system

Abstract

The system according to this embodiment aims to streamline the document creation process and make effective use of time. [Solution] The system according to the embodiment comprises a selection unit, a designation unit, a generation unit, and a confirmation unit. The selection unit selects a model. The designation unit specifies data based on the model selected by the selection unit. The generation unit generates materials based on the data specified by the designation unit. The confirmation unit performs a final confirmation of the materials generated by the generation unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of creating materials requires time and effort and is difficult to perform efficiently.

[0005] The system according to the embodiment aims to streamline the process of creating materials and effectively utilize time.

Means for Solving the Problems

[0006] The system according to the embodiment includes a selection unit, a specification unit, a generation unit, and a confirmation unit. The selection unit selects a model example. The specification unit specifies data based on the model example selected by the selection unit. The generation unit generates materials based on the data specified by the specification unit. The confirmation unit finally confirms the materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the document creation process and make effective use of time. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The document creation system according to an embodiment of the present invention is a system for streamlining the manual process of document creation. This document creation system works by allowing the user to select a "template" and specify the data they want to include. The generating AI then creates the document based on the specified data, and the user performs a final visual check. First, the user selects a "template" and specifies the data they want to include. At this time, the user can choose a "template" that matches the format and content of the document they want to create. For example, templates in various formats, such as presentation materials and reports, are available. The user selects the most suitable template from these and specifies the data they want to include. Next, the generating AI creates the document based on the specified data. The generating AI analyzes the data specified by the user and automatically generates the document based on the template. For example, if the data specified by the user is a table or graph, the generating AI incorporates them into the document in an appropriate format. If it is text data, the generating AI places it in the appropriate position and adjusts the overall layout. The created document is then visually checked by the user. The user can check the document created by the generating AI and make corrections or additional edits as needed. For example, the user can change the color or font of the graph automatically generated by the generating AI, or correct the content of the text. This allows users to create materials that match their intentions. This system enables efficient use of time spent on material creation by leveraging AI. Users can eliminate the need to manually create materials and instead work efficiently based on materials automatically generated by the AI. For example, when creating presentation materials, the AI ​​automatically generates slides, and the user only needs to review and revise them, significantly reducing work time. This allows the material creation system to enable users to efficiently create and finalize materials.

[0029] The document creation system according to the embodiment comprises a selection unit, a specification unit, a generation unit, and a confirmation unit. The selection unit selects a template that matches the format and content of the document the user wants to create. For example, templates in various formats, such as presentation materials and reports, are available. For example, if the user is creating presentation materials, the selection unit displays a template suitable for presentations. The selection unit can also display a template suitable for reports if the user is creating a report. Furthermore, if the user is creating marketing materials, the selection unit can also display a template suitable for marketing. The specification unit specifies data based on the template selected by the selection unit. For example, if the data specified by the user is a table or graph, the specification unit specifies them in an appropriate format. The specification unit can also specify text data. For example, the specification unit places the text data specified by the user in an appropriate position and adjusts the overall layout. The generation unit generates the document based on the data specified by the specification unit using a generation AI. The generation AI analyzes the data specified by the user and automatically generates the document based on the template. For example, if the data specified by the user is a table or graph, the generation AI incorporates them into the document in an appropriate format. Furthermore, the generation AI can also position text data appropriately and arrange the overall layout. The generation AI generates documents using, for example, a text generation AI (e.g., LLM). The generation AI can also generate documents using a multimodal generation AI. The verification unit performs a final visual check of the documents generated by the generation unit. The user can review the documents created by the generation AI and make corrections or additional edits as needed. For example, the verification unit can change the color and font of graphs automatically generated by the generation AI. The verification unit can also modify the text content. This allows the user to create documents that match their intentions. Thus, the document creation system according to this embodiment allows the user to efficiently create documents and perform a final check.

[0030] The document creation system includes a presentation section that displays different types of templates. The presentation section presents the types of templates the user can select. For example, the presentation section can display types such as business documents, technical documents, and educational materials. For example, as business documents, the presentation section might display reports, presentation materials, and marketing materials. It can also display technical documents such as technical reports, specifications, and design drawings. Furthermore, it can display educational materials such as teaching materials, lecture notes, and exam questions. This allows the user to select a template that suits their purpose. Some or all of the above processing in the presentation section may be performed using AI, or not. For example, the presentation section could use AI to analyze the user's selection history and present the most suitable template. This allows the user to efficiently select a template. Thus, the document creation system can provide the user with appropriate templates to efficiently create documents.

[0031] The document creation system includes an analysis unit that analyzes the data format. The analysis unit analyzes the data format specified by the user. For example, the analysis unit can analyze data formats such as CSV, JSON, and XML. For example, the analysis unit can analyze CSV data and extract the contents of each field. The analysis unit can also analyze JSON data and extract each key-value pair. Furthermore, the analysis unit can analyze XML data and extract the contents of each tag. In this way, the analysis unit can appropriately analyze the data format specified by the user and extract the information necessary for document creation. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input data into a generation AI, and the generation AI can analyze the data format. In this way, the document creation system can appropriately analyze the data format specified by the user and extract the information necessary for document creation.

[0032] The document creation system includes an editorial department that edits the generated documents. The editorial department edits the documents generated by the generation department. For example, the editorial department can change the color and font of graphs automatically generated by the generation AI. The editorial department can also modify the content of text. For example, the editorial department can modify the content of text automatically generated by the generation AI to make it more in line with the user's intentions. Furthermore, the editorial department can adjust the layout of the documents. For example, the editorial department can adjust the layout of documents automatically generated by the generation AI to make them easier to read. This allows users to create documents that meet their intentions. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can have the AI ​​analyze the documents automatically generated by the generation AI and suggest the optimal editing method. This allows the document creation system to provide users with an appropriate method for efficiently editing documents.

[0033] The selection unit can analyze past selection history and automatically suggest suitable examples for the user. For example, the selection unit can automatically display examples that the user has frequently used in the past as candidates. For example, the selection unit can also analyze the trends of examples that the user has selected in the past and suggest the optimal example. Furthermore, the selection unit can suggest examples suitable for a specific project based on the user's past selection history. In this way, by analyzing past selection history, the system can suggest the most suitable examples for the user. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can have AI analyze the user's selection history data and present the optimal example. In this way, the document creation system can provide appropriate examples for the user to create documents efficiently.

[0034] The selection section can filter the sample options based on the user's current project and objectives. For example, if the user is creating a presentation, the selection section will prioritize displaying samples suitable for presentations. For example, if the user is creating a report, the selection section can also display samples suitable for reports. Furthermore, if the user is creating marketing materials, the selection section can display samples suitable for marketing. This allows for the creation of appropriate materials by providing samples tailored to the user's project and objectives. Some or all of the above processing in the selection section may be performed using AI, for example, or not. For example, the selection section can use AI to analyze the user's project data and present the most suitable sample. This allows the material creation system to provide appropriate samples for the user to create materials efficiently.

[0035] The selection section can prioritize displaying highly relevant examples, taking into account the user's geographical location. For example, if the user is in a specific region, the selection section will prioritize displaying examples related to that region. For example, if the user is traveling, the selection section can also display examples related to travel. Furthermore, if the user is participating in a specific event, the selection section can display examples related to that event. This enables the creation of appropriate materials by providing examples based on the user's geographical location. Some or all of the above processing in the selection section may be performed using AI, for example, or not. For example, the selection section can use AI to analyze the user's geographical location data and present the most suitable examples. This allows the material creation system to provide appropriate examples for the user to create materials efficiently.

[0036] The selection unit can analyze a user's social media activity and suggest relevant examples. For example, the selection unit can suggest relevant examples based on what the user has shared on social media. For example, the selection unit can also suggest examples based on the activity of accounts the user follows on social media. Furthermore, the selection unit can suggest examples based on the activity of groups the user participates in on social media. This enables the creation of appropriate materials by providing examples based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can use AI to analyze the user's social media data and present the most suitable examples. This allows the material creation system to provide appropriate examples for the user to create materials efficiently.

[0037] The specification unit can analyze past data specification history and suggest a suitable specification method. For example, the specification unit can automatically display data that the user has frequently specified in the past as a candidate. For example, the specification unit can also prioritize suggesting data specification methods (voice, text, etc.) that the user has used in the past. Furthermore, the specification unit can suggest a data specification method suitable for a specific project based on the user's past data specification history. In this way, by analyzing past data specification history, the system can suggest the optimal data specification method to the user. Some or all of the above processing in the specification unit may be performed using AI, for example, or not using AI. For example, the specification unit can have AI analyze the user's data specification history data and present the optimal data specification method. In this way, the document creation system can provide the user with an appropriate data specification method to efficiently create documents.

[0038] The specification unit can apply different specification algorithms depending on the type of data being specified. For example, in the case of text data, the specification unit can analyze the data using natural language processing and provide an appropriate specification method. For example, in the case of image data, the specification unit can also analyze the data using image recognition technology and provide an appropriate specification method. Furthermore, in the case of numerical data, the specification unit can analyze the data using statistical analysis and provide an appropriate specification method. This enables the creation of appropriate documents by providing specification algorithms according to the type of data. Some or all of the above processing in the specification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the specification unit can input data into a generative AI, and the generative AI can apply a specification algorithm according to the type of data. This allows the document creation system to provide an appropriate data specification method for users to efficiently create documents.

[0039] The specification unit can prioritize highly relevant data when specifying data, taking into account the user's geographical location. For example, if the user is in a specific region, the specification unit can prioritize data related to that region. For example, if the user is traveling, the specification unit can prioritize data related to travel. Furthermore, if the user is participating in a specific event, the specification unit can prioritize data related to that event. This enables the creation of appropriate materials by providing data based on the user's geographical location. Some or all of the above processing in the specification unit may be performed using AI, for example, or not. For example, the specification unit can use AI to analyze the user's geographical location data and present the most suitable data. This allows the material creation system to provide an appropriate data specification method for users to efficiently create materials.

[0040] The specification unit can analyze the user's social media activity and specify relevant data when specifying data. For example, the specification unit can specify relevant data based on what the user has shared on social media. For example, the specification unit can also specify relevant data based on the activity of accounts the user follows on social media. Furthermore, the specification unit can specify relevant data based on the activity of groups the user participates in on social media. This enables the creation of appropriate materials by providing data based on the user's social media activity. Some or all of the above processing in the specification unit may be performed using AI, for example, or not using AI. For example, the specification unit can have AI analyze the user's social media data and present the optimal data. This allows the material creation system to provide an appropriate data specification method for users to efficiently create materials.

[0041] The generation unit can adjust the level of detail generated for the document based on the importance of the specified data. For example, if the data is important, the generation AI will generate a document that includes a detailed explanation. For example, if the data is general, the generation AI can generate a document that includes a concise explanation. Furthermore, if the data is supplementary, the generation AI can generate a document that includes only the main points. This allows for the creation of appropriate documents by providing a level of detail in the document generation that corresponds to the importance of the data. Some or all of the above processing in the generation unit may be performed using the generation AI, or not. For example, the generation unit can input data into the generation AI, and the generation AI can adjust the level of detail based on the importance of the data. This allows the document creation system to provide an appropriate document generation method for users to create documents efficiently.

[0042] The generation unit can apply different generation algorithms depending on the specified data category. For example, in the case of text data, the generation unit generates materials using natural language processing. For example, in the case of image data, the generation unit can also generate materials using image recognition technology. Furthermore, in the case of numerical data, the generation unit can also generate materials using statistical analysis. This enables the creation of appropriate materials by providing generation algorithms according to the data category. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data into a generation AI, and the generation AI can apply a generation algorithm according to the data category. This allows the material creation system to provide an appropriate material generation method for users to efficiently create materials.

[0043] The generation unit can determine the generation priority based on the data submission date when generating documents. For example, the generation unit may prioritize generating data with an approaching deadline. For example, the generation unit may postpone generating data with a distant submission date. The generation unit may also generate data with an unknown submission date after the generation of other data has been completed. This enables the creation of appropriate documents by providing a generation priority based on the data submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data into a generation AI, which can then determine the generation priority based on the data submission date. This allows the document creation system to provide an appropriate document generation method that enables users to create documents efficiently.

[0044] The generation unit can adjust the generation order based on the relevance of the data when generating documents. For example, the generation unit may generate highly relevant data first. For example, the generation unit may postpone generating less relevant data. The generation unit may also generate data whose relevance is unknown after the generation of other data is complete. This allows for the creation of appropriate documents by providing a generation order based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data into a generation AI, and the generation AI can adjust the generation order based on the relevance of the data. This allows the document creation system to provide an appropriate document generation method that enables users to create documents efficiently.

[0045] The verification unit can suggest a suitable verification method by referring to past verification history during the verification process. For example, the verification unit may prioritize suggesting verification methods previously used by the user. For example, the verification unit may also suggest a verification method suitable for a specific project based on the user's past verification history. Furthermore, the verification unit can analyze the user's past verification history and suggest the most efficient verification method. This allows the system to suggest the optimal verification method to the user by referring to past verification history. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or not. For example, the verification unit may use AI to analyze the user's verification history data and present the optimal verification method. This allows the document creation system to provide the user with an appropriate verification method for efficiently reviewing documents.

[0046] The verification unit can apply different verification methods depending on the category of the document during verification. For example, in the case of presentation materials, the verification unit can provide a method for verifying each slide. For example, in the case of reports, the verification unit can also provide a method for verifying each chapter. Furthermore, in the case of marketing materials, the verification unit can also provide a method for verifying each section. By providing verification methods according to the category of the document, appropriate document verification becomes possible. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can use AI to analyze the user's document category data and suggest the optimal verification method. This allows the document creation system to provide users with appropriate verification methods for efficiently reviewing documents.

[0047] The verification unit can adjust the order of verification based on the submission dates of the documents during the verification process. For example, the verification unit can prioritize documents with approaching deadlines. For example, the verification unit can postpone documents with later submission dates. The verification unit can also verify documents with unknown submission dates after the verification of other documents has been completed. This allows for appropriate document verification by providing a verification order based on the submission dates of the documents. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input document submission date data into AI, and the AI ​​can adjust the verification order based on the submission dates. This allows the document creation system to provide an appropriate verification method for users to efficiently review documents.

[0048] The verification unit can adjust the order of verification based on the relevance of the documents during the verification process. For example, the verification unit may verify highly relevant documents first. For example, the verification unit may postpone verification of less relevant documents. The verification unit may also verify documents whose relevance is unclear after the verification of other documents has been completed. This allows for appropriate document verification by providing a verification order based on the relevance of the documents. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input document relevance data into AI, and the AI ​​can adjust the verification order based on relevance. This allows the document creation system to provide an appropriate verification method for users to efficiently review documents.

[0049] The presentation unit can analyze past presentation history and automatically present the most suitable example to the user. For example, the presentation unit can automatically present examples that the user has frequently used in the past as candidates. For example, the presentation unit can also analyze the trends of examples that the user has selected in the past and present the most suitable example. Furthermore, the presentation unit can present examples suitable for a specific project based on the user's past presentation history. In this way, by analyzing past presentation history, the best example can be presented to the user. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can have AI analyze the user's presentation history data and present the most suitable example. In this way, the document creation system can provide appropriate examples for the user to create documents efficiently.

[0050] The presentation unit can prioritize presenting highly relevant examples, taking into account the user's geographical location. For example, if the user is in a specific region, the presentation unit will prioritize presenting examples related to that region. For example, if the user is traveling, the presentation unit can also present examples related to travel. Furthermore, if the user is participating in a specific event, the presentation unit can also present examples related to that event. This enables the creation of appropriate materials by providing examples based on the user's geographical location. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can use AI to analyze the user's geographical location data and present the most suitable examples. This allows the material creation system to provide appropriate examples for the user to create materials efficiently.

[0051] The analysis unit can apply the most suitable analysis algorithm by referring to past analysis history. For example, the analysis unit may prioritize applying analysis algorithms previously used by the user. For example, the analysis unit may apply an analysis algorithm suitable for a specific project based on the user's past analysis history. The analysis unit can also analyze the user's past analysis history and apply the most efficient analysis algorithm. In this way, the system can apply the most suitable analysis algorithm to the user by referring to past analysis history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past analysis history data into a generative AI, and the generative AI can apply the most suitable analysis algorithm. In this way, the document creation system can provide the user with an appropriate data analysis method for efficiently creating documents.

[0052] The analysis unit can adjust the order of analysis based on the data submission date during data analysis. For example, the analysis unit may prioritize analyzing data with approaching deadlines. For example, the analysis unit may postpone analyzing data with distant submission dates. Furthermore, the analysis unit may analyze data with unknown submission dates after the analysis of other data has been completed. This allows for the creation of appropriate documents by providing an analysis order based on the data submission dates. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the analysis unit can input data into a generation AI, which can then adjust the analysis order based on the data submission dates. This allows the document creation system to provide an appropriate data analysis method for users to efficiently create documents.

[0053] The editorial department can suggest the optimal editing method by referring to past editing history during the editing process. For example, the editorial department may prioritize suggesting editing methods previously used by the user. For example, the editorial department may suggest an editing method suitable for a specific project based on the user's past editing history. The editorial department can also analyze the user's past editing history and suggest the most efficient editing method. This allows the editorial department to suggest the optimal editing method to the user by referring to past editing history. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department may use AI to analyze the user's editing history data and present the optimal editing method. This allows the document creation system to provide the user with an appropriate editing method to efficiently create documents.

[0054] The editorial department can adjust the editing order based on the submission dates of the materials during the editing process. For example, the editorial department can prioritize editing materials with approaching deadlines. For example, the editorial department can postpone editing materials with later submission dates. The editorial department can also edit materials with unknown submission dates after other materials have been edited. This allows for the creation of appropriate materials by providing an editing order based on the submission dates of the materials. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department can input material submission date data into AI, and the AI ​​can adjust the editing order based on the submission dates. This allows the material creation system to provide users with an appropriate editing method for creating materials efficiently.

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

[0056] The document creation system may also include an environmental monitoring unit that monitors the user's work environment. This unit can, for example, detect noise levels and lighting brightness around the user and provide advice to ensure an optimal work environment. For instance, if the noise level is high, it can display a notification recommending the use of noise-canceling headphones. If the lighting is dim, it can suggest appropriate lighting adjustments. This allows the user to create documents in a comfortable work environment.

[0057] The document creation system can also include a progress tracking unit to track the user's work progress. This unit can, for example, display in real time how much work the user has completed and visually indicate the remaining workload. For instance, a progress bar can be used to show what percentage of the current work the user has completed. The progress tracking unit can also display alerts if the user is behind schedule compared to the deadline they have set. This allows the user to work more efficiently.

[0058] The document creation system can also include a schedule suggestion unit that analyzes the user's work history and proposes an optimal work schedule. For example, the schedule suggestion unit can analyze when the user has worked most efficiently in the past and suggest working during those times. For instance, if the user tends to work most efficiently in the morning, the schedule suggestion unit can suggest scheduling important tasks for the morning. The schedule suggestion unit can also suggest appropriate break times for the user. This allows the user to work more efficiently.

[0059] The document creation system can also be equipped with a health monitoring unit that monitors the user's health status. This unit can, for example, detect the user's heart rate and stress level and suggest appropriate breaks. For instance, if the user's heart rate is high, it can notify them to take a short break. If their stress level is high, it can also provide advice on how to relax. This allows users to work efficiently while maintaining their health.

[0060] The document creation system may also include a performance evaluation unit that assesses the user's work performance and suggests areas for improvement. For example, the performance evaluation unit can analyze the efficiency and quality of past work performed by the user and suggest areas for improvement. If a user is spending too much time on a particular task, the unit can suggest ways to improve the efficiency of that task. It can also provide specific advice to help the user improve the quality of their work. This allows the user to improve their own work performance.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The selection section allows the user to choose a template that matches the format and content of the document they want to create. For example, templates for various formats such as presentation materials and reports are available. If the user is creating a presentation, the selection section will display a template suitable for presentations. Similarly, if the user is creating a report, it can display a template suitable for reports. Furthermore, if the user is creating marketing materials, it can display a template suitable for marketing. Step 2: The specification section specifies the data based on the example selected by the selection section. For example, if the data specified by the user is a table or graph, the specification section will specify them in the appropriate format. The specification section can also specify text data. For example, it will place the text data specified by the user in the appropriate position and adjust the overall layout. Step 3: The generation unit uses a generation AI to generate materials based on the data specified by the specification unit. For example, the generation AI can analyze user-specified data and automatically generate materials based on a model. For example, if the user-specified data is a table or graph, the generation AI will incorporate them into the materials in the appropriate format. The generation AI can also place text data in the appropriate positions and adjust the overall layout. For example, the generation AI can generate materials using a text generation AI (e.g., LLM). The generation AI can also generate materials using a multimodal generation AI. Step 4: The verification unit visually performs a final check of the materials generated by the generation unit. The user can review the materials created by the generation AI and make corrections or additional edits as needed. For example, the verification unit can change the color and font of graphs automatically generated by the generation AI. It can also modify the text content. This allows the user to create materials that match their intentions.

[0063] (Example of form 2) The document creation system according to an embodiment of the present invention is a system for streamlining the manual process of document creation. This document creation system works by allowing the user to select a "template" and specify the data they want to include. The generating AI then creates the document based on the specified data, and the user performs a final visual check. First, the user selects a "template" and specifies the data they want to include. At this time, the user can choose a "template" that matches the format and content of the document they want to create. For example, templates in various formats, such as presentation materials and reports, are available. The user selects the most suitable template from these and specifies the data they want to include. Next, the generating AI creates the document based on the specified data. The generating AI analyzes the data specified by the user and automatically generates the document based on the template. For example, if the data specified by the user is a table or graph, the generating AI incorporates them into the document in an appropriate format. If it is text data, the generating AI places it in the appropriate position and adjusts the overall layout. The created document is then visually checked by the user. The user can check the document created by the generating AI and make corrections or additional edits as needed. For example, the user can change the color or font of the graph automatically generated by the generating AI, or correct the content of the text. This allows users to create materials that match their intentions. This system enables efficient use of time spent on material creation by leveraging AI. Users can eliminate the need to manually create materials and instead work efficiently based on materials automatically generated by the AI. For example, when creating presentation materials, the AI ​​automatically generates slides, and the user only needs to review and revise them, significantly reducing work time. This allows the material creation system to enable users to efficiently create and finalize materials.

[0064] The document creation system according to the embodiment comprises a selection unit, a specification unit, a generation unit, and a confirmation unit. The selection unit selects a template that matches the format and content of the document the user wants to create. For example, templates in various formats, such as presentation materials and reports, are available. For example, if the user is creating presentation materials, the selection unit displays a template suitable for presentations. The selection unit can also display a template suitable for reports if the user is creating a report. Furthermore, if the user is creating marketing materials, the selection unit can also display a template suitable for marketing. The specification unit specifies data based on the template selected by the selection unit. For example, if the data specified by the user is a table or graph, the specification unit specifies them in an appropriate format. The specification unit can also specify text data. For example, the specification unit places the text data specified by the user in an appropriate position and adjusts the overall layout. The generation unit generates the document based on the data specified by the specification unit using a generation AI. The generation AI analyzes the data specified by the user and automatically generates the document based on the template. For example, if the data specified by the user is a table or graph, the generation AI incorporates them into the document in an appropriate format. Furthermore, the generation AI can also position text data appropriately and arrange the overall layout. The generation AI generates documents using, for example, a text generation AI (e.g., LLM). The generation AI can also generate documents using a multimodal generation AI. The verification unit performs a final visual check of the documents generated by the generation unit. The user can review the documents created by the generation AI and make corrections or additional edits as needed. For example, the verification unit can change the color and font of graphs automatically generated by the generation AI. The verification unit can also modify the text content. This allows the user to create documents that match their intentions. Thus, the document creation system according to this embodiment allows the user to efficiently create documents and perform a final check.

[0065] The document creation system includes a presentation section that displays different types of templates. The presentation section presents the types of templates the user can select. For example, the presentation section can display types such as business documents, technical documents, and educational materials. For example, as business documents, the presentation section might display reports, presentation materials, and marketing materials. It can also display technical documents such as technical reports, specifications, and design drawings. Furthermore, it can display educational materials such as teaching materials, lecture notes, and exam questions. This allows the user to select a template that suits their purpose. Some or all of the above processing in the presentation section may be performed using AI, or not. For example, the presentation section could use AI to analyze the user's selection history and present the most suitable template. This allows the user to efficiently select a template. Thus, the document creation system can provide the user with appropriate templates to efficiently create documents.

[0066] The document creation system includes an analysis unit that analyzes the data format. The analysis unit analyzes the data format specified by the user. For example, the analysis unit can analyze data formats such as CSV, JSON, and XML. For example, the analysis unit can analyze CSV data and extract the contents of each field. The analysis unit can also analyze JSON data and extract each key-value pair. Furthermore, the analysis unit can analyze XML data and extract the contents of each tag. In this way, the analysis unit can appropriately analyze the data format specified by the user and extract the information necessary for document creation. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input data into a generation AI, and the generation AI can analyze the data format. In this way, the document creation system can appropriately analyze the data format specified by the user and extract the information necessary for document creation.

[0067] The document creation system includes an editorial department that edits the generated documents. The editorial department edits the documents generated by the generation department. For example, the editorial department can change the color and font of graphs automatically generated by the generation AI. The editorial department can also modify the content of text. For example, the editorial department can modify the content of text automatically generated by the generation AI to make it more in line with the user's intentions. Furthermore, the editorial department can adjust the layout of the documents. For example, the editorial department can adjust the layout of documents automatically generated by the generation AI to make them easier to read. This allows users to create documents that meet their intentions. Some or all of the above processes in the editorial department may be performed using AI, for example, or not using AI. For example, the editorial department can have the AI ​​analyze the documents automatically generated by the generation AI and suggest the optimal editing method. This allows the document creation system to provide users with an appropriate method for efficiently editing documents.

[0068] The selection unit can estimate the user's emotions and narrow down the example options based on those emotions. For example, if the user is stressed, the selection unit will prioritize displaying simple and intuitive examples. For example, if the user is relaxed, the selection unit may also display examples that allow for detailed customization. Furthermore, if the user is in a hurry, the selection unit may prioritize displaying examples that allow for quick document creation. This enables appropriate document creation by providing example options that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can use AI to analyze the user's emotion data and present the optimal example. This allows the document creation system to provide appropriate examples for the user to create documents efficiently.

[0069] The selection unit can analyze past selection history and automatically suggest suitable examples for the user. For example, the selection unit can automatically display examples that the user has frequently used in the past as candidates. For example, the selection unit can also analyze the trends of examples that the user has selected in the past and suggest the optimal example. Furthermore, the selection unit can suggest examples suitable for a specific project based on the user's past selection history. In this way, by analyzing past selection history, the system can suggest the most suitable examples for the user. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can have AI analyze the user's selection history data and present the optimal example. In this way, the document creation system can provide appropriate examples for the user to create documents efficiently.

[0070] The selection section can filter the sample options based on the user's current project and objectives. For example, if the user is creating a presentation, the selection section will prioritize displaying samples suitable for presentations. For example, if the user is creating a report, the selection section can also display samples suitable for reports. Furthermore, if the user is creating marketing materials, the selection section can display samples suitable for marketing. This allows for the creation of appropriate materials by providing samples tailored to the user's project and objectives. Some or all of the above processing in the selection section may be performed using AI, for example, or not. For example, the selection section can use AI to analyze the user's project data and present the most suitable sample. This allows the material creation system to provide appropriate samples for the user to create materials efficiently.

[0071] The selection unit can estimate the user's emotions and adjust the display order of examples based on those emotions. For example, if the user is stressed, the selection unit can first display a simple and intuitive example. For example, if the user is relaxed, the selection unit can first display an example that allows for detailed customization. Also, if the user is in a hurry, the selection unit can first display an example that allows for quick document creation. This enables appropriate document creation by providing an order of examples that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can have AI analyze the user's emotion data and present the most suitable example. This allows the document creation system to provide appropriate examples for the user to create documents efficiently.

[0072] The selection section can prioritize displaying highly relevant examples, taking into account the user's geographical location. For example, if the user is in a specific region, the selection section will prioritize displaying examples related to that region. For example, if the user is traveling, the selection section can also display examples related to travel. Furthermore, if the user is participating in a specific event, the selection section can display examples related to that event. This enables the creation of appropriate materials by providing examples based on the user's geographical location. Some or all of the above processing in the selection section may be performed using AI, for example, or not. For example, the selection section can use AI to analyze the user's geographical location data and present the most suitable examples. This allows the material creation system to provide appropriate examples for the user to create materials efficiently.

[0073] The selection unit can analyze a user's social media activity and suggest relevant examples. For example, the selection unit can suggest relevant examples based on what the user has shared on social media. For example, the selection unit can also suggest examples based on the activity of accounts the user follows on social media. Furthermore, the selection unit can suggest examples based on the activity of groups the user participates in on social media. This enables the creation of appropriate materials by providing examples based on the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can use AI to analyze the user's social media data and present the most suitable examples. This allows the material creation system to provide appropriate examples for the user to create materials efficiently.

[0074] The specification unit can estimate the user's emotions and adjust the data specification method based on those emotions. For example, if the user is stressed, the specification unit can provide a simple interface and minimize the data specification procedure. For example, if the user is relaxed, the specification unit can provide detailed data specification options and suggest a customizable specification method. Also, if the user is in a hurry, the specification unit can prioritize voice input to allow for quick data specification. This enables appropriate document creation by providing a data specification method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the specification unit may be performed using AI or not. For example, the specification unit can have AI analyze the user's emotion data and suggest the optimal data specification method. This allows the document creation system to provide an appropriate data specification method for the user to create documents efficiently.

[0075] The specification unit can analyze past data specification history and suggest a suitable specification method. For example, the specification unit can automatically display data that the user has frequently specified in the past as a candidate. For example, the specification unit can also prioritize suggesting data specification methods (voice, text, etc.) that the user has used in the past. Furthermore, the specification unit can suggest a data specification method suitable for a specific project based on the user's past data specification history. In this way, by analyzing past data specification history, the system can suggest the optimal data specification method to the user. Some or all of the above processing in the specification unit may be performed using AI, for example, or not using AI. For example, the specification unit can have AI analyze the user's data specification history data and present the optimal data specification method. In this way, the document creation system can provide the user with an appropriate data specification method to efficiently create documents.

[0076] The specification unit can apply different specification algorithms depending on the type of data being specified. For example, in the case of text data, the specification unit can analyze the data using natural language processing and provide an appropriate specification method. For example, in the case of image data, the specification unit can also analyze the data using image recognition technology and provide an appropriate specification method. Furthermore, in the case of numerical data, the specification unit can analyze the data using statistical analysis and provide an appropriate specification method. This enables the creation of appropriate documents by providing specification algorithms according to the type of data. Some or all of the above processing in the specification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the specification unit can input data into a generative AI, and the generative AI can apply a specification algorithm according to the type of data. This allows the document creation system to provide an appropriate data specification method for users to efficiently create documents.

[0077] The specification unit can estimate the user's emotions and adjust the order in which data is specified based on those emotions. For example, if the user is stressed, the specification unit may adjust the order so that important data is specified first. For example, if the user is relaxed, the specification unit may also adjust the order so that detailed data is specified first. Furthermore, if the user is in a hurry, the specification unit may adjust the order so that data that can be specified quickly is specified first. This enables the creation of appropriate documents by providing a data specification order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the specification unit may be performed using AI, for example, or not using AI. For example, the specification unit may have AI analyze the user's emotion data and suggest the optimal data specification order. This allows the document creation system to provide an appropriate data specification method for the user to create documents efficiently.

[0078] The specification unit can prioritize highly relevant data when specifying data, taking into account the user's geographical location. For example, if the user is in a specific region, the specification unit can prioritize data related to that region. For example, if the user is traveling, the specification unit can prioritize data related to travel. Furthermore, if the user is participating in a specific event, the specification unit can prioritize data related to that event. This enables the creation of appropriate materials by providing data based on the user's geographical location. Some or all of the above processing in the specification unit may be performed using AI, for example, or not. For example, the specification unit can use AI to analyze the user's geographical location data and present the most suitable data. This allows the material creation system to provide an appropriate data specification method for users to efficiently create materials.

[0079] The specification unit can analyze the user's social media activity and specify relevant data when specifying data. For example, the specification unit can specify relevant data based on what the user has shared on social media. For example, the specification unit can also specify relevant data based on the activity of accounts the user follows on social media. Furthermore, the specification unit can specify relevant data based on the activity of groups the user participates in on social media. This enables the creation of appropriate materials by providing data based on the user's social media activity. Some or all of the above processing in the specification unit may be performed using AI, for example, or not using AI. For example, the specification unit can have AI analyze the user's social media data and present the optimal data. This allows the material creation system to provide an appropriate data specification method for users to efficiently create materials.

[0080] The generation unit can estimate the user's emotions and adjust the method of generating materials based on those emotions. For example, if the user is relaxed, the generation AI can generate materials that proceed at a leisurely pace. For example, if the user is in a hurry, the generation AI can generate materials that can be completed in the shortest possible time. Furthermore, if the user is excited, the generation AI can generate materials with visually stimulating effects. This allows for the creation of appropriate materials by providing a method of generating materials that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can have AI analyze the user's emotion data and suggest the optimal method of generating materials. This allows the material creation system to provide an appropriate method of generating materials that allows the user to create materials efficiently.

[0081] The generation unit can adjust the level of detail generated for the document based on the importance of the specified data. For example, if the data is important, the generation AI will generate a document that includes a detailed explanation. For example, if the data is general, the generation AI can generate a document that includes a concise explanation. Furthermore, if the data is supplementary, the generation AI can generate a document that includes only the main points. This allows for the creation of appropriate documents by providing a level of detail in the document generation that corresponds to the importance of the data. Some or all of the above processing in the generation unit may be performed using the generation AI, or not. For example, the generation unit can input data into the generation AI, and the generation AI can adjust the level of detail based on the importance of the data. This allows the document creation system to provide an appropriate document generation method for users to create documents efficiently.

[0082] The generation unit can apply different generation algorithms depending on the specified data category. For example, in the case of text data, the generation unit generates materials using natural language processing. For example, in the case of image data, the generation unit can also generate materials using image recognition technology. Furthermore, in the case of numerical data, the generation unit can also generate materials using statistical analysis. This enables the creation of appropriate materials by providing generation algorithms according to the data category. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data into a generation AI, and the generation AI can apply a generation algorithm according to the data category. This allows the material creation system to provide an appropriate material generation method for users to efficiently create materials.

[0083] The generation unit can estimate the user's emotions and adjust the order in which data is generated based on those emotions. For example, if the user is stressed, the generation unit can adjust the order to generate important data first. For example, if the user is relaxed, the generation unit can adjust the order to generate detailed data first. Also, if the user is in a hurry, the generation unit can adjust the order to generate data that can be generated quickly first. This enables appropriate data creation by providing a data generation order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can have an AI analyze the user's emotion data and suggest the optimal data generation order. This allows the data creation system to provide an appropriate data generation method for users to create data efficiently.

[0084] The generation unit can determine the generation priority based on the data submission date when generating documents. For example, the generation unit may prioritize generating data with an approaching deadline. For example, the generation unit may postpone generating data with a distant submission date. The generation unit may also generate data with an unknown submission date after the generation of other data has been completed. This enables the creation of appropriate documents by providing a generation priority based on the data submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data into a generation AI, which can then determine the generation priority based on the data submission date. This allows the document creation system to provide an appropriate document generation method that enables users to create documents efficiently.

[0085] The generation unit can adjust the generation order based on the relevance of the data when generating documents. For example, the generation unit may generate highly relevant data first. For example, the generation unit may postpone generating less relevant data. The generation unit may also generate data whose relevance is unknown after the generation of other data is complete. This allows for the creation of appropriate documents by providing a generation order based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data into a generation AI, and the generation AI can adjust the generation order based on the relevance of the data. This allows the document creation system to provide an appropriate document generation method that enables users to create documents efficiently.

[0086] The confirmation unit can estimate the user's emotions and adjust the display method of the confirmation based on those emotions. For example, if the user is nervous, the confirmation unit provides a simple and highly visible display method. For example, if the user is relaxed, the confirmation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the confirmation unit can provide a display method that gets straight to the point. This enables appropriate document review by providing a confirmation display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or not using AI. For example, the confirmation unit can have AI analyze the user's emotion data and suggest the optimal confirmation display method. This allows the document creation system to provide an appropriate confirmation method for the user to efficiently review the document.

[0087] The verification unit can suggest a suitable verification method by referring to past verification history during the verification process. For example, the verification unit may prioritize suggesting verification methods previously used by the user. For example, the verification unit may also suggest a verification method suitable for a specific project based on the user's past verification history. Furthermore, the verification unit can analyze the user's past verification history and suggest the most efficient verification method. This allows the system to suggest the optimal verification method to the user by referring to past verification history. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or not. For example, the verification unit may use AI to analyze the user's verification history data and present the optimal verification method. This allows the document creation system to provide the user with an appropriate verification method for efficiently reviewing documents.

[0088] The verification unit can apply different verification methods depending on the category of the document during verification. For example, in the case of presentation materials, the verification unit can provide a method for verifying each slide. For example, in the case of reports, the verification unit can also provide a method for verifying each chapter. Furthermore, in the case of marketing materials, the verification unit can also provide a method for verifying each section. By providing verification methods according to the category of the document, appropriate document verification becomes possible. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can use AI to analyze the user's document category data and suggest the optimal verification method. This allows the document creation system to provide users with appropriate verification methods for efficiently reviewing documents.

[0089] The verification unit can estimate the user's emotions and determine the priority of the verification based on those emotions. For example, if the user is feeling stressed, the verification unit can adjust the order so that important parts are reviewed first. For example, if the user is relaxed, the verification unit can also adjust the order so that detailed parts are reviewed first. Furthermore, if the user is in a hurry, the verification unit can adjust the order so that parts that can be quickly reviewed are reviewed first. This enables appropriate document review by providing verification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can have AI analyze the user's emotion data and present the optimal verification priority. This allows the document creation system to provide an appropriate verification method for users to efficiently review documents.

[0090] The verification unit can adjust the order of verification based on the submission dates of the documents during the verification process. For example, the verification unit can prioritize documents with approaching deadlines. For example, the verification unit can postpone documents with later submission dates. The verification unit can also verify documents with unknown submission dates after the verification of other documents has been completed. This allows for appropriate document verification by providing a verification order based on the submission dates of the documents. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input document submission date data into AI, and the AI ​​can adjust the verification order based on the submission dates. This allows the document creation system to provide an appropriate verification method for users to efficiently review documents.

[0091] The verification unit can adjust the order of verification based on the relevance of the documents during the verification process. For example, the verification unit may verify highly relevant documents first. For example, the verification unit may postpone verification of less relevant documents. The verification unit may also verify documents whose relevance is unclear after the verification of other documents has been completed. This allows for appropriate document verification by providing a verification order based on the relevance of the documents. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input document relevance data into AI, and the AI ​​can adjust the verification order based on relevance. This allows the document creation system to provide an appropriate verification method for users to efficiently review documents.

[0092] The presentation unit can estimate the user's emotions and adjust the way examples are presented based on those emotions. For example, if the user is stressed, the presentation unit will prioritize presenting simple and intuitive examples. For example, if the user is relaxed, the presentation unit may also present examples that allow for detailed customization. Furthermore, if the user is in a hurry, the presentation unit may prioritize presenting examples that allow for quick document creation. This enables appropriate document creation by providing examples that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can have AI analyze the user's emotion data and present the optimal way to present examples. This allows the document creation system to provide appropriate examples for the user to create documents efficiently.

[0093] The presentation unit can analyze past presentation history and automatically present the most suitable example to the user. For example, the presentation unit can automatically present examples that the user has frequently used in the past as candidates. For example, the presentation unit can also analyze the trends of examples that the user has selected in the past and present the most suitable example. Furthermore, the presentation unit can present examples suitable for a specific project based on the user's past presentation history. In this way, by analyzing past presentation history, the best example can be presented to the user. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can have AI analyze the user's presentation history data and present the most suitable example. In this way, the document creation system can provide appropriate examples for the user to create documents efficiently.

[0094] The presentation unit can estimate the user's emotions and adjust the order in which examples are presented based on those emotions. For example, if the user is stressed, the presentation unit may first present a simple and intuitive example. For example, if the user is relaxed, the presentation unit may first present an example that allows for detailed customization. Also, if the user is in a hurry, the presentation unit may first present an example that allows for quick document creation. This enables appropriate document creation by providing an order of examples that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can have AI analyze the user's emotion data and present the optimal order of example presentations. This allows the document creation system to provide appropriate examples for the user to create documents efficiently.

[0095] The presentation unit can prioritize presenting highly relevant examples, taking into account the user's geographical location. For example, if the user is in a specific region, the presentation unit will prioritize presenting examples related to that region. For example, if the user is traveling, the presentation unit can also present examples related to travel. Furthermore, if the user is participating in a specific event, the presentation unit can also present examples related to that event. This enables the creation of appropriate materials by providing examples based on the user's geographical location. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not. For example, the presentation unit can use AI to analyze the user's geographical location data and present the most suitable examples. This allows the material creation system to provide appropriate examples for the user to create materials efficiently.

[0096] The analysis unit can estimate the user's emotions and adjust the data analysis method based on those emotions. For example, if the user is stressed, the analysis unit can provide a simple and rapid analysis method. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can provide an analysis method that yields results quickly. This enables the creation of appropriate materials by providing a data analysis method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI analyze the user's emotion data and suggest the optimal data analysis method. This allows the material creation system to provide an appropriate data analysis method for the user to create materials efficiently.

[0097] The analysis unit can apply the most suitable analysis algorithm by referring to past analysis history. For example, the analysis unit may prioritize applying analysis algorithms previously used by the user. For example, the analysis unit may apply an analysis algorithm suitable for a specific project based on the user's past analysis history. The analysis unit can also analyze the user's past analysis history and apply the most efficient analysis algorithm. In this way, the system can apply the most suitable analysis algorithm to the user by referring to past analysis history. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past analysis history data into a generative AI, and the generative AI can apply the most suitable analysis algorithm. In this way, the document creation system can provide the user with an appropriate data analysis method for efficiently creating documents.

[0098] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, the analysis unit can adjust the order to analyze important data first. For example, if the user is relaxed, the analysis unit can also adjust the order to analyze detailed data first. Furthermore, if the user is in a hurry, the analysis unit can adjust the order to analyze data that can be quickly analyzed first. This allows for the creation of appropriate materials by providing analysis priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI analyze the user's emotion data and present the optimal analysis priority. This allows the material creation system to provide an appropriate data analysis method for the user to create materials efficiently.

[0099] The analysis unit can adjust the order of analysis based on the data submission date during data analysis. For example, the analysis unit may prioritize analyzing data with approaching deadlines. For example, the analysis unit may postpone analyzing data with distant submission dates. Furthermore, the analysis unit may analyze data with unknown submission dates after the analysis of other data has been completed. This allows for the creation of appropriate documents by providing an analysis order based on the data submission dates. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the analysis unit can input data into a generation AI, which can then adjust the analysis order based on the data submission dates. This allows the document creation system to provide an appropriate data analysis method for users to efficiently create documents.

[0100] The editorial team can estimate the user's emotions and adjust the display method of the edits based on those emotions. For example, if the user is stressed, the editorial team can provide a simple and highly visible display method. For example, if the user is relaxed, the editorial team can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the editorial team can provide a display method that gets straight to the point. This enables the creation of appropriate materials by providing an edit display method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI, for example, or not using AI. For example, the editorial team can have AI analyze the user's emotion data and suggest the optimal edit display method. This allows the material creation system to provide appropriate editing methods for users to create materials efficiently.

[0101] The editorial department can suggest the optimal editing method by referring to past editing history during the editing process. For example, the editorial department may prioritize suggesting editing methods previously used by the user. For example, the editorial department may suggest an editing method suitable for a specific project based on the user's past editing history. The editorial department can also analyze the user's past editing history and suggest the most efficient editing method. This allows the editorial department to suggest the optimal editing method to the user by referring to past editing history. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department may use AI to analyze the user's editing history data and present the optimal editing method. This allows the document creation system to provide the user with an appropriate editing method to efficiently create documents.

[0102] The editorial team can estimate the user's emotions and determine editing priorities based on those emotions. For example, if the user is stressed, the editorial team can adjust the editing order to prioritize important sections first. For example, if the user is relaxed, the editorial team can adjust the editing order to prioritize detailed sections first. Also, if the user is in a hurry, the editorial team can adjust the editing order to prioritize sections that can be edited quickly. This allows for the creation of appropriate documents by providing editing priorities that correspond to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI or not. For example, the editorial team can have AI analyze the user's emotion data and suggest optimal editing priorities. This allows the document creation system to provide users with appropriate editing methods to create documents efficiently.

[0103] The editorial department can adjust the editing order based on the submission dates of the materials during the editing process. For example, the editorial department can prioritize editing materials with approaching deadlines. For example, the editorial department can postpone editing materials with later submission dates. The editorial department can also edit materials with unknown submission dates after other materials have been edited. This allows for the creation of appropriate materials by providing an editing order based on the submission dates of the materials. Some or all of the above processes in the editorial department may be performed using AI, for example, or not. For example, the editorial department can input material submission date data into AI, and the AI ​​can adjust the editing order based on the submission dates. This allows the material creation system to provide users with an appropriate editing method for creating materials efficiently. === Hard Collateral 1-1 === Each of the multiple elements described above, including the selection unit, specification unit, generation unit, confirmation unit, presentation unit, analysis unit, and editing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14 and selects a model that matches the format and content of the document the user wants to create. The specification unit is implemented by the control unit 46A of the smart device 14 and specifies data based on the selected model. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates the document based on the specified data. The confirmation unit is implemented by the control unit 46A of the smart device 14 and performs a final visual confirmation of the generated document. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents the type of model. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the format of the specified data. The editing unit is implemented by the control unit 46A of the smart device 14 and edits the generated document. === Hard Collateral 1-2 === Each of the multiple elements described above, including the selection unit, specification unit, generation unit, confirmation unit, presentation unit, analysis unit, and editing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214 and selects a model that matches the format and content of the document the user wants to create. The specification unit is implemented by the control unit 46A of the smart glasses 214 and specifies data based on the selected model. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates the document based on the specified data. The confirmation unit is implemented by the control unit 46A of the smart glasses 214 and performs a final visual confirmation of the generated document. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents the type of model. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the format of the specified data. The editing unit is implemented by the control unit 46A of the smart glasses 214 and edits the generated document. === Hard Collateral 1-3 === Each of the multiple elements described above, including the selection unit, specification unit, generation unit, confirmation unit, presentation unit, analysis unit, and editing unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314 and selects a model that matches the format and content of the document the user wants to create. The specification unit is implemented by the control unit 46A of the headset terminal 314 and specifies data based on the selected model. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates the document based on the specified data. The confirmation unit is implemented by the control unit 46A of the headset terminal 314 and performs a final visual confirmation of the generated document. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents the type of model. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the format of the specified data. The editing unit is implemented by the control unit 46A of the headset terminal 314 and edits the generated document. === Hard Collateral 1-4 === Each of the multiple elements described above, including the selection unit, specification unit, generation unit, confirmation unit, presentation unit, analysis unit, and editing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414 and selects a model that matches the format and content of the document the user wants to create. The specification unit is implemented by the control unit 46A of the robot 414 and specifies data based on the selected model. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates the document based on the specified data. The confirmation unit is implemented by the control unit 46A of the robot 414 and performs a final visual check of the generated document. The presentation unit is implemented by the control unit 46A of the robot 414 and presents the type of model. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the format of the specified data. The editing unit is implemented by the control unit 46A of the robot 414 and edits the generated document.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The document creation system may also include an environmental monitoring unit that monitors the user's work environment. This unit can, for example, detect noise levels and lighting brightness around the user and provide advice to ensure an optimal work environment. For instance, if the noise level is high, it can display a notification recommending the use of noise-canceling headphones. If the lighting is dim, it can suggest appropriate lighting adjustments. This allows the user to create documents in a comfortable work environment.

[0106] The document creation system can also include a progress tracking unit to track the user's work progress. This unit can, for example, display in real time how much work the user has completed and visually indicate the remaining workload. For instance, a progress bar can be used to show what percentage of the current work the user has completed. The progress tracking unit can also display alerts if the user is behind schedule compared to the deadline they have set. This allows the user to work more efficiently.

[0107] The document creation system can also include a schedule suggestion unit that analyzes the user's work history and proposes an optimal work schedule. For example, the schedule suggestion unit can analyze when the user has worked most efficiently in the past and suggest working during those times. For instance, if the user tends to work most efficiently in the morning, the schedule suggestion unit can suggest scheduling important tasks for the morning. The schedule suggestion unit can also suggest appropriate break times for the user. This allows the user to work more efficiently.

[0108] The document creation system can also be equipped with a health monitoring unit that monitors the user's health status. This unit can, for example, detect the user's heart rate and stress level and suggest appropriate breaks. For instance, if the user's heart rate is high, it can notify them to take a short break. If their stress level is high, it can also provide advice on how to relax. This allows users to work efficiently while maintaining their health.

[0109] The document creation system may also include a priority adjustment unit that estimates the user's emotions and adjusts the priority of tasks based on those emotions. For example, if the user is feeling stressed, the priority adjustment unit can suggest prioritizing simple tasks. For example, if the user is relaxed, it can suggest prioritizing complex tasks. Also, if the user is in a hurry, it can suggest prioritizing tasks that can be completed quickly. This allows the user to perform tasks that are optimal for their emotions.

[0110] The document creation system may also include a process adjustment unit that estimates the user's emotions and adjusts the work process based on those emotions. For example, if the user is stressed, the process adjustment unit can provide a simple and intuitive work procedure. If the user is relaxed, it can provide a highly customizable work procedure. Furthermore, if the user is in a hurry, it can provide a procedure that allows for quick completion. This allows the user to select the optimal work procedure according to their emotions.

[0111] The document creation system may also include a feedback unit that estimates the user's emotions and provides feedback based on those emotions. For example, if the user is stressed, the feedback unit can prioritize providing positive feedback. If the user is relaxed, it can provide detailed feedback. If the user is in a hurry, it can provide concise feedback. This allows the user to receive optimal feedback tailored to their emotions.

[0112] The document creation system may also include an interface adjustment unit that estimates the user's emotions and adjusts the work interface based on those emotions. For example, if the user is stressed, the interface adjustment unit can provide a simple and highly visible interface. For example, if the user is relaxed, it can provide a highly customizable interface. And if the user is in a hurry, it can provide a quick and easy-to-use interface. This allows the user to use the optimal interface according to their emotions.

[0113] The document creation system may also include a notification method adjustment unit that estimates the user's emotions and adjusts the notification method based on those emotions. For example, if the user is stressed, the notification method adjustment unit can provide simple and intuitive notifications. For example, if the user is relaxed, it can provide detailed notifications. It can also provide notifications that can be quickly understood if the user is in a hurry. This allows the user to receive notifications that are optimal for their emotions.

[0114] The document creation system may also include a performance evaluation unit that assesses the user's work performance and suggests areas for improvement. For example, the performance evaluation unit can analyze the efficiency and quality of past work performed by the user and suggest areas for improvement. If a user is spending too much time on a particular task, the unit can suggest ways to improve the efficiency of that task. It can also provide specific advice to help the user improve the quality of their work. This allows the user to improve their own work performance.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The selection section allows the user to choose a template that matches the format and content of the document they want to create. For example, templates for various formats such as presentation materials and reports are available. If the user is creating a presentation, the selection section will display a template suitable for presentations. Similarly, if the user is creating a report, it can display a template suitable for reports. Furthermore, if the user is creating marketing materials, it can display a template suitable for marketing. Step 2: The specification section specifies the data based on the example selected by the selection section. For example, if the data specified by the user is a table or graph, the specification section will specify them in the appropriate format. The specification section can also specify text data. For example, it will place the text data specified by the user in the appropriate position and adjust the overall layout. Step 3: The generation unit uses a generation AI to generate materials based on the data specified by the specification unit. For example, the generation AI can analyze user-specified data and automatically generate materials based on a model. For example, if the user-specified data is a table or graph, the generation AI will incorporate them into the materials in the appropriate format. The generation AI can also place text data in the appropriate positions and adjust the overall layout. For example, the generation AI can generate materials using a text generation AI (e.g., LLM). The generation AI can also generate materials using a multimodal generation AI. Step 4: The verification unit visually performs a final check of the materials generated by the generation unit. The user can review the materials created by the generation AI and make corrections or additional edits as needed. For example, the verification unit can change the color and font of graphs automatically generated by the generation AI. It can also modify the text content. This allows the user to create materials that match their intentions.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A selection section for choosing an example, A specification unit that specifies data based on the example selected by the selection unit, A generation unit that generates data based on the data specified by the designation unit, The system includes a verification unit that performs a final check of the materials generated by the generation unit. A system characterized by the following features.

2. It includes a display section that shows the types of examples. The system according to feature 1.

3. It includes an analysis unit that analyzes the data format. The system according to feature 1.

4. It includes an editorial department to edit the generated materials. The system according to feature 1.

5. The aforementioned selection unit is It estimates the user's emotions and narrows down the sample options based on those emotions. The system according to feature 1.

6. The aforementioned selection unit is It analyzes past selection history and automatically suggests suitable examples for the user. The system according to feature 1.

7. The aforementioned selection unit is Filter the example options based on the user's current project and objectives. The system according to feature 1.

8. The aforementioned selection unit is It estimates the user's emotions and adjusts the display order of examples based on those emotions. The system according to feature 1.

9. The aforementioned selection unit is The system prioritizes displaying relevant examples based on the user's geographical location. The system according to feature 1.

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