System

Abstract

To provide a system that efficiently extracts, analyzes and visualizes necessary information from past data in a company.SOLUTION: A system according to the embodiment comprises a reception unit, an extraction unit, an analysis unit, and a graphing unit. The reception unit receives instructions from users. The extraction unit extracts data based on the instructions received by the reception unit. The analysis unit analyzes the data extracted by the extraction unit. The graphing unit generates a graph based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently extract, analyze, and visualize the necessary information from a company's historical data.

[0005] The system according to the embodiment aims to efficiently extract, analyze, and visualize necessary information from past data within a company. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an extraction unit, an analysis unit, and a graphing unit. The reception unit receives instructions from a user. The extraction unit extracts data based on the instructions received by the reception unit. The analysis unit analyzes the data extracted by the extraction unit. The graphing unit generates a graph based on the data analyzed by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently extract, analyze, and visualize necessary information from historical data within a company. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The data extraction and graphing system according to the embodiment of the present invention is a system that uses a bot that has learned all past data in the company isolated from the outside in an environment where employees have limited access rights to extract data for necessary items in a voice or conversational format, and further performs graphing. This system begins with a user instructing the bot to extract the necessary data in a voice or conversational format. Next, the bot uses a generation AI that has learned all past data in the company to extract the instructed data. Furthermore, based on the extracted data, the bot automatically graphs the data by giving variables such as achievement rate and national comparison. This system allows the user to leave the data extraction and graphing work to the bot, and to focus on other tasks. For example, a user instructs the bot in a voice or conversational format, such as "I want you to extract last year's sales data and graph it." This instruction is input to the bot. Next, the bot uses a generation AI that has learned all past data in the company to extract the instructed data. The generation AI analyzes all past data in the company and identifies the instructed data. For example, when extracting sales data for last year, the generation AI analyzes the past sales data and identifies the data for last year. Furthermore, the bot automatically creates graphs based on the extracted data by providing variables such as achievement rates and national comparisons. For example, the achievement rate is calculated for last year's sales data, and the results are graphed. This graph is provided to the user. This allows the user to leave the data extraction and graphing tasks to the bot, and to focus on other tasks. For example, a sales representative can leave the analysis of sales data to the bot and focus on customer service and new sales activities. Furthermore, a manager can concentrate on strategy planning by having the bot extract data necessary for planning a management strategy. This allows the data extraction and graphing system to improve the user's work efficiency.

[0029] The data extraction and graphing system according to the embodiment includes a reception unit, an extraction unit, an analysis unit, and a graphing unit. The reception unit receives a user's instruction. The user's instruction includes, but is not limited to, for example, text input, voice input, and gesture input. The reception unit can receive a user's voice instruction using, for example, a voice recognition technology. The reception unit can also understand a user's conversational instruction using a natural language processing technology. For example, when a user gives an instruction such as "I want you to extract and graph last year's sales data," the reception unit understands the instruction and passes it to the next process. The extraction unit uses a generation AI to extract data based on the instruction received by the reception unit. The generation AI has learned all past data within the company and can extract the instructed data with high accuracy. For example, in order to extract last year's sales data, the extraction unit inputs a prompt to the generation AI saying, "Please extract last year's sales data," and the generation AI analyzes the past sales data to identify last year's data. The analysis unit analyzes the data extracted by the extraction unit. The analysis includes, for example, statistical analysis and the use of machine learning algorithms, but is not limited to such examples. For example, the analysis unit calculates an achievement rate for the extracted sales data. The achievement rate is calculated as a ratio of the actual value to the target value. The analysis unit can also calculate a variable for a national comparison. A national comparison is a method of comparing data by region, for example, comparing sales data of a specific region with the national average. The graphing unit generates a graph based on the data analyzed by the analysis unit. The graph generation includes, for example, a bar graph, a line graph, a pie chart, and other formats, but is not limited to such examples. For example, the graphing unit generates a bar graph showing the achievement rate and provides it to the user. The graphing unit can also generate a graph in a format specified by the user. For example, when the user instructs "I want to display it in a line graph," the graphing unit generates a line graph. As a result, the data extraction and graphing system according to the embodiment can improve the user's work efficiency by extracting, analyzing, and graphing data based on the user's instructions.

[0030] The reception unit receives a user's instruction. The user's instruction includes, but is not limited to, for example, text input, voice input, gesture input, and the like. The reception unit can receive a user's voice instruction using, for example, a voice recognition technology. Specifically, the voice recognition technology converts the user's utterance into text data and understands the instruction content by analyzing the text data. The reception unit can also understand the user's conversational instruction using a natural language processing technology. The natural language processing technology includes an algorithm for analyzing the user's utterance and text input and understanding the context and intent. For example, when the user gives an instruction such as "I want you to extract last year's sales data and graph it," the reception unit understands the instruction and passes it on to the next process. Furthermore, the reception unit is equipped with a sensor and a camera for receiving gesture input, and can detect the user's hand movement and body movement to receive an instruction. For example, the user can instruct the system to perform a specific operation by performing a specific gesture. This allows the reception unit to support a variety of input methods and improve user convenience. Furthermore, the reception unit can learn the user's past instruction history and grasp the user's preferences and patterns, thereby understanding instructions more quickly and accurately. For example, the reception unit can memorize instructions that the user frequently gives and predict and quickly respond to subsequent instructions. This allows the reception unit to efficiently receive user instructions and smoothly pass them on to the next process.

[0031] The extraction unit uses the generation AI to extract data based on the instructions received by the reception unit. The generation AI has learned all past data within the company and can extract the instructed data with high accuracy. Specifically, the generation AI uses natural language processing technology to analyze the user's instructions and identify the necessary data. For example, in order to extract sales data from last year, the extraction unit inputs a prompt to the generation AI saying, "Please extract last year's sales data," and the generation AI analyzes the past sales data to identify last year's data. By learning past data, the generation AI understands the structure and patterns of the data and achieves highly accurate data extraction. In addition, the generation AI can cross-search multiple tables and fields in the database and efficiently extract the necessary data. Furthermore, the extraction unit regularly updates the learning model of the generation AI to respond to new data and trends. This allows the extraction unit to always achieve highly accurate data extraction based on the latest data. For example, the extraction unit can simultaneously extract multiple related data, such as sales data, customer data, and product data, to support comprehensive data analysis. This enables the extraction unit to quickly and accurately extract the necessary data based on the user's instructions and pass it on to the next analysis process.

[0032] The analysis unit analyzes the data extracted by the extraction unit. The analysis includes, for example, statistical analysis and the use of machine learning algorithms, but is not limited to such examples. Specifically, the analysis unit calculates the achievement rate for the extracted sales data. The achievement rate is calculated as a ratio of the actual value to the target value. For example, if the sales target for last year was 10 million yen and the actual value was 9 million yen, the achievement rate is 90%. The analysis unit can also calculate a variable for a national comparison. A national comparison is a method of comparing data for each region, for example, comparing sales data for a specific region with the national average. This makes it possible to evaluate how well a specific region is performing relative to the national average. Furthermore, the analysis unit can also analyze trends and patterns of data using machine learning algorithms and make future predictions. For example, a time series analysis of the sales data is performed to make future sales predictions. Furthermore, the analysis unit can detect abnormal patterns and values ​​from the data using an anomaly detection algorithm. This makes it possible to quickly find abnormal transactions and signs of fraud from the sales data. Furthermore, the analysis unit can visualize the data and display the analysis results as graphs and charts. This allows the user to intuitively understand the analysis results. By combining these analysis techniques, the analysis unit can perform multifaceted analysis of data and provide useful information to the user.

[0033] The graphing unit generates a graph based on the data analyzed by the analysis unit. The graph generation includes, but is not limited to, formats such as bar graphs, line graphs, and pie charts. Specifically, the graphing unit generates a bar graph showing the achievement rate and provides it to the user. For example, a bar graph showing the sales achievement rate for each month is generated so that the user can grasp the performance for each month at a glance. The graphing unit can also generate a graph in a format specified by the user. For example, if the user instructs "I want it to be displayed as a line graph," the graphing unit generates a line graph. Furthermore, the graphing unit also has a function for customizing the color and design of the graph, and can generate a graph according to the user's preferences. For example, a specific color can be used or the design can be changed to emphasize a specific data point. This allows the graphing unit to provide the user with visually easy-to-understand information and promote understanding of the data. Furthermore, the graphing unit also has a function for saving the generated graph as a PDF or image file, and the user can use the graph for other materials or presentations. This allows the graphing unit to visually express the analysis results and support the user's decision-making.

[0034] The reception unit can receive the user's instructions by voice or in a conversational format. The reception unit, for example, uses a voice recognition technology to receive the user's voice instructions. For example, when the user gives a voice instruction such as "I want you to extract last year's sales data and graph it," the reception unit recognizes the voice and passes it to the next process. The reception unit can also understand the user's conversational instruction by using a natural language processing technology. For example, when the user gives a conversational instruction such as "I want you to extract last year's sales data and graph it," the reception unit understands the instruction and passes it to the next process. As a result, by receiving instructions by voice or in a conversational format, the convenience of the user is improved. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, when the reception unit receives the user's voice instruction using the voice recognition technology, the reception unit can convert the voice data into text data using an AI model.

[0035] The extraction unit can extract data using a generation AI that has learned past data within the company. The extraction unit uses the generation AI to extract data based on instructions received by the reception unit. The generation AI has learned all past data within the company and can extract the instructed data with high accuracy. For example, in order to extract sales data for last year, the extraction unit inputs a prompt to the generation AI saying, "Please extract sales data for last year," and the generation AI analyzes the past sales data to identify the data for last year. The generation AI is trained using a deep learning model and uses all past data within the company as a training data set. This improves the accuracy of data extraction by using the generation AI. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, when extracting data using the generation AI, the extraction unit can identify and extract data using an AI model.

[0036] The analysis unit can analyze the extracted data and calculate the achievement rate and national comparison variables. The analysis unit analyzes the data extracted by the extraction unit. The analysis includes, for example, statistical analysis and the use of machine learning algorithms, but is not limited to such examples. For example, the analysis unit calculates the achievement rate for the extracted sales data. The achievement rate is calculated as a ratio of the actual value to the target value. The analysis unit can also calculate national comparison variables. National comparison is a method of comparing data by region, for example, comparing sales data of a specific region with the national average. This makes it possible to provide important information such as the achievement rate and national comparison by analyzing the data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the extracted data into an AI model and have AI perform the calculation of the achievement rate and national comparison.

[0037] The graphing unit can generate a graph based on the analyzed data and provide it to the user. The graphing unit generates a graph based on the data analyzed by the analysis unit. The graph generation includes, but is not limited to, formats such as a bar graph, a line graph, and a pie chart. For example, the graphing unit generates a bar graph showing the achievement rate and provides it to the user. The graphing unit can also generate a graph in a format specified by the user. For example, when the user instructs "I want it to be displayed in a line graph," the graphing unit generates a line graph. This helps the user understand by providing the analysis result in a visually easy-to-understand manner. A part or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the analyzed data into an AI model and have the AI ​​generate the graph.

[0038] The graphing unit can generate a graph in a format specified by the user. The graphing unit generates a graph in a format specified by the user based on the data analyzed by the analysis unit. Formats that can be specified by the user include, but are not limited to, bar graphs, line graphs, and pie charts. For example, when a user instructs "I want to display it in a line graph," the graphing unit generates a line graph. Also, when a user instructs "I want to display it in a pie chart," the graphing unit generates a pie chart. This improves convenience by generating a graph in a format according to the needs of the user. Part or all of the above-mentioned processing in the graphing unit may be performed, for example, using AI, or may be performed without using AI. For example, when generating a graph in a format specified by the user, the graphing unit can generate the graph using an AI model.

[0039] The reception unit can analyze the user's past instruction history and select an appropriate reception method. The reception unit analyzes the user's past instruction history and selects the optimal reception method. For example, the reception unit automatically displays instructions that the user frequently performed in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest instructions to be performed in a specific time period from the user's past instruction history. This improves user convenience by providing an optimal reception method based on the past instruction history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past instruction history data into an AI model and cause AI to select the optimal reception method.

[0040] The reception unit can perform filtering based on the user's current work situation and field of interest when receiving an instruction. The reception unit performs filtering based on the user's current work situation and field of interest when receiving an instruction. For example, the reception unit preferentially receives instructions related to a project currently in progress by the user. The reception unit can also filter and display related instructions based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate instructions according to the user's work situation. This makes it possible to perform work efficiently by providing instructions according to the user's work situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation data into an AI model and have the AI ​​perform filtering.

[0041] The reception unit can preferentially accept highly relevant instructions in consideration of the geographical location information of the user when receiving an instruction. The reception unit preferentially accepts highly relevant instructions in consideration of the geographical location information of the user when receiving an instruction. For example, when the user is in a specific location, the reception unit preferentially accepts instructions related to the location. The reception unit can also preferentially accept instructions related to a location close to the user's current location. Furthermore, the reception unit can also suggest optimal instructions based on the geographical location information of the user. This improves convenience by providing instructions based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user into an AI model and cause the AI ​​to select highly relevant instructions.

[0042] The reception unit can analyze the user's social media activity and receive related instructions when receiving the instruction. The reception unit analyzes the user's social media activity and receives related instructions when receiving the instruction. For example, the reception unit receives related instructions based on the content mentioned by the user on social media. The reception unit can also preferentially receive instructions of high interest from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and suggest optimal instructions. This improves convenience by providing instructions based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data into an AI model and cause the AI ​​to select related instructions.

[0043] The extraction unit can select an appropriate extraction method by referring to the past data extraction history when extracting data. The extraction unit can select the optimal extraction method by referring to the past data extraction history when extracting data. For example, the extraction unit automatically displays data that the user has frequently extracted in the past as candidates. The extraction unit can also preferentially suggest extraction methods that the user has used in the past. Furthermore, the extraction unit can predict and suggest data to be extracted in a specific time period from the user's past data extraction history. This improves user convenience by providing an optimal extraction method based on the past data extraction history. Part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI or may be performed without using AI. For example, the extraction unit can input past data extraction history data into an AI model and cause AI to select the optimal extraction method.

[0044] The extraction unit can filter the extracted data based on the user's work situation when extracting data. The extraction unit filters the extracted data based on the user's work situation when extracting data. For example, the extraction unit preferentially extracts data related to a project currently in progress by the user. The extraction unit can also filter and display related data according to the user's work situation. Furthermore, the extraction unit can also suggest optimal data based on the user's work situation. This makes it possible to perform work efficiently by providing data according to the user's work situation. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the user's work situation data into an AI model and have the AI ​​perform filtering.

[0045] The extraction unit can preferentially extract highly relevant data in consideration of the geographical location information of the user when extracting data. The extraction unit preferentially extracts highly relevant data in consideration of the geographical location information of the user when extracting data. For example, when the user is in a specific location, the extraction unit preferentially extracts data related to that location. The extraction unit can also preferentially extract data related to a location close to the user's current location. Furthermore, the extraction unit can also suggest optimal data based on the geographical location information of the user. This improves convenience by providing data based on the geographical location information of the user. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI or may be performed without using AI. For example, the extraction unit can input the geographical location information data of the user into an AI model and cause the AI ​​to select highly relevant data.

[0046] The extraction unit can analyze the user's social media activity and extract related data at the time of data extraction. The extraction unit analyzes the user's social media activity and extracts related data at the time of data extraction. For example, the extraction unit extracts related data based on the content mentioned by the user on social media. The extraction unit can also preferentially extract data of high interest from the user's social media activity. Furthermore, the extraction unit can analyze the user's social media activity and suggest optimal data. This improves convenience by providing data based on the user's social media activity. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI or may be performed without using AI. For example, the extraction unit can input the user's social media activity data into an AI model and cause the AI ​​to select related data.

[0047] The analysis unit can select the optimal analysis method by referring to the past analysis history when analyzing data. The analysis unit selects the optimal analysis method by referring to the past analysis history when analyzing data. For example, the analysis unit automatically displays the analysis methods that the user has frequently used in the past as candidates. The analysis unit can also preferentially suggest the analysis methods that the user has used in the past. Furthermore, the analysis unit can predict and suggest the analysis method to be performed in a specific time period from the user's past analysis history. This improves the user's convenience by providing the optimal analysis method based on the past analysis history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input past analysis history data into an AI model and cause AI to select the optimal analysis method.

[0048] The analysis unit can filter the analysis data based on the user's work situation when analyzing data. The analysis unit filters the analysis data based on the user's work situation when analyzing data. For example, the analysis unit preferentially analyzes data related to a project currently in progress by the user. The analysis unit can also filter and analyze related data according to the user's work situation. Furthermore, the analysis unit can also suggest an optimal analysis method based on the user's work situation. This makes it possible to efficiently carry out work by providing data according to the user's work situation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's work situation data into an AI model and have the AI ​​perform filtering.

[0049] The analysis unit can prioritize analysis of highly relevant data in consideration of the geographical location information of the user when analyzing data. The analysis unit prioritizes analysis of highly relevant data in consideration of the geographical location information of the user when analyzing data. For example, when the user is in a specific location, the analysis unit prioritizes analysis of data related to that location. The analysis unit can also prioritize analysis of data related to a location close to the user's current location. Furthermore, the analysis unit can also suggest optimal data based on the geographical location information of the user. This improves convenience by providing data based on the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the geographical location information data of the user into an AI model and cause the AI ​​to select highly relevant data.

[0050] The analysis unit can analyze the user's social media activity and analyze related data during data analysis. The analysis unit analyzes the user's social media activity and analyzes related data during data analysis. For example, the analysis unit analyzes related data based on the content mentioned by the user on social media. The analysis unit can also preferentially analyze data of high interest from the user's social media activity. Furthermore, the analysis unit can analyze the user's social media activity and suggest optimal data. This improves convenience by providing data based on the user's social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into an AI model and cause the AI ​​to select related data.

[0051] The graphing unit can select the optimal generation method by referring to the past graph generation history when generating a graph. The graphing unit can select the optimal generation method by referring to the past graph generation history when generating a graph. For example, the graphing unit automatically displays the format of the graph that the user frequently generated in the past as a candidate. The graphing unit can also preferentially suggest the graph generation method that the user used in the past. Furthermore, the graphing unit can predict and suggest the format of the graph to be generated in a specific time period from the user's past graph generation history. This improves the user's convenience by providing the optimal generation method based on the past graph generation history. Part or all of the above-mentioned processing in the graphing unit may be performed, for example, using AI or may be performed without using AI. For example, the graphing unit can input past graph generation history data into an AI model and cause AI to select the optimal generation method.

[0052] The graphing unit can filter the graph based on the user's work situation when generating the graph. The graphing unit filters the graph based on the user's work situation when generating the graph. For example, the graphing unit preferentially generates a graph related to a project currently in progress by the user. The graphing unit can also filter and display related graphs according to the user's work situation. Furthermore, the graphing unit can also suggest an optimal graph based on the user's work situation. This makes it possible to efficiently carry out work by providing a graph according to the user's work situation. A part or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the user's work situation data into an AI model and have the AI ​​perform filtering.

[0053] The graphing unit can generate a graph with high relevance by considering the geographical location information of the user at the time of graph generation. The graphing unit generates a graph with high relevance by considering the geographical location information of the user at the time of graph generation. For example, when the user is in a specific location, the graphing unit generates a graph related to the location with high relevance by considering the geographical location information of the user. The graphing unit can also generate a graph related to a location close to the user's current location with high relevance by considering the geographical location information of the user. Furthermore, the graphing unit can also propose an optimal graph based on the geographical location information of the user. This improves convenience by providing a graph based on the geographical location information of the user. A part or all of the above-mentioned processing in the graphing unit may be performed, for example, using AI or may be performed without using AI. For example, the graphing unit can input the geographical location information data of the user into an AI model and cause the AI ​​to select a graph with high relevance.

[0054] The graphing unit can analyze the user's social media activity and generate a related graph when generating a graph. The graphing unit can analyze the user's social media activity and generate a related graph when generating a graph. For example, the graphing unit generates a related graph based on the content mentioned by the user on social media. The graphing unit can also generate a graph of high interest from the user's social media activity with priority. Furthermore, the graphing unit can analyze the user's social media activity and suggest an optimal graph. This improves convenience by providing a graph based on the user's social media activity. A part or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the user's social media activity data into an AI model and cause the AI ​​to select a related graph.

[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] When receiving a user's instruction, the reception unit can analyze the user's past behavioral patterns and provide an optimal interface. For example, if the user has used voice input frequently in the past, the reception unit can display the voice input preferentially. Also, if the user has a tendency to give specific instructions during a specific time period, the reception unit can automatically suggest instructions related to that time period. Furthermore, it is also possible to display frequently used instructions as shortcuts based on the history of instructions given by the user in the past. This improves user convenience and enables efficient operation.

[0057] When extracting data, the extraction unit can refer to the user's past data extraction history and select the optimal extraction method. For example, data that the user has frequently extracted in the past can be automatically displayed as candidates. It can also preferentially suggest extraction methods that the user has used in the past. Furthermore, it is also possible to predict and suggest data to be extracted in a specific time period based on the user's past data extraction history. This improves user convenience by providing the optimal extraction method based on the past data extraction history.

[0058] When analyzing data, the analysis unit can adjust the analysis method taking into account the user's current work situation. For example, data related to a project currently underway by the user is given priority in analysis. Also, related data can be filtered and analyzed according to the user's work situation. Furthermore, it is possible to suggest the optimal analysis method based on the user's work situation. This allows for efficient work execution by providing data according to the user's work situation.

[0059] When generating a graph, the graphing unit can prioritize generating a highly relevant graph by taking into consideration the geographical location information of the user. For example, when the user is in a specific location, a graph related to that location is generated with priority. It is also possible to prioritize generating a graph related to a location close to the user's current location. Furthermore, it is also possible to suggest an optimal graph based on the geographical location information of the user. This improves convenience by providing a graph based on the geographical location information of the user.

[0060] When accepting instructions from a user, the accepting unit can analyze the user's social media activity and accept related instructions. For example, related instructions can be accepted based on the contents mentioned by the user on social media. Also, instructions that are of high interest to the user can be preferentially accepted based on the user's social media activity. Furthermore, it is also possible to analyze the user's social media activity and suggest optimal instructions. This improves convenience by providing instructions based on the user's social media activity.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives user instructions. User instructions include text input, voice input, gesture input, and the like. For example, the reception unit can receive voice instructions from the user using voice recognition technology, and can also understand conversational instructions from the user using natural language processing technology. When the user gives an instruction such as "I want last year's sales data extracted and graphed," the instruction is understood and passed on to the next process. Step 2: The extraction unit uses the generation AI to extract data based on the instructions received by the reception unit. The generation AI has learned all of the company's past data and can extract the instructed data with high accuracy. For example, to extract sales data from the previous year, the extraction unit inputs a prompt to the generation AI saying, "Please extract the sales data from the previous year," and the generation AI analyzes the past sales data to identify the data from the previous year. Step 3: The analysis unit analyzes the data extracted by the extraction unit. Analysis can include the use of statistical analysis and machine learning algorithms. For example, the analysis unit calculates the achievement rate for the extracted sales data. The achievement rate is calculated as the ratio of the actual value to the target value. The analysis unit can also calculate a national comparison variable. A national comparison is a method of comparing data by region, where the sales data of a particular region is compared to the national average. Step 4: The graphing unit generates a graph based on the data analyzed by the analysis unit. The graphs generated can be in a variety of formats, including bar graphs, line graphs, and pie charts. For example, the graphing unit generates a bar graph showing the achievement rate and provides it to the user. The graphing unit can also generate a graph in a format specified by the user. For example, if the user instructs the graphing unit to display the graph in a line graph, the graphing unit generates a line graph.

[0063] (Example 2) The data extraction and graphing system according to the embodiment of the present invention is a system that uses a bot that has learned all past data in the company isolated from the outside in an environment where employees have limited access rights to extract data for necessary items in a voice or conversational format, and further performs graphing. This system begins with a user instructing the bot to extract the necessary data in a voice or conversational format. Next, the bot uses a generation AI that has learned all past data in the company to extract the instructed data. Furthermore, based on the extracted data, the bot automatically graphs the data by giving variables such as achievement rate and national comparison. This system allows the user to leave the data extraction and graphing work to the bot, and to focus on other tasks. For example, a user instructs the bot in a voice or conversational format, such as "I want you to extract last year's sales data and graph it." This instruction is input to the bot. Next, the bot uses a generation AI that has learned all past data in the company to extract the instructed data. The generation AI analyzes all past data in the company and identifies the instructed data. For example, when extracting sales data for last year, the generation AI analyzes the past sales data and identifies the data for last year. Furthermore, the bot automatically creates graphs based on the extracted data by providing variables such as achievement rates and national comparisons. For example, the achievement rate is calculated for last year's sales data, and the results are graphed. This graph is provided to the user. This allows the user to leave the data extraction and graphing tasks to the bot, and to focus on other tasks. For example, a sales representative can leave the analysis of sales data to the bot and focus on customer service and new sales activities. Furthermore, a manager can concentrate on strategy planning by having the bot extract data necessary for planning a management strategy. This allows the data extraction and graphing system to improve the user's work efficiency.

[0064] The data extraction and graphing system according to the embodiment includes a reception unit, an extraction unit, an analysis unit, and a graphing unit. The reception unit receives a user's instruction. The user's instruction includes, but is not limited to, for example, text input, voice input, and gesture input. The reception unit can receive a user's voice instruction using, for example, a voice recognition technology. The reception unit can also understand a user's conversational instruction using a natural language processing technology. For example, when a user gives an instruction such as "I want you to extract and graph last year's sales data," the reception unit understands the instruction and passes it to the next process. The extraction unit uses a generation AI to extract data based on the instruction received by the reception unit. The generation AI has learned all past data within the company and can extract the instructed data with high accuracy. For example, in order to extract last year's sales data, the extraction unit inputs a prompt to the generation AI saying, "Please extract last year's sales data," and the generation AI analyzes the past sales data to identify last year's data. The analysis unit analyzes the data extracted by the extraction unit. The analysis includes, for example, statistical analysis and the use of machine learning algorithms, but is not limited to such examples. For example, the analysis unit calculates an achievement rate for the extracted sales data. The achievement rate is calculated as a ratio of the actual value to the target value. The analysis unit can also calculate a variable for a national comparison. A national comparison is a method of comparing data by region, for example, comparing sales data of a specific region with the national average. The graphing unit generates a graph based on the data analyzed by the analysis unit. The graph generation includes, for example, a bar graph, a line graph, a pie chart, and other formats, but is not limited to such examples. For example, the graphing unit generates a bar graph showing the achievement rate and provides it to the user. The graphing unit can also generate a graph in a format specified by the user. For example, when the user instructs "I want to display it in a line graph," the graphing unit generates a line graph. As a result, the data extraction and graphing system according to the embodiment can improve the user's work efficiency by extracting, analyzing, and graphing data based on the user's instructions.

[0065] The reception unit receives a user's instruction. The user's instruction includes, but is not limited to, for example, text input, voice input, gesture input, and the like. The reception unit can receive a user's voice instruction using, for example, a voice recognition technology. Specifically, the voice recognition technology converts the user's utterance into text data and understands the instruction content by analyzing the text data. The reception unit can also understand the user's conversational instruction using a natural language processing technology. The natural language processing technology includes an algorithm for analyzing the user's utterance and text input and understanding the context and intent. For example, when the user gives an instruction such as "I want you to extract last year's sales data and graph it," the reception unit understands the instruction and passes it on to the next process. Furthermore, the reception unit is equipped with a sensor and a camera for receiving gesture input, and can detect the user's hand movement and body movement to receive an instruction. For example, the user can instruct the system to perform a specific operation by performing a specific gesture. This allows the reception unit to support a variety of input methods and improve user convenience. Furthermore, the reception unit can learn the user's past instruction history and grasp the user's preferences and patterns, thereby understanding instructions more quickly and accurately. For example, the reception unit can memorize instructions that the user frequently gives and predict and quickly respond to subsequent instructions. This allows the reception unit to efficiently receive user instructions and smoothly pass them on to the next process.

[0066] The extraction unit uses the generation AI to extract data based on the instructions received by the reception unit. The generation AI has learned all past data within the company and can extract the instructed data with high accuracy. Specifically, the generation AI uses natural language processing technology to analyze the user's instructions and identify the necessary data. For example, in order to extract sales data from last year, the extraction unit inputs a prompt to the generation AI saying, "Please extract last year's sales data," and the generation AI analyzes the past sales data to identify last year's data. By learning past data, the generation AI understands the structure and patterns of the data and achieves highly accurate data extraction. In addition, the generation AI can cross-search multiple tables and fields in the database and efficiently extract the necessary data. Furthermore, the extraction unit regularly updates the learning model of the generation AI to respond to new data and trends. This allows the extraction unit to always achieve highly accurate data extraction based on the latest data. For example, the extraction unit can simultaneously extract multiple related data, such as sales data, customer data, and product data, to support comprehensive data analysis. This enables the extraction unit to quickly and accurately extract the necessary data based on the user's instructions and pass it on to the next analysis process.

[0067] The analysis unit analyzes the data extracted by the extraction unit. The analysis includes, for example, statistical analysis and the use of machine learning algorithms, but is not limited to such examples. Specifically, the analysis unit calculates the achievement rate for the extracted sales data. The achievement rate is calculated as a ratio of the actual value to the target value. For example, if the sales target for last year was 10 million yen and the actual value was 9 million yen, the achievement rate is 90%. The analysis unit can also calculate a variable for a national comparison. A national comparison is a method of comparing data for each region, for example, comparing sales data for a specific region with the national average. This makes it possible to evaluate how well a specific region is performing relative to the national average. Furthermore, the analysis unit can also analyze trends and patterns of data using machine learning algorithms and make future predictions. For example, a time series analysis of the sales data is performed to make future sales predictions. Furthermore, the analysis unit can detect abnormal patterns and values ​​from the data using an anomaly detection algorithm. This makes it possible to quickly find abnormal transactions and signs of fraud from the sales data. Furthermore, the analysis unit can visualize the data and display the analysis results as graphs and charts. This allows the user to intuitively understand the analysis results. By combining these analysis techniques, the analysis unit can perform multifaceted analysis of data and provide useful information to the user.

[0068] The graphing unit generates a graph based on the data analyzed by the analysis unit. The graph generation includes, but is not limited to, formats such as bar graphs, line graphs, and pie charts. Specifically, the graphing unit generates a bar graph showing the achievement rate and provides it to the user. For example, a bar graph showing the sales achievement rate for each month is generated so that the user can grasp the performance for each month at a glance. The graphing unit can also generate a graph in a format specified by the user. For example, if the user instructs "I want it to be displayed as a line graph," the graphing unit generates a line graph. Furthermore, the graphing unit also has a function for customizing the color and design of the graph, and can generate a graph according to the user's preferences. For example, a specific color can be used or the design can be changed to emphasize a specific data point. This allows the graphing unit to provide the user with visually easy-to-understand information and promote understanding of the data. Furthermore, the graphing unit also has a function for saving the generated graph as a PDF or image file, and the user can use the graph for other materials or presentations. This allows the graphing unit to visually express the analysis results and support the user's decision-making.

[0069] The reception unit can receive the user's instructions by voice or in a conversational format. The reception unit, for example, uses a voice recognition technology to receive the user's voice instructions. For example, when the user gives a voice instruction such as "I want you to extract last year's sales data and graph it," the reception unit recognizes the voice and passes it to the next process. The reception unit can also understand the user's conversational instruction by using a natural language processing technology. For example, when the user gives a conversational instruction such as "I want you to extract last year's sales data and graph it," the reception unit understands the instruction and passes it to the next process. As a result, by receiving instructions by voice or in a conversational format, the convenience of the user is improved. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, when the reception unit receives the user's voice instruction using the voice recognition technology, the reception unit can convert the voice data into text data using an AI model.

[0070] The extraction unit can extract data using a generation AI that has learned past data within the company. The extraction unit uses the generation AI to extract data based on instructions received by the reception unit. The generation AI has learned all past data within the company and can extract the instructed data with high accuracy. For example, in order to extract sales data for last year, the extraction unit inputs a prompt to the generation AI saying, "Please extract sales data for last year," and the generation AI analyzes the past sales data to identify the data for last year. The generation AI is trained using a deep learning model and uses all past data within the company as a training data set. This improves the accuracy of data extraction by using the generation AI. Some or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI, or may be performed without using AI. For example, when extracting data using the generation AI, the extraction unit can identify and extract data using an AI model.

[0071] The analysis unit can analyze the extracted data and calculate the achievement rate and national comparison variables. The analysis unit analyzes the data extracted by the extraction unit. The analysis includes, for example, statistical analysis and the use of machine learning algorithms, but is not limited to such examples. For example, the analysis unit calculates the achievement rate for the extracted sales data. The achievement rate is calculated as a ratio of the actual value to the target value. The analysis unit can also calculate national comparison variables. National comparison is a method of comparing data by region, for example, comparing sales data of a specific region with the national average. This makes it possible to provide important information such as the achievement rate and national comparison by analyzing the data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the extracted data into an AI model and have AI perform the calculation of the achievement rate and national comparison.

[0072] The graphing unit can generate a graph based on the analyzed data and provide it to the user. The graphing unit generates a graph based on the data analyzed by the analysis unit. The graph generation includes, but is not limited to, formats such as a bar graph, a line graph, and a pie chart. For example, the graphing unit generates a bar graph showing the achievement rate and provides it to the user. The graphing unit can also generate a graph in a format specified by the user. For example, when the user instructs "I want it to be displayed in a line graph," the graphing unit generates a line graph. This helps the user understand by providing the analysis result in a visually easy-to-understand manner. A part or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the analyzed data into an AI model and have the AI ​​generate the graph.

[0073] The graphing unit can generate a graph in a format specified by the user. The graphing unit generates a graph in a format specified by the user based on the data analyzed by the analysis unit. Formats that can be specified by the user include, but are not limited to, bar graphs, line graphs, and pie charts. For example, when a user instructs "I want to display it in a line graph," the graphing unit generates a line graph. Also, when a user instructs "I want to display it in a pie chart," the graphing unit generates a pie chart. This improves convenience by generating a graph in a format according to the needs of the user. Part or all of the above-mentioned processing in the graphing unit may be performed, for example, using AI, or may be performed without using AI. For example, when generating a graph in a format specified by the user, the graphing unit can generate the graph using an AI model.

[0074] The reception unit can estimate the user's emotion and adjust the instruction reception method based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes input procedures. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and quickly accept instructions. This improves the user's satisfaction by providing an instruction reception method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can analyze the user's past instruction history and select an appropriate reception method. The reception unit analyzes the user's past instruction history and selects the optimal reception method. For example, the reception unit automatically displays instructions that the user frequently performed in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest instructions to be performed in a specific time period from the user's past instruction history. This improves user convenience by providing an optimal reception method based on the past instruction history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past instruction history data into an AI model and cause AI to select the optimal reception method.

[0076] The reception unit can perform filtering based on the user's current work situation and field of interest when receiving an instruction. The reception unit performs filtering based on the user's current work situation and field of interest when receiving an instruction. For example, the reception unit preferentially receives instructions related to a project currently in progress by the user. The reception unit can also filter and display related instructions based on the user's field of interest. Furthermore, the reception unit can also suggest appropriate instructions according to the user's work situation. This makes it possible to perform work efficiently by providing instructions according to the user's work situation and field of interest. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation data into an AI model and have the AI ​​perform filtering.

[0077] The reception unit can estimate the user's emotion and determine the priority of the instruction to be received based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of the instruction to be received based on the estimated user's emotion. For example, when the user is nervous, the reception unit preferentially receives important instructions. Also, when the user is relaxed, the reception unit can preferentially receive detailed instructions. Furthermore, when the user is in a hurry, the reception unit can preferentially receive instructions that can be quickly processed. This improves the satisfaction of the user by providing the priority of instructions according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0078] The reception unit can preferentially accept highly relevant instructions in consideration of the geographical location information of the user when receiving an instruction. The reception unit preferentially accepts highly relevant instructions in consideration of the geographical location information of the user when receiving an instruction. For example, when the user is in a specific location, the reception unit preferentially accepts instructions related to the location. The reception unit can also preferentially accept instructions related to a location close to the user's current location. Furthermore, the reception unit can also suggest optimal instructions based on the geographical location information of the user. This improves convenience by providing instructions based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user into an AI model and cause the AI ​​to select highly relevant instructions.

[0079] The reception unit can analyze the user's social media activity and receive related instructions when receiving the instruction. The reception unit analyzes the user's social media activity and receives related instructions when receiving the instruction. For example, the reception unit receives related instructions based on the content mentioned by the user on social media. The reception unit can also preferentially receive instructions of high interest from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity and suggest optimal instructions. This improves convenience by providing instructions based on the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's social media activity data into an AI model and cause the AI ​​to select related instructions.

[0080] The extraction unit can estimate the user's emotion and adjust the data extraction method based on the estimated user's emotion. The extraction unit estimates the user's emotion and adjusts the data extraction method based on the estimated user's emotion. For example, the extraction unit extracts detailed data when the user is relaxed. Also, the extraction unit can quickly extract the minimum necessary data when the user is in a hurry. Furthermore, the extraction unit can extract simple data when the user is stressed. This improves the user's satisfaction by providing a data extraction method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0081] The extraction unit can select an appropriate extraction method by referring to the past data extraction history when extracting data. The extraction unit can select the optimal extraction method by referring to the past data extraction history when extracting data. For example, the extraction unit automatically displays data that the user has frequently extracted in the past as candidates. The extraction unit can also preferentially suggest extraction methods that the user has used in the past. Furthermore, the extraction unit can predict and suggest data to be extracted in a specific time period from the user's past data extraction history. This improves user convenience by providing an optimal extraction method based on the past data extraction history. Part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI or may be performed without using AI. For example, the extraction unit can input past data extraction history data into an AI model and cause AI to select the optimal extraction method.

[0082] The extraction unit can filter the extracted data based on the user's work situation when extracting data. The extraction unit filters the extracted data based on the user's work situation when extracting data. For example, the extraction unit preferentially extracts data related to a project currently in progress by the user. The extraction unit can also filter and display related data according to the user's work situation. Furthermore, the extraction unit can also suggest optimal data based on the user's work situation. This makes it possible to perform work efficiently by providing data according to the user's work situation. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, AI, or may be performed without using AI. For example, the extraction unit can input the user's work situation data into an AI model and have the AI ​​perform filtering.

[0083] The extraction unit can estimate the user's emotion and determine the priority of data to be extracted based on the estimated user's emotion. The extraction unit estimates the user's emotion and determines the priority of data to be extracted based on the estimated user's emotion. For example, when the user is nervous, the extraction unit preferentially extracts important data. In addition, when the user is relaxed, the extraction unit can also preferentially extract detailed data. Furthermore, when the user is in a hurry, the extraction unit can also preferentially extract data that can be processed quickly. This improves the user's satisfaction by providing a priority order of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the extraction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the extraction unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The extraction unit can preferentially extract highly relevant data in consideration of the geographical location information of the user when extracting data. The extraction unit preferentially extracts highly relevant data in consideration of the geographical location information of the user when extracting data. For example, when the user is in a specific location, the extraction unit preferentially extracts data related to that location. The extraction unit can also preferentially extract data related to a location close to the user's current location. Furthermore, the extraction unit can also suggest optimal data based on the geographical location information of the user. This improves convenience by providing data based on the geographical location information of the user. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI or may be performed without using AI. For example, the extraction unit can input the geographical location information data of the user into an AI model and cause the AI ​​to select highly relevant data.

[0085] The extraction unit can analyze the user's social media activity and extract related data at the time of data extraction. The extraction unit analyzes the user's social media activity and extracts related data at the time of data extraction. For example, the extraction unit extracts related data based on the content mentioned by the user on social media. The extraction unit can also preferentially extract data of high interest from the user's social media activity. Furthermore, the extraction unit can analyze the user's social media activity and suggest optimal data. This improves convenience by providing data based on the user's social media activity. A part or all of the above-mentioned processing in the extraction unit may be performed, for example, using AI or may be performed without using AI. For example, the extraction unit can input the user's social media activity data into an AI model and cause the AI ​​to select related data.

[0086] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the analysis method based on the estimated user's emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. Also, the analysis unit can quickly perform a minimum necessary analysis when the user is in a hurry. Furthermore, the analysis unit can perform a simple analysis when the user is stressed. This improves the user's satisfaction by providing an analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0087] The analysis unit can select the optimal analysis method by referring to the past analysis history when analyzing data. The analysis unit selects the optimal analysis method by referring to the past analysis history when analyzing data. For example, the analysis unit automatically displays the analysis methods that the user has frequently used in the past as candidates. The analysis unit can also preferentially suggest the analysis methods that the user has used in the past. Furthermore, the analysis unit can predict and suggest the analysis method to be performed in a specific time period from the user's past analysis history. This improves the user's convenience by providing the optimal analysis method based on the past analysis history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input past analysis history data into an AI model and cause AI to select the optimal analysis method.

[0088] The analysis unit can filter the analysis data based on the user's work situation when analyzing data. The analysis unit filters the analysis data based on the user's work situation when analyzing data. For example, the analysis unit preferentially analyzes data related to a project currently in progress by the user. The analysis unit can also filter and analyze related data according to the user's work situation. Furthermore, the analysis unit can also suggest an optimal analysis method based on the user's work situation. This makes it possible to efficiently carry out work by providing data according to the user's work situation. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's work situation data into an AI model and have the AI ​​perform filtering.

[0089] The analysis unit can estimate the user's emotion and determine the priority of data to be analyzed based on the estimated user's emotion. The analysis unit estimates the user's emotion and determines the priority of data to be analyzed based on the estimated user's emotion. For example, when the user is nervous, the analysis unit prioritizes analysis of important data. In addition, when the user is relaxed, the analysis unit can also prioritize analysis of detailed data. Furthermore, when the user is in a hurry, the analysis unit can also prioritize analysis of data that can be processed quickly. This improves user satisfaction by providing a priority order of data according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0090] The analysis unit can prioritize analysis of highly relevant data in consideration of the geographical location information of the user when analyzing data. The analysis unit prioritizes analysis of highly relevant data in consideration of the geographical location information of the user when analyzing data. For example, when the user is in a specific location, the analysis unit prioritizes analysis of data related to that location. The analysis unit can also prioritize analysis of data related to a location close to the user's current location. Furthermore, the analysis unit can also suggest optimal data based on the geographical location information of the user. This improves convenience by providing data based on the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the geographical location information data of the user into an AI model and cause the AI ​​to select highly relevant data.

[0091] The analysis unit can analyze the user's social media activity and analyze related data during data analysis. The analysis unit analyzes the user's social media activity and analyzes related data during data analysis. For example, the analysis unit analyzes related data based on the content mentioned by the user on social media. The analysis unit can also preferentially analyze data of high interest from the user's social media activity. Furthermore, the analysis unit can analyze the user's social media activity and suggest optimal data. This improves convenience by providing data based on the user's social media activity. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into an AI model and cause the AI ​​to select related data.

[0092] The graphing unit can estimate the user's emotion and adjust the graph generation method based on the estimated user's emotion. The graphing unit estimates the user's emotion and adjusts the graph generation method based on the estimated user's emotion. For example, the graphing unit generates a detailed graph when the user is relaxed. Also, the graphing unit can quickly generate a simple graph when the user is in a hurry. Furthermore, the graphing unit can generate a visually easy-to-understand graph when the user is stressed. This improves the user's satisfaction by providing a graph generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the graphing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the graphing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The graphing unit can select the optimal generation method by referring to the past graph generation history when generating a graph. The graphing unit can select the optimal generation method by referring to the past graph generation history when generating a graph. For example, the graphing unit automatically displays the format of the graph that the user frequently generated in the past as a candidate. The graphing unit can also preferentially suggest the graph generation method that the user used in the past. Furthermore, the graphing unit can predict and suggest the format of the graph to be generated in a specific time period from the user's past graph generation history. This improves the user's convenience by providing the optimal generation method based on the past graph generation history. Part or all of the above-mentioned processing in the graphing unit may be performed, for example, using AI or may be performed without using AI. For example, the graphing unit can input past graph generation history data into an AI model and cause AI to select the optimal generation method.

[0094] The graphing unit can filter the graph based on the user's work situation when generating the graph. The graphing unit filters the graph based on the user's work situation when generating the graph. For example, the graphing unit preferentially generates a graph related to a project currently in progress by the user. The graphing unit can also filter and display related graphs according to the user's work situation. Furthermore, the graphing unit can also suggest an optimal graph based on the user's work situation. This makes it possible to efficiently carry out work by providing a graph according to the user's work situation. A part or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the user's work situation data into an AI model and have the AI ​​perform filtering.

[0095] The graphing unit can estimate the user's emotion and determine the priority of the graph to be generated based on the estimated user's emotion. The graphing unit estimates the user's emotion and determines the priority of the graph to be generated based on the estimated user's emotion. For example, when the user is nervous, the graphing unit generates important graphs with priority. In addition, when the user is relaxed, the graphing unit can also generate detailed graphs with priority. Furthermore, when the user is in a hurry, the graphing unit can also generate graphs that can be processed quickly with priority. This improves the satisfaction of the user by providing a priority order of graphs according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the graphing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the graphing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0096] The graphing unit can generate a graph with high relevance by considering the geographical location information of the user at the time of graph generation. The graphing unit generates a graph with high relevance by considering the geographical location information of the user at the time of graph generation. For example, when the user is in a specific location, the graphing unit generates a graph related to the location with high relevance by considering the geographical location information of the user. The graphing unit can also generate a graph related to a location close to the user's current location with high relevance by considering the geographical location information of the user. Furthermore, the graphing unit can also propose an optimal graph based on the geographical location information of the user. This improves convenience by providing a graph based on the geographical location information of the user. A part or all of the above-mentioned processing in the graphing unit may be performed, for example, using AI or may be performed without using AI. For example, the graphing unit can input the geographical location information data of the user into an AI model and cause the AI ​​to select a graph with high relevance.

[0097] The graphing unit can analyze the user's social media activity and generate a related graph when generating a graph. The graphing unit can analyze the user's social media activity and generate a related graph when generating a graph. For example, the graphing unit generates a related graph based on the content mentioned by the user on social media. The graphing unit can also generate a graph of high interest from the user's social media activity with priority. Furthermore, the graphing unit can analyze the user's social media activity and suggest an optimal graph. This improves convenience by providing a graph based on the user's social media activity. A part or all of the above-mentioned processing in the graphing unit may be performed using, for example, AI, or may be performed without using AI. For example, the graphing unit can input the user's social media activity data into an AI model and cause the AI ​​to select a related graph.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] When receiving a user's instruction, the reception unit can analyze the user's past behavioral patterns and provide an optimal interface. For example, if the user has used voice input frequently in the past, the reception unit can display the voice input preferentially. Also, if the user has a tendency to give specific instructions during a specific time period, the reception unit can automatically suggest instructions related to that time period. Furthermore, it is also possible to display frequently used instructions as shortcuts based on the history of instructions given by the user in the past. This improves user convenience and enables efficient operation.

[0100] When accepting instructions from the user, the accepting unit can estimate the user's current emotional state and provide an appropriate interface. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to simplify the operation. Also, if the user is relaxed, detailed options can be provided and a customizable interface can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to quickly accept instructions. In this way, by providing an interface according to the user's emotions, the satisfaction of the user can be improved.

[0101] When extracting data, the extraction unit can refer to the user's past data extraction history and select the optimal extraction method. For example, data that the user has frequently extracted in the past can be automatically displayed as candidates. It can also preferentially suggest extraction methods that the user has used in the past. Furthermore, it is also possible to predict and suggest data to be extracted in a specific time period based on the user's past data extraction history. This improves user convenience by providing the optimal extraction method based on the past data extraction history.

[0102] When analyzing data, the analysis unit can adjust the analysis method taking into account the user's current work situation. For example, data related to a project currently underway by the user is given priority in analysis. Also, related data can be filtered and analyzed according to the user's work situation. Furthermore, it is possible to suggest the optimal analysis method based on the user's work situation. This allows for efficient work execution by providing data according to the user's work situation.

[0103] When generating a graph, the graphing unit can prioritize generating a highly relevant graph by taking into consideration the geographical location information of the user. For example, when the user is in a specific location, a graph related to that location is generated with priority. It is also possible to prioritize generating a graph related to a location close to the user's current location. Furthermore, it is also possible to suggest an optimal graph based on the geographical location information of the user. This improves convenience by providing a graph based on the geographical location information of the user.

[0104] The reception unit can estimate the user's emotion and adjust the method of receiving instructions based on the estimated user's emotion. For example, when the user is stressed, a simple interface can be provided to minimize input steps. When the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized to quickly receive instructions. This improves user satisfaction by providing a method of receiving instructions according to the user's emotion.

[0105] The extraction unit can estimate the user's emotions and adjust the data extraction method based on the estimated user's emotions. For example, when the user is relaxed, detailed data is extracted. When the user is in a hurry, the minimum necessary data can be quickly extracted. Furthermore, when the user is stressed, simple data can be extracted. This provides a data extraction method according to the user's emotions, thereby improving user satisfaction.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a minimum necessary analysis can be performed quickly. Furthermore, if the user is feeling stressed, a simple analysis can be performed. This provides an analysis method according to the user's emotions, thereby improving user satisfaction.

[0107] The graphing unit can estimate the user's emotions and adjust the graph generation method based on the estimated user's emotions. For example, if the user is relaxed, a detailed graph can be generated. If the user is in a hurry, a simple graph can be quickly generated. Furthermore, if the user is stressed, a visually easy-to-understand graph can be generated. This provides a graph generation method according to the user's emotions, thereby improving user satisfaction.

[0108] When accepting instructions from a user, the accepting unit can analyze the user's social media activity and accept related instructions. For example, related instructions can be accepted based on the contents mentioned by the user on social media. Also, instructions that are of high interest to the user can be preferentially accepted based on the user's social media activity. Furthermore, it is also possible to analyze the user's social media activity and suggest optimal instructions. This improves convenience by providing instructions based on the user's social media activity.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit receives user instructions. User instructions include text input, voice input, gesture input, and the like. For example, the reception unit can receive voice instructions from the user using voice recognition technology, and can also understand conversational instructions from the user using natural language processing technology. When the user gives an instruction such as "I want last year's sales data extracted and graphed," the instruction is understood and passed on to the next process. Step 2: The extraction unit uses the generation AI to extract data based on the instructions received by the reception unit. The generation AI has learned all of the company's past data and can extract the instructed data with high accuracy. For example, to extract sales data from the previous year, the extraction unit inputs a prompt to the generation AI saying, "Please extract the sales data from the previous year," and the generation AI analyzes the past sales data to identify the data from the previous year. Step 3: The analysis unit analyzes the data extracted by the extraction unit. Analysis can include the use of statistical analysis and machine learning algorithms. For example, the analysis unit calculates the achievement rate for the extracted sales data. The achievement rate is calculated as the ratio of the actual value to the target value. The analysis unit can also calculate a national comparison variable. A national comparison is a method of comparing data by region, where the sales data of a particular region is compared to the national average. Step 4: The graphing unit generates a graph based on the data analyzed by the analysis unit. The graphs generated can be in a variety of formats, including bar graphs, line graphs, and pie charts. For example, the graphing unit generates a bar graph showing the achievement rate and provides it to the user. The graphing unit can also generate a graph in a format specified by the user. For example, if the user instructs the graphing unit to display the graph in a line graph, the graphing unit generates a line graph.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, extraction unit, analysis unit, and graphing 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 receives a voice instruction from the user. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts data using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the extracted data. The graphing unit is realized, for example, by the control unit 46A of the smart device 14 and generates a graph based on the analyzed data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

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

[0122] Fig. 4 shows an example of 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.

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

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

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

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

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned reception unit, extraction unit, analysis unit, and graphing 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 receives a voice instruction from a user. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts data using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the extracted data. The graphing unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates a graph based on the analyzed data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The 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.

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

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

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

[0138] 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.

[0139] 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.

[0140] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, extraction unit, analysis unit, and graphing 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 receives a voice instruction from the user. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts data using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the extracted data. The graphing unit is realized, for example, by the control unit 46A of the headset type terminal 314 and generates a graph based on the analyzed data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0163] Each of the multiple elements including the above-mentioned reception unit, extraction unit, analysis unit, and graphing 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 receives a voice instruction from a user. The extraction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and extracts data using a generation AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the extracted data. The graphing unit is realized, for example, by the control unit 46A of the robot 414 and generates a graph based on the analyzed data. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A reception unit that receives an instruction from a user; an extraction unit that extracts data based on the instruction received by the reception unit; an analysis unit that analyzes the data extracted by the extraction unit; A graphing unit that generates a graph based on the data analyzed by the analysis unit. A system characterized by: (Appendix 2) The reception unit is Accepts user instructions through voice or conversation 2. The system of claim 1, (Appendix 3) The extraction unit is Extract data using generative AI that has learned from the company's past data 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze the extracted data and calculate achievement rates and national comparison variables. 2. The system of claim 1, (Appendix 5) The graphing unit is Generate graphs based on the analyzed data and provide them to the user 2. The system of claim 1, (Appendix 6) The graphing unit is Generates a graph in a user-specified format 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust a method of receiving an instruction based on the estimated user's emotion 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past instruction history and select the appropriate reception method 2. The system of claim 1, (Appendix 9) The reception unit is When receiving instructions, filter them based on the user's current work situation and areas of interest 2. The system of claim 1, (Appendix 10) The reception unit is A user's emotion is estimated, and a priority order of instructions to be accepted is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting instructions, the system takes into account the user's geographic location information and prioritizes accepting instructions that are more relevant to the user. 2. The system of claim 1, (Appendix 12) The reception unit is Upon receiving an instruction, analyze the user's social media activity and receive relevant instructions. 2. The system of claim 1, (Appendix 13) The extraction unit is Infer user sentiment and adjust data extraction methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The extraction unit is When extracting data, refer to past data extraction history to select the appropriate extraction method 2. The system of claim 1, (Appendix 15) The extraction unit is When extracting data, filter the extracted data based on the user's work situation. 2. The system of claim 1, (Appendix 16) The extraction unit is Infer user sentiment and prioritize data to be extracted based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The extraction unit is When extracting data, the system takes into account the user's geographic location information to prioritize the extraction of data that is highly relevant 2. The system of claim 1, (Appendix 18) The extraction unit is At the time of data extraction, we analyze users' social media activity and extract relevant data. 2. The system of claim 1, (Appendix 19) The analysis unit is Inferring user emotions and adjusting the analysis method based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The analysis unit is When analyzing data, refer to past analysis history to select the optimal analysis method 2. The system of claim 1, (Appendix 21) The analysis unit is When analyzing data, filter the analysis data based on the user's work situation. 2. The system of claim 1, (Appendix 22) The analysis unit is Infer user sentiment and prioritize data to be analyzed based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The analysis unit is When analyzing data, the system takes into account the user's geographic location information and prioritizes the analysis of data that is highly relevant. 2. The system of claim 1, (Appendix 24) The analysis unit is During data analysis, analyze users' social media activities and analyze related data 2. The system of claim 1, (Appendix 25) The graphing unit is Infer user emotions and adjust the graph generation method based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The graphing unit is When generating a graph, the optimal generation method is selected by referring to the history of past graph generation. 2. The system of claim 1, (Appendix 27) The graphing unit is When generating graphs, filter the graphs based on the user's work situation. 2. The system of claim 1, (Appendix 28) The graphing unit is Infer user emotions and prioritize graphs to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The graphing unit is When generating graphs, the system takes into account the user's geographic location information and prioritizes the generation of graphs with high relevance. 2. The system of claim 1, (Appendix 30) The graphing unit is When generating graphs, analyze users' social media activity and generate relevant graphs 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives an instruction from a user; an extraction unit that extracts data based on the instruction received by the reception unit; an analysis unit that analyzes the data extracted by the extraction unit; A graphing unit that generates a graph based on the data analyzed by the analysis unit. A system characterized by:

2. The reception unit is Accepts user instructions through voice or conversation 2. The system of claim 1 .

3. The extraction unit is Extract data using generative AI that has learned from the company's past data 2. The system of claim 1 .

4. The analysis unit is Analyze the extracted data and calculate achievement rates and national comparison variables 2. The system of claim 1 .

5. The graphing unit is Generate graphs based on the analyzed data and provide them to the user 2. The system of claim 1 .

6. The graphing unit is Generates a graph in a user-specified format 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust a method of receiving an instruction based on the estimated user's emotion 2. The system of claim 1 .

8. The reception unit is Analyze the user's past instruction history and select the appropriate reception method 2. The system of claim 1 .

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