System

Abstract

An object of the system according to the embodiment is to customize a material in consideration of a person to whom a description is given.SOLUTION: A system includes a reception unit, an analysis unit, a storage unit, and a customization unit. The reception unit inputs a material created by a user. The analysis unit analyzes the model material input by the reception unit. The storage unit stores the learning result of the model material analyzed by the analysis unit. The customization unit customizes the material of the user based on the learning result stored in the storage unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology uses generative AI to assist in document creation, but there is an issue in that it is not sufficiently customized to take into account the audience.

[0005] The system according to the embodiment aims to customize materials with consideration for the person to whom the materials will be explained. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a storage unit, and a customization unit. The reception unit inputs materials created by a user. The analysis unit analyzes the model materials input by the reception unit. The storage unit stores the learning results of the model materials analyzed by the analysis unit. The customization unit customizes the user's materials based on the learning results stored in the storage unit. [Effects of the Invention]

[0007] The system according to the embodiment can customize materials taking into consideration the person to whom the materials are being explained. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A document creation assistance tool according to an embodiment of the present invention is a system that automatically customizes documents created by a user. This system inputs documents created by the user, and a generation AI analyzes model documents and customizes the user's documents to a format that is easy to understand for the recipient. For example, when inputting documents created by the user, previously used presentation materials or reports are set as model documents. The generation AI learns the structure and expression methods of the model documents and optimizes the user's documents based on those materials. This creates documents that are easy to understand for the recipient. For example, the generation AI learns the layout, fonts, color usage, etc. of the model documents and applies them to the user's documents. The generation AI customizes the user's documents to a format that is easy to understand for the recipient. For example, it increases font size or changes colors to emphasize important points. It is also possible to add charts and graphs to make the documents visually easier to understand. In this way, the document creation assistance tool efficiently customizes the user's documents to create documents that are easy to understand for the recipient. In this way, the document creation assistance tool efficiently customizes the user's documents to create documents that are easy to understand for the recipient. For example, it can be used in a variety of situations, such as sales presentations and internal company reports.

[0029] A material creation assistance tool according to an embodiment includes a reception unit, an analysis unit, a storage unit, and a customization unit. The reception unit inputs materials created by a user. The materials created by a user include, but are not limited to, text documents, presentation materials, and image files. The reception unit can input, for example, materials created by a user in digital format. The reception unit also has a function for inputting model materials to be referenced by the user. For example, previously used presentation materials or reports may be used as model materials. The analysis unit uses a generation AI to analyze the model materials input by the reception unit. The analysis may be performed using, for example, text analysis, image analysis, data mining, or other methods. For example, the generation AI may learn the structure and expression method of the model materials and customize the user's materials based on the learned results. The storage unit stores the learning results of the model materials analyzed by the analysis unit. The learning results may include, for example, but are not limited to, model parameters and statistical information on the analysis results. The storage unit stores the learning results in a database for later reference. The customization unit uses the generation AI to customize the user's materials based on the learning results stored in the storage unit. Customization can be performed, for example, by changing font size, color usage, or layout adjustment, but is not limited to these examples. For example, the customization unit may increase font size or change color to emphasize important points. The customization unit can also add charts and graphs to make the materials visually easier to understand. This allows the material creation assistance tool according to the embodiment to efficiently customize the user's materials and create materials that are easy for the recipient to understand. Some or all of the above-described processing in the customization unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the customization unit may input the learning results stored in the storage unit into the generation AI and cause the generation AI to customize the user's materials.

[0030] The reception unit may have a function for inputting model materials referenced by the user. Examples of model materials include, but are not limited to, standard presentation materials and template documents. The reception unit may input, for example, presentation materials or reports previously used by the user as model materials. The reception unit may also input the model materials referenced by the user in digital format. For example, the reception unit may simultaneously input materials created by the user and the model materials. This improves the accuracy of customization by inputting the model materials referenced by the user. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the model materials input by the user into the generation AI and have the generation AI analyze the model materials.

[0031] The analysis unit can learn the structure and expression of the model material. Examples of the structure and expression include, but are not limited to, the chapter structure, the arrangement of figures and tables, and the format of the document. The analysis unit, for example, uses a generative AI to learn the structure and expression of the model material. For example, the generative AI learns the layout, font, color usage, etc. of the model material and customizes the user's material based on that. The analysis unit can also analyze the content of the model material and extract important points. For example, the generative AI analyzes the text of the model material and extracts important keywords and phrases. This allows the structure and expression of the model material to be learned, thereby more appropriately customizing the user's material. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generative AI. For example, the analysis unit can input the model material into the generative AI and have the generative AI learn the structure and expression.

[0032] The customization unit can apply specific customization methods, such as changing font size, changing color, and adding charts and graphs. Examples of font size changes include, but are not limited to, standard font size and highlighted font size. The customization unit can, for example, use a generation AI to change the font size of the user's materials. For example, the font size can be increased to emphasize important points. Examples of color changes include, but are not limited to, highlighted colors, background colors, and text colors. The customization unit can, for example, use a generation AI to change the color of the user's materials. For example, the color can be changed to emphasize important points. Examples of adding charts and graphs include, but are not limited to, graph types and chart placement methods. The customization unit can, for example, use a generation AI to add charts and graphs to the user's materials. For example, graphs can be added to the materials to make them more visually understandable. By applying specific customization methods, the visual understandability of the materials can be improved. Some or all of the above-described processing in the customization unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the customization unit can input the user's materials into the generation AI and have the generation AI change the font size, change the color scheme, or add charts and graphs.

[0033] The customization unit can change the font size and color to emphasize important points. Important points include, but are not limited to, keywords, key data points, etc. The customization unit can, for example, use the generation AI to emphasize important points in the user's materials. For example, the font size can be increased to emphasize important points. The customization unit can also change the color to emphasize important points. For example, the color can be changed to emphasize important points. By emphasizing important points, materials that are easy for the recipient to understand can be created. Some or all of the above-described processing in the customization unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the customization unit can input the user's materials into the generation AI and have the generation AI emphasize the important points.

[0034] The customization unit can add charts and graphs to make the material more visually understandable. Methods for making the material more visually understandable include, but are not limited to, color usage, layout, font size, etc. The customization unit can add charts and graphs to the user's materials, for example, using a generation AI. For example, a graph can be added to the material to make it more visually understandable. The customization unit can also adjust the layout of the material to make it more visually understandable. For example, the layout can be changed to highlight important information. By adding charts and graphs, the material can be more visually understandable. Some or all of the above-described processing in the customization unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the customization unit can input the user's materials into the generation AI and cause the generation AI to add charts and graphs.

[0035] The reception unit can analyze the user's past document creation history and select the optimal input method. The past document creation history includes, but is not limited to, past documents, editing history, and templates used. The reception unit can, for example, use a generation AI to analyze the user's past document creation history. For example, the generation AI can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also automatically select a specific format or template from the user's past document creation history. For example, the generation AI can analyze the content of documents created by the user in the past and suggest the optimal input method for creating similar documents. In this way, by analyzing the past document creation history, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past document creation history into the generation AI and have the generation AI select the optimal input method.

[0036] When inputting materials, the reception unit can filter the materials based on the user's current project or area of ​​interest. Examples of current projects and areas of interest include, but are not limited to, project themes and keywords related to the area of ​​interest. The reception unit can, for example, use a generation AI to identify the user's current project or area of ​​interest. For example, the generation AI can analyze data from the user's project management tool or social media to identify the current project or area of ​​interest. The reception unit can also filter the materials based on the identified project or area of ​​interest. For example, the reception unit can filter the materials so that only materials related to the user's current project are input. Related materials can also be preferentially input based on the user's area of ​​interest. Furthermore, related materials can be filtered and input based on topics in which the user has previously shown interest. This allows for efficient input of highly relevant materials by filtering the materials based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input data on the user's project or area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0037] When inputting materials, the reception unit can select the optimal input means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. The reception unit, for example, uses a generation AI to identify the user's input method. For example, the generation AI can analyze the user's past input history and current situation and select the optimal input means. The reception unit can also provide the optimal input means depending on the identified input method. For example, if the user desires voice input, the generation AI can input the materials using voice recognition technology. If the user desires text input, the generation AI can provide a text editor to input the materials. Furthermore, if the user desires image input, the generation AI can input the materials using image recognition technology. This enables efficient input of materials by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input data on the user's input method into the generation AI and have the generation AI select the optimal input means.

[0038] When inputting materials, the reception unit can prioritize inputting highly relevant materials by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. The reception unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI can acquire location information of the user's smartphone or computer. The reception unit can also prioritize inputting highly relevant materials based on the acquired geographical location information. For example, if the user is in a specific area, materials related to that area can be prioritized. Also, if the user is on a business trip, materials related to the business trip destination can be prioritized. Furthermore, if the user is at home, materials related to the home can be prioritized. This allows highly relevant materials to be efficiently input by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant materials.

[0039] When inputting materials, the reception unit can analyze the user's social media activity and input relevant materials. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit can, for example, use a generation AI to analyze the user's social media activity. For example, the generation AI can input materials related to articles the user shared on social media. The reception unit can also analyze the content of the user's social media posts and input relevant materials. For example, the generation AI can input relevant materials based on the activities of the user's friends on social media. This allows for efficient input of highly relevant materials by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input data on the user's social media activity into the generation AI and have the generation AI select relevant materials.

[0040] When inputting materials, the reception unit can customize the input method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user comments and evaluation scores. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the generation AI can prioritize the use of input methods for which the user has provided feedback in the past. The reception unit can also suggest an optimal input method based on the user's past feedback. For example, the generation AI can avoid input methods for which the user has previously expressed dissatisfaction and suggest other input methods. This allows the optimal input method to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input data of the user's past feedback into the generation AI and have the generation AI customize the input method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. Examples of the importance of the document include, but are not limited to, project priority and the evaluations of stakeholders. The analysis unit, for example, uses a generation AI to evaluate the importance of the document. For example, the generation AI can evaluate the importance of the document based on the project priority and the evaluations of stakeholders. The analysis unit can also adjust the level of detail of the analysis based on the evaluated importance of the document. For example, the generation AI can perform a detailed analysis of highly important documents. For less important documents, the generation AI can perform a concise analysis. Furthermore, the generation AI can dynamically adjust the level of detail of the analysis depending on the importance of the document. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the document. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the importance of the document into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the document. Examples of document categories include, but are not limited to, technical documents, marketing materials, and presentation materials. The analysis unit, for example, uses a generation AI to identify the category of the document. For example, the generation AI can analyze the content and format of the document to identify the category. The analysis unit can then apply different analysis algorithms depending on the identified category. For example, the generation AI can apply a visual analysis algorithm to presentation materials. The generation AI can apply a text analysis algorithm to reports. The generation AI can also apply a specialized analysis algorithm to technical documents. This allows for more appropriate analysis results by applying different analysis algorithms depending on the category of the document. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input data on the document category into the generation AI and have the generation AI apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past reports and analysis logs. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the generation AI can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also suggest an optimal analysis method by referring to the user's past analysis results. For example, the generation AI can analyze the user's past analysis results and adjust optimal analysis parameters. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the submission date of the documents. The submission date of the documents includes, but is not limited to, a submission deadline and a submission date and time. The analysis unit, for example, uses a generation AI to obtain the submission date of the documents. For example, the generation AI can analyze data from a user's project management tool or calendar to identify the submission date. The analysis unit can also determine the analysis priority based on the identified submission date. For example, documents with an upcoming deadline can be analyzed first. Furthermore, documents with a more distant submission date can be postponed. Furthermore, the generation AI can dynamically adjust the analysis priority according to the submission date of the documents. This enables efficient analysis by determining the analysis priority based on the submission date of the documents. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the submission date of the documents into the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the analysis order based on the relevance of the materials. Examples of the relevance of the materials include, but are not limited to, the degree of theme similarity and keyword commonality. The analysis unit, for example, uses a generation AI to evaluate the relevance of the materials. For example, the generation AI can analyze the themes and keywords of the materials and evaluate the relevance. The analysis unit can also adjust the analysis order based on the evaluated relevance. For example, highly relevant materials can be analyzed preferentially. Less relevant materials can be postponed. Furthermore, the generation AI can dynamically adjust the analysis order according to the relevance of the materials. This enables efficient analysis by adjusting the analysis order based on the relevance of the materials. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the relevance of the materials into the generation AI and have the generation AI adjust the analysis order.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of technical terms include, but are not limited to, qualifications and past work experience. The analysis unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation AI can analyze the user's resume and work history to evaluate the user's level of expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the evaluated level of expertise. For example, if the user has technical expertise, the generation AI can use a lot of technical terms. On the other hand, if the user does not have technical expertise, the generation AI can avoid technical terms. Furthermore, the generation AI can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terms.

[0047] The storage unit can optimize the storage algorithm by referring to past stored data during storage. Examples of storage algorithms include, but are not limited to, data compression algorithms and database indexing methods. The storage unit can analyze the past stored data, for example, using a generation AI. For example, the generation AI can propose an optimal storage algorithm based on the past stored data. The storage unit can also dynamically adjust the storage algorithm based on the past stored data. For example, the generation AI can improve storage efficiency by referring to the past stored data. This makes it possible to optimize the storage algorithm by referring to the past stored data. Some or all of the above-described processing in the storage unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the storage unit can input the past stored data into the generation AI and have the generation AI optimize the storage algorithm.

[0048] The storage unit can update the stored data by reflecting user feedback when saving the data. Updating the stored data includes, for example, data version management and update frequency, but is not limited to these examples. The storage unit can, for example, analyze user feedback using a generation AI. For example, the generation AI can update the stored data based on feedback provided by the user. The storage unit can also optimize the structure of the stored data by reflecting user feedback. For example, the generation AI can update the content of the stored data by referring to the user feedback. This allows the stored data to be updated efficiently by reflecting user feedback. Some or all of the above-mentioned processing in the storage unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the storage unit can input user feedback data into the generation AI and have the generation AI update the stored data.

[0049] The storage unit can weight the stored data based on the submission date of the document when storing the data. Weighting of the stored data includes, but is not limited to, importance and frequency of use. The storage unit, for example, uses a generation AI to obtain the submission date of the document. For example, the generation AI can analyze data from a user's project management tool or calendar to identify the submission date. The storage unit can also weight the stored data based on the identified submission date. For example, data of documents with an upcoming deadline can be stored preferentially. Data of documents with a distant submission date can be postponed. Furthermore, the generation AI can dynamically adjust the weighting of the stored data depending on the submission date of the document. This enables efficient data storage by weighting the stored data based on the submission date of the document. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the storage unit can input data on the submission date of the document into the generation AI and have the generation AI weight the stored data.

[0050] The storage unit can integrate information from different data sources to enrich the stored data when storing the data. Examples of different data sources include, but are not limited to, external APIs, databases, and file systems. The storage unit can acquire information from different data sources using, for example, a generation AI. For example, the generation AI can acquire information from external APIs and databases, integrate the information, and store it. The storage unit can also enrich the stored data based on the acquired information. For example, the generation AI can analyze information from different data sources and enrich the stored data in an optimal manner. Furthermore, the storage unit can integrate and optimize the stored data based on data sources provided by the user. This enables the enrichment of the stored data by integrating information from different data sources. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the storage unit can input data from different data sources into the generation AI and have the generation AI integrate the information.

[0051] During customization, the customization unit can adjust the level of customization detail based on the importance of the material. Examples of the level of customization detail include, but are not limited to, detailed adjustments and overall layout changes. The customization unit, for example, uses a generation AI to evaluate the importance of the material. For example, the generation AI can evaluate the importance of the material based on project priorities and evaluations by stakeholders. The customization unit can also adjust the level of customization detail based on the evaluated importance of the material. For example, the generation AI can perform detailed customization for highly important material. For less important material, the generation AI can perform simple customization. Furthermore, the generation AI can dynamically adjust the level of customization detail depending on the importance of the material. This enables efficient material customization by adjusting the level of customization detail based on the importance of the material. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input data on the importance of the material to the generation AI and cause the generation AI to adjust the level of customization detail.

[0052] During customization, the customization unit can apply different customization algorithms depending on the category of the document. Examples of customization algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. The customization unit, for example, uses a generation AI to identify the category of the document. For example, the generation AI can analyze the content and format of the document to identify the category. The customization unit can then apply different customization algorithms depending on the identified category. For example, the generation AI can apply a visual customization algorithm to presentation materials. The generation AI can apply a text customization algorithm to reports. The generation AI can also apply a specialized customization algorithm to technical documents. This allows for more appropriate document customization by applying different customization algorithms depending on the category of the document. Some or all of the above-described processing in the customization unit can be performed using, or without, the generation AI. For example, the customization unit can input data on the category of the document into the generation AI and have the generation AI apply the customization algorithm.

[0053] During customization, the customization unit can improve the accuracy of the customization by referring to the user's past customization results. Past customization results include, but are not limited to, past reports and customization logs. The customization unit, for example, uses a generation AI to analyze the user's past customization results. For example, the generation AI can improve the accuracy of the customization based on the user's past customization results. The customization unit can also suggest an optimal customization method by referring to the user's past customization results. For example, the generation AI can analyze the user's past customization results and adjust optimal customization parameters. This improves the accuracy of the customization by referring to the user's past customization results. Some or all of the above-described processing in the customization unit may be performed, for example, using the generation AI or without using the generation AI. For example, the customization unit can input data on the user's past customization results into the generation AI and cause the generation AI to improve the accuracy of the customization.

[0054] During customization, the customization unit can adjust the order of customization based on the submission dates of the documents. Examples of the order of customization include, but are not limited to, submission deadlines and importance. The customization unit, for example, uses a generation AI to obtain the submission dates of the documents. For example, the generation AI can analyze data from a user's project management tool or calendar to identify the submission dates. The customization unit can also adjust the order of customization based on the identified submission dates. For example, customization of documents with upcoming deadlines can be prioritized. Furthermore, customization of documents with distant submission dates can be postponed. Furthermore, the generation AI can dynamically adjust the order of customization depending on the submission dates of the documents. This enables efficient document customization by adjusting the order of customization based on the submission dates of the documents. Some or all of the above-described processing in the customization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the customization unit can input data on the submission dates of the documents into the generation AI and have the generation AI adjust the order of customization.

[0055] During customization, the customization unit can adjust the order of customization based on the relevance of the materials. Examples of the relevance of the materials include, but are not limited to, the degree of theme similarity and keyword commonality. The customization unit, for example, uses a generation AI to evaluate the relevance of the materials. For example, the generation AI can analyze the themes and keywords of the materials and evaluate the relevance. The customization unit can also adjust the order of customization based on the evaluated relevance. For example, the customization of highly relevant materials can be prioritized. Furthermore, the customization of less relevant materials can be postponed. Furthermore, the generation AI can dynamically adjust the order of customization based on the relevance of the materials. This enables efficient material customization by adjusting the order of customization based on the relevance of the materials. Some or all of the above-described processing in the customization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the customization unit can input data on the relevance of the materials into the generation AI and have the generation AI adjust the order of customization.

[0056] During customization, the customization unit can adjust the use of technical terms in the customization according to the user's level of expertise. The use of technical terms includes, but is not limited to, the definition and frequency of use of technical terms. The customization unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation AI can analyze the user's resume and work history to evaluate the user's level of expertise. The customization unit can also adjust the use of technical terms in the customization according to the evaluated level of expertise. For example, if the user has technical expertise, the generation AI can use a lot of technical terms. On the other hand, if the user does not have technical expertise, the generation AI can avoid technical terms. Furthermore, the generation AI can dynamically adjust the use of technical terms in the customization according to the user's level of expertise. This enables more appropriate document customization by adjusting the use of technical terms in the customization according to the user's level of expertise. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terms.

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

[0058] The reception unit can analyze the user's past document creation history and select the optimal input method. The past document creation history includes, but is not limited to, past documents, editing history, and templates used. The reception unit can, for example, use a generation AI to analyze the user's past document creation history. For example, the generation AI can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also automatically select a specific format or template from the user's past document creation history. For example, the generation AI can analyze the content of documents created by the user in the past and suggest the optimal input method for creating similar documents. In this way, by analyzing the past document creation history, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past document creation history into the generation AI and have the generation AI select the optimal input method.

[0059] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. Examples of the importance of the document include, but are not limited to, project priority and the evaluations of stakeholders. The analysis unit, for example, uses a generation AI to evaluate the importance of the document. For example, the generation AI can evaluate the importance of the document based on the project priority and the evaluations of stakeholders. The analysis unit can also adjust the level of detail of the analysis based on the evaluated importance of the document. For example, the generation AI can perform a detailed analysis of highly important documents. For less important documents, the generation AI can perform a concise analysis. Furthermore, the generation AI can dynamically adjust the level of detail of the analysis depending on the importance of the document. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the document. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the importance of the document into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0060] During customization, the customization unit can apply different customization algorithms depending on the category of the document. Examples of customization algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. The customization unit, for example, uses a generation AI to identify the category of the document. For example, the generation AI can analyze the content and format of the document to identify the category. The customization unit can then apply different customization algorithms depending on the identified category. For example, the generation AI can apply a visual customization algorithm to presentation materials. The generation AI can apply a text customization algorithm to reports. The generation AI can also apply a specialized customization algorithm to technical documents. This allows for more appropriate document customization by applying different customization algorithms depending on the category of the document. Some or all of the above-described processing in the customization unit can be performed using, or without, the generation AI. For example, the customization unit can input data on the category of the document into the generation AI and have the generation AI apply the customization algorithm.

[0061] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the document. Examples of document categories include, but are not limited to, technical documents, marketing materials, and presentation materials. The analysis unit, for example, uses a generation AI to identify the category of the document. For example, the generation AI can analyze the content and format of the document to identify the category. The analysis unit can then apply different analysis algorithms depending on the identified category. For example, the generation AI can apply a visual analysis algorithm to presentation materials. The generation AI can apply a text analysis algorithm to reports. The generation AI can also apply a specialized analysis algorithm to technical documents. This allows for more appropriate analysis results by applying different analysis algorithms depending on the category of the document. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input data on the document category into the generation AI and have the generation AI apply the analysis algorithm.

[0062] The storage unit can integrate information from different data sources to enrich the stored data when storing the data. Examples of different data sources include, but are not limited to, external APIs, databases, and file systems. The storage unit can acquire information from different data sources using, for example, a generation AI. For example, the generation AI can acquire information from external APIs and databases, integrate the information, and store it. The storage unit can also enrich the stored data based on the acquired information. For example, the generation AI can analyze information from different data sources and enrich the stored data in an optimal manner. Furthermore, the storage unit can integrate and optimize the stored data based on data sources provided by the user. This enables the enrichment of the stored data by integrating information from different data sources. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the storage unit can input data from different data sources into the generation AI and have the generation AI integrate the information.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit inputs materials created by the user. Materials created by the user include text documents, presentation materials, image files, etc. The reception unit can input these materials in digital format. The reception unit also has a function to input model materials that the user will refer to, and past presentation materials, reports, etc. can be used as model materials. Step 2: The analysis unit uses the generation AI to analyze the model materials entered by the reception unit. The analysis is performed using methods such as text analysis, image analysis, and data mining. The generation AI learns the structure and expression methods of the model materials and customizes the user's materials based on that information. Step 3: The storage unit stores the learning results of the model data analyzed by the analysis unit. The learning results include model parameters and statistical information on the analysis results. The storage unit stores these learning results in a database so that they can be referenced later. Step 4: The customization unit uses the generative AI to customize the user's materials based on the learning results stored in the storage unit. Customization is performed by changing the font size, color, and layout, for example. For example, you can increase the font size or change the color to emphasize important points. You can also add charts and graphs to make the materials easier to understand visually.

[0065] (Example 2) A document creation assistance tool according to an embodiment of the present invention is a system that automatically customizes documents created by a user. This system inputs documents created by the user, and a generation AI analyzes model documents and customizes the user's documents to a format that is easy to understand for the recipient. For example, when inputting documents created by the user, previously used presentation materials or reports are set as model documents. The generation AI learns the structure and expression methods of the model documents and optimizes the user's documents based on those materials. This creates documents that are easy to understand for the recipient. For example, the generation AI learns the layout, fonts, color usage, etc. of the model documents and applies them to the user's documents. The generation AI customizes the user's documents to a format that is easy to understand for the recipient. For example, it increases font size or changes colors to emphasize important points. It is also possible to add charts and graphs to make the documents visually easier to understand. In this way, the document creation assistance tool efficiently customizes the user's documents to create documents that are easy to understand for the recipient. In this way, the document creation assistance tool efficiently customizes the user's documents to create documents that are easy to understand for the recipient. For example, it can be used in a variety of situations, such as sales presentations and internal company reports.

[0066] A material creation assistance tool according to an embodiment includes a reception unit, an analysis unit, a storage unit, and a customization unit. The reception unit inputs materials created by a user. The materials created by a user include, but are not limited to, text documents, presentation materials, and image files. The reception unit can input, for example, materials created by a user in digital format. The reception unit also has a function for inputting model materials to be referenced by the user. For example, previously used presentation materials or reports may be used as model materials. The analysis unit uses a generation AI to analyze the model materials input by the reception unit. The analysis may be performed using, for example, text analysis, image analysis, data mining, or other methods. For example, the generation AI may learn the structure and expression method of the model materials and customize the user's materials based on the learned results. The storage unit stores the learning results of the model materials analyzed by the analysis unit. The learning results may include, for example, but are not limited to, model parameters and statistical information on the analysis results. The storage unit stores the learning results in a database for later reference. The customization unit uses the generation AI to customize the user's materials based on the learning results stored in the storage unit. Customization can be performed, for example, by changing font size, color usage, or layout adjustment, but is not limited to these examples. For example, the customization unit may increase font size or change color to emphasize important points. The customization unit can also add charts and graphs to make the materials visually easier to understand. This allows the material creation assistance tool according to the embodiment to efficiently customize the user's materials and create materials that are easy for the recipient to understand. Some or all of the above-described processing in the customization unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the customization unit may input the learning results stored in the storage unit into the generation AI and cause the generation AI to customize the user's materials.

[0067] The reception unit may have a function for inputting model materials referenced by the user. Examples of model materials include, but are not limited to, standard presentation materials and template documents. The reception unit may input, for example, presentation materials or reports previously used by the user as model materials. The reception unit may also input the model materials referenced by the user in digital format. For example, the reception unit may simultaneously input materials created by the user and the model materials. This improves the accuracy of customization by inputting the model materials referenced by the user. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the model materials input by the user into the generation AI and have the generation AI analyze the model materials.

[0068] The analysis unit can learn the structure and expression of the model material. Examples of the structure and expression include, but are not limited to, the chapter structure, the arrangement of figures and tables, and the format of the document. The analysis unit, for example, uses a generative AI to learn the structure and expression of the model material. For example, the generative AI learns the layout, font, color usage, etc. of the model material and customizes the user's material based on that. The analysis unit can also analyze the content of the model material and extract important points. For example, the generative AI analyzes the text of the model material and extracts important keywords and phrases. This allows the structure and expression of the model material to be learned, thereby more appropriately customizing the user's material. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generative AI. For example, the analysis unit can input the model material into the generative AI and have the generative AI learn the structure and expression.

[0069] The customization unit can apply specific customization methods, such as changing font size, changing color, and adding charts and graphs. Examples of font size changes include, but are not limited to, standard font size and highlighted font size. The customization unit can, for example, use a generation AI to change the font size of the user's materials. For example, the font size can be increased to emphasize important points. Examples of color changes include, but are not limited to, highlighted colors, background colors, and text colors. The customization unit can, for example, use a generation AI to change the color of the user's materials. For example, the color can be changed to emphasize important points. Examples of adding charts and graphs include, but are not limited to, graph types and chart placement methods. The customization unit can, for example, use a generation AI to add charts and graphs to the user's materials. For example, graphs can be added to the materials to make them more visually understandable. By applying specific customization methods, the visual understandability of the materials can be improved. Some or all of the above-described processing in the customization unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the customization unit can input the user's materials into the generation AI and have the generation AI change the font size, change the color scheme, or add charts and graphs.

[0070] The customization unit can change the font size and color to emphasize important points. Important points include, but are not limited to, keywords, key data points, etc. The customization unit can, for example, use the generation AI to emphasize important points in the user's materials. For example, the font size can be increased to emphasize important points. The customization unit can also change the color to emphasize important points. For example, the color can be changed to emphasize important points. By emphasizing important points, materials that are easy for the recipient to understand can be created. Some or all of the above-described processing in the customization unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the customization unit can input the user's materials into the generation AI and have the generation AI emphasize the important points.

[0071] The customization unit can add charts and graphs to make the material more visually understandable. Methods for making the material more visually understandable include, but are not limited to, color usage, layout, font size, etc. The customization unit can add charts and graphs to the user's materials, for example, using a generation AI. For example, a graph can be added to the material to make it more visually understandable. The customization unit can also adjust the layout of the material to make it more visually understandable. For example, the layout can be changed to highlight important information. By adding charts and graphs, the material can be more visually understandable. Some or all of the above-described processing in the customization unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the customization unit can input the user's materials into the generation AI and cause the generation AI to add charts and graphs.

[0072] The reception unit can estimate the user's emotions and adjust the timing of document input based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, concentration, and fatigue. The reception unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The reception unit can also adjust the timing of document input based on the estimated user emotions. For example, if the user is stressed, the generation AI can delay the input timing to allow the user to relax. If the user is concentrating, the generation AI can speed up the input timing to allow the user to input documents efficiently. Furthermore, if the user is tired, the generation AI can adjust the input timing to allow the user to input documents while taking breaks. This allows for efficient document creation by adjusting the timing of document input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0073] The reception unit can analyze the user's past document creation history and select the optimal input method. The past document creation history includes, but is not limited to, past documents, editing history, and templates used. The reception unit can, for example, use a generation AI to analyze the user's past document creation history. For example, the generation AI can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also automatically select a specific format or template from the user's past document creation history. For example, the generation AI can analyze the content of documents created by the user in the past and suggest the optimal input method for creating similar documents. In this way, by analyzing the past document creation history, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past document creation history into the generation AI and have the generation AI select the optimal input method.

[0074] When inputting materials, the reception unit can filter the materials based on the user's current project or area of ​​interest. Examples of current projects and areas of interest include, but are not limited to, project themes and keywords related to the area of ​​interest. The reception unit can, for example, use a generation AI to identify the user's current project or area of ​​interest. For example, the generation AI can analyze data from the user's project management tool or social media to identify the current project or area of ​​interest. The reception unit can also filter the materials based on the identified project or area of ​​interest. For example, the reception unit can filter the materials so that only materials related to the user's current project are input. Related materials can also be preferentially input based on the user's area of ​​interest. Furthermore, related materials can be filtered and input based on topics in which the user has previously shown interest. This allows for efficient input of highly relevant materials by filtering the materials based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input data on the user's project or area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0075] When inputting materials, the reception unit can select the optimal input means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. The reception unit, for example, uses a generation AI to identify the user's input method. For example, the generation AI can analyze the user's past input history and current situation and select the optimal input means. The reception unit can also provide the optimal input means depending on the identified input method. For example, if the user desires voice input, the generation AI can input the materials using voice recognition technology. If the user desires text input, the generation AI can provide a text editor to input the materials. Furthermore, if the user desires image input, the generation AI can input the materials using image recognition technology. This enables efficient input of materials by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input data on the user's input method into the generation AI and have the generation AI select the optimal input means.

[0076] The reception unit can estimate the user's emotions and determine the priority of materials to be input based on the estimated user emotions. The priority of materials can include, but is not limited to, importance, urgency, and relevance. The reception unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The reception unit can also determine the priority of materials to be input based on the estimated user emotions. For example, if the user is stressed, the generation AI can postpone less important materials and prioritize input of more important materials. Furthermore, if the user is relaxed, the generation AI can input all materials equally. Furthermore, if the user is in a hurry, the generation AI can prioritize input of the most important materials. Thus, by determining the priority of materials according to the user's emotions, important materials can be input preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and have the generation AI determine the priority of materials.

[0077] When inputting materials, the reception unit can prioritize inputting highly relevant materials by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. The reception unit, for example, uses a generation AI to acquire the user's geographical location information. For example, the generation AI can acquire location information of the user's smartphone or computer. The reception unit can also prioritize inputting highly relevant materials based on the acquired geographical location information. For example, if the user is in a specific area, materials related to that area can be prioritized. Also, if the user is on a business trip, materials related to the business trip destination can be prioritized. Furthermore, if the user is at home, materials related to the home can be prioritized. This allows highly relevant materials to be efficiently input by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant materials.

[0078] When inputting materials, the reception unit can analyze the user's social media activity and input relevant materials. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit can, for example, use a generation AI to analyze the user's social media activity. For example, the generation AI can input materials related to articles the user shared on social media. The reception unit can also analyze the content of the user's social media posts and input relevant materials. For example, the generation AI can input relevant materials based on the activities of the user's friends on social media. This allows for efficient input of highly relevant materials by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input data on the user's social media activity into the generation AI and have the generation AI select relevant materials.

[0079] When inputting materials, the reception unit can customize the input method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user comments and evaluation scores. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the generation AI can prioritize the use of input methods for which the user has provided feedback in the past. The reception unit can also suggest an optimal input method based on the user's past feedback. For example, the generation AI can avoid input methods for which the user has previously expressed dissatisfaction and suggest other input methods. This allows the optimal input method to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input data of the user's past feedback into the generation AI and have the generation AI customize the input method.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Examples of the presentation method of the analysis include, but are not limited to, graph types and report formats. The analysis unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The analysis unit can also adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide detailed analysis results. If the user is in a hurry, the generation AI can provide concise analysis results that focus on the main points. If the user is excited, the generation AI can provide visually stimulating analysis results. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expressing the analysis.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. Examples of the importance of the document include, but are not limited to, project priority and the evaluations of stakeholders. The analysis unit, for example, uses a generation AI to evaluate the importance of the document. For example, the generation AI can evaluate the importance of the document based on the project priority and the evaluations of stakeholders. The analysis unit can also adjust the level of detail of the analysis based on the evaluated importance of the document. For example, the generation AI can perform a detailed analysis of highly important documents. For less important documents, the generation AI can perform a concise analysis. Furthermore, the generation AI can dynamically adjust the level of detail of the analysis depending on the importance of the document. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the document. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the importance of the document into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the document. Examples of document categories include, but are not limited to, technical documents, marketing materials, and presentation materials. The analysis unit, for example, uses a generation AI to identify the category of the document. For example, the generation AI can analyze the content and format of the document to identify the category. The analysis unit can then apply different analysis algorithms depending on the identified category. For example, the generation AI can apply a visual analysis algorithm to presentation materials. The generation AI can apply a text analysis algorithm to reports. The generation AI can also apply a specialized analysis algorithm to technical documents. This allows for more appropriate analysis results by applying different analysis algorithms depending on the category of the document. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input data on the document category into the generation AI and have the generation AI apply the analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, past reports and analysis logs. The analysis unit, for example, uses a generation AI to analyze the user's past analysis results. For example, the generation AI can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit can also suggest an optimal analysis method by referring to the user's past analysis results. For example, the generation AI can analyze the user's past analysis results and adjust optimal analysis parameters. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. Examples of the length of the analysis include, but are not limited to, the number of pages and the number of analysis items. The analysis unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The analysis unit can also adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the generation AI can perform a short, concise analysis. If the user is relaxed, the generation AI can perform a detailed analysis. If the user is excited, the generation AI can perform a visually stimulating analysis. This enables efficient analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to, these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0085] During analysis, the analysis unit can determine the analysis priority based on the submission date of the documents. The submission date of the documents includes, but is not limited to, a submission deadline and a submission date and time. The analysis unit, for example, uses a generation AI to obtain the submission date of the documents. For example, the generation AI can analyze data from a user's project management tool or calendar to identify the submission date. The analysis unit can also determine the analysis priority based on the identified submission date. For example, documents with an upcoming deadline can be analyzed first. Furthermore, documents with a more distant submission date can be postponed. Furthermore, the generation AI can dynamically adjust the analysis priority according to the submission date of the documents. This enables efficient analysis by determining the analysis priority based on the submission date of the documents. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the submission date of the documents into the generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the analysis order based on the relevance of the materials. Examples of the relevance of the materials include, but are not limited to, the degree of theme similarity and keyword commonality. The analysis unit, for example, uses a generation AI to evaluate the relevance of the materials. For example, the generation AI can analyze the themes and keywords of the materials and evaluate the relevance. The analysis unit can also adjust the analysis order based on the evaluated relevance. For example, highly relevant materials can be analyzed preferentially. Less relevant materials can be postponed. Furthermore, the generation AI can dynamically adjust the analysis order according to the relevance of the materials. This enables efficient analysis by adjusting the analysis order based on the relevance of the materials. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the relevance of the materials into the generation AI and have the generation AI adjust the analysis order.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of technical terms include, but are not limited to, qualifications and past work experience. The analysis unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation AI can analyze the user's resume and work history to evaluate the user's level of expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the evaluated level of expertise. For example, if the user has technical expertise, the generation AI can use a lot of technical terms. On the other hand, if the user does not have technical expertise, the generation AI can avoid technical terms. Furthermore, the generation AI can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terms.

[0088] The storage unit can estimate the user's emotions and select data to save based on the estimated user emotions. The selection of data to save includes, but is not limited to, importance, frequency, and relevance. The storage unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The storage unit can also select data to save based on the estimated user emotions. For example, if the user is stressed, the generation AI can save only data of high importance. Furthermore, if the user is relaxed, the generation AI can save all data. Furthermore, if the user is in a hurry, the generation AI can prioritize saving the most important data. This enables efficient data storage by selecting data to save based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the storage unit may input user emotion data into the generation AI and have the generation AI select the data to be stored.

[0089] The storage unit can optimize the storage algorithm by referring to past stored data during storage. Examples of storage algorithms include, but are not limited to, data compression algorithms and database indexing methods. The storage unit can analyze the past stored data, for example, using a generation AI. For example, the generation AI can propose an optimal storage algorithm based on the past stored data. The storage unit can also dynamically adjust the storage algorithm based on the past stored data. For example, the generation AI can improve storage efficiency by referring to the past stored data. This makes it possible to optimize the storage algorithm by referring to the past stored data. Some or all of the above-described processing in the storage unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the storage unit can input the past stored data into the generation AI and have the generation AI optimize the storage algorithm.

[0090] The storage unit can update the stored data by reflecting user feedback when saving the data. Updating the stored data includes, for example, data version management and update frequency, but is not limited to these examples. The storage unit can, for example, analyze user feedback using a generation AI. For example, the generation AI can update the stored data based on feedback provided by the user. The storage unit can also optimize the structure of the stored data by reflecting user feedback. For example, the generation AI can update the content of the stored data by referring to the user feedback. This allows the stored data to be updated efficiently by reflecting user feedback. Some or all of the above-mentioned processing in the storage unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the storage unit can input user feedback data into the generation AI and have the generation AI update the stored data.

[0091] The storage unit can estimate the user's emotions and adjust the frequency of saving based on the estimated user emotions. Examples of the frequency of saving include, but are not limited to, periodic saving and event-triggered saving. The storage unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The storage unit can also adjust the frequency of saving based on the estimated user emotions. For example, if the user is stressed, the generation AI can reduce the frequency of saving to reduce the user's burden. Also, if the user is relaxed, the generation AI can increase the frequency of saving to ensure data safety. Furthermore, if the user is in a hurry, the generation AI can prioritize saving the most important data. This enables efficient data storage by adjusting the frequency of saving according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the storage unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the storage unit may input user emotion data into the generation AI and have the generation AI adjust the frequency of saving.

[0092] The storage unit can weight the stored data based on the submission date of the document when storing the data. Weighting of the stored data includes, but is not limited to, importance and frequency of use. The storage unit, for example, uses a generation AI to obtain the submission date of the document. For example, the generation AI can analyze data from a user's project management tool or calendar to identify the submission date. The storage unit can also weight the stored data based on the identified submission date. For example, data of documents with an upcoming deadline can be stored preferentially. Data of documents with a distant submission date can be postponed. Furthermore, the generation AI can dynamically adjust the weighting of the stored data depending on the submission date of the document. This enables efficient data storage by weighting the stored data based on the submission date of the document. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the storage unit can input data on the submission date of the document into the generation AI and have the generation AI weight the stored data.

[0093] The storage unit can integrate information from different data sources to enrich the stored data when storing the data. Examples of different data sources include, but are not limited to, external APIs, databases, and file systems. The storage unit can acquire information from different data sources using, for example, a generation AI. For example, the generation AI can acquire information from external APIs and databases, integrate the information, and store it. The storage unit can also enrich the stored data based on the acquired information. For example, the generation AI can analyze information from different data sources and enrich the stored data in an optimal manner. Furthermore, the storage unit can integrate and optimize the stored data based on data sources provided by the user. This enables the enrichment of the stored data by integrating information from different data sources. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the storage unit can input data from different data sources into the generation AI and have the generation AI integrate the information.

[0094] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user's emotions. Examples of customization methods include, but are not limited to, changing font size, changing colors, and adjusting layout. The customization unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The customization unit can also adjust the customization method based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can perform detailed customization. If the user is in a hurry, the generation AI can perform concise customization that focuses on the main points. Furthermore, if the user is excited, the generation AI can perform visually stimulating customization. This enables efficient document customization by adjusting the customization method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the customization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the customization unit may input user emotion data into the generation AI and cause the generation AI to adjust the customization method.

[0095] During customization, the customization unit can adjust the level of customization detail based on the importance of the material. Examples of the level of customization detail include, but are not limited to, detailed adjustments and overall layout changes. The customization unit, for example, uses a generation AI to evaluate the importance of the material. For example, the generation AI can evaluate the importance of the material based on project priorities and evaluations by stakeholders. The customization unit can also adjust the level of customization detail based on the evaluated importance of the material. For example, the generation AI can perform detailed customization for highly important material. For less important material, the generation AI can perform simple customization. Furthermore, the generation AI can dynamically adjust the level of customization detail depending on the importance of the material. This enables efficient material customization by adjusting the level of customization detail based on the importance of the material. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input data on the importance of the material to the generation AI and cause the generation AI to adjust the level of customization detail.

[0096] During customization, the customization unit can apply different customization algorithms depending on the category of the document. Examples of customization algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. The customization unit, for example, uses a generation AI to identify the category of the document. For example, the generation AI can analyze the content and format of the document to identify the category. The customization unit can then apply different customization algorithms depending on the identified category. For example, the generation AI can apply a visual customization algorithm to presentation materials. The generation AI can apply a text customization algorithm to reports. The generation AI can also apply a specialized customization algorithm to technical documents. This allows for more appropriate document customization by applying different customization algorithms depending on the category of the document. Some or all of the above-described processing in the customization unit can be performed using, or without, the generation AI. For example, the customization unit can input data on the category of the document into the generation AI and have the generation AI apply the customization algorithm.

[0097] During customization, the customization unit can improve the accuracy of the customization by referring to the user's past customization results. Past customization results include, but are not limited to, past reports and customization logs. The customization unit, for example, uses a generation AI to analyze the user's past customization results. For example, the generation AI can improve the accuracy of the customization based on the user's past customization results. The customization unit can also suggest an optimal customization method by referring to the user's past customization results. For example, the generation AI can analyze the user's past customization results and adjust optimal customization parameters. This improves the accuracy of the customization by referring to the user's past customization results. Some or all of the above-described processing in the customization unit may be performed, for example, using the generation AI or without using the generation AI. For example, the customization unit can input data on the user's past customization results into the generation AI and cause the generation AI to improve the accuracy of the customization.

[0098] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user emotions. Examples of customization priorities include, but are not limited to, importance, urgency, and relevance. The customization unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The customization unit can also determine the priority of customization based on the estimated user emotions. For example, if the user is stressed, the generation AI can prioritize customizations with high importance. Also, if the user is relaxed, the generation AI can perform all customizations equally. Furthermore, if the user is in a hurry, the generation AI can prioritize the most important customization. This enables efficient material customization by determining the priority of customizations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the customization unit may input user emotion data into the generation AI and have the generation AI determine the priority of customization.

[0099] During customization, the customization unit can adjust the order of customization based on the submission dates of the documents. Examples of the order of customization include, but are not limited to, submission deadlines and importance. The customization unit, for example, uses a generation AI to obtain the submission dates of the documents. For example, the generation AI can analyze data from a user's project management tool or calendar to identify the submission dates. The customization unit can also adjust the order of customization based on the identified submission dates. For example, customization of documents with upcoming deadlines can be prioritized. Furthermore, customization of documents with distant submission dates can be postponed. Furthermore, the generation AI can dynamically adjust the order of customization depending on the submission dates of the documents. This enables efficient document customization by adjusting the order of customization based on the submission dates of the documents. Some or all of the above-described processing in the customization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the customization unit can input data on the submission dates of the documents into the generation AI and have the generation AI adjust the order of customization.

[0100] During customization, the customization unit can adjust the order of customization based on the relevance of the materials. Examples of the relevance of the materials include, but are not limited to, the degree of theme similarity and keyword commonality. The customization unit, for example, uses a generation AI to evaluate the relevance of the materials. For example, the generation AI can analyze the themes and keywords of the materials and evaluate the relevance. The customization unit can also adjust the order of customization based on the evaluated relevance. For example, the customization of highly relevant materials can be prioritized. Furthermore, the customization of less relevant materials can be postponed. Furthermore, the generation AI can dynamically adjust the order of customization based on the relevance of the materials. This enables efficient material customization by adjusting the order of customization based on the relevance of the materials. Some or all of the above-described processing in the customization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the customization unit can input data on the relevance of the materials into the generation AI and have the generation AI adjust the order of customization.

[0101] During customization, the customization unit can adjust the use of technical terms in the customization according to the user's level of expertise. The use of technical terms includes, but is not limited to, the definition and frequency of use of technical terms. The customization unit, for example, uses a generation AI to evaluate the user's level of expertise. For example, the generation AI can analyze the user's resume and work history to evaluate the user's level of expertise. The customization unit can also adjust the use of technical terms in the customization according to the evaluated level of expertise. For example, if the user has technical expertise, the generation AI can use a lot of technical terms. On the other hand, if the user does not have technical expertise, the generation AI can avoid technical terms. Furthermore, the generation AI can dynamically adjust the use of technical terms in the customization according to the user's level of expertise. This enables more appropriate document customization by adjusting the use of technical terms in the customization according to the user's level of expertise. Some or all of the above-described processing in the customization unit may be performed using, or without, the generation AI. For example, the customization unit can input data on the user's level of expertise into the generation AI and have the generation AI adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and customization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input materials created by a user using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the model materials using a generative AI. The storage unit stores the analysis results in the database 24 of the data processing device 12. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the user's materials based on the stored learning results. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and customization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input materials created by the user using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the model materials using a generative AI. The storage unit stores the analysis results in the database 24 of the data processing device 12. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the user's materials based on the stored learning results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and customization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input materials created by the user using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the model materials using a generative AI. The storage unit stores the analysis results in the database 24 of the data processing device 12. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the user's materials based on the stored learning results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, storage unit, and customization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input materials created by the user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the model materials using a generative AI. The storage unit stores the analysis results in the database 24 of the data processing device 12. The customization unit is realized by the specific processing unit 290 of the data processing device 12 and customizes the user's materials based on the stored learning results.

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

[0103] The reception unit can analyze the user's past document creation history and select the optimal input method. The past document creation history includes, but is not limited to, past documents, editing history, and templates used. The reception unit can, for example, use a generation AI to analyze the user's past document creation history. For example, the generation AI can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also automatically select a specific format or template from the user's past document creation history. For example, the generation AI can analyze the content of documents created by the user in the past and suggest the optimal input method for creating similar documents. In this way, by analyzing the past document creation history, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past document creation history into the generation AI and have the generation AI select the optimal input method.

[0104] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. Examples of the importance of the document include, but are not limited to, project priority and the evaluations of stakeholders. The analysis unit, for example, uses a generation AI to evaluate the importance of the document. For example, the generation AI can evaluate the importance of the document based on the project priority and the evaluations of stakeholders. The analysis unit can also adjust the level of detail of the analysis based on the evaluated importance of the document. For example, the generation AI can perform a detailed analysis of highly important documents. For less important documents, the generation AI can perform a concise analysis. Furthermore, the generation AI can dynamically adjust the level of detail of the analysis depending on the importance of the document. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the document. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the importance of the document into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0105] During customization, the customization unit can apply different customization algorithms depending on the category of the document. Examples of customization algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. The customization unit, for example, uses a generation AI to identify the category of the document. For example, the generation AI can analyze the content and format of the document to identify the category. The customization unit can then apply different customization algorithms depending on the identified category. For example, the generation AI can apply a visual customization algorithm to presentation materials. The generation AI can apply a text customization algorithm to reports. The generation AI can also apply a specialized customization algorithm to technical documents. This allows for more appropriate document customization by applying different customization algorithms depending on the category of the document. Some or all of the above-described processing in the customization unit can be performed using, or without, the generation AI. For example, the customization unit can input data on the category of the document into the generation AI and have the generation AI apply the customization algorithm.

[0106] The reception unit can estimate the user's emotions and adjust the timing of document input based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, concentration, and fatigue. The reception unit estimates the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The reception unit can also adjust the timing of document input based on the estimated user emotions. For example, if the user is stressed, the generation AI can delay the input timing to allow the user to relax. If the user is concentrating, the generation AI can speed up the input timing to allow the user to input documents efficiently. Furthermore, if the user is tired, the generation AI can adjust the input timing to allow the user to input documents while taking breaks. This allows for efficient document creation by adjusting the timing of document input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0107] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Examples of the presentation method of the analysis include, but are not limited to, graph types and report formats. The analysis unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The analysis unit can also adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can provide detailed analysis results. If the user is in a hurry, the generation AI can provide concise analysis results that focus on the main points. If the user is excited, the generation AI can provide visually stimulating analysis results. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expressing the analysis.

[0108] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user's emotions. Examples of customization methods include, but are not limited to, changing font size, changing colors, and adjusting layout. The customization unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The customization unit can also adjust the customization method based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can perform detailed customization. If the user is in a hurry, the generation AI can perform concise customization that focuses on the main points. Furthermore, if the user is excited, the generation AI can perform visually stimulating customization. This enables efficient document customization by adjusting the customization method according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the customization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the customization unit may input user emotion data into the generation AI and cause the generation AI to adjust the customization method.

[0109] The storage unit can estimate the user's emotions and select data to save based on the estimated user emotions. The selection of data to save includes, but is not limited to, importance, frequency, and relevance. The storage unit can estimate the user's emotions using, for example, a generation AI. For example, the generation AI can estimate the user's emotions using facial expression recognition or voice analysis. The storage unit can also select data to save based on the estimated user emotions. For example, if the user is stressed, the generation AI can save only data of high importance. Furthermore, if the user is relaxed, the generation AI can save all data. Furthermore, if the user is in a hurry, the generation AI can prioritize saving the most important data. This enables efficient data storage by selecting data to save based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the storage unit may input user emotion data into the generation AI and have the generation AI select the data to be stored.

[0110] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the document. Examples of document categories include, but are not limited to, technical documents, marketing materials, and presentation materials. The analysis unit, for example, uses a generation AI to identify the category of the document. For example, the generation AI can analyze the content and format of the document to identify the category. The analysis unit can then apply different analysis algorithms depending on the identified category. For example, the generation AI can apply a visual analysis algorithm to presentation materials. The generation AI can apply a text analysis algorithm to reports. The generation AI can also apply a specialized analysis algorithm to technical documents. This allows for more appropriate analysis results by applying different analysis algorithms depending on the category of the document. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input data on the document category into the generation AI and have the generation AI apply the analysis algorithm.

[0111] The storage unit can integrate information from different data sources to enrich the stored data when storing the data. Examples of different data sources include, but are not limited to, external APIs, databases, and file systems. The storage unit can acquire information from different data sources using, for example, a generation AI. For example, the generation AI can acquire information from external APIs and databases, integrate the information, and store it. The storage unit can also enrich the stored data based on the acquired information. For example, the generation AI can analyze information from different data sources and enrich the stored data in an optimal manner. Furthermore, the storage unit can integrate and optimize the stored data based on data sources provided by the user. This enables the enrichment of the stored data by integrating information from different data sources. Some or all of the above-described processing in the storage unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the storage unit can input data from different data sources into the generation AI and have the generation AI integrate the information.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit inputs materials created by the user. Materials created by the user include text documents, presentation materials, image files, etc. The reception unit can input these materials in digital format. The reception unit also has a function to input model materials that the user will refer to, and past presentation materials, reports, etc. can be used as model materials. Step 2: The analysis unit uses the generation AI to analyze the model materials entered by the reception unit. The analysis is performed using methods such as text analysis, image analysis, and data mining. The generation AI learns the structure and expression methods of the model materials and customizes the user's materials based on that information. Step 3: The storage unit stores the learning results of the model data analyzed by the analysis unit. The learning results include model parameters and statistical information on the analysis results. The storage unit stores these learning results in a database so that they can be referenced later. Step 4: The customization unit uses the generative AI to customize the user's materials based on the learning results stored in the storage unit. Customization is performed by changing the font size, color, and layout, for example. For example, you can increase the font size or change the color to emphasize important points. You can also add charts and graphs to make the materials easier to understand visually.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting documents created by a user; an analysis unit that analyzes the model material input by the reception unit; a storage unit for storing the learning results of the model data analyzed by the analysis unit; a customization unit that customizes the user's materials based on the learning results stored in the storage unit. A system characterized by:

2. The reception unit Provides a function for users to input reference model materials 2. The system of claim 1.

3. The analysis unit Learn the structure and presentation of model materials 2. The system of claim 1.

4. The customization unit Apply specific customizations such as changing font size, changing colors, or adding charts and figures 2. The system of claim 1.

5. The customization unit Change font size and color to highlight important points 2. The system of claim 1.

6. The customization unit Add charts and graphs for visual clarity 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of inputting materials based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past document creation history and select the optimal input method 2. The system of claim 1.

Images