System

Abstract

To provide a system that automates business operations such as document creation, numerical value analysis, and inquiry response and improves efficiency.SOLUTION: A system according to an embodiment comprises: a reception unit; a creation unit: an analysis unit; a generation unit; and a provision unit. The reception unit receives data. The creation unit creates documents on the basis of the data input by the reception unit. The analysis unit analyzes numerical values on the basis of the documents created by the creation unit. The generation unit generates responses to inquiries on the basis of the analysis results obtained by the analysis unit. The provision unit provides the responses generated by the generation 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, tasks such as document creation, numerical analysis, and answering inquiries are done manually, resulting in low efficiency and room for improvement.

[0005] The system according to the embodiment aims to automate tasks such as document creation, numerical analysis, and answering inquiries, thereby improving efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a creation unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs data. The creation unit creates materials based on the data input by the reception unit. The analysis unit analyzes numerical values ​​based on the materials created by the creation unit. The generation unit generates an answer to the inquiry based on the analysis results obtained by the analysis unit. The provision unit provides the answer generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can automate tasks such as document creation, numerical analysis, and answering inquiries, thereby improving efficiency. [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 office work efficiency system according to the embodiment of the present invention utilizes a generation AI to automatically perform tasks such as creating documents using PowerPoint, Excel, and Gmail, performing numerical analysis, and answering inquiries about work content. This system can take over the work of partner sales, and by reducing or reassigning sales personnel, this leads to improved profits and investment in new businesses. For example, the generation AI creates documents using PowerPoint or Excel. The generation AI selects appropriate layouts and designs based on the input data and creates visually easy-to-understand documents. Next, the generation AI performs numerical analysis. The generation AI analyzes sales data and customer data to extract trends and patterns. The generation AI analyzes data using complex mathematical formulas and statistical methods and outputs the results as graphs and charts. Furthermore, the generation AI responds to inquiries about work content. The generation AI generates replies quickly and accurately based on a database that has been learned in advance in response to inquiries about product specifications and prices. This mechanism makes it possible to streamline partner sales work and reduce or reassign sales personnel. This allows companies to improve profits and use the freed up resources to invest in new businesses. In addition, because humans are specialized in coming up with new ideas based on data, it is possible to divide the work between generative AI and humans. As a result, the office work efficiency system can significantly improve the work efficiency of companies, leading to improved profits and investment in new businesses.

[0029] The office work efficiency improvement system according to the embodiment includes a reception unit, a creation unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs data. The data includes, for example, text data, numerical data, image data, and the like, but is not limited to such examples. The reception unit inputs data using, for example, a presentation tool or a spreadsheet tool. The presentation tool includes, for example, Microsoft (registered trademark) PowerPoint and Google (registered trademark) Slides. The spreadsheet tool includes, for example, Microsoft Excel and Google Sheets. The creation unit creates materials based on the data input by the reception unit using a generation AI. The materials include, for example, reports, presentation materials, analysis reports, and the like, but are not limited to such examples. The creation unit selects, for example, an appropriate layout and design based on the input data, and creates materials that are visually easy to understand. The generation AI creates materials using a text generation AI (for example, LLM). The creation unit can also use the generation AI to extract important parts of the data and create materials. For example, the generation AI uses a keyword extraction technique to pick up particularly important information from the data, and creates materials based on that information. The analysis unit analyzes the numerical values ​​based on the data input by the reception unit. The analysis of the numerical values ​​includes, but is not limited to, statistical analysis, regression analysis, data mining, and the like. The analysis unit, for example, analyzes sales data and customer data to extract trends and patterns. The analysis unit analyzes data using complex mathematical formulas and statistical methods, and outputs the results as graphs and charts. The generation unit generates an answer to an inquiry based on the analysis results obtained by the analysis unit. The generation unit generates an appropriate answer to an inquiry regarding product specifications and prices, for example, based on a database that has been learned in advance. The generation unit can generate an answer quickly and accurately using a generation AI. The provision unit provides the answer generated by the generation unit. The provision unit provides, for example, the generated answer quickly and accurately. The provision unit can also provide the answer using a generation AI. For example, the provision unit provides the generated answer through a web application or a mobile application.As a result, the office work efficiency improvement system according to the embodiment automates processes from data entry to document creation, numerical analysis, and response to inquiries, thereby improving work efficiency.

[0030] The reception unit inputs data. The data includes, but is not limited to, text data, numerical data, and image data. The reception unit inputs data using, for example, a presentation tool or a spreadsheet tool. The presentation tool includes, for example, Microsoft PowerPoint and Google Slides. The spreadsheet tool includes, for example, Microsoft Excel and Google Sheets. The reception unit plays a role of efficiently collecting data input by the user using these tools and incorporating it into the system. Specifically, the reception unit uses the presentation tool to automatically extract the contents of slides, graphs, and text boxes created by the user and save them in a database. In addition, the reception unit uses the spreadsheet tool to analyze numerical data and calculation formulas input by the user, extract necessary information, and incorporate it into the system. Furthermore, the reception unit can also extract text information from scanned documents and image data using OCR (optical character recognition) technology. This makes it possible to incorporate paper documents and handwritten notes as digital data. The reception unit displays the data input status and progress in real time through a user interface, making it easy for the user to check and correct the data input. This enables the reception unit to streamline the data input process and collect data accurately and quickly.

[0031] The creation unit uses the generation AI to create materials based on the data input by the reception unit. The materials include, but are not limited to, reports, presentation materials, and analysis reports. For example, the creation unit selects an appropriate layout and design based on the input data to create materials that are visually easy to understand. The generation AI creates materials using a text generation AI (for example, LLM). The creation unit can also use the generation AI to extract important parts of data to create materials. For example, the generation AI uses keyword extraction technology to pick up particularly important information from the data and create materials based on that. Specifically, the generation AI uses natural language processing technology to analyze the input text data, understand the context and meaning, and generate appropriate sentences. Furthermore, the generation AI can automatically create graphs and charts according to the content of the data to visually convey information. For example, in a report based on sales data, a line graph showing the progress of sales and a pie chart showing sales by region are automatically generated. The generation AI can also learn the user's preferences and past document creation history to propose layouts and designs that are more suitable for the user. This allows the creation unit to quickly create high-quality materials without the user having to take time and effort, thereby improving work efficiency.

[0032] The analysis unit analyzes the numerical values ​​based on the data input by the reception unit. The analysis of the numerical values ​​includes, but is not limited to, statistical analysis, regression analysis, data mining, and the like. For example, the analysis unit analyzes sales data and customer data to extract trends and patterns. Specifically, the analysis unit uses statistical analysis to calculate basic statistical indicators such as the average value, standard deviation, and variance of the data, and grasps the distribution and trends of the data. In addition, the analysis unit uses regression analysis to clarify the relationship between multiple variables, such as the relationship between sales and advertising costs, and the relationship between customer satisfaction and repurchase rate. Furthermore, the analysis unit uses data mining technology to discover useful patterns and rules from large amounts of data, which are useful for business decision-making. For example, the analysis unit analyzes customer data to clarify what products a specific customer group prefers, and uses the results in planning a marketing strategy. The analysis unit visually displays these analysis results as graphs and charts, allowing the user to intuitively understand them. For example, the analysis unit automatically generates line graphs showing sales trends and histograms showing the distribution of customer satisfaction. Furthermore, the analysis unit can also use AI to integrate past data and external data sources to predict future trends and risks. This enables the analysis department to understand the current state of the business through data analysis and provide useful information for planning future strategies.

[0033] The generation unit generates an answer to the inquiry based on the analysis result obtained by the analysis unit. For example, the generation unit generates an appropriate answer to an inquiry about product specifications or price based on a database that has been learned in advance. The generation unit can generate an answer quickly and accurately using the generation AI. Specifically, the generation AI uses natural language processing technology to analyze the content of the inquiry from the user and generate an appropriate answer. For example, when a user inquires about product specifications, the generation AI searches for the relevant information from the product database and generates an answer in an appropriate format. In addition, for an inquiry about price, an accurate answer is provided based on the latest price information. Furthermore, the generation unit can learn the user's past inquiry history and the inquiry patterns of other users to provide a more appropriate answer. For example, if a similar inquiry is made multiple times, the generation AI can learn the pattern and respond quickly to the next inquiry and thereafter. As a result, the generation unit can provide a quick and accurate answer to the user's inquiry, thereby improving the efficiency of business operations.

[0034] The providing unit provides the answer generated by the generating unit. The providing unit, for example, provides the generated answer quickly and accurately. The providing unit can also provide the answer using the generating AI. For example, the providing unit provides the generated answer through a web application or a mobile application. Specifically, the providing unit displays the generated answer in real time through a web portal or a mobile application accessed by the user. In addition, the providing unit can customize the format and display method of the answer according to the user's preferences. For example, it is possible to provide not only a text-format answer but also a visual answer using a graph or chart. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy of the answer and the method of providing it. For example, the user can leave an evaluation or comment on the provided answer, and based on that feedback, the learning data of the generating AI is updated and the accuracy of the answer from the next time onwards is improved. As a result, the providing unit can provide a quick and accurate answer to the user, thereby improving the efficiency of the work.

[0035] The reception unit can input data using a presentation tool or a spreadsheet tool. Examples of the presentation tool include Microsoft PowerPoint and Google Slides. Examples of the spreadsheet tool include Microsoft Excel and Google Sheets. The reception unit inputs data using, for example, a presentation tool. For example, the reception unit can input data in a slide format using PowerPoint. The reception unit can also input data using a spreadsheet tool. For example, the reception unit can input data in a table format using Excel. As a result, the efficiency of data input is improved by using a presentation tool or a spreadsheet tool. 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 data input using a presentation tool or a spreadsheet tool to an AI, and cause the AI ​​to analyze and process the data.

[0036] The creation unit can select an appropriate layout or design based on the input data and create visually easy-to-understand materials. The creation unit uses the generation AI to select an appropriate layout or design based on the input data and create visually easy-to-understand materials. For example, the creation unit can automate the creation of sales reports and presentation materials. The generation AI selects an appropriate layout or design based on the input data and creates visually easy-to-understand materials. For example, the generation AI extracts important parts of the data and creates materials based on them. The generation AI creates materials using a text generation AI (e.g., LLM). The generation AI can also create the contents of the materials using a multimodal generation AI. For example, the generation AI uses keyword extraction technology to pick up particularly important information in the data and create materials based on that. This improves the efficiency of document creation by automatically creating visually easy-to-understand materials. Part or all of the above-mentioned processing in the creation unit is performed using the generation AI. For example, the creation unit can input the input data to the generation AI and have the generation AI execute the creation of the materials.

[0037] The analysis unit can analyze sales data or customer data and extract trends and patterns. The analysis unit can analyze sales data or customer data and extract trends and patterns. The sales data includes, for example, monthly sales and sales by product. The customer data includes, for example, customer attribute information and purchase history. The analysis unit can analyze, for example, sales data and extract trends and patterns. For example, the analysis unit can analyze the increase / decrease trend of sales based on the sales data. The analysis unit can also analyze customer data and extract trends and patterns. For example, the analysis unit can analyze the purchasing tendency of customers based on the customer data. This makes it possible to automate the analysis of sales data and customer data, thereby making it more efficient to extract trends and patterns. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input sales data and customer data into AI and have AI execute the analysis of the data.

[0038] The generation unit can generate a response to an inquiry regarding product specifications and prices based on a pre-trained database. The generation unit generates a response to an inquiry regarding product specifications and prices based on a pre-trained database. The pre-trained database includes, for example, a machine learning model and a training dataset. The product specifications and prices include, for example, technical specifications and price lists. The generation unit generates a response to an inquiry regarding product specifications based on a technical specification. The generation unit can also generate a response to an inquiry regarding price based on a price list. This makes it possible to provide a quick and accurate response to an inquiry regarding product specifications and prices. A part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input a pre-trained database into the generation AI and cause the generation AI to generate a response to the inquiry.

[0039] The providing unit can provide the generated answer quickly and accurately. The providing unit provides the generated answer quickly and accurately. Providing quickly and accurately includes criteria such as response time and error rate. The providing unit provides the generated answer, for example, through a web application or a mobile application. For example, the providing unit displays the generated answer in a web application so that the user can access it. The providing unit can also display the generated answer in a mobile application so that the user can access it on a smartphone or tablet. This improves the efficiency of responding to inquiries by providing the generated answer quickly and accurately. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the generated answer to AI and cause the AI ​​to provide the answer.

[0040] The reception unit can analyze the user's past data input history and select an input method. For example, the reception unit preferentially suggests an input method (such as voice input or text input) that the user has frequently used in the past. The reception unit can also suggest an optimal input method for a specific time period from the user's past data input history. Furthermore, the reception unit can also suggest an efficient data input procedure based on the user's past input history. In this way, the optimal input method is suggested by analyzing the user's past data input history, improving the efficiency of data input. 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 past data input history to AI and cause the AI ​​to select an input method.

[0041] The reception unit can filter the data based on the user's current project or field of interest when inputting the data. For example, the reception unit preferentially inputs only data related to the project the user is currently working on. The reception unit can also filter and input highly relevant data based on the user's field of interest. Furthermore, the reception unit can select and input necessary data according to the progress of the user's project. This allows highly relevant data to be efficiently input by filtering data based on the user's current project or 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 data related to the user's current project or field of interest to AI, and have the AI ​​perform filtering.

[0042] The reception unit can preferentially input highly relevant data based on the geographical location information of the user when inputting data. For example, when the user is in a specific area, the reception unit preferentially inputs data related to the area. In addition, when the user is moving, the reception unit can also input highly relevant data based on the current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially input data related to the location. In this way, by taking into account the geographical location information of the user, highly relevant data can be efficiently input. 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 of the user to AI and cause the AI ​​to select highly relevant data.

[0043] The reception unit can input relevant data based on the user's social media activity at the time of data input. The reception unit inputs relevant data based on, for example, information shared by the user on social media. The reception unit can also input data related to topics of interest from the user's social media activity. Furthermore, the reception unit can analyze the activities of the user's social media followers and friends and input relevant data. In this way, highly relevant data can be efficiently input by analyzing 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 to AI and cause the AI ​​to select related data.

[0044] The creation unit can adjust the level of detail of the material in consideration of the importance of the data when creating the material. The creation unit uses the generation AI to adjust the level of detail of the material based on the importance of the data when creating the material. For example, the creation unit uses the generation AI to create a material including a detailed explanation and a graph for data with high importance. In addition, the creation unit can create a material including only a concise explanation and the main points for data with low importance. Furthermore, the creation unit can adjust the number of pages of the material and the level of detail of the content according to the importance of the data. This makes it possible to efficiently create materials by adjusting the level of detail of the material according to the importance of the data. Part or all of the above-mentioned processing in the creation unit is performed using the generation AI. For example, the creation unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the material.

[0045] The creation unit can apply different layouts or designs based on the categories of data when creating documents. The creation unit uses the generation AI to apply different layouts and designs based on the categories of data when creating documents. For example, the creation unit causes the generation AI to apply a layout that makes extensive use of graphs and charts to sales data. The creation unit can also cause the generation AI to apply a table-format layout to customer data. Furthermore, the creation unit can also cause the generation AI to apply a timeline-format layout to project progress data. This allows the creation of visually easy-to-understand documents by adjusting the layout and design according to the categories of data. Some or all of the above-mentioned processes in the creation unit are performed using the generation AI. For example, the creation unit can input the category of data to the generation AI and have the generation AI apply the layout and design.

[0046] The creation department can determine the priority of the materials in consideration of the submission time of the data when creating the materials. The creation department uses the generation AI to determine the priority of the materials based on the submission time of the data when creating the materials. For example, the creation department prioritizes the creation of materials for data whose deadline is approaching. Also, the creation department can postpone the creation of materials for data whose submission time is far away. Furthermore, the creation department can adjust the schedule for creating materials according to the submission time. This enables efficient creation of materials by determining the priority of materials based on the submission time of data. Part or all of the above-mentioned processing in the creation department is performed using the generation AI. For example, the creation department can input the submission time of data to the generation AI and have the generation AI execute the determination of the priority of the materials.

[0047] The creation unit can adjust the order of the materials in consideration of the relevance of the data when creating the materials. The creation unit uses the generation AI to adjust the order of the materials based on the relevance of the data when creating the materials. For example, the creation unit preferentially arranges highly relevant data in the first half of the materials. The creation unit can also arrange less relevant data in the second half of the materials. Furthermore, the creation unit can adjust the chapter structure and sections of the materials in accordance with the relevance of the data. This allows the creation of materials that are visually easy to understand by adjusting the order of the materials based on the relevance of the data. A part or all of the above-mentioned processing in the creation unit is performed using the generation AI. For example, the creation unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of the materials.

[0048] The analysis unit can improve the accuracy of the analysis based on the interrelationships of data during the analysis. The analysis unit uses AI to improve the accuracy of the analysis by considering the interrelationships of data during the analysis. For example, the analysis unit extracts trends by considering the interrelationships of sales data and customer data. The analysis unit can also propose efficient resource allocation by considering the interrelationships of project progress data and resource data. Furthermore, the analysis unit can also analyze market trends by considering the interrelationships of product data and market data. This improves the accuracy of the analysis by considering the interrelationships of data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the interrelationships of data to AI and cause AI to improve the accuracy of the analysis.

[0049] The analysis unit can perform the analysis based on the geographical distribution of the data at the time of analysis. The analysis unit performs the analysis by taking into account the geographical distribution of the data at the time of analysis using AI. For example, the analysis unit can analyze trends by region by taking into account the geographical distribution of sales data. The analysis unit can also analyze customer characteristics by region by taking into account the geographical distribution of customer data. Furthermore, the analysis unit can also analyze market needs by region by taking into account the geographical distribution of product data. This makes it possible to perform an analysis that reflects the characteristics of each region by taking into account the geographical distribution of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution of the data into AI and have the AI ​​execute the analysis.

[0050] The analysis unit can improve the accuracy of the analysis based on the literature related to the data during the analysis. The analysis unit uses AI to improve the accuracy of the analysis by referring to the literature related to the data during the analysis. For example, the analysis unit refers to related market research reports when analyzing sales data. In addition, the analysis unit can refer to related academic papers when analyzing customer data. Furthermore, the analysis unit can also refer to related technical literature when analyzing product data. In this way, the accuracy of the analysis is improved by referring to the literature related to the data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the literature related to the data into AI and cause the AI ​​to improve the accuracy of the analysis.

[0051] The generation unit can generate an answer based on past inquiry data when generating an answer. The generation unit uses AI to generate an optimal answer by referring to past inquiry data when generating an answer. For example, if a similar inquiry has been made in the past, the generation unit generates an optimal answer based on the answer. The generation unit can also generate answers to frequently asked questions from past inquiry data. Furthermore, the generation unit can analyze past inquiry data and generate the most effective answer. This makes it possible to quickly generate an optimal answer by referring to past inquiry data. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input past inquiry data to AI and cause AI to generate an answer.

[0052] The generation unit can apply an answer algorithm based on the category of the inquiry when generating an answer. The generation unit uses AI to apply different answer algorithms depending on the category of the inquiry when generating an answer. For example, the generation unit applies an answer algorithm including technical details to an inquiry about product specifications. The generation unit can also apply an answer algorithm including a price list and discount information to an inquiry about prices. Furthermore, the generation unit can also apply an answer algorithm including procedures and FAQs to an inquiry about support. This makes it possible to provide an optimal answer according to the category of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the category of the inquiry to AI and cause the AI ​​to apply the answer algorithm.

[0053] The generation unit can determine the priority of the answers based on the time of submission of the inquiry when generating the answer. The generation unit uses AI to determine the priority of the answers based on the time of submission of the inquiry when generating the answer. For example, the generation unit generates an answer quickly for an urgent inquiry. Also, the generation unit can postpone generating an answer for an inquiry submitted earlier. Furthermore, the generation unit can adjust the schedule for generating the answer depending on the submission time. This enables a quick response by determining the priority of the answers based on the time of submission of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the time of submission of the inquiry to AI and cause the AI ​​to execute the determination of the priority of the answers.

[0054] The generation unit can adjust the order of answers based on the relevance of the inquiries when generating answers. The generation unit adjusts the order of answers based on the relevance of the inquiries using AI when generating answers. For example, the generation unit generates answers preferentially for inquiries with high relevance. Also, the generation unit can generate answers later for inquiries with low relevance. Furthermore, the generation unit can adjust the order of answers according to the relevance of the inquiries. This makes it possible to provide an efficient answer by adjusting the order of answers based on the relevance of the inquiries. A part or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the inquiries to AI and cause the AI ​​to adjust the order of answers.

[0055] The providing unit can select a providing method based on the user's past inquiry history when providing an answer. The providing unit uses AI to select the optimal providing method by referring to the user's past inquiry history when providing an answer. For example, the providing unit preferentially provides a providing method (email, chat, etc.) that the user has used in the past. The providing unit can also select the optimal providing method from the user's past inquiry history. Furthermore, the providing unit can also select an efficient providing method based on the user's past inquiry history. In this way, the optimal providing method can be selected by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past inquiry history into AI and cause the AI ​​to select the providing method.

[0056] The providing unit can select a providing method based on the device information of the user when providing an answer. The providing unit uses AI to select the optimal providing method by considering the device information of the user when providing an answer. For example, when the user is using a smartphone, the providing unit selects a providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can select a providing method optimized for a large screen. Furthermore, when the user is using a desktop, the providing unit can also select a providing method that includes detailed information. In this way, the optimal providing method can be selected by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to AI and cause the AI ​​to select the providing method.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The office work efficiency system further includes a notification unit. The notification unit is responsible for notifying the user of the processing results from the creation unit, analysis unit, and generation unit. For example, when the creation unit completes a document, the notification unit can send a notification to the user by email or chat. In addition, when the analysis unit discovers a new trend, the notification unit can inform the user of the result in real time. Furthermore, when the generation unit generates a response to an inquiry, the notification unit can quickly provide the response to the user. This allows the user to receive important information in a timely manner, further improving work efficiency.

[0059] The reception unit can analyze the user's past data input history and select an input method. For example, it preferentially suggests input methods (such as voice input and text input) that the user has used frequently in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past data input history. Furthermore, the reception unit can also suggest an efficient data input procedure based on the user's past input history. In this way, the optimal input method is suggested by analyzing the user's past data input history, improving the efficiency of data input.

[0060] When creating documents, the creation department can adjust the level of detail of the documents taking into account the importance of the data. For example, for data with high importance, the creation department can have the generation AI create documents that include detailed explanations and graphs. In addition, for data with low importance, the creation department can have the generation AI create documents that include only concise explanations and key points. Furthermore, the creation department can adjust the number of pages of the documents and the level of detail of the contents according to the importance of the data. This allows for efficient document creation by adjusting the level of detail of the documents according to the importance of the data.

[0061] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between data. For example, the analysis unit extracts trends by considering the interrelationships between sales data and customer data. The analysis unit can also propose efficient resource allocation by considering the interrelationships between project progress data and resource data. Furthermore, the analysis unit can analyze market trends by considering the interrelationships between product data and market data. In this way, the accuracy of the analysis is improved by considering the interrelationships between data.

[0062] When generating an answer, the generation unit can generate the answer based on past inquiry data. For example, when a similar inquiry has been made in the past, the generation unit generates an optimal answer based on the answer. The generation unit can also generate answers to frequently asked questions from past inquiry data. Furthermore, the generation unit can analyze past inquiry data and generate the most effective answer. This allows the optimal answer to be generated quickly by referring to past inquiry data.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit inputs data. The data includes, for example, text data, numerical data, image data, etc. The reception unit inputs the data using a presentation tool (e.g., Microsoft PowerPoint, Google Slides) or a spreadsheet tool (e.g., Microsoft Excel, Google Sheets). Step 2: The creation department uses the generation AI to create materials based on the data entered by the reception department. The materials include reports, presentation materials, analysis reports, etc. The creation department selects an appropriate layout and design based on the entered data to create materials that are visually easy to understand. The generation AI can also create materials by using a text generation AI (e.g. LLM) and extracting important parts of the data. Step 3: The analysis unit analyzes the numbers based on the data entered by the reception unit. Numerical analysis includes statistical analysis, regression analysis, data mining, etc. The analysis unit analyzes sales data and customer data to extract trends and patterns. Complex mathematical formulas and statistical methods are used to analyze the data, and the results are output as graphs and charts. Step 4: The generation unit generates answers to inquiries based on the analysis results obtained by the analysis unit. The generation unit generates appropriate answers to inquiries about product specifications and prices based on a pre-trained database. Answers are generated quickly and accurately using a generation AI. Step 5: The providing unit provides the answer generated by the generating unit. The providing unit provides the generated answer quickly and accurately. The providing unit uses the generating AI to provide the answer through a web application or a mobile application.

[0065] (Example 2) The office work efficiency system according to the embodiment of the present invention utilizes a generation AI to automatically perform tasks such as creating documents using PowerPoint, Excel, and Gmail, performing numerical analysis, and answering inquiries about work content. This system can take over the work of partner sales, and by reducing or reassigning sales personnel, this leads to improved profits and investment in new businesses. For example, the generation AI creates documents using PowerPoint or Excel. The generation AI selects appropriate layouts and designs based on the input data and creates visually easy-to-understand documents. Next, the generation AI performs numerical analysis. The generation AI analyzes sales data and customer data to extract trends and patterns. The generation AI analyzes data using complex mathematical formulas and statistical methods and outputs the results as graphs and charts. Furthermore, the generation AI responds to inquiries about work content. The generation AI generates replies quickly and accurately based on a database that has been learned in advance in response to inquiries about product specifications and prices. This mechanism makes it possible to streamline partner sales work and reduce or reassign sales personnel. This allows companies to improve profits and use the freed up resources to invest in new businesses. In addition, because humans are specialized in coming up with new ideas based on data, it is possible to divide the work between generative AI and humans. As a result, the office work efficiency system can significantly improve the work efficiency of companies, leading to improved profits and investment in new businesses.

[0066] The office work efficiency improvement system according to the embodiment includes a reception unit, a creation unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs data. The data includes, for example, text data, numerical data, image data, and the like, but is not limited to such examples. The reception unit inputs data using, for example, a presentation tool or a spreadsheet tool. The presentation tool includes, for example, Microsoft PowerPoint, Google Slides, and the like. The spreadsheet tool includes, for example, Microsoft Excel, Google Sheets, and the like. The creation unit uses a generation AI to create materials based on the data input by the reception unit. The materials include, for example, reports, presentation materials, analysis reports, and the like, but are not limited to such examples. The creation unit selects, for example, an appropriate layout and design based on the input data, and creates materials that are visually easy to understand. The generation AI creates materials using a text generation AI (for example, LLM). The creation unit can also use the generation AI to extract important parts of the data and create materials. For example, the generation AI uses a keyword extraction technique to pick up particularly important information from the data, and creates materials based on that information. The analysis unit analyzes the numerical values ​​based on the data input by the reception unit. The analysis of the numerical values ​​includes, but is not limited to, statistical analysis, regression analysis, data mining, and the like. The analysis unit, for example, analyzes sales data and customer data to extract trends and patterns. The analysis unit analyzes data using complex mathematical formulas and statistical methods, and outputs the results as graphs and charts. The generation unit generates an answer to an inquiry based on the analysis results obtained by the analysis unit. The generation unit generates an appropriate answer to an inquiry regarding product specifications and prices, for example, based on a database that has been learned in advance. The generation unit can generate an answer quickly and accurately using a generation AI. The provision unit provides the answer generated by the generation unit. The provision unit provides, for example, the generated answer quickly and accurately. The provision unit can also provide the answer using a generation AI. For example, the provision unit provides the generated answer through a web application or a mobile application.As a result, the office work efficiency improvement system according to the embodiment automates processes from data entry to document creation, numerical analysis, and response to inquiries, thereby improving work efficiency.

[0067] The reception unit inputs data. The data includes, but is not limited to, text data, numerical data, and image data. The reception unit inputs data using, for example, a presentation tool or a spreadsheet tool. The presentation tool includes, for example, Microsoft PowerPoint and Google Slides. The spreadsheet tool includes, for example, Microsoft Excel and Google Sheets. The reception unit plays a role of efficiently collecting data input by the user using these tools and incorporating it into the system. Specifically, the reception unit uses the presentation tool to automatically extract the contents of slides, graphs, and text boxes created by the user and save them in a database. In addition, the reception unit uses the spreadsheet tool to analyze numerical data and calculation formulas input by the user, extract necessary information, and incorporate it into the system. Furthermore, the reception unit can also extract text information from scanned documents and image data using OCR (optical character recognition) technology. This makes it possible to incorporate paper documents and handwritten notes as digital data. The reception unit displays the data input status and progress in real time through a user interface, making it easy for the user to check and correct the data input. This enables the reception unit to streamline the data input process and collect data accurately and quickly.

[0068] The creation unit uses the generation AI to create materials based on the data input by the reception unit. The materials include, but are not limited to, reports, presentation materials, and analysis reports. For example, the creation unit selects an appropriate layout and design based on the input data to create materials that are visually easy to understand. The generation AI creates materials using a text generation AI (for example, LLM). The creation unit can also use the generation AI to extract important parts of data to create materials. For example, the generation AI uses keyword extraction technology to pick up particularly important information from the data and create materials based on that. Specifically, the generation AI uses natural language processing technology to analyze the input text data, understand the context and meaning, and generate appropriate sentences. Furthermore, the generation AI can automatically create graphs and charts according to the content of the data to visually convey information. For example, in a report based on sales data, a line graph showing the progress of sales and a pie chart showing sales by region are automatically generated. The generation AI can also learn the user's preferences and past document creation history to propose layouts and designs that are more suitable for the user. This allows the creation unit to quickly create high-quality materials without the user having to take time and effort, thereby improving work efficiency.

[0069] The analysis unit analyzes the numerical values ​​based on the data input by the reception unit. The analysis of the numerical values ​​includes, but is not limited to, statistical analysis, regression analysis, data mining, and the like. For example, the analysis unit analyzes sales data and customer data to extract trends and patterns. Specifically, the analysis unit uses statistical analysis to calculate basic statistical indicators such as the average value, standard deviation, and variance of the data, and grasps the distribution and trends of the data. In addition, the analysis unit uses regression analysis to clarify the relationship between multiple variables, such as the relationship between sales and advertising costs, and the relationship between customer satisfaction and repurchase rate. Furthermore, the analysis unit uses data mining technology to discover useful patterns and rules from large amounts of data, which are useful for business decision-making. For example, the analysis unit analyzes customer data to clarify what products a specific customer group prefers, and uses the results in planning a marketing strategy. The analysis unit visually displays these analysis results as graphs and charts, allowing the user to intuitively understand them. For example, the analysis unit automatically generates line graphs showing sales trends and histograms showing the distribution of customer satisfaction. Furthermore, the analysis unit can also use AI to integrate past data and external data sources to predict future trends and risks. This enables the analysis department to understand the current state of the business through data analysis and provide useful information for planning future strategies.

[0070] The generation unit generates an answer to the inquiry based on the analysis result obtained by the analysis unit. For example, the generation unit generates an appropriate answer to an inquiry about product specifications or price based on a database that has been learned in advance. The generation unit can generate an answer quickly and accurately using the generation AI. Specifically, the generation AI uses natural language processing technology to analyze the content of the inquiry from the user and generate an appropriate answer. For example, when a user inquires about product specifications, the generation AI searches for the relevant information from the product database and generates an answer in an appropriate format. In addition, for an inquiry about price, an accurate answer is provided based on the latest price information. Furthermore, the generation unit can learn the user's past inquiry history and the inquiry patterns of other users to provide a more appropriate answer. For example, if a similar inquiry is made multiple times, the generation AI can learn the pattern and respond quickly to the next inquiry and thereafter. As a result, the generation unit can provide a quick and accurate answer to the user's inquiry, thereby improving the efficiency of business operations.

[0071] The providing unit provides the answer generated by the generating unit. The providing unit, for example, provides the generated answer quickly and accurately. The providing unit can also provide the answer using the generating AI. For example, the providing unit provides the generated answer through a web application or a mobile application. Specifically, the providing unit displays the generated answer in real time through a web portal or a mobile application accessed by the user. In addition, the providing unit can customize the format and display method of the answer according to the user's preferences. For example, it is possible to provide not only a text-format answer but also a visual answer using a graph or chart. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy of the answer and the method of providing it. For example, the user can leave an evaluation or comment on the provided answer, and based on that feedback, the learning data of the generating AI is updated and the accuracy of the answer from the next time onwards is improved. As a result, the providing unit can provide a quick and accurate answer to the user, thereby improving the efficiency of the work.

[0072] The reception unit can input data using a presentation tool or a spreadsheet tool. Examples of the presentation tool include Microsoft PowerPoint and Google Slides. Examples of the spreadsheet tool include Microsoft Excel and Google Sheets. The reception unit inputs data using, for example, a presentation tool. For example, the reception unit can input data in a slide format using PowerPoint. The reception unit can also input data using a spreadsheet tool. For example, the reception unit can input data in a table format using Excel. As a result, the efficiency of data input is improved by using a presentation tool or a spreadsheet tool. 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 data input using a presentation tool or a spreadsheet tool to an AI, and cause the AI ​​to analyze and process the data.

[0073] The creation unit can select an appropriate layout or design based on the input data and create visually easy-to-understand materials. The creation unit uses the generation AI to select an appropriate layout or design based on the input data and create visually easy-to-understand materials. For example, the creation unit can automate the creation of sales reports and presentation materials. The generation AI selects an appropriate layout or design based on the input data and creates visually easy-to-understand materials. For example, the generation AI extracts important parts of the data and creates materials based on them. The generation AI creates materials using a text generation AI (e.g., LLM). The generation AI can also create the contents of the materials using a multimodal generation AI. For example, the generation AI uses keyword extraction technology to pick up particularly important information in the data and create materials based on that. This improves the efficiency of document creation by automatically creating visually easy-to-understand materials. Part or all of the above-mentioned processing in the creation unit is performed using the generation AI. For example, the creation unit can input the input data to the generation AI and have the generation AI execute the creation of the materials.

[0074] The analysis unit can analyze sales data or customer data and extract trends and patterns. The analysis unit can analyze sales data or customer data and extract trends and patterns. The sales data includes, for example, monthly sales and sales by product. The customer data includes, for example, customer attribute information and purchase history. The analysis unit can analyze, for example, sales data and extract trends and patterns. For example, the analysis unit can analyze the increase / decrease trend of sales based on the sales data. The analysis unit can also analyze customer data and extract trends and patterns. For example, the analysis unit can analyze the purchasing tendency of customers based on the customer data. This makes it possible to automate the analysis of sales data and customer data, thereby making it more efficient to extract trends and patterns. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input sales data and customer data into AI and have AI execute the analysis of the data.

[0075] The generation unit can generate a response to an inquiry regarding product specifications and prices based on a pre-trained database. The generation unit generates a response to an inquiry regarding product specifications and prices based on a pre-trained database. The pre-trained database includes, for example, a machine learning model and a training dataset. The product specifications and prices include, for example, technical specifications and price lists. The generation unit generates a response to an inquiry regarding product specifications based on a technical specification. The generation unit can also generate a response to an inquiry regarding price based on a price list. This makes it possible to provide a quick and accurate response to an inquiry regarding product specifications and prices. A part or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can input a pre-trained database into the generation AI and cause the generation AI to generate a response to the inquiry.

[0076] The providing unit can provide the generated answer quickly and accurately. The providing unit provides the generated answer quickly and accurately. Providing quickly and accurately includes criteria such as response time and error rate. The providing unit provides the generated answer, for example, through a web application or a mobile application. For example, the providing unit displays the generated answer in a web application so that the user can access it. The providing unit can also display the generated answer in a mobile application so that the user can access it on a smartphone or tablet. This improves the efficiency of responding to inquiries by providing the generated answer quickly and accurately. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the generated answer to AI and cause the AI ​​to provide the answer.

[0077] The reception unit can estimate the user's emotion and adjust the timing of data input based on the estimated user's emotion. For example, when the user is stressed, the reception unit can delay the timing of data input so that the user can input in a relaxed state. In addition, when the user is concentrating, the reception unit can advance the timing of data input so that the input can proceed efficiently. Furthermore, when the user is tired, the reception unit can adjust the timing of data input and input data while taking a break. In this way, by adjusting the timing of data input according to the user's emotion, the user's stress can be reduced and efficient data input can be achieved. 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 emotion data to the generation AI and cause the generation AI to adjust the timing of data input.

[0078] The reception unit can analyze the user's past data input history and select an input method. For example, the reception unit preferentially suggests an input method (such as voice input or text input) that the user has frequently used in the past. The reception unit can also suggest an optimal input method for a specific time period from the user's past data input history. Furthermore, the reception unit can also suggest an efficient data input procedure based on the user's past input history. In this way, the optimal input method is suggested by analyzing the user's past data input history, improving the efficiency of data input. 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 past data input history to AI and cause the AI ​​to select an input method.

[0079] The reception unit can filter the data based on the user's current project or field of interest when inputting the data. For example, the reception unit preferentially inputs only data related to the project the user is currently working on. The reception unit can also filter and input highly relevant data based on the user's field of interest. Furthermore, the reception unit can select and input necessary data according to the progress of the user's project. This allows highly relevant data to be efficiently input by filtering data based on the user's current project or 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 data related to the user's current project or field of interest to AI, and have the AI ​​perform filtering.

[0080] The reception unit can estimate the user's emotion and determine the priority of data to be input based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit postpones data of low importance and inputs data of high importance preferentially. In addition, when the user is relaxed, the reception unit can input all data evenly. Furthermore, when the user is in a hurry, the reception unit can also input the most important data preferentially. In this way, by determining the priority of data according to the user's emotion, important data can be input preferentially. 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 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 emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the data.

[0081] The reception unit can preferentially input highly relevant data based on the geographical location information of the user when inputting data. For example, when the user is in a specific area, the reception unit preferentially inputs data related to the area. In addition, when the user is moving, the reception unit can also input highly relevant data based on the current location. Furthermore, when the user is in a specific location, the reception unit can also preferentially input data related to the location. In this way, by taking into account the geographical location information of the user, highly relevant data can be efficiently input. 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 of the user to AI and cause the AI ​​to select highly relevant data.

[0082] The reception unit can input relevant data based on the user's social media activity at the time of data input. The reception unit inputs relevant data based on, for example, information shared by the user on social media. The reception unit can also input data related to topics of interest from the user's social media activity. Furthermore, the reception unit can analyze the activities of the user's social media followers and friends and input relevant data. In this way, highly relevant data can be efficiently input by analyzing 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 to AI and cause the AI ​​to select related data.

[0083] The creation unit can estimate the user's emotion and adjust the expression method of the material based on the estimated user's emotion. The creation unit uses the generation AI to estimate the user's emotion and adjust the expression method of the material based on the estimated user's emotion. For example, when the user is relaxed, the creation unit uses the generation AI to create colorful and visually attractive materials. Also, when the user is in a hurry, the creation unit can use the generation AI to create materials that are simple and to the point. Furthermore, when the user is stressed, the creation unit can also use the generation AI to create materials that visually reduce stress with calm colors. In this way, visually attractive materials can be created by adjusting the expression method of the material 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 creation unit is performed using the generation AI. For example, the creation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the material.

[0084] The creation unit can adjust the level of detail of the material in consideration of the importance of the data when creating the material. The creation unit uses the generation AI to adjust the level of detail of the material based on the importance of the data when creating the material. For example, the creation unit uses the generation AI to create a material including a detailed explanation and a graph for data with high importance. In addition, the creation unit can create a material including only a concise explanation and the main points for data with low importance. Furthermore, the creation unit can adjust the number of pages of the material and the level of detail of the content according to the importance of the data. This makes it possible to efficiently create materials by adjusting the level of detail of the material according to the importance of the data. Part or all of the above-mentioned processing in the creation unit is performed using the generation AI. For example, the creation unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the material.

[0085] The creation unit can apply different layouts or designs based on the categories of data when creating documents. The creation unit uses the generation AI to apply different layouts and designs based on the categories of data when creating documents. For example, the creation unit causes the generation AI to apply a layout that makes extensive use of graphs and charts to sales data. The creation unit can also cause the generation AI to apply a table-format layout to customer data. Furthermore, the creation unit can also cause the generation AI to apply a timeline-format layout to project progress data. This allows the creation of visually easy-to-understand documents by adjusting the layout and design according to the categories of data. Some or all of the above-mentioned processes in the creation unit are performed using the generation AI. For example, the creation unit can input the category of data to the generation AI and have the generation AI apply the layout and design.

[0086] The creation unit can estimate the user's emotion and adjust the length of the material based on the estimated user's emotion. The creation unit uses the generation AI to estimate the user's emotion and adjust the length of the material based on the estimated user's emotion. For example, when the user is in a hurry, the creation unit uses the generation AI to create a short material that covers the main points. Also, when the user is relaxed, the creation unit can use the generation AI to create a longer material that includes detailed explanations. Furthermore, when the user is stressed, the creation unit can also use the generation AI to create a material of a moderate length to visually reduce stress. This allows efficient material creation by adjusting the length of the material 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 creation unit is performed using the generation AI. For example, the creation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the material.

[0087] The creation department can determine the priority of the materials in consideration of the submission time of the data when creating the materials. The creation department uses the generation AI to determine the priority of the materials based on the submission time of the data when creating the materials. For example, the creation department prioritizes the creation of materials for data whose deadline is approaching. Also, the creation department can postpone the creation of materials for data whose submission time is far away. Furthermore, the creation department can adjust the schedule for creating materials according to the submission time. This enables efficient creation of materials by determining the priority of materials based on the submission time of data. Part or all of the above-mentioned processing in the creation department is performed using the generation AI. For example, the creation department can input the submission time of data to the generation AI and have the generation AI execute the determination of the priority of the materials.

[0088] The creation unit can adjust the order of the materials in consideration of the relevance of the data when creating the materials. The creation unit uses the generation AI to adjust the order of the materials based on the relevance of the data when creating the materials. For example, the creation unit preferentially arranges highly relevant data in the first half of the materials. The creation unit can also arrange less relevant data in the second half of the materials. Furthermore, the creation unit can adjust the chapter structure and sections of the materials in accordance with the relevance of the data. This allows the creation of materials that are visually easy to understand by adjusting the order of the materials based on the relevance of the data. A part or all of the above-mentioned processing in the creation unit is performed using the generation AI. For example, the creation unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of the materials.

[0089] The analysis unit can estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. The analysis unit uses AI to estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. For example, the analysis unit applies detailed analysis criteria when the user is relaxed. Also, the analysis unit can apply concise analysis criteria when the user is in a hurry. Furthermore, the analysis unit can also apply analysis criteria for visually reducing stress when the user is stressed. This enables efficient data analysis by adjusting the analysis criteria 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 (e.g., 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 is performed using AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis criteria.

[0090] The analysis unit can improve the accuracy of the analysis based on the interrelationships of data during the analysis. The analysis unit uses AI to improve the accuracy of the analysis by considering the interrelationships of data during the analysis. For example, the analysis unit extracts trends by considering the interrelationships of sales data and customer data. The analysis unit can also propose efficient resource allocation by considering the interrelationships of project progress data and resource data. Furthermore, the analysis unit can also analyze market trends by considering the interrelationships of product data and market data. This improves the accuracy of the analysis by considering the interrelationships of data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the interrelationships of data to AI and cause AI to improve the accuracy of the analysis.

[0091] The analysis unit can perform the analysis based on the geographical distribution of the data at the time of analysis. The analysis unit performs the analysis by taking into account the geographical distribution of the data at the time of analysis using AI. For example, the analysis unit can analyze trends by region by taking into account the geographical distribution of sales data. The analysis unit can also analyze customer characteristics by region by taking into account the geographical distribution of customer data. Furthermore, the analysis unit can also analyze market needs by region by taking into account the geographical distribution of product data. This makes it possible to perform an analysis that reflects the characteristics of each region by taking into account the geographical distribution of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution of the data into AI and have the AI ​​execute the analysis.

[0092] The analysis unit can improve the accuracy of the analysis based on the literature related to the data during the analysis. The analysis unit uses AI to improve the accuracy of the analysis by referring to the literature related to the data during the analysis. For example, the analysis unit refers to related market research reports when analyzing sales data. In addition, the analysis unit can refer to related academic papers when analyzing customer data. Furthermore, the analysis unit can also refer to related technical literature when analyzing product data. In this way, the accuracy of the analysis is improved by referring to the literature related to the data. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the literature related to the data into AI and cause the AI ​​to improve the accuracy of the analysis.

[0093] The generation unit can estimate the user's emotion and adjust the answer generation method based on the estimated user's emotion. The generation unit uses AI to estimate the user's emotion and adjust the answer generation method based on the estimated user's emotion. For example, the generation unit generates a detailed answer when the user is relaxed. Also, the generation unit can generate a concise and to the point answer when the user is in a hurry. Furthermore, the generation unit can also generate an answer for visually reducing stress when the user is stressed. This allows a more appropriate answer to be provided by adjusting the answer 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 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 generation unit is performed using AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the answer generation method.

[0094] The generation unit can generate an answer based on past inquiry data when generating an answer. The generation unit uses AI to generate an optimal answer by referring to past inquiry data when generating an answer. For example, if a similar inquiry has been made in the past, the generation unit generates an optimal answer based on the answer. The generation unit can also generate answers to frequently asked questions from past inquiry data. Furthermore, the generation unit can analyze past inquiry data and generate the most effective answer. This makes it possible to quickly generate an optimal answer by referring to past inquiry data. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input past inquiry data to AI and cause AI to generate an answer.

[0095] The generation unit can apply an answer algorithm based on the category of the inquiry when generating an answer. The generation unit uses AI to apply different answer algorithms depending on the category of the inquiry when generating an answer. For example, the generation unit applies an answer algorithm including technical details to an inquiry about product specifications. The generation unit can also apply an answer algorithm including a price list and discount information to an inquiry about prices. Furthermore, the generation unit can also apply an answer algorithm including procedures and FAQs to an inquiry about support. This makes it possible to provide an optimal answer according to the category of the inquiry. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the category of the inquiry to AI and cause the AI ​​to apply the answer algorithm.

[0096] The generation unit can estimate the user's emotion and determine the priority of answers based on the estimated user's emotion. The generation unit uses AI to estimate the user's emotion and determine the priority of answers based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit can provide the most important answer preferentially. Furthermore, when the user is relaxed, the generation unit can also provide a detailed answer preferentially. Furthermore, when the user is stressed, the generation unit can also provide an answer for visually reducing stress preferentially. In this way, by determining the priority of answers according to the user's emotion, it is possible to provide important answers preferentially. 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 generation unit is performed using AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of answers.

[0097] The generation unit can determine the priority of the answers based on the time of submission of the inquiry when generating the answer. The generation unit uses AI to determine the priority of the answers based on the time of submission of the inquiry when generating the answer. For example, the generation unit generates an answer quickly for an urgent inquiry. Also, the generation unit can postpone generating an answer for an inquiry submitted earlier. Furthermore, the generation unit can adjust the schedule for generating the answer depending on the submission time. This enables a quick response by determining the priority of the answers based on the time of submission of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the time of submission of the inquiry to AI and cause the AI ​​to execute the determination of the priority of the answers.

[0098] The generation unit can adjust the order of answers based on the relevance of the inquiries when generating answers. The generation unit adjusts the order of answers based on the relevance of the inquiries using AI when generating answers. For example, the generation unit generates answers preferentially for inquiries with high relevance. Also, the generation unit can generate answers later for inquiries with low relevance. Furthermore, the generation unit can adjust the order of answers according to the relevance of the inquiries. This makes it possible to provide an efficient answer by adjusting the order of answers based on the relevance of the inquiries. A part or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the inquiries to AI and cause the AI ​​to adjust the order of answers.

[0099] The providing unit can estimate the user's emotion and adjust the answer providing method based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and adjust the answer providing method based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides an answer including a detailed explanation. Also, when the user is in a hurry, the providing unit can provide a concise answer that covers the main points. Furthermore, when the user is stressed, the providing unit can also provide an answer for visually reducing stress. This allows a more appropriate answer to be provided by adjusting the answer providing 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 providing unit is performed using AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the answer providing method.

[0100] The providing unit can select a providing method based on the user's past inquiry history when providing an answer. The providing unit uses AI to select the optimal providing method by referring to the user's past inquiry history when providing an answer. For example, the providing unit preferentially provides a providing method (email, chat, etc.) that the user has used in the past. The providing unit can also select the optimal providing method from the user's past inquiry history. Furthermore, the providing unit can also select an efficient providing method based on the user's past inquiry history. In this way, the optimal providing method can be selected by referring to the user's past inquiry history. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the user's past inquiry history into AI and cause the AI ​​to select the providing method.

[0101] The providing unit can estimate the user's emotion and adjust the order of answer provision based on the estimated user's emotion. The providing unit uses AI to estimate the user's emotion and adjust the order of answer provision based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides detailed answers preferentially. Also, when the user is in a hurry, the providing unit can provide the most important answers preferentially. Furthermore, when the user is stressed, the providing unit can also provide answers for visually reducing stress preferentially. In this way, by adjusting the order of answer provision according to the user's emotion, important answers can be provided preferentially. 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 providing unit is performed using AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the order of answer provision.

[0102] The providing unit can select a providing method based on the device information of the user when providing an answer. The providing unit uses AI to select the optimal providing method by considering the device information of the user when providing an answer. For example, when the user is using a smartphone, the providing unit selects a providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can select a providing method optimized for a large screen. Furthermore, when the user is using a desktop, the providing unit can also select a providing method that includes detailed information. In this way, the optimal providing method can be selected by considering the device information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to AI and cause the AI ​​to select the providing method.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The office work efficiency system further includes a notification unit. The notification unit is responsible for notifying the user of the processing results from the creation unit, analysis unit, and generation unit. For example, when the creation unit completes a document, the notification unit can send a notification to the user by email or chat. In addition, when the analysis unit discovers a new trend, the notification unit can inform the user of the result in real time. Furthermore, when the generation unit generates a response to an inquiry, the notification unit can quickly provide the response to the user. This allows the user to receive important information in a timely manner, further improving work efficiency.

[0105] The reception unit can analyze the user's past data input history and select an input method. For example, it preferentially suggests input methods (such as voice input and text input) that the user has used frequently in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past data input history. Furthermore, the reception unit can also suggest an efficient data input procedure based on the user's past input history. In this way, the optimal input method is suggested by analyzing the user's past data input history, improving the efficiency of data input.

[0106] When creating documents, the creation department can adjust the level of detail of the documents taking into account the importance of the data. For example, for data with high importance, the creation department can have the generation AI create documents that include detailed explanations and graphs. In addition, for data with low importance, the creation department can have the generation AI create documents that include only concise explanations and key points. Furthermore, the creation department can adjust the number of pages of the documents and the level of detail of the contents according to the importance of the data. This allows for efficient document creation by adjusting the level of detail of the documents according to the importance of the data.

[0107] During analysis, the analysis unit can improve the accuracy of the analysis based on the interrelationships between data. For example, the analysis unit extracts trends by considering the interrelationships between sales data and customer data. The analysis unit can also propose efficient resource allocation by considering the interrelationships between project progress data and resource data. Furthermore, the analysis unit can analyze market trends by considering the interrelationships between product data and market data. In this way, the accuracy of the analysis is improved by considering the interrelationships between data.

[0108] When generating an answer, the generation unit can generate the answer based on past inquiry data. For example, when a similar inquiry has been made in the past, the generation unit generates an optimal answer based on the answer. The generation unit can also generate answers to frequently asked questions from past inquiry data. Furthermore, the generation unit can analyze past inquiry data and generate the most effective answer. This allows the optimal answer to be generated quickly by referring to past inquiry data.

[0109] The reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user's emotions. For example, if the user is stressed, the timing of data input can be delayed to allow the user to input in a relaxed state. Also, if the user is concentrating, the timing of data input can be advanced to allow efficient data input. Furthermore, if the user is tired, the timing of data input can be adjusted to allow input to be performed while taking breaks. In this way, by adjusting the timing of data input according to the user's emotions, the user's stress can be reduced and efficient data input can be achieved.

[0110] The creation unit can estimate the user's emotions and adjust the way the materials are expressed based on the estimated user's emotions. For example, when the user is relaxed, the creation unit causes the generation AI to create colorful and visually appealing materials. When the user is in a hurry, the creation unit can also create materials that are simple and to the point. Furthermore, when the user is feeling stressed, the creation unit can also create materials that visually reduce stress with calming colors. In this way, visually appealing materials can be created by adjusting the way the materials are expressed according to the user's emotions.

[0111] The analysis unit can estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. For example, the analysis unit can apply detailed analysis criteria when the user is relaxed. Also, the analysis unit can apply concise analysis criteria when the user is in a hurry. Furthermore, the analysis unit can apply analysis criteria for visually reducing stress when the user is feeling stressed. This allows efficient data analysis by adjusting the analysis criteria according to the user's emotion.

[0112] The generation unit can estimate the user's emotions and adjust the answer generation method based on the estimated user's emotions. For example, the generation unit generates a detailed answer when the user is relaxed. Also, the generation unit can generate a concise answer that is to the point when the user is in a hurry. Furthermore, the generation unit can generate an answer for visually reducing stress when the user is feeling stressed. In this way, a more appropriate answer can be provided by adjusting the answer generation method according to the user's emotions.

[0113] The providing unit can estimate the user's emotions and adjust the method of providing an answer based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can provide an answer including a detailed explanation. When the user is in a hurry, the providing unit can provide a concise answer that is to the point. Furthermore, when the user is feeling stressed, the providing unit can also provide an answer for visually reducing stress. In this way, by adjusting the method of providing an answer according to the user's emotions, a more appropriate answer can be provided.

[0114] The process flow of the second embodiment will be briefly described below.

[0115] Step 1: The reception unit inputs data. The data includes, for example, text data, numerical data, image data, etc. The reception unit inputs the data using a presentation tool (e.g., Microsoft PowerPoint, Google Slides) or a spreadsheet tool (e.g., Microsoft Excel, Google Sheets). Step 2: The creation department uses the generation AI to create materials based on the data entered by the reception department. The materials include reports, presentation materials, analysis reports, etc. The creation department selects an appropriate layout and design based on the entered data to create materials that are visually easy to understand. The generation AI can also create materials by using a text generation AI (e.g. LLM) and extracting important parts of the data. Step 3: The analysis unit analyzes the numbers based on the data entered by the reception unit. Numerical analysis includes statistical analysis, regression analysis, data mining, etc. The analysis unit analyzes sales data and customer data to extract trends and patterns. Complex mathematical formulas and statistical methods are used to analyze the data, and the results are output as graphs and charts. Step 4: The generation unit generates answers to inquiries based on the analysis results obtained by the analysis unit. The generation unit generates appropriate answers to inquiries about product specifications and prices based on a pre-trained database. Answers are generated quickly and accurately using a generation AI. Step 5: The providing unit provides the answer generated by the generating unit. The providing unit provides the generated answer quickly and accurately. The providing unit uses the generating AI to provide the answer through a web application or a mobile application.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements including the above-mentioned reception unit, creation unit, analysis unit, generation unit, and provision 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 reception device 38 of the smart device 14 and inputs data. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates materials using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes numerical values. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer to an inquiry. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated answer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements including the above-mentioned reception unit, creation unit, analysis unit, generation unit, and provision 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 microphone 238 of the smart glasses 214 and inputs data. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates materials using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes numerical values. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer to an inquiry. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated answer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements including the above-mentioned reception unit, creation unit, analysis unit, generation unit, and provision 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 microphone 238 of the headset type terminal 314 and inputs data. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates materials using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes numerical values. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer to an inquiry. The provision unit is realized by the display 343 of the headset type terminal 314 and provides the generated answer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements including the above-mentioned reception unit, creation unit, analysis unit, generation unit, and provision 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 microphone 238 of the robot 414 and inputs data. The creation unit is realized by the specific processing unit 290 of the data processing device 12 and creates materials using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes numerical values. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer to an inquiry. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated answer. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Appendix 1) A reception section for inputting data; a creation unit that creates materials based on the data input by the reception unit; an analysis unit that analyzes numerical values ​​based on the materials created by the creation unit; a generation unit that generates an answer to the inquiry based on the analysis result obtained by the analysis unit; a providing unit that provides the answer generated by the generating unit; A system characterized by: (Appendix 2) The reception unit is Enter data using a presentation or spreadsheet tool 2. The system of claim 1, (Appendix 3) The creation unit is Select an appropriate layout or design based on the input data to create visually easy-to-understand materials 2. The system of claim 1, (Appendix 4) The analysis unit includes: Analyze sales or customer data to extract trends and patterns 2. The system of claim 1, (Appendix 5) The generation unit is Generates answers to inquiries about product specifications and prices based on a pre-trained database 2. The system of claim 1, (Appendix 6) The providing unit is Providing generated answers quickly and accurately 2. The system of claim 1, (Appendix 7) The reception unit is Infer user emotions and adjust the timing of data input based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past data entry history and select the input method 2. The system of claim 1, (Appendix 9) The reception unit is Filter data entry based on the user's current project or area of ​​interest 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and prioritize input data based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The reception unit is During data entry, prioritize relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is During data entry, populate relevant data based on the user's social media activity 2. The system of claim 1, (Appendix 13) The creation unit is Estimate the user's emotions and adjust the presentation of materials based on the estimated user's emotions 2. The system of claim 1, (Appendix 14) The creation unit is When creating documents, adjust the level of detail based on the importance of the data. 2. The system of claim 1, (Appendix 15) The creation unit is When creating a document, apply different layouts or designs based on the category of data 2. The system of claim 1, (Appendix 16) The creation unit is Estimate the user's emotions and adjust the length of the material based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The creation unit is When preparing documents, determine the priority of documents by considering the time of data submission. 2. The system of claim 1, (Appendix 18) The creation unit is When creating documents, adjust the order of documents taking into account the relevance of the data 2. The system of claim 1, (Appendix 19) The analysis unit includes: Infer user sentiment and adjust analysis criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The analysis unit includes: During analysis, improve the accuracy of your analysis based on data correlations 2. The system of claim 1, (Appendix 21) The analysis unit includes: During analysis, the analysis is based on the geographic distribution of the data. 2. The system of claim 1, (Appendix 22) The analysis unit includes: During analysis, improve the accuracy of the analysis based on the relevant literature of the data 2. The system of claim 1, (Appendix 23) The generation unit is Infer user sentiment and adjust answer generation method based on the inferred user sentiment 2. The system of claim 1, (Appendix 24) The generation unit is When generating answers, generate answers based on past inquiry data 2. The system of claim 1, (Appendix 25) The generation unit is Applying answer algorithms based on the query category while generating answers 2. The system of claim 1, (Appendix 26) The generation unit is Infer user sentiment and prioritize answers based on the inferred sentiment 2. The system of claim 1, (Appendix 27) The generation unit is When generating responses, prioritize responses by taking into account the time of the query submission 2. The system of claim 1, (Appendix 28) The generation unit is When generating answers, adjust the order of answers based on the relevance of the query 2. The system of claim 1, (Appendix 29) The providing unit is Inferring user sentiment and adjusting answer delivery based on the inferred user sentiment 2. The system of claim 1, (Appendix 30) The providing unit is When providing an answer, the method of providing it is selected based on the user's past inquiry history. 2. The system of claim 1, (Appendix 31) The providing unit is Inferring user sentiment and adjusting the order in which answers are provided based on the estimated user sentiment 2. The system of claim 1, (Appendix 32) The providing unit is When providing answers, the method of providing them is selected based on the user's device information. 2. The system of claim 1, [Explanation of symbols]

[0188] 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 section for inputting data; a creation unit that creates materials based on the data input by the reception unit; an analysis unit that analyzes numerical values ​​based on the materials created by the creation unit; a generation unit that generates an answer to the inquiry based on the analysis result obtained by the analysis unit; a providing unit that provides the answer generated by the generating unit; A system characterized by:

2. The reception unit is Enter data using a presentation or spreadsheet tool 2. The system of claim 1 .

3. The creation unit is Select an appropriate layout or design based on the input data to create visually easy-to-understand materials 2. The system of claim 1 .

4. The analysis unit includes: Analyze sales or customer data to extract trends and patterns 2. The system of claim 1 .

5. The generation unit is Generates answers to inquiries about product specifications and prices based on a pre-trained database 2. The system of claim 1 .

6. The reception unit is Infer user emotions and adjust the timing of data input based on the estimated user emotions 2. The system of claim 1 .

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

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