information in a format that is easy for the user to understand.

[0065] A data analyst support system according to an embodiment includes a reception unit, an analysis unit, a collection unit, and a visualization unit. The reception unit provides an interface for a user to input a prompt. For example, the reception unit can accept prompts in text, audio, image, or other formats. The analysis unit uses a generation AI to analyze the prompt accepted by the reception unit. The analysis unit understands the content of the prompt and identifies relevant data using, for example, natural language processing technology. The collection unit collects the data identified by the analysis unit. The collection unit collects data from, for example, market data, public information, internal closed data, etc. The visualization unit analyzes the data collected by the collection unit and visualizes it in a format that is easy for users to understand. The visualization unit displays the data in a format such as a graph or chart. As a result, the data analyst support system according to an embodiment can collect, analyze, and visualize data and provide information in a format that is easy for users to understand, simply by the user inputting a prompt.

[0066] The collection unit can collect market data, public information, and internal private data. Market data includes, for example, sales data, customer data, and competitor data. The collection unit can collect market data, for example, from public information on the Internet and a company's internal database. Public information includes, for example, news articles, government statistics, and public reports. The collection unit can collect public information using, for example, web scraping technology. Private data includes, for example, internal company data, confidential information, and internal reports. The collection unit can collect private data, for example, from a company's internal network or database. This allows the collection unit to provide comprehensive information by collecting data from a variety of data sources.

[0067] The visualization unit can display the collected data in the form of a graph or chart. Examples of graphs include bar graphs, line graphs, and pie charts. For example, the visualization unit can display the collected data as a bar graph to visually show data fluctuations. Furthermore, the visualization unit can display the collected data as a line graph to visually show data trends. Furthermore, the visualization unit can display the collected data as a pie chart to visually show data proportions. Examples of charts include histograms, scatter plots, and radar charts. For example, the visualization unit can display the collected data as a histogram to visually show data distribution. Furthermore, the visualization unit can display the collected data as a scatter plot to visually show data correlations. Furthermore, the visualization unit can display the collected data as a radar chart to visually show multidimensional characteristics of the data. In this way, the visualization unit can visually display data to make it easier for users to intuitively understand.

[0068] The analyzer can understand the content of the prompt and identify relevant data. The analyzer can analyze the content of the prompt using, for example, natural language processing technology. For example, the analyzer can extract keywords from the prompt and identify relevant data based on the keywords. The analyzer can also understand the context of the prompt using context analysis technology and identify relevant data. Furthermore, the analyzer can understand the meaning of the prompt using semantic analysis technology and identify relevant data. This allows the analyzer to identify appropriate data based on the content of the prompt.

[0069] The visualization unit can display data in a form that allows the user to intuitively understand it. The visualization unit can visually display data using, for example, infographics. For example, the visualization unit can generate infographics that visually show the main points of the data, allowing the user to intuitively understand it. The visualization unit can also display data using interactive visualization. For example, the visualization unit can generate interactive graphs and charts that allow the user to manipulate the data, allowing the user to check the details of the data. In this way, the visualization unit can allow the user to intuitively understand the data.

[0070] The collection unit may include a security unit for ensuring the security of the data. The security unit may, for example, encrypt the data. For example, the security unit may encrypt the collected data to ensure the confidentiality of the data. The security unit may also perform access control. For example, the security unit may limit access to the data to only authorized users to prevent unauthorized access to the data. Furthermore, the security unit may perform data masking. For example, the security unit may mask part of the data to protect confidential information in the data. In this way, the collection unit may ensure the security of the data and prevent information leakage.

[0071] The reception unit may include a guide unit that guides the user to input an appropriate prompt. The guide unit may, for example, guide the user on the format of the prompt. For example, the guide unit may present the user with a prompt format such as text, audio, or image, and guide the user to input the prompt in an appropriate format. The guide unit may also guide the user on the content of the prompt. For example, the guide unit may present the user with examples of prompt content and guide the user to input a prompt with appropriate content. Furthermore, the guide unit may guide the user on the length of the prompt. For example, the guide unit may present the user with an appropriate length of the prompt and guide the user to input a prompt with an appropriate length. In this way, the reception unit can support the user to input an appropriate prompt.

[0072] The data analyst support system further includes a reception unit that estimates a user's emotions and adjusts a prompt input interface based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable prompt input quickly. This allows the reception unit to provide a more comfortable input environment by adjusting the interface according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the reception unit to adjust the prompt input interface based on the user's emotions.

[0073] The reception unit can analyze the user's past prompt input history and suggest an appropriate input method. For example, the reception unit can automatically display prompts that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest prompts to be used in a specific time period based on the user's past input history. This allows the reception unit to improve user convenience by suggesting the optimal input method based on the past input history. Methods for collecting and analyzing the past prompt input history include, for example, analyzing log data and saving history data. This allows the reception unit to analyze the user's past prompt input history and suggest the optimal input method.

[0074] The reception unit can provide input guidance based on the user's current work situation and areas of interest when the user inputs a prompt. For example, when the user inputs a prompt related to a current project, the reception unit can automatically suggest related data sources. The reception unit can also display related keywords and phrases as input guidance based on the user's areas of interest. Furthermore, the reception unit can present examples of appropriate prompts based on the user's work situation and support the input. In this way, the reception unit can support appropriate prompt input by providing guidance based on the user's work situation and areas of interest. Methods and criteria for identifying the work situation include, for example, the progress of work and task priority. Methods and criteria for identifying areas of interest include, for example, past activity history and topics of interest. In this way, the reception unit can provide input guidance based on the user's current work situation and areas of interest.

[0075] The data analyst support system further includes a reception unit that estimates a user's emotion and determines the input priority of prompts based on the estimated user's emotion. For example, the reception unit can guide the user to input important prompts with priority when the user is nervous. Furthermore, the reception unit can encourage the user to input detailed prompts when the user is relaxed. Furthermore, the reception unit can enable the user to quickly input the most important prompts when the user is in a hurry. In this way, the reception unit can determine the input priority according to the user's emotion, thereby enabling the input of important prompts with priority. The user's emotion is estimated using techniques such as facial expression recognition, voice analysis, and text analysis. In this way, the reception unit can determine the input priority of prompts based on the user's emotion.

[0076] The reception unit can provide relevant input guidance based on the user's geographical location information when the user inputs a prompt. For example, if the user is in a specific area, the reception unit can suggest data sources related to that area. The reception unit can also display relevant market data or public information as input guidance based on the user's current location. Furthermore, if the user is moving, the reception unit can present example prompts based on the user's current location. This allows the reception unit to support appropriate prompt input by providing relevant guidance based on the user's geographical location information. Methods for collecting and using geographical location information include, for example, GPS data and location information services. This allows the reception unit to provide relevant input guidance based on the user's geographical location information.

[0077] The reception unit can analyze the user's social media activity when entering a prompt and provide appropriate input guidance. For example, the reception unit can present examples of relevant prompts based on topics the user has shown interest in on social media. The reception unit can also suggest relevant data sources from the user's social media activity. Furthermore, the reception unit can guide the user to enter an appropriate prompt based on information shared by the user on social media. In this way, the reception unit can support appropriate prompt entry by providing relevant guidance based on the user's social media activity. Methods for collecting and analyzing social media activity include, for example, analyzing post content and followers. In this way, the reception unit can analyze the user's social media activity and provide appropriate input guidance.

[0078] The data analyst support system further includes an analysis unit that estimates a user's emotions and adjusts a prompt analysis method based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive data. Furthermore, when the user is in a hurry, the analysis unit can perform a quick analysis and provide data that focuses on the main points. Furthermore, when the user is excited, the analysis unit can provide analysis results that include visually stimulating effects. This allows the analysis unit to adjust the analysis method according to the user's emotions and provide more appropriate analysis results. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the analysis unit to adjust the prompt analysis method based on the user's emotions.

[0079] During prompt analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the prompt. For example, the analysis unit can perform a detailed analysis on a prompt with a high level of importance and provide comprehensive data. The analysis unit can also perform a concise analysis on a prompt with a low level of importance and provide data that focuses on the main points. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the prompt and provide data efficiently. This allows the analysis unit to provide data efficiently by adjusting the level of detail of the analysis based on the importance of the prompt. Methods and criteria for evaluating the importance of a prompt include, for example, an impact evaluation and an urgency evaluation. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the prompt.

[0080] When analyzing a prompt, the analysis unit can apply different analysis algorithms depending on the category of the prompt. For example, the analysis unit can apply a specific market analysis algorithm to a prompt related to market data. The analysis unit can also apply a public information analysis algorithm to a prompt related to public information. The analysis unit can also apply a closed data analysis algorithm to a prompt related to internal closed data. This allows the analysis unit to provide appropriate analysis results by applying an analysis algorithm depending on the category of the prompt. Examples of methods and criteria for classifying prompt categories include by topic, by purpose, by format, etc. Examples of types and application methods of analysis algorithms include clustering algorithms and classification algorithms, etc. This allows the analysis unit to apply different analysis algorithms depending on the category of the prompt.

[0081] The data analyst support system further includes an analysis unit that estimates a user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis unit to provide more appropriate analysis results by adjusting the display method according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the analysis unit to adjust the display method of the analysis results based on the user's emotions.

[0082] When analyzing prompts, the analysis unit can determine the priority of analysis based on the submission time of the prompt. For example, the analysis unit can prioritize analysis of prompts that are submitted early. Furthermore, the analysis unit can postpone analysis of prompts that are submitted late. Furthermore, the analysis unit can adjust the analysis schedule according to the submission time and provide data efficiently. In this way, the analysis unit can provide data efficiently by determining the priority of analysis according to the submission time. Methods and criteria for identifying the submission time include, for example, the submission date and time and the submission frequency. In this way, the analysis unit can determine the priority of analysis based on the submission time of the prompt.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the prompts when analyzing the prompts. For example, the analysis unit can prioritize analysis of highly relevant prompts. Furthermore, the analysis unit can postpone analysis of less relevant prompts. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the prompts and provide data efficiently. As a result, the analysis unit can provide data efficiently by adjusting the order of analysis based on the relevance of the prompts. Methods and criteria for evaluating the relevance of prompts include, for example, similarity and correlation of content. As a result, the analysis unit can adjust the order of analysis based on the relevance of the prompts.

[0084] The data analyst support system further includes a collection unit that estimates a user's emotions and adjusts the timing of data collection based on the estimated user's emotions. For example, if the user is relaxed, the collection unit can collect data at a leisurely pace. Furthermore, if the user is in a hurry, the collection unit can collect data quickly. Furthermore, if the user is excited, the collection unit can collect data with visually stimulating effects. This allows the collection unit to adjust the timing of data collection according to the user's emotions, thereby enabling more appropriate data collection. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the collection unit to adjust the timing of data collection based on the user's emotions.

[0085] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit can prioritize collection of data sources that the user has frequently collected from in the past. The collection unit can also predict the optimal collection timing from the user's past data collection history. Furthermore, the collection unit can analyze the user's past data collection history and suggest an efficient collection method. This allows the collection unit to select the optimal collection method based on the past data collection history, thereby enabling efficient data collection. Methods for collecting and analyzing the data collection history include, for example, analyzing log data and storing history data. This allows the collection unit to analyze the user's past data collection history and select the optimal collection method.

[0086] The collection unit can perform filtering based on the user's current work situation and areas of interest when collecting data. For example, the collection unit can prioritize collecting data related to a project currently underway by the user. The collection unit can also filter related data sources based on the user's areas of interest. Furthermore, the collection unit can collect appropriate data according to the user's work situation. This allows the collection unit to provide more appropriate data by collecting data according to the user's work situation and areas of interest. Filtering methods and criteria include, for example, keyword filtering and attribute filtering. This allows the collection unit to perform filtering based on the user's current work situation and areas of interest when collecting data.

[0087] The data analyst support system further includes a collection unit that estimates a user's emotions and determines the priority of data to be collected based on the estimated user's emotions. For example, the collection unit can prioritize collecting important data when the user is nervous. Furthermore, the collection unit can collect detailed data when the user is relaxed. Furthermore, the collection unit can quickly collect the most important data when the user is in a hurry. This allows the collection unit to prioritize data collection based on the user's emotions, thereby allowing important data to be collected preferentially. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the collection unit to prioritize data to be collected based on the user's emotions.

[0088] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting data. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also collect relevant market data and public information based on the user's current location. Furthermore, when the user is moving, the collection unit can prioritize collecting data according to the user's current location. This allows the collection unit to provide appropriate data by collecting highly relevant data based on the user's geographical location information. Methods for collecting and using geographical location information include, for example, GPS data and location information services. This allows the collection unit to prioritize collecting highly relevant data based on the user's geographical location information when collecting data.

[0089] The collection unit can analyze the user's social media activity and collect appropriate data when collecting data. For example, the collection unit can collect relevant data based on topics in which the user has shown interest on social media. The collection unit can also identify relevant data sources from the user's social media activity. Furthermore, the collection unit can collect appropriate data based on information shared by the user on social media. This allows the collection unit to provide appropriate data by collecting relevant data based on the user's social media activity. Methods for collecting and analyzing social media activity include, for example, analyzing posted content and followers. This allows the collection unit to analyze the user's social media activity and collect appropriate data when collecting data.

[0090] The data analyst support system further includes a visualization unit that estimates a user's emotions and adjusts a data visualization method based on the estimated user's emotions. For example, if the user is nervous, the visualization unit can provide a simple, highly visible graph or chart. Furthermore, if the user is relaxed, the visualization unit can provide a graph or chart containing detailed information. Furthermore, if the user is in a hurry, the visualization unit can provide a graph or chart that focuses on the main points. This allows the visualization unit to adjust the visualization method according to the user's emotions, thereby enabling more appropriate data display. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the visualization unit to adjust the data visualization method based on the user's emotions.

[0091] The visualization unit can adjust the level of detail of the visualization based on the importance of the data when visualizing the data. For example, the visualization unit can provide detailed graphs and charts for data with high importance. Furthermore, the visualization unit can provide simple graphs and charts for data with low importance. Furthermore, the visualization unit can determine the priority of visualization according to the importance of the data and efficiently display the data. As a result, the visualization unit can efficiently display the data by adjusting the level of detail of the visualization according to the importance of the data. Methods and criteria for evaluating the importance of data include, for example, an impact evaluation and an urgency evaluation. As a result, the visualization unit can adjust the level of detail of the visualization based on the importance of the data.

[0092] The visualization unit can apply different visualization algorithms depending on the data category when visualizing data. For example, the visualization unit can apply a specific market analysis algorithm to data related to market data. The visualization unit can also apply a public information analysis algorithm to data related to public information. The visualization unit can also apply a closed data analysis algorithm to data related to internal closed data. This allows the visualization unit to apply a visualization algorithm depending on the data category, enabling appropriate data display. Examples of data classification methods and criteria include by topic, by use, by format, etc. Types and application methods of visualization algorithms include, for example, clustering algorithms and classification algorithms. This allows the visualization unit to apply different visualization algorithms depending on the data category when visualizing data.

[0093] The data analyst support system further includes a visualization unit that estimates a user's emotions and adjusts the display method of the visualization results based on the estimated user emotions. For example, if the user is nervous, the visualization unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the visualization unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the visualization unit can provide a display method that focuses on the main points. This allows the visualization unit to adjust the display method according to the user's emotions, thereby enabling more appropriate data display. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the visualization unit to adjust the display method of the visualization results based on the user's emotions.

[0094] The visualization unit can determine the priority of visualization based on the time of data submission when visualizing data. For example, the visualization unit can prioritize visualization of data submitted early. Also, the visualization unit can postpone visualization of data submitted late. Furthermore, the visualization unit can adjust the visualization schedule according to the time of submission and efficiently display data. In this way, the visualization unit can efficiently display data by determining the priority of visualization based on the time of submission. Methods and criteria for specifying the time of submission include, for example, the submission date and time and the submission frequency. In this way, the visualization unit can determine the priority of visualization based on the time of data submission when visualizing data.

[0095] The visualization unit can adjust the visualization order based on the relevance of the data when visualizing the data. For example, the visualization unit can prioritize visualization of highly relevant data. Furthermore, the visualization unit can postpone visualization of less relevant data. Furthermore, the visualization unit can adjust the visualization order according to the relevance of the data and efficiently display the data. As a result, the visualization unit can efficiently display the data by adjusting the visualization order according to the relevance of the data. Methods and criteria for evaluating the relevance of the data include, for example, similarity and correlation of content. As a result, the visualization unit can adjust the visualization order based on the relevance of the data when visualizing the data.

[0096] The data analyst support system further includes a security unit that estimates a user's emotions and adjusts the security level of data based on the estimated user emotions. For example, the security unit can apply the highest level of security when the user is nervous. Furthermore, the security unit can apply a standard level of security when the user is relaxed. Furthermore, the security unit can adjust the security level to enable quick access when the user is in a hurry. This allows the security unit to provide appropriate security measures by adjusting the security level according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the security unit to adjust the security level of data based on the user's emotions.

[0097] The security unit can strengthen security measures based on the confidentiality of the data when collecting data. For example, the security unit can apply strong encryption to highly confidential data. Furthermore, the security unit can apply standard security measures to less confidential data. Furthermore, the security unit can adjust the level of security measures according to the confidentiality of the data. This allows the security unit to prevent information leaks by strengthening security measures according to the confidentiality of the data. Methods and standards for evaluating the confidentiality of data include, for example, the importance of the data and the extent to which the data is made public. This allows the security unit to strengthen security measures based on the confidentiality of the data when collecting data.

[0098] The data analyst support system further includes a security unit that estimates a user's emotions and prioritizes security measures based on the estimated user emotions. For example, if the user is nervous, the security unit can prioritize important security measures. Furthermore, if the user is relaxed, the security unit can prioritize standard security measures. Furthermore, if the user is in a hurry, the security unit can adjust the priority of security measures to enable quick access. Thus, the security unit prioritizes security measures based on the user's emotions, thereby enabling important security measures to be prioritized. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the security unit to prioritize security measures based on the user's emotions.

[0099] The security department can adjust security measures taking into account the geographic distribution of data when collecting data. For example, for data related to a specific region, the security department can comply with the security regulations of that region. The security department can also apply appropriate security measures based on the geographic distribution of data. Furthermore, the security department can select a data storage location and strengthen security measures according to the geographic distribution. This allows the security department to prevent information leaks by providing appropriate security measures based on the geographic distribution of data. Methods and criteria for identifying geographic distribution include, for example, data distribution by region or by country. This allows the security department to adjust security measures taking into account the geographic distribution of data when collecting data.

[0100] The data analyst support system further includes a guide unit that estimates a user's emotion and adjusts the content of a prompt input guide based on the estimated user's emotion. For example, the guide unit can provide simple and intuitive guidance when the user is nervous. Furthermore, the guide unit can provide detailed guidance when the user is relaxed. Furthermore, the guide unit can provide concise guidance to enable quick input when the user is in a hurry. This allows the guide unit to support appropriate prompt input by adjusting the content of the guidance according to the user's emotion. The user's emotion is estimated using technologies such as facial expression recognition, voice analysis, and text analysis. This allows the guide unit to adjust the content of the prompt input guide based on the user's emotion.

[0101] The guide unit can provide optimal guidance by referring to the user's past input history when entering prompts. The guide unit can provide relevant guidance based on, for example, prompts that the user has frequently entered in the past. The guide unit can also suggest an optimal input method from the user's past input history. Furthermore, the guide unit can analyze the user's past input history and provide efficient input guidance. This allows the guide unit to improve user convenience by providing optimal guidance based on the past input history. Methods for collecting and analyzing the past input history include, for example, analyzing log data and storing history data. This allows the guide unit to provide optimal guidance by referring to the user's past input history when entering prompts.

[0102] The data analyst support system further includes a guide unit that estimates a user's emotions and determines the priority of guidance based on the estimated user emotions. For example, the guide unit can provide important guidance with priority when the user is nervous. Furthermore, the guide unit can provide detailed guidance when the user is relaxed. Furthermore, the guide unit can provide concise guidance to enable quick input when the user is in a hurry. In this way, the guide unit can prioritize important guidance by determining the priority of guidance according to the user's emotions. The user's emotions are estimated using technologies such as facial expression recognition, voice analysis, and text analysis. In this way, the guide unit can prioritize guidance based on the user's emotions.

[0103] The guide unit can provide optimal guidance by taking into account the user's device information when entering prompts. For example, if the user is using a smartphone, the guide unit can provide guidance tailored to the screen size. Furthermore, if the user is using a tablet, the guide unit can provide guidance optimized for a large screen. Furthermore, if the user is using a smartwatch, the guide unit can provide concise and highly visible guidance. This allows the guide unit to support appropriate prompt input by providing optimal guidance based on the user's device information. Methods for collecting and using device information include, for example, the type of device and device setting information. This allows the guide unit to provide optimal guidance by taking into account the user's device information when entering prompts. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, collection unit, and visualization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for a user to input a prompt. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the prompt using a generation AI. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects related data. The visualization unit is realized, for example, by the control unit 46A of the smart device 14 and visualizes the collected data in the form of a graph, chart, or the like. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, collection unit, and visualization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for a user to input a prompt. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the prompt using a generation AI. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects related data. The visualization unit is realized, for example, by the control unit 46A of the smart glasses 214 and visualizes the collected data in the form of a graph, chart, or the like. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, collection unit, and visualization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for the user to input a prompt. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the prompt using a generation AI. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects related data. The visualization unit is realized, for example, by the control unit 46A of the headset type terminal 314 and visualizes the collected data in the form of a graph, chart, or the like. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, collection unit, and visualization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for a user to input a prompt. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the prompt using a generation AI. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects related data. The visualization unit is realized, for example, by the control unit 46A of the robot 414 and visualizes the collected data in the form of a graph, chart, or the like.

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

[0105] The reception unit can analyze the input content in real time when the user enters a prompt and provide an appropriate suggestion function. For example, if the user starts entering "market trends in 2022," the reception unit can automatically suggest related keywords and phrases to support the user in entering more specific prompts. The reception unit can also analyze the user's input speed and typing pattern and present appropriate completion candidates as the user enters information. Furthermore, the reception unit can refer to the history of prompts entered by the user in the past and suggest similar prompts, allowing the user to enter information efficiently. In this way, the reception unit can support the user's input in real time and achieve efficient prompt input.

[0106] The collection unit can adjust the timing of data collection based on the user's work schedule when collecting data. For example, if the user is in a meeting, the collection unit can temporarily delay data collection and resume collection after the meeting ends. The collection unit can also predict the optimal collection timing based on the user's work schedule and collect data efficiently. Furthermore, the collection unit can adjust the priority of data to be collected according to the user's work schedule and collect important data preferentially. As a result, the collection unit can achieve efficient data collection by adjusting the collection timing based on the user's work schedule.

[0107] The visualization unit can display collected data in a format that can be customized according to the user's preferences. For example, if a user prefers a particular color or font, the visualization unit can adjust the design of a graph or chart according to the user's preference. Also, if a user prefers a particular data format (e.g., a bar graph or a pie chart), the visualization unit can display the data in that format. Furthermore, the visualization unit provides an interface that allows the user to adjust the level of detail of the data, and can display detailed data or summarized data as needed. In this way, the visualization unit can realize a customizable data display according to the user's preferences.

[0108] The analysis unit can evaluate the reliability of the related data based on the content of the prompt. For example, the analysis unit can evaluate the source and update frequency of the collected data and prioritize analysis of highly reliable data. The analysis unit can also check the consistency and integrity of the data and exclude less reliable data. Furthermore, the analysis unit can display the reliability of the analysis results based on the reliability of the data, which can be used as a reference when the user judges the analysis results. This allows the analysis unit to evaluate the reliability of the data based on the content of the prompt and provide highly reliable analysis results.

[0109] The visualization unit may provide interactive data exploration capabilities to enable users to gain a deeper understanding of the data. For example, a user may click on a specific data point on a graph or chart to display detailed information related to that data point. The visualization unit may also provide an interface that enables users to filter and sort data, allowing users to narrow down the data based on specific criteria. Furthermore, the visualization unit may provide multiple visualization options that enable users to analyze the data from different perspectives. In this way, the visualization unit may enable users to interactively explore and gain a deeper understanding of the data.

[0110] The reception unit can estimate the user's emotions and provide prompt input assistance based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide simple and intuitive input assistance to minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable prompt input quickly. In this way, the reception unit can provide a more comfortable input environment by providing input assistance according to the user's emotions.

[0111] The collection unit can estimate the user's emotions and adjust the priority of data collection based on the estimated user's emotions. For example, if the user is nervous, the collection unit can prioritize collecting important data. If the user is relaxed, detailed data can be collected. Furthermore, if the user is in a hurry, the most important data can be collected quickly. In this way, the collection unit can prioritize collecting important data by adjusting the priority of data collection according to the user's emotions.

[0112] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the analysis unit can provide more appropriate analysis results by adjusting the display method according to the user's emotions.

[0113] The visualization unit can estimate the user's emotions and adjust the data visualization method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible graph or chart can be provided. If the user is relaxed, a graph or chart containing detailed information can be provided. Furthermore, if the user is in a hurry, a graph or chart that focuses on the main points can be provided. In this way, the visualization unit can adjust the visualization method according to the user's emotions, enabling more appropriate data display.

[0114] The security unit can estimate the user's emotions and adjust the security level of data based on the estimated user's emotions. For example, if the user is nervous, the highest level of security can be applied. If the user is relaxed, the standard level of security can be applied. Furthermore, if the user is in a hurry, the security level can be adjusted to allow for quick access. In this way, the security unit can provide appropriate security measures by adjusting the security level according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit provides an interface for the user to input a prompt. For example, the reception unit can receive prompts in text format, audio format, image format, and the like. Step 2: The analysis unit uses the generation AI to analyze the prompt received by the reception unit. The analysis unit uses, for example, natural language processing technology to understand the content of the prompt and identify related data. Step 3: The collection unit collects the data identified by the analysis unit. The collection unit collects data from, for example, market data, public information, internal closed data, etc. Step 4: The analysis unit analyzes the data collected by the collection unit. Step 5: The visualization unit analyzes the data collected by the collection unit and visualizes it in a format that is easy for the user to understand. For example, the visualization unit displays the data in the form of a graph or chart.

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

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0123] The data processing device 12 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

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

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

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

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

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

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

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

Claims

1. a reception unit that receives a prompt; an analysis unit that analyzes the prompt received by the reception unit; a collection unit that collects data based on the prompt analyzed by the analysis unit; an analysis unit that analyzes the data collected by the collection unit; a visualization unit that visualizes the data analyzed by the analysis unit; A system comprising:

2. The collecting unit Collect market data, public information, and internal, private data 2. The system of claim 1.

3. The visualization unit Display the collected data in the form of graphs or charts 2. The system of claim 1.

4. The analysis unit Understand prompts and identify relevant data 2. The system of claim 1.

5. The visualization unit Present data in a way that is intuitive to the user 2. The system of claim 1.

6. The collecting unit Equipped with a security department to ensure data security 2. The system of claim 1.

7. The reception unit A guide section is provided to guide the user to input appropriate prompts.

2. The system of claim 1.

8. The reception unit Estimating a user's emotion and adjusting a prompt input interface based on the estimated user's emotion 2. The system of claim 1.

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