system

Abstract

The system according to this embodiment aims to efficiently generate standardized reports based on data and information, and to propose revisions. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a proposal unit. The reception unit receives user input information. The generation unit generates a report corresponding to the information received by the reception unit, which includes figures such as graphs and charts. The proposal unit proposes revisions to the report generated by the generation unit based on past documents.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a fixed-form report based on data and information has not been sufficiently generated efficiently and an amendment has not been proposed.

[0005] The system according to the embodiment aims to efficiently generate a fixed-form report based on data and information and propose an amendment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a proposal unit. The reception unit receives user input information. The generation unit generates a report corresponding to the information received by the reception unit, which includes figures such as graphs and charts. The proposal unit proposes revisions to the report generated by the generation unit based on past documents. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate standardized reports based on data and information, and propose revisions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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), etc.

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The document and report automatic generation system according to the embodiment of the present invention is a system that automatically generates standardized reports based on data and information. This system has the function of receiving user input information, generating a corresponding report, and suggesting revisions based on past documents. For example, the user inputs information such as sales data and customer feedback. This information is received by the reception unit. Next, the generation unit generates a report based on the information received by the reception unit. Figures such as graphs and charts are automatically inserted into this report to support data visualization. For example, a graph of sales trends is inserted based on sales data. Furthermore, the generated report is revised by the suggestion unit. The suggestion unit refers to past documents and makes suggestions and revisions to the wording. For example, appropriate expressions and wording are suggested based on past reports. This allows the user to efficiently create high-quality reports. In addition, the generation unit can generate standardized reports using generation AI. For example, the generation AI analyzes sales data and automatically generates a standardized sales report. Furthermore, the generation unit can estimate the user's emotions and generate different reports according to the estimated emotions. For example, if the user shows positive emotions, the unit generates a report that makes extensive use of positive expressions. Furthermore, the reception system can filter incoming information based on the user's current projects and areas of interest. For example, if a user is interested in marketing projects, it will prioritize receiving only relevant information. In addition, the reception system can estimate the user's emotions and prioritize the information received based on those emotions. For example, if a user is feeling stressed, it will prioritize receiving important information. The reception system can also prioritize receiving highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it will prioritize receiving information related to that region. This allows users to efficiently obtain the information they need. As a result, the automated document and report generation system can efficiently create high-quality reports by automatically generating reports based on user input and suggesting revisions.

[0029] The automated document and report generation system according to the embodiment comprises a reception unit, a generation unit, and a proposal unit. The reception unit receives user input information. User input information includes, but is not limited to, text, numerical data, and images. The reception unit receives information such as sales data and customer feedback entered by the user. The reception unit can also filter information based on the user's current projects and areas of interest. For example, if the user is interested in a marketing project, it will prioritize receiving only relevant information. Furthermore, the reception unit can estimate the user's emotions and determine the priority of the information to receive based on the estimated emotions. For example, if the user is stressed, it will prioritize receiving important information. The reception unit can also prioritize receiving highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize receiving information related to that region. The generation unit generates a report based on the information received by the reception unit. The report will automatically include diagrams such as graphs and charts to support data visualization. For example, a graph of sales trends may be inserted based on sales data. The generation unit can generate standardized reports using generation AI. For example, the generation AI analyzes sales data and automatically generates a standardized sales report. The generation unit can also estimate the user's emotions and generate different reports according to the estimated emotions. For example, if the user shows positive emotions, it will generate a report that uses a lot of positive language. The suggestion unit proposes revisions to the reports generated by the generation unit based on past documents. The suggestion unit uses generation AI to suggest and revise wording. For example, appropriate expressions and wording are suggested based on past reports. As a result, the automated document and report generation system according to this embodiment can efficiently create high-quality reports by automatically generating reports based on user input information and proposing revisions.

[0030] The reception desk receives user input information. User input information includes, but is not limited to, text, numerical data, and images. The reception desk receives information such as sales data and customer feedback entered by users. Specifically, data entered by users through web forms or dedicated applications is sent to the reception desk. This data is stored in the reception desk's database in real time and used for subsequent processing. The reception desk can also filter information based on the user's current projects and areas of interest. For example, if a user is interested in a marketing project, only relevant information will be prioritized. This ensures that the information the user needs is processed quickly and irrelevant data is filtered out. Furthermore, the reception desk can estimate the user's emotions and prioritize the information to be received based on those emotions. For example, if a user is feeling stressed, important information will be prioritized. Emotion estimation uses AI technology that analyzes the user's input content, input speed, and, in the case of voice input, the tone of voice. The reception desk can also prioritize highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, information related to that region will be prioritized. This allows region-specific data and trends to be reflected in reports, enabling the provision of more accurate information. By integrating these functions, the reception department can achieve flexible data reception tailored to user needs, improving the overall efficiency and accuracy of the system.

[0031] The generation unit generates reports based on information received by the reception unit. These reports automatically include diagrams such as graphs and charts to support data visualization. Specifically, a graph showing sales trends is inserted based on sales data. The generation unit can generate standardized reports using generation AI. The generation AI utilizes natural language processing technology to analyze user-provided data and create reports in appropriate context. For example, it analyzes sales data, automatically generates graphs showing sales increases / decreases and trends, and inserts explanatory text based on these graphs. Furthermore, the generation unit can estimate the user's emotions and generate different reports accordingly. For example, if the user expresses positive emotions, it generates a report that uses many positive expressions. Emotion estimation uses AI technology that analyzes user input and past feedback. In addition, the generation unit can automatically adjust the report layout and design. For example, it selects the optimal layout based on the type and amount of data to create a visually easy-to-understand report. This allows the generation unit to quickly generate high-quality reports based on user input, significantly improving the user's work efficiency.

[0032] The Proposal Department proposes revisions to reports generated by the Generation Department based on past documents. The Proposal Department uses generation AI to suggest and revise wording. Specifically, it stores past reports and documents in a database and analyzes them to suggest appropriate expressions and wording. For example, based on past reports, it might revise explanatory text for sales data to be more specific and easier to understand. Furthermore, the Proposal Department can continuously improve its suggestions based on user feedback. For instance, if a user adopts a suggested revision, that information is stored as training data to improve the accuracy of future suggestions. The Proposal Department can also provide revisions tailored to different industries and fields. For example, it can customize reports to meet user needs, such as using specialized terminology in medical reports or emphasizing trend analysis in marketing reports. This allows the Proposal Department to further improve the quality of generated reports and provide users with optimal documents. The Proposal Department's capabilities enable users to efficiently create high-quality reports, significantly improving their work productivity.

[0033] The proposal department can use generative AI to suggest and revise wording. For example, the proposal department can use generative AI to suggest appropriate expressions and wording based on past reports. For example, the proposal department can have the generative AI analyze past reports, extract appropriate wording, and suggest it. The proposal department can also revise wording using generative AI. For example, the proposal department can have the generative AI generate revised wording and propose it to the user. This improves the accuracy of wording suggestions and revisions by using generative AI. The generative AI can suggest and revise wording using, for example, text generation AI (e.g., LLM) or natural language processing technology. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or without using generative AI. For example, the proposal department can suggest and revise wording using a generative AI model that takes past reports as input and outputs appropriate wording.

[0034] The generation unit can generate standardized reports using a generation AI. For example, the generation unit can analyze sales data using a generation AI and automatically generate a standardized sales report. For example, the generation unit takes sales data as input and outputs a standardized sales report. The generation unit can also analyze customer feedback using a generation AI and generate a standardized customer feedback report. For example, the generation unit takes customer feedback as input and outputs a standardized customer feedback report. This makes the generation of standardized reports more efficient by using a generation AI. The generation AI can generate standardized reports using, for example, a text generation AI (e.g., LLM) or natural language processing technology. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can generate standardized reports using a generation AI model that takes sales data as input and outputs a standardized sales report.

[0035] The reception unit can filter input information based on the user's current projects and areas of interest. For example, if the user is interested in a marketing project, the reception unit will prioritize receiving only relevant information. For instance, the reception unit analyzes the information entered by the user, extracts information related to the marketing project, and prioritizes receiving that information. The reception unit can also filter information based on the user's areas of interest. For example, the reception unit will prioritize receiving information related to areas the user has indicated interest in. This allows for the priority of receiving highly relevant information by filtering information based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's input information into an AI and have the AI ​​perform the process of filtering information based on areas of interest.

[0036] The reception unit can prioritize receiving highly relevant information based on the user's geographical location when receiving input information. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. For example, the reception unit will analyze the user's geographical location and extract and prioritize receiving information related to that region. The reception unit can also filter information considering the user's geographical location. For example, the reception unit will prioritize receiving information related to regions that the user has shown interest in. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into AI and have the AI ​​perform the process of filtering highly relevant information.

[0037] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can analyze the user's past input history, extract frequently used input methods, and suggest them preferentially. The reception desk can also analyze patterns in the information the user has entered in the past and automatically generate the optimal input form. For example, the reception desk can analyze the user's past input patterns and generate the optimal input form. The reception desk can also consider the time of day the user has entered information in the past and select the most suitable reception method for that time of day. For example, the reception desk can analyze the user's past input time periods and suggest the most suitable reception method for that time of day. In this way, the optimal reception method can be selected by analyzing past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI and have the AI ​​perform the process of selecting the optimal reception method.

[0038] The generation unit can adjust the level of detail in graphs and charts based on the importance of the data during generation. For example, the generation unit can insert detailed graphs and charts for important data to visually highlight it. For instance, the generation unit can display particularly important parts of sales data in a detailed graph. The generation unit can also insert simplified graphs and charts for less important data. For example, the generation unit can display less important parts of sales data in a simplified graph. Furthermore, the generation unit can adjust the color and design of graphs and charts according to the importance of the data. For example, the generation unit can use bright colors for important data and light colors for less important data. This allows for visual emphasis by adjusting the level of detail in graphs and charts based on the importance of the data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can adjust the level of detail using a generation AI model that takes data importance as input and outputs the level of detail in graphs and charts.

[0039] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit can apply an algorithm that generates a graph showing sales trends to sales data. For example, the generation unit uses a generation AI model that takes sales data as input and outputs a graph showing sales trends. The generation unit can also apply an algorithm that performs sentiment analysis to customer feedback. For example, the generation unit uses a generation AI model that takes customer feedback as input and outputs the results of sentiment analysis. The generation unit can also apply an algorithm that generates a graph showing inventory trends to inventory data. For example, the generation unit uses a generation AI model that takes inventory data as input and outputs a graph showing inventory trends. By applying different generation algorithms depending on the data category, more appropriate reports can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can take the data category as input and have the generation AI perform the process of selecting the generation algorithm to apply.

[0040] The generation unit can determine the priority of reports based on the data submission date during generation. For example, the generation unit can prioritize generating reports for urgent data. For example, the generation unit can analyze the data submission date and time and prioritize generating reports for urgent data. The generation unit can also generate reports with normal priority for regular data. For example, the generation unit can generate reports with normal priority for regularly submitted data. The generation unit can also generate reports with lower priority for historical data. For example, the generation unit can generate reports with lower priority for data submitted in the past. This allows for priority processing of urgent data by determining the report priority based on the data submission date. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can determine priority using a generation AI model that takes the data submission date as input and outputs the report priority.

[0041] The generation unit can adjust the order of the report based on the relevance of the data during generation. For example, the generation unit can place important data first and organize the report in order of relevance. For example, the generation unit can analyze the relevance of the data and place important data first. Alternatively, the generation unit can postpone less relevant data and place it at the end of the report. For example, the generation unit can place less relevant data at the end of the report. The generation unit can also rearrange the sections of the report according to the relevance of the data. For example, the generation unit rearranges the sections based on the relevance of the data. This allows important data to be placed first by adjusting the order of the report based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can adjust the order using a generative AI model that takes data relevance as input and outputs the order of the report.

[0042] The proposal unit can adjust the level of detail in the proposed revisions based on the importance of past documents. For example, the proposal unit can propose detailed revisions for important documents. For example, the proposal unit can analyze the importance of past documents and propose detailed revisions for important documents. The proposal unit can also propose simplified revisions for less important documents. For example, the proposal unit can analyze the importance of past documents and propose simplified revisions for less important documents. The proposal unit can also adjust the content of the revisions according to the importance of the document. For example, the proposal unit can analyze the importance of past documents and adjust the content of the revisions according to their importance. In this way, by adjusting the level of detail in the revisions based on the importance of past documents, detailed revisions can be provided for important documents. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or not using generative AI. For example, the proposal unit can adjust the level of detail using a generative AI model that takes the importance of past documents as input and outputs the level of detail of the revisions.

[0043] The proposal unit can apply different proposal algorithms depending on the category of past documents when making a proposal. For example, the proposal unit can apply an algorithm that proposes revisions based on sales data to sales reports. For example, the proposal unit can use a generative AI model that takes sales reports as input and outputs revisions based on sales data. The proposal unit can also apply an algorithm that performs sentiment analysis to customer feedback. For example, the proposal unit can use a generative AI model that takes customer feedback as input and outputs the results of sentiment analysis. The proposal unit can also apply an algorithm that proposes revisions based on inventory data to inventory reports. For example, the proposal unit can use a generative AI model that takes inventory reports as input and outputs revisions based on inventory data. By applying different proposal algorithms depending on the category of past documents, more appropriate revisions can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without generative AI. For example, the proposal unit can take the category of past documents as input and have the generative AI perform the process of selecting the proposal algorithm to apply.

[0044] The proposal department can determine the priority of proposed revisions based on the submission dates of past documents. For example, the proposal department can prioritize proposals for urgent documents. For example, the proposal department can analyze the submission dates of past documents and prioritize proposals for urgent documents. The proposal department can also propose revisions with normal priority to regularly submitted documents. For example, the proposal department can propose revisions with normal priority to regularly submitted documents. The proposal department can also propose revisions with lower priority to older documents. For example, the proposal department can propose revisions with lower priority to documents submitted in the past. This allows for priority processing of urgent documents by determining the priority of revisions based on the submission dates of past documents. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can determine priority using a generative AI model that takes the submission dates of past documents as input and outputs the priority of proposed revisions.

[0045] The proposal unit can adjust the order of proposed revisions based on the relevance of past documents during the proposal process. For example, the proposal unit can place important documents first and propose revisions in order of relevance. For example, the proposal unit can analyze the relevance of past documents and place important documents first. The proposal unit can also postpone less relevant documents and place them at the end of the proposed revisions. For example, the proposal unit can place less relevant documents at the end of the proposed revisions. The proposal unit can also rearrange the sections of the proposed revisions according to the relevance of the documents. For example, the proposal unit can rearrange the sections based on the relevance of past documents. This allows important documents to be placed first by adjusting the order of the proposed revisions based on the relevance of past documents. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or not using generative AI. For example, the proposal unit can adjust the order using a generative AI model that takes the relevance of past documents as input and outputs the order of proposed revisions.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The reception desk can analyze the user's past input history when receiving user input information and suggest the most suitable input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). The reception desk can also analyze patterns in the information the user has previously entered and automatically generate the most suitable input form. For example, it can consider the time of day the user previously entered information and select the most suitable reception method for that time of day. In this way, the reception desk can select the most suitable reception method by analyzing past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI and have the AI ​​perform the process of selecting the most suitable reception method.

[0048] The generation unit can adjust the level of detail in graphs and charts based on the importance of the data during generation. For example, it can insert detailed graphs and charts for important data to visually highlight them. It can also insert simplified graphs and charts for less important data. Furthermore, the generation unit can adjust the color and design of graphs and charts according to the importance of the data. This allows for visual emphasis by adjusting the level of detail in graphs and charts based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes the importance of the data as input and outputs the level of detail in graphs and charts.

[0049] The proposal unit can adjust the level of detail in the proposed revisions based on the importance of past documents. For example, it can propose detailed revisions for important documents and simplified revisions for less important documents. The proposal unit can also adjust the content of the revisions according to the importance of the document. This allows for detailed revisions to be provided for important documents by adjusting the level of detail based on the importance of past documents. Some or all of the above processing in the proposal unit may be performed using generative AI or not. For example, the proposal unit can adjust the level of detail using a generative AI model that takes the importance of past documents as input and outputs the level of detail of the revisions.

[0050] The generation unit can apply different generation algorithms depending on the data category during generation. For example, an algorithm that generates a graph showing sales trends can be applied to sales data, and an algorithm that performs sentiment analysis can be applied to customer feedback. Inventory data can also be applied to an algorithm that generates a graph showing inventory trends. By applying different generation algorithms depending on the data category, more appropriate reports can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can take the data category as input and have the generation AI perform the process of selecting the generation algorithm to apply.

[0051] The proposal department can determine the priority of proposed revisions based on the submission dates of past documents. For example, it can propose revisions with priority to urgent documents and with normal priority to regular documents. It can also propose revisions with lower priority to older documents. This allows for priority processing of urgent documents by determining the priority of revisions based on the submission dates of past documents. Some or all of the above processing in the proposal department may be performed using generative AI, or not. For example, the proposal department can determine the priority using a generative AI model that takes the submission dates of past documents as input and outputs the priority of proposed revisions.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The reception desk receives user input information. User input information includes text, numerical data, images, etc. For example, it receives information such as sales data and customer feedback entered by the user. The reception desk can also filter information based on the user's current projects and areas of interest. Furthermore, the reception desk can estimate the user's emotions and determine the priority of the information to receive based on those emotions. For example, if the user is feeling stressed, important information will be prioritized. The reception desk can also prioritize highly relevant information by considering the user's geographical location. Step 2: The generation unit generates a report based on the information received by the reception unit. The report automatically includes graphs, charts, and other visual aids to support data visualization. For example, a graph showing sales trends is inserted based on sales data. The generation unit can also generate standardized reports using generation AI. For example, the generation AI analyzes sales data and automatically generates a standardized sales report. Furthermore, the generation unit can estimate the user's emotions and generate different reports based on the estimated emotions. For example, if the user expresses positive emotions, it will generate a report that uses more positive language. Step 3: The proposal team proposes revisions to the report generated by the generation team, based on past documents. The proposal team uses generation AI to suggest and revise wording. For example, appropriate expressions and wording are suggested based on past reports.

[0054] (Example of form 2) The document and report automatic generation system according to the embodiment of the present invention is a system that automatically generates standardized reports based on data and information. This system has the function of receiving user input information, generating a corresponding report, and suggesting revisions based on past documents. For example, the user inputs information such as sales data and customer feedback. This information is received by the reception unit. Next, the generation unit generates a report based on the information received by the reception unit. Figures such as graphs and charts are automatically inserted into this report to support data visualization. For example, a graph of sales trends is inserted based on sales data. Furthermore, the generated report is revised by the suggestion unit. The suggestion unit refers to past documents and makes suggestions and revisions to the wording. For example, appropriate expressions and wording are suggested based on past reports. This allows the user to efficiently create high-quality reports. In addition, the generation unit can generate standardized reports using generation AI. For example, the generation AI analyzes sales data and automatically generates a standardized sales report. Furthermore, the generation unit can estimate the user's emotions and generate different reports according to the estimated emotions. For example, if the user shows positive emotions, the unit generates a report that makes extensive use of positive expressions. Furthermore, the reception system can filter incoming information based on the user's current projects and areas of interest. For example, if a user is interested in marketing projects, it will prioritize receiving only relevant information. In addition, the reception system can estimate the user's emotions and prioritize the information received based on those emotions. For example, if a user is feeling stressed, it will prioritize receiving important information. The reception system can also prioritize receiving highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, it will prioritize receiving information related to that region. This allows users to efficiently obtain the information they need. As a result, the automated document and report generation system can efficiently create high-quality reports by automatically generating reports based on user input and suggesting revisions.

[0055] The automated document and report generation system according to the embodiment comprises a reception unit, a generation unit, and a proposal unit. The reception unit receives user input information. User input information includes, but is not limited to, text, numerical data, and images. The reception unit receives information such as sales data and customer feedback entered by the user. The reception unit can also filter information based on the user's current projects and areas of interest. For example, if the user is interested in a marketing project, it will prioritize receiving only relevant information. Furthermore, the reception unit can estimate the user's emotions and determine the priority of the information to receive based on the estimated emotions. For example, if the user is stressed, it will prioritize receiving important information. The reception unit can also prioritize receiving highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize receiving information related to that region. The generation unit generates a report based on the information received by the reception unit. The report will automatically include diagrams such as graphs and charts to support data visualization. For example, a graph of sales trends may be inserted based on sales data. The generation unit can generate standardized reports using generation AI. For example, the generation AI analyzes sales data and automatically generates a standardized sales report. The generation unit can also estimate the user's emotions and generate different reports according to the estimated emotions. For example, if the user shows positive emotions, it will generate a report that uses a lot of positive language. The suggestion unit proposes revisions to the reports generated by the generation unit based on past documents. The suggestion unit uses generation AI to suggest and revise wording. For example, appropriate expressions and wording are suggested based on past reports. As a result, the automated document and report generation system according to this embodiment can efficiently create high-quality reports by automatically generating reports based on user input information and proposing revisions.

[0056] The reception desk receives user input information. User input information includes, but is not limited to, text, numerical data, and images. The reception desk receives information such as sales data and customer feedback entered by users. Specifically, data entered by users through web forms or dedicated applications is sent to the reception desk. This data is stored in the reception desk's database in real time and used for subsequent processing. The reception desk can also filter information based on the user's current projects and areas of interest. For example, if a user is interested in a marketing project, only relevant information will be prioritized. This ensures that the information the user needs is processed quickly and irrelevant data is filtered out. Furthermore, the reception desk can estimate the user's emotions and prioritize the information to be received based on those emotions. For example, if a user is feeling stressed, important information will be prioritized. Emotion estimation uses AI technology that analyzes the user's input content, input speed, and, in the case of voice input, the tone of voice. The reception desk can also prioritize highly relevant information by considering the user's geographical location. For example, if a user is in a specific region, information related to that region will be prioritized. This allows region-specific data and trends to be reflected in reports, enabling the provision of more accurate information. By integrating these functions, the reception department can achieve flexible data reception tailored to user needs, improving the overall efficiency and accuracy of the system.

[0057] The generation unit generates reports based on information received by the reception unit. These reports automatically include diagrams such as graphs and charts to support data visualization. Specifically, a graph showing sales trends is inserted based on sales data. The generation unit can generate standardized reports using generation AI. The generation AI utilizes natural language processing technology to analyze user-provided data and create reports in appropriate context. For example, it analyzes sales data, automatically generates graphs showing sales increases / decreases and trends, and inserts explanatory text based on these graphs. Furthermore, the generation unit can estimate the user's emotions and generate different reports accordingly. For example, if the user expresses positive emotions, it generates a report that uses many positive expressions. Emotion estimation uses AI technology that analyzes user input and past feedback. In addition, the generation unit can automatically adjust the report layout and design. For example, it selects the optimal layout based on the type and amount of data to create a visually easy-to-understand report. This allows the generation unit to quickly generate high-quality reports based on user input, significantly improving the user's work efficiency.

[0058] The Proposal Department proposes revisions to reports generated by the Generation Department based on past documents. The Proposal Department uses generation AI to suggest and revise wording. Specifically, it stores past reports and documents in a database and analyzes them to suggest appropriate expressions and wording. For example, based on past reports, it might revise explanatory text for sales data to be more specific and easier to understand. Furthermore, the Proposal Department can continuously improve its suggestions based on user feedback. For instance, if a user adopts a suggested revision, that information is stored as training data to improve the accuracy of future suggestions. The Proposal Department can also provide revisions tailored to different industries and fields. For example, it can customize reports to meet user needs, such as using specialized terminology in medical reports or emphasizing trend analysis in marketing reports. This allows the Proposal Department to further improve the quality of generated reports and provide users with optimal documents. The Proposal Department's capabilities enable users to efficiently create high-quality reports, significantly improving their work productivity.

[0059] The proposal department can use generative AI to suggest and revise wording. For example, the proposal department can use generative AI to suggest appropriate expressions and wording based on past reports. For example, the proposal department can have the generative AI analyze past reports, extract appropriate wording, and suggest it. The proposal department can also revise wording using generative AI. For example, the proposal department can have the generative AI generate revised wording and propose it to the user. This improves the accuracy of wording suggestions and revisions by using generative AI. The generative AI can suggest and revise wording using, for example, text generation AI (e.g., LLM) or natural language processing technology. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or without using generative AI. For example, the proposal department can suggest and revise wording using a generative AI model that takes past reports as input and outputs appropriate wording.

[0060] The generation unit can generate standardized reports using a generation AI. For example, the generation unit can analyze sales data using a generation AI and automatically generate a standardized sales report. For example, the generation unit takes sales data as input and outputs a standardized sales report. The generation unit can also analyze customer feedback using a generation AI and generate a standardized customer feedback report. For example, the generation unit takes customer feedback as input and outputs a standardized customer feedback report. This makes the generation of standardized reports more efficient by using a generation AI. The generation AI can generate standardized reports using, for example, a text generation AI (e.g., LLM) or natural language processing technology. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can generate standardized reports using a generation AI model that takes sales data as input and outputs a standardized sales report.

[0061] The generation unit can estimate the user's emotions and generate different reports according to the estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate. This allows for the generation of reports tailored to the user's emotions, thereby providing more appropriate reports. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input image data of the user captured by the camera into the generation AI, and have the generation AI perform the estimation of the user's emotions.

[0062] The reception unit can filter input information based on the user's current projects and areas of interest. For example, if the user is interested in a marketing project, the reception unit will prioritize receiving only relevant information. For instance, the reception unit analyzes the information entered by the user, extracts information related to the marketing project, and prioritizes receiving that information. The reception unit can also filter information based on the user's areas of interest. For example, the reception unit will prioritize receiving information related to areas the user has indicated interest in. This allows for the priority of receiving highly relevant information by filtering information based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's input information into an AI and have the AI ​​perform the process of filtering information based on areas of interest.

[0063] The reception unit can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate. This allows the reception unit to prioritize information according to the user's emotions, thereby prioritizing the reception of important information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI, which can then perform an estimation of the user's emotions.

[0064] The reception unit can prioritize receiving highly relevant information based on the user's geographical location when receiving input information. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. For example, the reception unit will analyze the user's geographical location and extract and prioritize receiving information related to that region. The reception unit can also filter information considering the user's geographical location. For example, the reception unit will prioritize receiving information related to regions that the user has shown interest in. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into AI and have the AI ​​perform the process of filtering highly relevant information.

[0065] The reception unit can estimate the user's emotions and adjust the timing of receiving input information based on the estimated emotions. For example, if the user is stressed, the reception unit can temporarily delay receiving input information and wait until the user is relaxed. For example, the reception unit can capture the user's facial expression with a camera, estimate their emotions using an emotion estimation algorithm, and delay reception if the user is stressed. The reception unit can also immediately accept input information if the user is focused, thus maintaining the user's concentration. For example, the reception unit can record the user's voice, estimate their emotions using voice analysis technology, and immediately accept the information if the user is focused. Furthermore, if the user is tired, the reception unit can divide the input information reception into shorter sessions to complete them more quickly. For example, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and divide the reception if the user is tired. This allows for information to be received at a more appropriate time by adjusting the reception timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the reception area may be performed using AI, or not using AI. For example, the reception area may input image data of the user captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0066] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can analyze the user's past input history, extract frequently used input methods, and suggest them preferentially. The reception desk can also analyze patterns in the information the user has entered in the past and automatically generate the optimal input form. For example, the reception desk can analyze the user's past input patterns and generate the optimal input form. The reception desk can also consider the time of day the user has entered information in the past and select the most suitable reception method for that time of day. For example, the reception desk can analyze the user's past input time periods and suggest the most suitable reception method for that time of day. In this way, the optimal reception method can be selected by analyzing past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI and have the AI ​​perform the process of selecting the optimal reception method.

[0067] The generation unit can estimate the user's emotions and adjust the way the report is presented based on the estimated emotions. For example, if the user is showing positive emotions, the generation unit will generate a report that uses a lot of positive language. For instance, the generation unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and use a lot of positive language if the user is showing positive emotions. The generation unit can also generate a report that uses a lot of calm and objective language if the user is showing negative emotions. For example, the generation unit can record the user's voice, estimate their emotions using voice analysis technology, and use a lot of calm and objective language if the user is showing negative emotions. The generation unit can also generate a report that uses balanced language if the user is showing neutral emotions. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and use balanced language if the user is showing neutral emotions. By adjusting the way the report is presented according to the user's emotions, a more appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generative AI, or not using the generative AI. For example, the generation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0068] The generation unit can adjust the level of detail in graphs and charts based on the importance of the data during generation. For example, the generation unit can insert detailed graphs and charts for important data to visually highlight it. For instance, the generation unit can display particularly important parts of sales data in a detailed graph. The generation unit can also insert simplified graphs and charts for less important data. For example, the generation unit can display less important parts of sales data in a simplified graph. Furthermore, the generation unit can adjust the color and design of graphs and charts according to the importance of the data. For example, the generation unit can use bright colors for important data and light colors for less important data. This allows for visual emphasis by adjusting the level of detail in graphs and charts based on the importance of the data. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can adjust the level of detail using a generation AI model that takes data importance as input and outputs the level of detail in graphs and charts.

[0069] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit can apply an algorithm that generates a graph showing sales trends to sales data. For example, the generation unit uses a generation AI model that takes sales data as input and outputs a graph showing sales trends. The generation unit can also apply an algorithm that performs sentiment analysis to customer feedback. For example, the generation unit uses a generation AI model that takes customer feedback as input and outputs the results of sentiment analysis. The generation unit can also apply an algorithm that generates a graph showing inventory trends to inventory data. For example, the generation unit uses a generation AI model that takes inventory data as input and outputs a graph showing inventory trends. By applying different generation algorithms depending on the data category, more appropriate reports can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can take the data category as input and have the generation AI perform the process of selecting the generation algorithm to apply.

[0070] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise report. For instance, the generation unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and generate a short report if the user is in a hurry. The generation unit can also generate a longer report with detailed explanations if the user is relaxed. For example, the generation unit can record the user's voice, estimate their emotions using voice analysis technology, and generate a longer report if the user is relaxed. The generation unit can also generate a report with visually stimulating effects if the user is excited. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and generate a visually stimulating report if the user is excited. This allows for the provision of more appropriate reports by adjusting the length of the report according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0071] The generation unit can determine the priority of reports based on the data submission date during generation. For example, the generation unit can prioritize generating reports for urgent data. For example, the generation unit can analyze the data submission date and time and prioritize generating reports for urgent data. The generation unit can also generate reports with normal priority for regular data. For example, the generation unit can generate reports with normal priority for regularly submitted data. The generation unit can also generate reports with lower priority for historical data. For example, the generation unit can generate reports with lower priority for data submitted in the past. This allows for priority processing of urgent data by determining the report priority based on the data submission date. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can determine priority using a generation AI model that takes the data submission date as input and outputs the report priority.

[0072] The generation unit can adjust the order of the report based on the relevance of the data during generation. For example, the generation unit can place important data first and organize the report in order of relevance. For example, the generation unit can analyze the relevance of the data and place important data first. Alternatively, the generation unit can postpone less relevant data and place it at the end of the report. For example, the generation unit can place less relevant data at the end of the report. The generation unit can also rearrange the sections of the report according to the relevance of the data. For example, the generation unit rearranges the sections based on the relevance of the data. This allows important data to be placed first by adjusting the order of the report based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the generation unit can adjust the order using a generative AI model that takes data relevance as input and outputs the order of the report.

[0073] The proposal unit can estimate the user's emotions and adjust the expression of the revised proposal based on the estimated emotions. For example, if the user is showing positive emotions, the proposal unit will propose a revised proposal that makes frequent use of positive expressions. For example, the proposal unit will capture the user's facial expressions with a camera, estimate the emotions using an emotion estimation algorithm, and use positive expressions if the user is showing positive emotions. The proposal unit can also propose a revised proposal that makes frequent use of calm and objective expressions if the user is showing negative emotions. For example, the proposal unit will record the user's voice, estimate the emotions using voice analysis technology, and use calm and objective expressions if the user is showing negative emotions. The proposal unit can also propose a revised proposal that uses balanced expressions if the user is showing neutral emotions. For example, the proposal unit will collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the emotions using an emotion estimation algorithm, and use balanced expressions if the user is showing neutral emotions. In this way, by adjusting the expression of the revised proposal according to the user's emotions, a more appropriate revised proposal can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the proposed unit may be performed using a generative AI, or not using a generative AI. For example, the proposed unit can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0074] The proposal unit can adjust the level of detail in the proposed revisions based on the importance of past documents. For example, the proposal unit can propose detailed revisions for important documents. For example, the proposal unit can analyze the importance of past documents and propose detailed revisions for important documents. The proposal unit can also propose simplified revisions for less important documents. For example, the proposal unit can analyze the importance of past documents and propose simplified revisions for less important documents. The proposal unit can also adjust the content of the revisions according to the importance of the document. For example, the proposal unit can analyze the importance of past documents and adjust the content of the revisions according to their importance. In this way, by adjusting the level of detail in the revisions based on the importance of past documents, detailed revisions can be provided for important documents. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or not using generative AI. For example, the proposal unit can adjust the level of detail using a generative AI model that takes the importance of past documents as input and outputs the level of detail of the revisions.

[0075] The proposal unit can apply different proposal algorithms depending on the category of past documents when making a proposal. For example, the proposal unit can apply an algorithm that proposes revisions based on sales data to sales reports. For example, the proposal unit can use a generative AI model that takes sales reports as input and outputs revisions based on sales data. The proposal unit can also apply an algorithm that performs sentiment analysis to customer feedback. For example, the proposal unit can use a generative AI model that takes customer feedback as input and outputs the results of sentiment analysis. The proposal unit can also apply an algorithm that proposes revisions based on inventory data to inventory reports. For example, the proposal unit can use a generative AI model that takes inventory reports as input and outputs revisions based on inventory data. By applying different proposal algorithms depending on the category of past documents, more appropriate revisions can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without generative AI. For example, the proposal unit can take the category of past documents as input and have the generative AI perform the process of selecting the proposal algorithm to apply.

[0076] The suggestion unit can estimate the user's emotions and prioritize suggested modifications based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggesting important modifications. For instance, the suggestion unit could capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and prioritize suggesting important modifications if the user is stressed. The suggestion unit can also prioritize suggesting detailed modifications if the user is relaxed. For example, the suggestion unit could record the user's voice, estimate their emotions using voice analysis technology, and prioritize suggesting detailed modifications if the user is relaxed. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting modifications that require quick processing. For example, the suggestion unit could collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and prioritize suggesting modifications that require quick processing if the user is in a hurry. This allows the system to prioritize suggestions based on the user's emotions, thereby providing important modifications preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the proposed unit may be performed using a generative AI, or not using a generative AI. For example, the proposed unit can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0077] The proposal department can determine the priority of proposed revisions based on the submission dates of past documents. For example, the proposal department can prioritize proposals for urgent documents. For example, the proposal department can analyze the submission dates of past documents and prioritize proposals for urgent documents. The proposal department can also propose revisions with normal priority to regularly submitted documents. For example, the proposal department can propose revisions with normal priority to regularly submitted documents. The proposal department can also propose revisions with lower priority to older documents. For example, the proposal department can propose revisions with lower priority to documents submitted in the past. This allows for priority processing of urgent documents by determining the priority of revisions based on the submission dates of past documents. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can determine priority using a generative AI model that takes the submission dates of past documents as input and outputs the priority of proposed revisions.

[0078] The proposal unit can adjust the order of proposed revisions based on the relevance of past documents during the proposal process. For example, the proposal unit can place important documents first and propose revisions in order of relevance. For example, the proposal unit can analyze the relevance of past documents and place important documents first. The proposal unit can also postpone less relevant documents and place them at the end of the proposed revisions. For example, the proposal unit can place less relevant documents at the end of the proposed revisions. The proposal unit can also rearrange the sections of the proposed revisions according to the relevance of the documents. For example, the proposal unit can rearrange the sections based on the relevance of past documents. This allows important documents to be placed first by adjusting the order of the proposed revisions based on the relevance of past documents. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or not using generative AI. For example, the proposal unit can adjust the order using a generative AI model that takes the relevance of past documents as input and outputs the order of proposed revisions.

[0079] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0080] The reception desk can analyze the user's past input history when receiving user input information and suggest the most suitable input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice or text). The reception desk can also analyze patterns in the information the user has previously entered and automatically generate the most suitable input form. For example, it can consider the time of day the user previously entered information and select the most suitable reception method for that time of day. In this way, the reception desk can select the most suitable reception method by analyzing past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history into AI and have the AI ​​perform the process of selecting the most suitable reception method.

[0081] The generation unit can adjust the level of detail in graphs and charts based on the importance of the data during generation. For example, it can insert detailed graphs and charts for important data to visually highlight them. It can also insert simplified graphs and charts for less important data. Furthermore, the generation unit can adjust the color and design of graphs and charts according to the importance of the data. This allows for visual emphasis by adjusting the level of detail in graphs and charts based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes the importance of the data as input and outputs the level of detail in graphs and charts.

[0082] The proposal unit can adjust the level of detail in the proposed revisions based on the importance of past documents. For example, it can propose detailed revisions for important documents and simplified revisions for less important documents. The proposal unit can also adjust the content of the revisions according to the importance of the document. This allows for detailed revisions to be provided for important documents by adjusting the level of detail based on the importance of past documents. Some or all of the above processing in the proposal unit may be performed using generative AI or not. For example, the proposal unit can adjust the level of detail using a generative AI model that takes the importance of past documents as input and outputs the level of detail of the revisions.

[0083] The generation unit can apply different generation algorithms depending on the data category during generation. For example, an algorithm that generates a graph showing sales trends can be applied to sales data, and an algorithm that performs sentiment analysis can be applied to customer feedback. Inventory data can also be applied to an algorithm that generates a graph showing inventory trends. By applying different generation algorithms depending on the data category, more appropriate reports can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can take the data category as input and have the generation AI perform the process of selecting the generation algorithm to apply.

[0084] The proposal department can determine the priority of proposed revisions based on the submission dates of past documents. For example, it can propose revisions with priority to urgent documents and with normal priority to regular documents. It can also propose revisions with lower priority to older documents. This allows for priority processing of urgent documents by determining the priority of revisions based on the submission dates of past documents. Some or all of the above processing in the proposal department may be performed using generative AI, or not. For example, the proposal department can determine the priority using a generative AI model that takes the submission dates of past documents as input and outputs the priority of proposed revisions.

[0085] The generation unit can estimate the user's emotions and adjust the way the report is presented based on the estimated emotions. For example, if the user shows positive emotions, it can generate a report that uses a lot of positive language, and if the user shows negative emotions, it can generate a report that uses a lot of calm and objective language. If the user shows neutral emotions, it can also generate a report that uses balanced language. In this way, by adjusting the way the report is presented according to the user's emotions, a more appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0086] The proposal unit can estimate the user's emotions and adjust the expression of the proposed revisions based on the estimated emotions. For example, if the user is showing positive emotions, it can propose a revised revision that uses a lot of positive expressions, and if the user is showing negative emotions, it can propose a revised revision that uses a lot of calm and objective expressions. If the user is showing neutral emotions, it can also propose a revised revision that uses balanced expressions. In this way, by adjusting the expression of the revised revisions according to the user's emotions, more appropriate revised revisions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the proposal unit may be performed using a generative AI or not using a generative AI. For example, the proposal unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The reception unit can estimate the user's emotions and adjust the timing of receiving input information based on the estimated emotions. For example, if the user is stressed, the reception of input information can be temporarily delayed, waiting until the user relaxes. Conversely, if the user is focused, the input information can be received immediately to maintain the user's concentration. Furthermore, if the user is tired, the reception of input information can be divided and adjusted to be completed in a shorter time. In this way, by adjusting the reception timing according to the user's emotions, information can be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0088] The suggestion unit can estimate the user's emotions and prioritize suggested revisions based on those emotions. For example, if the user is stressed, it can prioritize suggesting important revisions; if the user is relaxed, it can prioritize suggesting detailed revisions. Furthermore, if the user is in a hurry, it can prioritize suggesting revisions that require quick processing. This allows for the prioritization of important revisions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit can input user image data captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0089] The generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is in a hurry, it can generate a short, concise report; if the user is relaxed, it can generate a longer report with detailed explanations. If the user is excited, it can also generate a report with visually stimulating effects. This allows for the provision of more appropriate reports by adjusting the report length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processes in the generation unit may be performed using or without the generative AI. For example, the generation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the user's emotion estimation.

[0090] The following briefly describes the processing flow for example form 2.

[0091] Step 1: The reception desk receives user input information. User input information includes text, numerical data, images, etc. For example, it receives information such as sales data and customer feedback entered by the user. The reception desk can also filter information based on the user's current projects and areas of interest. Furthermore, the reception desk can estimate the user's emotions and determine the priority of the information to receive based on those emotions. For example, if the user is feeling stressed, important information will be prioritized. The reception desk can also prioritize highly relevant information by considering the user's geographical location. Step 2: The generation unit generates a report based on the information received by the reception unit. The report automatically includes graphs, charts, and other visual aids to support data visualization. For example, a graph showing sales trends is inserted based on sales data. The generation unit can also generate standardized reports using generation AI. For example, the generation AI analyzes sales data and automatically generates a standardized sales report. Furthermore, the generation unit can estimate the user's emotions and generate different reports based on the estimated emotions. For example, if the user expresses positive emotions, it will generate a report that uses more positive language. Step 3: The proposal team proposes revisions to the report generated by the generation team, based on past documents. The proposal team uses generation AI to suggest and revise wording. For example, appropriate expressions and wording are suggested based on past reports.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0094] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user input information. For example, text and voice data can be received using the touch panel 38A or microphone 38B of the reception device 38. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates a report based on the information received by the reception unit. For example, the identification processing unit 290 analyzes sales data using generation AI and automatically generates a standardized sales report. The proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes revised versions of reports generated based on past documents. For example, it estimates the user's emotions using the emotion identification model 59 and proposes appropriate expressions and wording. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0096] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0097] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0102] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0103] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0104] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0105] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0106] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0110] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user input information. For example, voice data can be received using the microphone 238. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates a report based on the information received by the reception unit. For example, the identification processing unit 290 analyzes sales data using generation AI and automatically generates a standardized sales report. The proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes revised versions of reports generated based on past documents. For example, it estimates the user's emotions using the emotion identification model 59 and proposes appropriate expressions and wording. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0112] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0113] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0116] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user input information. For example, voice data can be received using the microphone 238. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates a report based on the information received by the reception unit. For example, the identification processing unit 290 analyzes sales data using generation AI and automatically generates a standardized sales report. The proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes revised versions of reports generated based on past documents. For example, it estimates the user's emotions using the emotion identification model 59 and proposes appropriate expressions and wording. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0128] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0129] As shown in Figure 7, the 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.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0136] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives user input information. For example, voice data can be received using the microphone 238. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates a report based on the information received by the reception unit. For example, the identification processing unit 290 analyzes sales data using a generation AI and automatically generates a standardized sales report. The proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and proposes revised versions of reports generated based on past documents. For example, it estimates the user's emotions using an emotion identification model 59 and proposes appropriate expressions and wording. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0147] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0148] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0149] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0153] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0154] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0155] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0156] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0157] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0158] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0160] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0161] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0162] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0163] (Note 1) A reception desk that receives user input information, A generating unit that generates a report corresponding to the information received by the aforementioned receiving unit, the report having figures including graphs and charts inserted; The system comprises: a proposal unit that proposes revised versions of the report generated by the generation unit based on past documents; and A system characterized by the following features. (Note 2) The aforementioned proposal section is, Use generative AI to suggest and revise wording. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate standardized reports using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It estimates the user's emotions and generates different reports based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When receiving input information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving input information, the system prioritizes receiving information that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of receiving input information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates the user's emotions and adjusts the way the report is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, the level of detail in graphs and charts is adjusted based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the user's emotions and adjusts the length of the report based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, report priorities are determined based on the data submission date. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the order of reports is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the proposed revisions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposed revisions based on the importance of the previous documents. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the category of past documents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and prioritizes proposed revisions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting a proposal, prioritize the proposed revisions based on the submission dates of previous documents. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the order of the proposed revisions based on the relevance of previous documents. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives user input information, A generating unit that generates a report corresponding to the information received by the aforementioned receiving unit, the report having figures including graphs and charts inserted; The system comprises: a proposal unit that proposes revised versions of the report generated by the generation unit based on past documents; and A system characterized by the following features.

2. The aforementioned proposal section is, Use generative AI to suggest and revise wording. The system according to feature 1.

3. The generating unit is Generate standardized reports using generative AI. The system according to feature 1.

4. The generating unit is It estimates the user's emotions and generates different reports based on the estimated emotions. The system according to feature 1.

5. The aforementioned reception unit is When receiving input information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is When receiving input information, the system prioritizes receiving information that is highly relevant based on the user's geographical location. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of receiving input information based on the estimated emotions. The system according to feature 1.

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