system

Abstract

The system of the embodiment aims to enable anyone to easily collect, analyze, and visualize market data, public information, and internal closed data. [Solution] A system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a visualization unit. The reception unit inputs a prompt. The collection unit collects market data, public information, and in-house closed data based on the prompt input by the reception unit. The analysis unit analyzes the data collected by the collection unit. The visualization unit visualizes the analysis results obtained 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 faced the challenge of making it difficult to comprehensively collect, analyze, and visualize market data, public information, and internal closed data.

[0005] The system of the embodiment aims to enable anyone to easily collect, analyze, and visualize market data, public information, and internal closed data. [Means for solving the problem]

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

[0007] The system according to the embodiment allows anyone to easily collect, analyze, and visualize market data, public information, and internal closed 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. When a user enters a prompt, the system collects market data, public information, and internal closed data, analyzes the collected data, visualizes the analysis results, and provides them to the user. For example, when a user enters a prompt such as "Tell me the latest market trends," the system collects the necessary data from public information on the Internet and internal databases. The collected data is analyzed by an analysis unit within the system, and trend and correlation analyses are performed. The analysis results are displayed in the form of graphs, charts, and other formats by a visualization unit within the system. This mechanism allows users to easily access data and understand the analysis results. This allows anyone to easily fulfill the role of a data analyst. The data analyst support system allows users to quickly and efficiently collect, analyze, and visualize data simply by entering a prompt.

[0029] A data analyst support system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a visualization unit. The reception unit provides an interface for a user to input a prompt. For example, the reception unit may include a text input field or a voice input function, allowing the user to easily input the prompt. The collection unit collects market data, public information, and internal closed data based on the prompt input by the reception unit. For example, the collection unit may collect public information on the Internet using web scraping technology. The collection unit may also access internal databases to obtain necessary data. The collection unit may also collect data from external data sources using APIs. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit may perform trend analysis or correlation analysis of the data. The analysis unit may analyze the data using statistical analysis or machine learning algorithms to extract important insights. The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization unit may generate graphs or charts to visually display the analysis results. The visualization unit may also provide an interactive dashboard to enable a user to intuitively understand the analysis results. As a result, the data analyst support system according to the embodiment allows the user to quickly and efficiently collect, analyze, and visualize data simply by inputting a prompt.

[0030] The collection unit can collect data from publicly available information on the Internet or from an in-house database. For example, the collection unit collects publicly available information on the Internet using web scraping technology. For example, the collection unit automatically extracts necessary data from specific websites and stores it in a database. The collection unit can also access in-house databases to obtain necessary data. For example, the collection unit obtains data from CRM systems or ERP systems and uses it for analysis. Furthermore, the collection unit can collect data from external data sources using APIs. For example, the collection unit uses the API of a financial data provider to obtain the latest market data. This allows the collection unit to collect data from a variety of data sources.

[0031] The analysis unit can perform trend analysis and correlation analysis of the collected data. The analysis unit, for example, performs trend analysis of the collected data. For example, the analysis unit analyzes data trends using time-series data and predicts future trends. The analysis unit can also perform correlation analysis of the collected data. For example, the analysis unit analyzes correlations between data using the Pearson correlation coefficient or the Spearman rank correlation coefficient. Furthermore, the analysis unit can perform clustering and classification of data using machine learning algorithms. For example, the analysis unit divides data into clusters using K-means clustering and analyzes the characteristics of each cluster. This allows the analysis unit to analyze trends and correlations of data.

[0032] The visualization unit can display the analysis results in the form of a graph or chart. For example, the visualization unit displays the analysis results in the form of a bar graph or a line graph. For example, the visualization unit generates a line graph showing the trend of the data and provides it to the user. The visualization unit can also display the analysis results in the form of a pie chart or a histogram. For example, the visualization unit generates a histogram showing the distribution of the data and provides it to the user. Furthermore, the visualization unit can display the analysis results in the form of an interactive dashboard. For example, the visualization unit provides an interactive dashboard that allows the user to filter the data or view detailed information. In this way, the visualization unit can display the analysis results in a format that is visually easy to understand.

[0033] The collection unit may include a filtering unit that filters and cleanses data. The filtering unit, for example, filters collected data. For example, the filtering unit filters data based on specific conditions to extract only necessary data. The filtering unit can also cleanse the collected data. For example, the filtering unit complements missing values ​​and removes outliers to improve data quality. Furthermore, the filtering unit can normalize and standardize data. For example, the filtering unit scales data and converts it into a format appropriate for analysis. In this way, the filtering unit can improve the quality of the collected data.

[0034] The analysis unit may include a storage unit that stores the analysis results. The storage unit stores the analysis results in a database, for example. For example, the storage unit stores the analysis results in a relational database so that they can be referenced later. The storage unit may also store the analysis results in a file system. For example, the storage unit stores the analysis results in a CSV file or JSON file format and shares them with other systems. The storage unit may also store the analysis results in cloud storage. For example, the storage unit uploads the analysis results to a cloud storage service and makes them accessible. This allows the storage unit to store the analysis results and reference them later.

[0035] The reception unit can analyze the user's past prompt input history and provide optimal input assistance. For example, the reception unit stores the user's past prompt input history in a database and analyzes it. For example, the reception unit automatically displays prompts that have been frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests prompts that will be used in a specific time period based on the past input history. Furthermore, the reception unit can also suggest prompts related to a specific project or field of interest based on the user's past input history. For example, the reception unit preferentially displays prompts related to the current project based on the past input history. This allows the reception unit to provide optimal input assistance based on the user's past input history.

[0036] The reception unit can present input candidates based on the user's current areas of interest when the user inputs a prompt. For example, the reception unit stores the user's current areas of interest in a database and analyzes them. For example, the reception unit can suggest related prompts based on keywords recently searched by the user. The reception unit can also display input candidates based on topics in which the user has shown interest in the past. For example, the reception unit can suggest prompts related to the user's current areas of interest based on the user's past search history or browsing history. Furthermore, the reception unit can preferentially present prompts related to the user's current project. For example, the reception unit can suggest necessary prompts depending on the progress of the current project. This allows the reception unit to present appropriate input candidates based on the user's areas of interest.

[0037] The reception unit can present highly relevant input candidates in consideration of the user's geographical location information when the user enters a prompt. The reception unit, for example, acquires and analyzes the user's geographical location information from GPS data or an IP address. For example, the reception unit proposes relevant prompts based on the user's current location. The reception unit can also display relevant prompts based on locations the user has visited in the past. For example, the reception unit proposes prompts related to the user's current location based on the user's past location information history. Furthermore, the reception unit can present prompts including region-specific information based on the user's current location information. For example, the reception unit proposes prompts including local news and event information based on the user's current location. This allows the reception unit to present appropriate input candidates based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when entering a prompt and present related input candidates. The reception unit, for example, stores and analyzes the user's social media activity in a database. For example, the reception unit suggests related prompts based on information recently shared by the user on social media. The reception unit can also display related prompts based on the content of posts from accounts the user follows. For example, the reception unit suggests prompts related to the user's current field of interest based on the content of posts from the accounts the user follows. Furthermore, the reception unit can analyze the user's social media activity history and present prompts that are likely to interest the user. For example, the reception unit suggests related prompts based on the content of past posts and the number of likes. This allows the reception unit to present appropriate input candidates based on the user's social media activity.

[0039] The collection unit can analyze the user's past data collection history and select the optimal collection method. The collection unit, for example, stores the user's past data collection history in a database and analyzes it. For example, the collection unit suggests the optimal collection method based on the type of data collected in the past. The collection unit can also preferentially suggest data collection methods that the user has used in the past. For example, the collection unit predicts and suggests the optimal collection method for a specific time period based on the past collection history. Furthermore, the collection unit can also suggest collection methods related to a specific project or field of interest based on the user's past data collection history. For example, the collection unit preferentially suggests collection methods related to a current project based on the past collection history. This allows the collection unit to select the optimal collection method based on the user's past data collection history.

[0040] The collection unit can filter data based on the user's current project or areas of interest when collecting data. For example, the collection unit stores the user's current project or areas of interest in a database and analyzes it. For example, the collection unit prioritizes collecting data related to the project the user is currently working on. The collection unit can also filter and collect related data based on the user's areas of interest. For example, the collection unit prioritizes collecting data related to the current project from past project history or areas of interest. Furthermore, the collection unit can collect required data depending on the progress of the user's current project. For example, the collection unit prioritizes collecting specific data based on the progress of the project. This allows the collection unit to collect appropriate data based on the user's current project or areas of interest.

[0041] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, acquires and analyzes the user's geographical location information from GPS data or an IP address. For example, the collection unit prioritizes collecting relevant data based on the user's current location. The collection unit can also collect relevant data based on locations the user has visited in the past. For example, the collection unit prioritizes collecting data related to the user's current location from past location information history. Furthermore, the collection unit can prioritize collecting data including region-specific information based on the user's current location information. For example, the collection unit prioritizes collecting data including local news and event information based on the user's current location. This allows the collection unit to collect appropriate data based on the user's geographical location information.

[0042] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit stores the user's social media activities in a database and analyzes them. For example, the collection unit collects related data based on information recently shared by the user on social media. The collection unit can also collect related data based on the content posted by accounts the user follows. For example, the collection unit collects data related to the user's current areas of interest from the content posted by the accounts the user follows. Furthermore, the collection unit can analyze the user's social media activity history and collect data that may be of interest to the user. For example, the collection unit collects related data from the content of past posts and the number of likes. This allows the collection unit to collect appropriate data based on the user's social media activities.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the data based on business impact or technical value and performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs only basic statistical analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance and postpones analysis of data with low importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit identifies the category of data and applies the optimal analysis algorithm depending on the category. For example, the analysis unit performs trend analysis on time series data and correlation analysis on categorical data. The analysis unit can also perform regression analysis on numerical data. For example, the analysis unit applies linear regression or logistic regression to numerical data to analyze data relationships. Furthermore, the analysis unit can apply natural language processing technology to text data. For example, the analysis unit performs topic modeling or sentiment analysis on text data. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit identifies the time when the data was collected and prioritizes analysis of the most recent data. For example, the analysis unit identifies the time when the data was collected based on the collection date or collection period and prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. For example, the analysis unit performs only basic statistical analysis on older data. Furthermore, the analysis unit can adjust the priority of analysis depending on the time when the data was collected. For example, the analysis unit prioritizes analysis of data collected within a specific period based on the time of collection. This allows the analysis unit to adjust the priority of analysis depending on the time when the data was collected.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data based on correlations and co-occurrence relationships and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit performs only basic statistical analysis on less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of specific data based on the relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0047] The visualization unit can adjust the level of detail of the display based on the importance of the data during visualization. For example, the visualization unit evaluates the importance of the data and displays data with high importance in detail. For example, the visualization unit evaluates the importance of the data based on business impact or technical value and displays data with high importance in detail. The visualization unit can also display data with low importance in a simplified manner. For example, the visualization unit displays only a basic graph for data with low importance. Furthermore, the visualization unit can determine the priority of the display based on the importance of the data. For example, the visualization unit displays data with high importance first and data with low importance later. This allows the visualization unit to adjust the level of detail of the display based on the importance of the data.

[0048] The visualization unit can apply different visualization techniques depending on the category of data during visualization. For example, the visualization unit identifies the category of data and applies the optimal visualization technique depending on the category. For example, the visualization unit uses a line graph for time series data and a bar graph for categorical data. The visualization unit can also use a scatter plot for numerical data. For example, the visualization unit uses a scatter plot for numerical data to display the distribution of data. Furthermore, the visualization unit can also use a word cloud for text data. For example, the visualization unit uses a word cloud for text data to visually display important keywords. This allows the visualization unit to apply the optimal visualization technique depending on the category of data.

[0049] During visualization, the visualization unit can determine the display priority based on the time when the data was collected. For example, the visualization unit identifies the time when the data was collected and prioritizes displaying the most recent data. For example, the visualization unit identifies the time when the data was collected based on the collection date or collection period and prioritizes displaying the most recent data. The visualization unit can also postpone displaying older data. For example, the visualization unit only displays basic graphs for older data. Furthermore, the visualization unit can adjust the display priority based on the time when the data was collected. For example, the visualization unit prioritizes displaying data collected within a specific period based on the time when the data was collected. This allows the visualization unit to adjust the display priority based on the time when the data was collected.

[0050] The visualization unit can adjust the display order based on the relevance of the data during visualization. For example, the visualization unit evaluates the relevance of the data and prioritizes displaying highly relevant data. For example, the visualization unit evaluates the relevance of the data based on correlations or co-occurrence relationships and prioritizes displaying highly relevant data. The visualization unit can also postpone displaying data with low relevance. For example, the visualization unit only displays basic graphs for data with low relevance. Furthermore, the visualization unit can adjust the display order according to the relevance of the data. For example, the visualization unit prioritizes displaying specific data based on the relevance. This allows the visualization unit to adjust the display order according to the relevance of the data.

[0051] The filtering unit can adjust the level of filtering detail based on the importance of the data during filtering. For example, the filtering unit evaluates the importance of the data and performs detailed filtering on data with high importance. For example, the filtering unit evaluates the importance of the data based on business impact or technical value and performs detailed filtering on data with high importance. The filtering unit can also perform simplified filtering on data with low importance. For example, the filtering unit performs only basic filtering on data with low importance. Furthermore, the filtering unit can determine the priority of filtering according to the importance of the data. For example, the filtering unit preferentially filters data with high importance and postpones filtering of data with low importance. This allows the filtering unit to adjust the level of filtering detail according to the importance of the data.

[0052] During filtering, the filtering unit can adjust the filtering order based on the time when the data was collected. For example, the filtering unit identifies the time when the data was collected and prioritizes filtering of the most recent data. For example, the filtering unit identifies the time when the data was collected based on the collection date or collection period and prioritizes filtering of the most recent data. The filtering unit can also postpone filtering of older data. For example, the filtering unit performs only basic filtering on older data. Furthermore, the filtering unit can adjust the filtering order based on the time when the data was collected. For example, the filtering unit prioritizes filtering of data collected within a specific period based on the time when the data was collected. This allows the filtering unit to adjust the filtering order based on the time when the data was collected.

[0053] The storage unit can adjust the level of detail of storage based on the importance of the data when storing the data. For example, the storage unit evaluates the importance of the data and stores the data with high importance in detail. For example, the storage unit evaluates the importance of the data based on business impact or technical value and stores the data with high importance in detail. The storage unit can also perform simplified storage for data with low importance. For example, the storage unit performs only basic storage for data with low importance. Furthermore, the storage unit can determine the priority of storage according to the importance of the data. For example, the storage unit stores data with high importance first and stores data with low importance later. This allows the storage unit to adjust the level of detail of storage according to the importance of the data.

[0054] The storage unit can adjust the order of storage based on the time when the data was collected when storing the data. For example, the storage unit identifies the time when the data was collected and prioritizes storing the most recent data. For example, the storage unit identifies the time when the data was collected based on the collection date or collection period and prioritizes storing the most recent data. The storage unit can also postpone storing older data. For example, the storage unit only performs basic storage of older data. Furthermore, the storage unit can adjust the order of storage based on the time when the data was collected. For example, the storage unit prioritizes storing data collected within a specific period based on the time of collection. This allows the storage unit to adjust the order of storage based on the time when the data was collected.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past prompt input history and provide optimal input assistance. For example, the reception unit stores the user's past prompt input history in a database and analyzes it. For example, the reception unit automatically displays prompts that have been frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests prompts that will be used in a specific time period based on the past input history. Furthermore, the reception unit can also suggest prompts related to a specific project or field of interest based on the user's past input history. For example, the reception unit preferentially displays prompts related to the current project based on the past input history. This allows the reception unit to provide optimal input assistance based on the user's past input history.

[0057] The collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. For example, the collection unit acquires and analyzes the user's geographical location information from GPS data or an IP address. For example, the collection unit prioritizes collection of relevant data based on the user's current location. The collection unit can also collect relevant data based on locations the user has visited in the past. For example, the collection unit prioritizes collection of data related to the user's current location from past location information history. Furthermore, the collection unit can prioritize collection of data including region-specific information based on the user's current location information. For example, the collection unit prioritizes collection of data including local news and event information based on the user's current location. This allows the collection unit to collect appropriate data based on the user's geographical location information.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on highly important data. For example, the analysis unit evaluates the importance of the data based on business impact or technical value and performs a detailed analysis on highly important data. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit performs only basic statistical analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of more important data and postpones analysis of less important data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0059] The visualization unit can apply different visualization techniques depending on the category of data. For example, the visualization unit identifies the category of data and applies the optimal visualization technique depending on the category. For example, the visualization unit uses a line graph for time series data and a bar graph for categorical data. The visualization unit can also use a scatter plot for numerical data. For example, the visualization unit uses a scatter plot for numerical data to display the distribution of data. Furthermore, the visualization unit can also use a word cloud for text data. For example, the visualization unit uses a word cloud for text data to visually display important keywords. This allows the visualization unit to apply the optimal visualization technique depending on the category of data.

[0060] The filtering unit can adjust the filtering order based on the time when the data was collected. For example, the filtering unit identifies the time when the data was collected, and prioritizes filtering of the most recent data. For example, the filtering unit identifies the time when the data was collected based on the collection date or collection period, and prioritizes filtering of the most recent data. The filtering unit can also postpone filtering of older data. For example, the filtering unit performs only basic filtering on older data. Furthermore, the filtering unit can adjust the filtering order based on the time when the data was collected. For example, the filtering unit prioritizes filtering of data collected within a specific period based on the collection time. This allows the filtering unit to adjust the filtering order based on the time when the data was collected.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit provides an interface for the user to input a prompt. For example, the reception unit may have a text input field or a voice input function, allowing the user to easily input a prompt. Step 2: The collection unit collects market data, public information, and internal closed data based on the prompts entered by the reception unit. For example, the collection unit may use web scraping technology to collect public information on the Internet. The collection unit may also access internal databases to obtain the required data. Furthermore, the collection unit may use APIs to collect data from external data sources. Step 3: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit performs trend analysis and correlation analysis of the data. The analysis unit analyzes the data using statistical analysis and machine learning algorithms to extract important insights. Step 4: The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization unit generates graphs and charts to visually display the analysis results. The visualization unit can also provide an interactive dashboard so that the user can intuitively understand the analysis results.

[0063] (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. When a user enters a prompt, the system collects market data, public information, and internal closed data, analyzes the collected data, visualizes the analysis results, and provides them to the user. For example, when a user enters a prompt such as "Tell me the latest market trends," the system collects the necessary data from public information on the Internet and internal databases. The collected data is analyzed by an analysis unit within the system, and trend and correlation analyses are performed. The analysis results are displayed in the form of graphs, charts, and other formats by a visualization unit within the system. This mechanism allows users to easily access data and understand the analysis results. This allows anyone to easily fulfill the role of a data analyst. The data analyst support system allows users to quickly and efficiently collect, analyze, and visualize data simply by entering a prompt.

[0064] A data analyst support system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a visualization unit. The reception unit provides an interface for a user to input a prompt. For example, the reception unit may include a text input field or a voice input function, allowing the user to easily input the prompt. The collection unit collects market data, public information, and internal closed data based on the prompt input by the reception unit. For example, the collection unit may collect public information on the Internet using web scraping technology. The collection unit may also access internal databases to obtain necessary data. The collection unit may also collect data from external data sources using APIs. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit may perform trend analysis or correlation analysis of the data. The analysis unit may analyze the data using statistical analysis or machine learning algorithms to extract important insights. The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization unit may generate graphs or charts to visually display the analysis results. The visualization unit may also provide an interactive dashboard to enable a user to intuitively understand the analysis results. As a result, the data analyst support system according to the embodiment allows the user to quickly and efficiently collect, analyze, and visualize data simply by inputting a prompt.

[0065] The collection unit can collect data from publicly available information on the Internet or from an in-house database. For example, the collection unit collects publicly available information on the Internet using web scraping technology. For example, the collection unit automatically extracts necessary data from specific websites and stores it in a database. The collection unit can also access in-house databases to obtain necessary data. For example, the collection unit obtains data from CRM systems or ERP systems and uses it for analysis. Furthermore, the collection unit can collect data from external data sources using APIs. For example, the collection unit uses the API of a financial data provider to obtain the latest market data. This allows the collection unit to collect data from a variety of data sources.

[0066] The analysis unit can perform trend analysis and correlation analysis of the collected data. The analysis unit, for example, performs trend analysis of the collected data. For example, the analysis unit analyzes data trends using time-series data and predicts future trends. The analysis unit can also perform correlation analysis of the collected data. For example, the analysis unit analyzes correlations between data using the Pearson correlation coefficient or the Spearman rank correlation coefficient. Furthermore, the analysis unit can perform clustering and classification of data using machine learning algorithms. For example, the analysis unit divides data into clusters using K-means clustering and analyzes the characteristics of each cluster. This allows the analysis unit to analyze trends and correlations of data.

[0067] The visualization unit can display the analysis results in the form of a graph or chart. For example, the visualization unit displays the analysis results in the form of a bar graph or a line graph. For example, the visualization unit generates a line graph showing the trend of the data and provides it to the user. The visualization unit can also display the analysis results in the form of a pie chart or a histogram. For example, the visualization unit generates a histogram showing the distribution of the data and provides it to the user. Furthermore, the visualization unit can display the analysis results in the form of an interactive dashboard. For example, the visualization unit provides an interactive dashboard that allows the user to filter the data or view detailed information. In this way, the visualization unit can display the analysis results in a format that is visually easy to understand.

[0068] The collection unit may include a filtering unit that filters and cleanses data. The filtering unit, for example, filters collected data. For example, the filtering unit filters data based on specific conditions to extract only necessary data. The filtering unit can also cleanse the collected data. For example, the filtering unit complements missing values ​​and removes outliers to improve data quality. Furthermore, the filtering unit can normalize and standardize data. For example, the filtering unit scales data and converts it into a format appropriate for analysis. In this way, the filtering unit can improve the quality of the collected data.

[0069] The analysis unit may include a storage unit that stores the analysis results. The storage unit stores the analysis results in a database, for example. For example, the storage unit stores the analysis results in a relational database so that they can be referenced later. The storage unit may also store the analysis results in a file system. For example, the storage unit stores the analysis results in a CSV file or JSON file format and shares them with other systems. The storage unit may also store the analysis results in cloud storage. For example, the storage unit uploads the analysis results to a cloud storage service and makes them accessible. This allows the storage unit to store the analysis results and reference them later.

[0070] The reception unit can estimate the user's emotion and adjust the prompt input method based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and adjusts the prompt input method. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the prompt input method. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and adjusts the prompt input method. This allows the reception unit to provide an optimal prompt input method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The reception unit can analyze the user's past prompt input history and provide optimal input assistance. For example, the reception unit stores the user's past prompt input history in a database and analyzes it. For example, the reception unit automatically displays prompts that have been frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests prompts that will be used in a specific time period based on the past input history. Furthermore, the reception unit can also suggest prompts related to a specific project or field of interest based on the user's past input history. For example, the reception unit preferentially displays prompts related to the current project based on the past input history. This allows the reception unit to provide optimal input assistance based on the user's past input history.

[0072] The reception unit can present input candidates based on the user's current areas of interest when the user inputs a prompt. For example, the reception unit stores the user's current areas of interest in a database and analyzes them. For example, the reception unit can suggest related prompts based on keywords recently searched by the user. The reception unit can also display input candidates based on topics in which the user has shown interest in the past. For example, the reception unit can suggest prompts related to the user's current areas of interest based on the user's past search history or browsing history. Furthermore, the reception unit can preferentially present prompts related to the user's current project. For example, the reception unit can suggest necessary prompts depending on the progress of the current project. This allows the reception unit to present appropriate input candidates based on the user's areas of interest.

[0073] The reception unit can estimate the user's emotions and determine the priority of prompts based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions and determines the priority of prompts. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of prompts. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and determines the priority of prompts. This allows the reception unit to adjust the priority of prompts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The reception unit can present highly relevant input candidates in consideration of the user's geographical location information when the user enters a prompt. The reception unit, for example, acquires and analyzes the user's geographical location information from GPS data or an IP address. For example, the reception unit proposes relevant prompts based on the user's current location. The reception unit can also display relevant prompts based on locations the user has visited in the past. For example, the reception unit proposes prompts related to the user's current location based on the user's past location information history. Furthermore, the reception unit can present prompts including region-specific information based on the user's current location information. For example, the reception unit proposes prompts including local news and event information based on the user's current location. This allows the reception unit to present appropriate input candidates based on the user's geographical location information.

[0075] The reception unit can analyze the user's social media activity when entering a prompt and present related input candidates. The reception unit, for example, stores and analyzes the user's social media activity in a database. For example, the reception unit suggests related prompts based on information recently shared by the user on social media. The reception unit can also display related prompts based on the content of posts from accounts the user follows. For example, the reception unit suggests prompts related to the user's current field of interest based on the content of posts from the accounts the user follows. Furthermore, the reception unit can analyze the user's social media activity history and present prompts that are likely to interest the user. For example, the reception unit suggests related prompts based on the content of past posts and the number of likes. This allows the reception unit to present appropriate input candidates based on the user's social media activity.

[0076] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions and adjusts the timing of data collection. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of data collection. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations and adjusts the timing of data collection. This allows the collection unit to adjust the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI.

[0077] The collection unit can analyze the user's past data collection history and select the optimal collection method. The collection unit, for example, stores the user's past data collection history in a database and analyzes it. For example, the collection unit suggests the optimal collection method based on the type of data collected in the past. The collection unit can also preferentially suggest data collection methods that the user has used in the past. For example, the collection unit predicts and suggests the optimal collection method for a specific time period based on the past collection history. Furthermore, the collection unit can also suggest collection methods related to a specific project or field of interest based on the user's past data collection history. For example, the collection unit preferentially suggests collection methods related to a current project based on the past collection history. This allows the collection unit to select the optimal collection method based on the user's past data collection history.

[0078] The collection unit can filter data based on the user's current project or areas of interest when collecting data. For example, the collection unit stores the user's current project or areas of interest in a database and analyzes it. For example, the collection unit prioritizes collecting data related to the project the user is currently working on. The collection unit can also filter and collect related data based on the user's areas of interest. For example, the collection unit prioritizes collecting data related to the current project from past project history or areas of interest. Furthermore, the collection unit can collect required data depending on the progress of the user's current project. For example, the collection unit prioritizes collecting specific data based on the progress of the project. This allows the collection unit to collect appropriate data based on the user's current project or areas of interest.

[0079] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions and determines the priority of data to be collected. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of data to be collected. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations and determines the priority of data to be collected. This allows the collection unit to adjust the priority of data to be collected depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI.

[0080] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, acquires and analyzes the user's geographical location information from GPS data or an IP address. For example, the collection unit prioritizes collecting relevant data based on the user's current location. The collection unit can also collect relevant data based on locations the user has visited in the past. For example, the collection unit prioritizes collecting data related to the user's current location from past location information history. Furthermore, the collection unit can prioritize collecting data including region-specific information based on the user's current location information. For example, the collection unit prioritizes collecting data including local news and event information based on the user's current location. This allows the collection unit to collect appropriate data based on the user's geographical location information.

[0081] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit stores the user's social media activities in a database and analyzes them. For example, the collection unit collects related data based on information recently shared by the user on social media. The collection unit can also collect related data based on the content posted by accounts the user follows. For example, the collection unit collects data related to the user's current areas of interest from the content posted by the accounts the user follows. Furthermore, the collection unit can analyze the user's social media activity history and collect data that may be of interest to the user. For example, the collection unit collects related data from the content of past posts and the number of likes. This allows the collection unit to collect appropriate data based on the user's social media activities.

[0082] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the expression method of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the expression method of the analysis. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the expression method of the analysis. This allows the analysis unit to adjust the expression method of the analysis result according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the data based on business impact or technical value and performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs only basic statistical analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance and postpones analysis of data with low importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit identifies the category of data and applies the optimal analysis algorithm depending on the category. For example, the analysis unit performs trend analysis on time series data and correlation analysis on categorical data. The analysis unit can also perform regression analysis on numerical data. For example, the analysis unit applies linear regression or logistic regression to numerical data to analyze data relationships. Furthermore, the analysis unit can apply natural language processing technology to text data. For example, the analysis unit performs topic modeling or sentiment analysis on text data. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of data.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the length of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the analysis. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the length of the analysis. This allows the analysis unit to adjust the length of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. For example, the analysis unit identifies the time when the data was collected and prioritizes analysis of the most recent data. For example, the analysis unit identifies the time when the data was collected based on the collection date or collection period and prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of older data. For example, the analysis unit performs only basic statistical analysis on older data. Furthermore, the analysis unit can adjust the priority of analysis depending on the time when the data was collected. For example, the analysis unit prioritizes analysis of data collected within a specific period based on the time of collection. This allows the analysis unit to adjust the priority of analysis depending on the time when the data was collected.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data based on correlations and co-occurrence relationships and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit performs only basic statistical analysis on less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of specific data based on the relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0088] The visualization unit can estimate the user's emotion and adjust the display method of the visualization based on the estimated user emotion. For example, the visualization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on changes in facial expression and adjusts the display method of the visualization. The visualization unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the visualization unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the visualization. Furthermore, the visualization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the visualization. This allows the visualization unit to adjust the display method of the visualization according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The visualization unit can adjust the level of detail of the display based on the importance of the data during visualization. For example, the visualization unit evaluates the importance of the data and displays data with high importance in detail. For example, the visualization unit evaluates the importance of the data based on business impact or technical value and displays data with high importance in detail. The visualization unit can also display data with low importance in a simplified manner. For example, the visualization unit displays only a basic graph for data with low importance. Furthermore, the visualization unit can determine the priority of the display based on the importance of the data. For example, the visualization unit displays data with high importance first and data with low importance later. This allows the visualization unit to adjust the level of detail of the display based on the importance of the data.

[0090] The visualization unit can apply different visualization techniques depending on the category of data during visualization. For example, the visualization unit identifies the category of data and applies the optimal visualization technique depending on the category. For example, the visualization unit uses a line graph for time series data and a bar graph for categorical data. The visualization unit can also use a scatter plot for numerical data. For example, the visualization unit uses a scatter plot for numerical data to display the distribution of data. Furthermore, the visualization unit can also use a word cloud for text data. For example, the visualization unit uses a word cloud for text data to visually display important keywords. This allows the visualization unit to apply the optimal visualization technique depending on the category of data.

[0091] The visualization unit can estimate the user's emotion and adjust the length of visualization based on the estimated user emotion. For example, the visualization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on changes in facial expression and adjusts the length of visualization. The visualization unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the visualization unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of visualization. Furthermore, the visualization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on heart rate fluctuations and adjusts the length of visualization. This allows the visualization unit to adjust the length of visualization according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] During visualization, the visualization unit can determine the display priority based on the time when the data was collected. For example, the visualization unit identifies the time when the data was collected and prioritizes displaying the most recent data. For example, the visualization unit identifies the time when the data was collected based on the collection date or collection period and prioritizes displaying the most recent data. The visualization unit can also postpone displaying older data. For example, the visualization unit only displays basic graphs for older data. Furthermore, the visualization unit can adjust the display priority based on the time when the data was collected. For example, the visualization unit prioritizes displaying data collected within a specific period based on the time when the data was collected. This allows the visualization unit to adjust the display priority based on the time when the data was collected.

[0093] The visualization unit can adjust the display order based on the relevance of the data during visualization. For example, the visualization unit evaluates the relevance of the data and prioritizes displaying highly relevant data. For example, the visualization unit evaluates the relevance of the data based on correlations or co-occurrence relationships and prioritizes displaying highly relevant data. The visualization unit can also postpone displaying data with low relevance. For example, the visualization unit only displays basic graphs for data with low relevance. Furthermore, the visualization unit can adjust the display order according to the relevance of the data. For example, the visualization unit prioritizes displaying specific data based on the relevance. This allows the visualization unit to adjust the display order according to the relevance of the data.

[0094] The filtering unit can estimate the user's emotions and adjust filtering criteria based on the estimated user emotions. For example, the filtering unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the filtering unit calculates an emotion score based on changes in facial expressions and adjusts the filtering criteria. The filtering unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the filtering unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the filtering criteria. Furthermore, the filtering unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the filtering unit calculates an emotion score based on heart rate fluctuations and adjusts the filtering criteria. This allows the filtering unit to adjust the filtering criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The filtering unit can adjust the level of filtering detail based on the importance of the data during filtering. For example, the filtering unit evaluates the importance of the data and performs detailed filtering on data with high importance. For example, the filtering unit evaluates the importance of the data based on business impact or technical value and performs detailed filtering on data with high importance. The filtering unit can also perform simplified filtering on data with low importance. For example, the filtering unit performs only basic filtering on data with low importance. Furthermore, the filtering unit can determine the priority of filtering according to the importance of the data. For example, the filtering unit preferentially filters data with high importance and postpones filtering of data with low importance. This allows the filtering unit to adjust the level of filtering detail according to the importance of the data.

[0096] The filtering unit can estimate the user's emotions and determine filtering priorities based on the estimated user emotions. For example, the filtering unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the filtering unit calculates an emotion score based on changes in facial expressions and determines filtering priorities. The filtering unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the filtering unit analyzes the tone and speed of the voice, calculates an emotion score, and determines filtering priorities. Furthermore, the filtering unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the filtering unit calculates an emotion score based on heart rate fluctuations and determines filtering priorities. This allows the filtering unit to adjust filtering priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] During filtering, the filtering unit can adjust the filtering order based on the time when the data was collected. For example, the filtering unit identifies the time when the data was collected and prioritizes filtering of the most recent data. For example, the filtering unit identifies the time when the data was collected based on the collection date or collection period and prioritizes filtering of the most recent data. The filtering unit can also postpone filtering of older data. For example, the filtering unit performs only basic filtering on older data. Furthermore, the filtering unit can adjust the filtering order based on the time when the data was collected. For example, the filtering unit prioritizes filtering of data collected within a specific period based on the time when the data was collected. This allows the filtering unit to adjust the filtering order based on the time when the data was collected.

[0098] The storage unit can estimate the user's emotions and select data to be saved based on the estimated user emotions. For example, the storage unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on changes in facial expressions and selects data to save. The storage unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the storage unit analyzes the tone and speed of the voice, calculates an emotion score, and selects data to save. The storage unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on heart rate fluctuations and selects data to save. This allows the storage unit to adjust the selection of data to save depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI.

[0099] The storage unit can adjust the level of detail of storage based on the importance of the data when storing the data. For example, the storage unit evaluates the importance of the data and stores the data with high importance in detail. For example, the storage unit evaluates the importance of the data based on business impact or technical value and stores the data with high importance in detail. The storage unit can also perform simplified storage for data with low importance. For example, the storage unit performs only basic storage for data with low importance. Furthermore, the storage unit can determine the priority of storage according to the importance of the data. For example, the storage unit stores data with high importance first and stores data with low importance later. This allows the storage unit to adjust the level of detail of storage according to the importance of the data.

[0100] The storage unit can estimate the user's emotions and determine storage priorities based on the estimated user emotions. For example, the storage unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on changes in facial expressions and determines the storage priorities. The storage unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the storage unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the storage priorities. Furthermore, the storage unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on heart rate fluctuations and determines the storage priorities. This allows the storage unit to adjust the storage priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The storage unit can adjust the order of storage based on the time when the data was collected when storing the data. For example, the storage unit identifies the time when the data was collected and prioritizes storing the most recent data. For example, the storage unit identifies the time when the data was collected based on the collection date or collection period and prioritizes storing the most recent data. The storage unit can also postpone storing older data. For example, the storage unit only performs basic storage of older data. Furthermore, the storage unit can adjust the order of storage based on the time when the data was collected. For example, the storage unit prioritizes storing data collected within a specific period based on the time of collection. This allows the storage unit to adjust the order of storage based on the time when the data was collected. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis 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 the user to input a prompt. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects necessary data from public information on the Internet or an internal company database. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The visualization unit is realized, for example, by the control unit 46A of the smart device 14 and displays the analysis results 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, collection unit, analysis 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 the user to input prompts. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects necessary data from public information on the Internet or an internal company database. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The visualization unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays the analysis results 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, collection unit, analysis 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 collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects necessary data from public information on the Internet or an internal company database. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The visualization unit is realized, for example, by the control unit 46A of the headset type terminal 314 and displays the analysis results 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, collection unit, analysis 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 the user to input prompts. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects necessary data from public information on the Internet or an internal company database. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The visualization unit is realized, for example, by the control unit 46A of the robot 414 and displays the analysis results in the form of a graph, chart, or the like.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can analyze the user's past prompt input history and provide optimal input assistance. For example, the reception unit stores the user's past prompt input history in a database and analyzes it. For example, the reception unit automatically displays prompts that have been frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests prompts that will be used in a specific time period based on the past input history. Furthermore, the reception unit can also suggest prompts related to a specific project or field of interest based on the user's past input history. For example, the reception unit preferentially displays prompts related to the current project based on the past input history. This allows the reception unit to provide optimal input assistance based on the user's past input history.

[0104] The collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. For example, the collection unit acquires and analyzes the user's geographical location information from GPS data or an IP address. For example, the collection unit prioritizes collection of relevant data based on the user's current location. The collection unit can also collect relevant data based on locations the user has visited in the past. For example, the collection unit prioritizes collection of data related to the user's current location from past location information history. Furthermore, the collection unit can prioritize collection of data including region-specific information based on the user's current location information. For example, the collection unit prioritizes collection of data including local news and event information based on the user's current location. This allows the collection unit to collect appropriate data based on the user's geographical location information.

[0105] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on highly important data. For example, the analysis unit evaluates the importance of the data based on business impact or technical value and performs a detailed analysis on highly important data. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit performs only basic statistical analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of more important data and postpones analysis of less important data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0106] The visualization unit can apply different visualization techniques depending on the category of data. For example, the visualization unit identifies the category of data and applies the optimal visualization technique depending on the category. For example, the visualization unit uses a line graph for time series data and a bar graph for categorical data. The visualization unit can also use a scatter plot for numerical data. For example, the visualization unit uses a scatter plot for numerical data to display the distribution of data. Furthermore, the visualization unit can also use a word cloud for text data. For example, the visualization unit uses a word cloud for text data to visually display important keywords. This allows the visualization unit to apply the optimal visualization technique depending on the category of data.

[0107] The filtering unit can adjust the filtering order based on the time when the data was collected. For example, the filtering unit identifies the time when the data was collected, and prioritizes filtering of the most recent data. For example, the filtering unit identifies the time when the data was collected based on the collection date or collection period, and prioritizes filtering of the most recent data. The filtering unit can also postpone filtering of older data. For example, the filtering unit performs only basic filtering on older data. Furthermore, the filtering unit can adjust the filtering order based on the time when the data was collected. For example, the filtering unit prioritizes filtering of data collected within a specific period based on the collection time. This allows the filtering unit to adjust the filtering order based on the time when the data was collected.

[0108] The reception unit can estimate the user's emotion and adjust the prompt input method based on the estimated user's emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and adjusts the prompt input method. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the prompt input method. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on fluctuations in heart rate and adjusts the prompt input method. In this way, the reception unit can provide the optimal prompt input method according to the user's emotion.

[0109] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expression and adjusts the timing of data collection. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of data collection. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on fluctuations in heart rate and adjusts the timing of data collection. This allows the collection unit to adjust the timing of data collection according to the user's emotion.

[0110] The analysis unit can estimate the user's emotion and adjust the method of expression of the analysis based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the method of expression of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the method of expression of the analysis. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on fluctuations in heart rate and adjusts the method of expression of the analysis. This allows the analysis unit to adjust the method of expression of the analysis results according to the user's emotion.

[0111] The visualization unit can estimate the user's emotion and adjust the display method of the visualization based on the estimated user's emotion. For example, the visualization unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on changes in facial expression and adjusts the display method of the visualization. The visualization unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the visualization unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the visualization. Furthermore, the visualization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the visualization unit calculates an emotion score based on fluctuations in heart rate and adjusts the display method of the visualization. In this way, the visualization unit can adjust the display method of the visualization according to the user's emotion.

[0112] The storage unit can estimate the user's emotion and select data to store based on the estimated user's emotion. For example, the storage unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on changes in facial expression and selects data to store. The storage unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the storage unit analyzes the tone and speed of the voice, calculates an emotion score, and selects data to store. Furthermore, the storage unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the storage unit calculates an emotion score based on fluctuations in heart rate and selects data to store. This allows the storage unit to adjust the selection of data to store according to the user's emotion.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit provides an interface for the user to input a prompt. For example, the reception unit may have a text input field or a voice input function, allowing the user to easily input a prompt. Step 2: The collection unit collects market data, public information, and internal closed data based on the prompts entered by the reception unit. For example, the collection unit may use web scraping technology to collect public information on the Internet. The collection unit may also access internal databases to obtain the required data. Furthermore, the collection unit may use APIs to collect data from external data sources. Step 3: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit performs trend analysis and correlation analysis of the data. The analysis unit analyzes the data using statistical analysis and machine learning algorithms to extract important insights. Step 4: The visualization unit visualizes the analysis results obtained by the analysis unit. For example, the visualization unit generates graphs and charts to visually display the analysis results. The visualization unit can also provide an interactive dashboard so that the user can intuitively understand the analysis results.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 for inputting a prompt; a collection unit that collects market data, public information, and in-house closed data based on the prompts input by the reception unit; an analysis unit that analyzes the data collected by the collection unit; a visualization unit that visualizes the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect data from publicly available sources on the internet or internal company databases 2. The system of claim 1.

3. The analysis unit Conduct trend and correlation analysis of collected data 2. The system of claim 1.

4. The visualization unit Displaying analysis results in the form of graphs or charts 2. The system of claim 1.

5. The collecting unit Equipped with a filtering section that filters and cleanses data 2. The system of claim 1.

6. The analysis unit Equipped with a storage unit for saving analysis results 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting prompt input methods based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past prompt input history and provides optimal input assistance 2. The system of claim 1.

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