system

Abstract

The system according to the embodiment aims to reduce the number of steps required for creating and reviewing presentation materials. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a re-review unit. The reception unit accepts presentation materials from a user. The analysis unit analyzes the materials accepted by the reception unit. The proposal unit proposes revisions based on the results of the analysis by the analysis unit. The re-review unit re-reviews the materials after the revisions proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating and reviewing presentation materials required a lot of man-hours, making it difficult to run business efficiently.

[0005] The system according to the embodiment aims to reduce the number of steps required for creating and reviewing presentation materials. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a re-review unit. The reception unit accepts presentation materials from a user. The analysis unit analyzes the materials accepted by the reception unit. The proposal unit proposes revisions based on the results of the analysis by the analysis unit. The re-review unit re-reviews the materials after the revisions proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the number of steps required to create and review presentation materials. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A presentation material review system according to an embodiment of the present invention automatically reviews presentation materials created by a user and proposes revisions. In this system, presentation materials created by a user are input into an AI, which analyzes the content of the materials and detects grammatical and phrasal errors, logical inconsistencies, and visual layout issues. The AI ​​then proposes revisions based on the detected issues. Furthermore, the AI ​​re-reviews the revised materials and proposes additional revisions as needed. This improves the quality of the materials and reduces the number of reviews and revisions required. This system reduces the labor required for creating and reviewing presentation materials, thereby improving internal efficiency. For example, the reduction in the time required for creating materials and the reduction in the number of reviews allows for more time to focus on other tasks. Furthermore, the improvement in the quality of materials facilitates smoother internal and external communication. This allows the presentation material review system to efficiently review users' presentation materials and propose revisions, thereby reducing labor costs.

[0029] A presentation material review system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a re-review unit. The reception unit accepts presentation materials from a user. Presentation materials created by a user include, but are not limited to, slides, documents, and videos. The reception unit, for example, receives presentation materials uploaded by a user and inputs them into the system. The reception unit can also automatically receive materials sent by a user via email. The reception unit can also obtain materials from a cloud storage service. The analysis unit analyzes the materials accepted by the reception unit. The analysis unit performs, for example, grammatical analysis, content analysis, and visual analysis. The grammatical analysis detects grammatical errors in the materials. For example, the analysis unit identifies grammatical errors based on grammatical rules. The content analysis evaluates the consistency and logical flow of the content of the materials. For example, the analysis unit checks whether each section of the materials is logically connected. The visual analysis detects problems with the layout and design of the materials. For example, the analysis unit identifies inconsistencies in font size and color usage. The suggestion unit makes correction suggestions based on the results of the analysis by the analysis unit. The suggestion unit, for example, makes suggestions to correct grammatical errors. For example, the suggestion unit presents specific correction suggestions for correcting grammatical errors. The suggestion unit makes suggestions to improve the content. For example, the suggestion unit makes suggestions to improve the logical flow. The suggestion unit makes suggestions to change the visual layout. For example, the suggestion unit suggests unifying the font size and color usage. The re-review unit re-reviews the material after the corrections proposed by the suggestion unit. For example, the re-review unit re-grammatically analyzes the material after the corrections to check for additional grammatical errors. The re-review unit re-checks the content. For example, the re-review unit re-evaluates the logical consistency of the material after the corrections. The re-review unit performs a visual evaluation. For example, the re-review unit re-checks the layout and design of the material after the corrections. As a result, the presentation material review system according to the embodiment efficiently reviews a user's presentation materials and suggests corrections, thereby reducing labor hours.

[0030] The analysis unit can detect grammatical or phrasal errors, logical inconsistencies, and visual layout problems. The analysis unit, for example, performs grammatical analysis and detects grammatical errors. For example, the analysis unit identifies grammatical errors based on grammatical rules. The analysis unit can also detect expression errors. For example, the analysis unit identifies inappropriate expressions based on a style guide. The analysis unit also evaluates logical consistency. For example, the analysis unit checks whether each section of the document is logically connected. The analysis unit also detects visual layout problems. For example, the analysis unit identifies inconsistencies in font size or color usage. This improves the quality of the document by automatically detecting grammatical or phrasal errors, logical inconsistencies, and visual layout problems in the document. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model that uses natural language processing technology for grammatical analysis.

[0031] The suggestion unit can make correction suggestions based on the detected problems. The suggestion unit, for example, makes suggestions to correct grammatical errors. For example, the suggestion unit presents specific correction suggestions for correcting grammatical errors. The suggestion unit can also make suggestions to correct expression errors. For example, the suggestion unit makes suggestions to correct inappropriate expressions based on a style guide. The suggestion unit can also make suggestions to improve logical consistency. For example, the suggestion unit can make suggestions to correct each section of the document so that it is logically connected. The suggestion unit can also make suggestions to change the visual layout. For example, the suggestion unit can suggest standardizing font sizes and colors. This allows for efficient correction of the document by making correction suggestions based on the detected problems. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can use an AI model that uses natural language processing technology to make correction suggestions for grammatical errors.

[0032] The re-review unit can review the revised material again and make additional correction suggestions. For example, the re-review unit can perform grammatical analysis on the revised material again to check for additional grammatical errors. For example, the re-review unit can identify grammatical errors based on grammatical rules. The re-review unit can also re-check the content. For example, the re-review unit can re-evaluate the logical consistency of the revised material. Furthermore, the re-review unit can perform visual evaluation. For example, the re-review unit can re-check the layout and design of the revised material. This allows the revised material to be reviewed again and additional correction suggestions made as necessary, thereby further improving the quality of the material. Some or all of the above-mentioned processing in the re-review unit can be performed using, for example, AI, or can be performed without using AI. For example, the re-review unit can use an AI model that uses natural language processing technology for grammatical analysis.

[0033] The suggestion unit may make suggestions for correcting grammatical errors. For example, the suggestion unit may present specific suggestions for correcting grammatical errors. For example, the suggestion unit may identify grammatical errors based on grammatical rules and suggest how to correct them. The suggestion unit may also refer to a style guide for correcting grammatical errors. For example, the suggestion unit may make suggestions for correcting improper grammar based on the style guide. As a result, the suggestions for correcting grammatical errors improve the grammatical accuracy of the material. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may use an AI model that uses natural language processing technology to make correction suggestions for grammatical errors.

[0034] The suggestion unit can make suggestions to change expressions to more specific expressions. For example, the suggestion unit can make suggestions to change ambiguous expressions to more specific expressions. For example, the suggestion unit can make suggestions to modify ambiguous expressions based on specific examples or data. The suggestion unit can also suggest the use of technical terms. For example, the suggestion unit can suggest replacing general expressions with technical terms. This makes suggestions to change expressions to more specific expressions, thereby improving the expressiveness of the material. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use an AI model that uses natural language processing technology to change ambiguous expressions to more specific expressions.

[0035] The suggestion unit can make suggestions for unifying the slide layout. For example, the suggestion unit can suggest unifying fonts. For example, the suggestion unit can suggest using the same font for all slides in a document. The suggestion unit can also suggest unifying color usage. For example, the suggestion unit can suggest using the same color palette for all slides in a document. The suggestion unit can also suggest balancing the layout. For example, the suggestion unit can suggest evenly arranging text and images within a slide. This makes suggestions for unifying the slide layout, thereby improving the visual consistency of the document. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can use an AI model that uses image analysis technology to unify the slide layout.

[0036] The reception unit can analyze the user's past document submission history and select the optimal reception method. For example, the reception unit analyzes the time periods during which the user frequently submitted documents in the past and receives documents during those time periods. For example, the reception unit stores the user's submission history in a database and analyzes the pattern of submission time periods. The reception unit can also analyze the types of documents the user has previously submitted and prioritize receiving similar documents. For example, the reception unit identifies document categories from the user's submission history and prioritizes receiving documents of the same category. Furthermore, the reception unit selects the optimal reception method (online, offline, etc.) based on the user's past submission history. For example, based on the user's submission history, the reception unit prioritizes online reception if there are many online submissions, and prioritizes in-person reception if there are many in-person submissions. This allows the optimal reception method to be selected by analyzing the user's past document submission history, enabling efficient document reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input submission history data into the generation AI and have the generation AI select the optimal reception method.

[0037] When receiving materials, the reception unit can filter the materials based on the user's current project or area of ​​interest. For example, the reception unit prioritizes receiving materials related to the user's ongoing project. For example, the reception unit stores the user's project information in a database and identifies related materials. The reception unit can also filter and receive highly relevant materials based on the user's area of ​​interest. For example, the reception unit acquires the user's area of ​​interest from profile information and prioritizes receiving related materials. Furthermore, the reception unit prioritizes receiving necessary materials according to the progress of the user's project. For example, the reception unit monitors the progress of the project in real time and identifies necessary materials. This allows for efficient reception of highly relevant materials by filtering materials based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input project information and area of ​​interest data into a generation AI and have the generation AI filter highly relevant materials.

[0038] When accepting materials, the reception unit can prioritize accepting highly relevant materials by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting materials related to that area. For example, the reception unit acquires the user's geographical location information from GPS data and identifies relevant materials. Furthermore, if the user is on a business trip, the reception unit can prioritize accepting materials related to the user's business trip destination. For example, the reception unit stores the user's business trip destination information in a database and identifies relevant materials. Furthermore, if the user is at home, the reception unit prioritizes accepting materials that are easily accessible from home. For example, the reception unit acquires geographical location information from the user's IP address and identifies relevant materials. This allows for efficient acceptance of highly relevant materials by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input geographical location information data to a generation AI and cause the generation AI to identify highly relevant materials.

[0039] The reception unit can analyze the user's social media activity and receive relevant materials when receiving the materials. The reception unit, for example, prioritizes receiving relevant materials based on information shared by the user on social media. For example, the reception unit can analyze the user's social media account and identify the shared information. The reception unit can also receive materials related to topics the user follows on social media. For example, the reception unit can store the user's followed topics in a database and identify relevant materials. The reception unit can also analyze the user's social media activity history and receive highly relevant materials. For example, the reception unit can analyze the user's posts and reactions to identify relevant materials. This allows the user's social media activity to be analyzed and highly relevant materials to be efficiently received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input social media data into a generation AI and cause the generation AI to identify highly relevant materials.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. For example, the analysis unit performs a detailed analysis on documents with high importance. For example, the analysis unit evaluates the importance of the document based on the progress of the project and the user's needs. The analysis unit can also perform a brief analysis on documents with low importance. For example, the analysis unit adjusts the depth of the analysis based on the importance of the document. Furthermore, the analysis unit determines the priority of the analysis based on the importance of the document. For example, the analysis unit prioritizes analysis of documents with high importance and postpones analysis of documents with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the document. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the document. For example, the analysis unit applies a technical analysis algorithm to technical documents. For example, the analysis unit uses an algorithm that analyzes the content of the technical documents from a professional perspective. The analysis unit can also apply an analysis algorithm specialized for marketing to marketing documents. For example, the analysis unit uses an algorithm that analyzes the content of the marketing documents from a market analysis perspective. Furthermore, the analysis unit applies an analysis algorithm specialized for financial analysis to financial documents. For example, the analysis unit uses an algorithm that analyzes the content of the financial documents based on financial indicators. In this way, by applying different analysis algorithms depending on the category of the document, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document category data into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can determine the analysis priority based on the submission date of the documents. For example, the analysis unit prioritizes analysis of documents with an upcoming submission deadline. For example, the analysis unit stores the submission deadline of the documents in a database and identifies documents with an upcoming submission deadline. The analysis unit can also postpone documents with a distant submission deadline. For example, the analysis unit adjusts the analysis priority based on the submission deadline. Furthermore, the analysis unit determines the analysis priority according to the submission date. For example, the analysis unit determines the order of analysis based on the submission date or submission time. This enables efficient analysis by determining the analysis priority based on the submission date of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission date data to the generation AI and have the generation AI determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the materials. For example, the analysis unit prioritizes analysis of highly relevant materials. For example, the analysis unit evaluates the similarity of the themes or contents of the materials and identifies highly relevant materials. The analysis unit can also postpone less relevant materials. For example, the analysis unit adjusts the order of analysis based on the relevance of the materials. Furthermore, the analysis unit determines the priority of analysis according to the relevance of the materials. For example, the analysis unit determines the order of analysis based on thematic agreement or content similarity. This enables efficient analysis by adjusting the order of analysis based on the relevance of the materials. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the materials to the generation AI and have the generation AI adjust the order of analysis.

[0044] The suggestion unit can adjust the level of detail of the proposal based on the importance of the document when making a proposal. For example, the suggestion unit makes a detailed proposal for a document with a high level of importance. For example, the suggestion unit evaluates the importance of the document based on the progress of the project and the user's needs. The suggestion unit can also make a concise proposal for a document with a low level of importance. For example, the suggestion unit adjusts the depth of the proposal based on the importance of the document. Furthermore, the suggestion unit determines the priority of the proposal based on the importance of the document. For example, the suggestion unit prioritizes the proposal of a document with a high level of importance and postpones the proposal of a document with a low level of importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the document. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data on the importance of the document to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0045] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the document. For example, the proposal unit applies a technical proposal algorithm to technical documents. For example, the proposal unit uses an algorithm that analyzes the content of the technical documents from a professional perspective and makes appropriate proposals. The proposal unit can also apply a proposal algorithm specialized for marketing to marketing documents. For example, the proposal unit analyzes the content of the marketing documents from a market analysis perspective and uses an algorithm that makes appropriate proposals. Furthermore, the proposal unit applies a proposal algorithm specialized for financial analysis to financial documents. For example, the proposal unit analyzes the content of the financial documents based on financial indicators and uses an algorithm that makes appropriate proposals. In this way, by applying different proposal algorithms depending on the category of the document, more appropriate proposals can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input document category data into a generation AI and cause the generation AI to select an appropriate proposal algorithm.

[0046] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the document. For example, the proposal unit prioritizes proposals based on the upcoming submission deadline. For example, the proposal unit stores the submission deadline of the document in a database and identifies the document with the upcoming submission deadline. The proposal unit can also postpone documents with a distant submission deadline. For example, the proposal unit adjusts the priority of the proposal based on the submission deadline. Furthermore, the proposal unit determines the priority of the proposal according to the submission date. For example, the proposal unit determines the order of the proposals based on the submission date or submission time. This enables efficient proposals by determining the priority of the proposals based on the submission date of the document. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input submission date data into a generation AI and have the generation AI determine the priority of the proposals.

[0047] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the materials. For example, the suggestion unit prioritizes the proposal of highly relevant materials. For example, the suggestion unit evaluates the similarity of the themes or contents of the materials and identifies highly relevant materials. The suggestion unit can also postpone less relevant materials. For example, the suggestion unit adjusts the order of proposals based on the relevance of the materials. Furthermore, the suggestion unit determines the priority of proposals according to the relevance of the materials. For example, the suggestion unit determines the order of proposals based on thematic agreement or content similarity. This enables efficient proposals by adjusting the order of proposals based on the relevance of the materials. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input relevance data of the materials to a generation AI and cause the generation AI to adjust the order of proposals.

[0048] During a re-review, the re-review unit can select an optimal re-review method by referring to the user's past review history. The re-review unit, for example, analyzes the trends of reviews the user has made in the past and selects the optimal re-review method. For example, the re-review unit stores the user's past review history in a database and analyzes the review trends. The re-review unit can also focus the re-review on specific issues based on the user's past review history. For example, the re-review unit identifies specific issues based on the user's past review history and adjusts the level of detail of the re-review. Furthermore, the re-review unit determines the priority of the re-review based on the user's past review history. For example, the re-review unit prioritizes important re-reviews based on the past review history. This allows the optimal re-review method to be selected by referring to the user's past review history, enabling efficient re-review. Some or all of the above-described processing in the re-review unit may be performed using, or without, AI. For example, the re-review unit can input past review history data into a generation AI and have the generation AI select an optimal re-review method.

[0049] During re-review, the re-review unit can customize the re-review method based on the user's current project status. For example, the re-review unit may focus on re-reviewing materials related to the user's ongoing project. For example, the re-review unit may store the user's project information in a database and identify related materials. The re-review unit may also customize the re-review method according to the progress of the user's project. For example, the re-review unit may monitor the project progress in real time and adjust the re-review method. Furthermore, the re-review unit may adjust the order of re-reviews based on the user's project priority. For example, the re-review unit may prioritize important re-reviews based on the project priority. This enables efficient re-reviews by customizing the re-review method based on the user's current project status. Some or all of the above-described processing in the re-review unit may be performed using, or without, AI. For example, the re-review unit may input project information data into a generation AI and cause the generation AI to customize the re-review method.

[0050] During re-review, the re-review unit can select the optimal re-review method by taking into account the user's geographic location information. For example, if the user is in a specific area, the re-review unit prioritizes re-reviewing materials related to that area. For example, the re-review unit may obtain the user's geographic location information from GPS data and identify relevant materials. Furthermore, if the user is on a business trip, the re-review unit may prioritize re-reviewing materials related to the user's business trip destination. For example, the re-review unit may store the user's business trip destination information in a database and identify relevant materials. Furthermore, if the user is at home, the re-review unit prioritizes re-reviewing materials that are easily accessible from home. For example, the re-review unit may obtain geographic location information from the user's IP address and identify relevant materials. This allows for the selection of the optimal re-review method by taking the user's geographic location information into account, enabling efficient re-review. Some or all of the above-described processing in the re-review unit may be performed using, or without, AI. For example, the re-review unit may input geographic location information data into a generation AI and cause the generation AI to select the optimal re-review method.

[0051] During re-review, the re-review unit can analyze the user's social media activity and suggest a re-review method. The re-review unit, for example, prioritizes re-reviewing relevant materials based on information shared by the user on social media. For example, the re-review unit analyzes the user's social media account to identify the shared information. The re-review unit can also re-review materials related to topics the user follows on social media. For example, the re-review unit stores the user's followed topics in a database to identify relevant materials. Furthermore, the re-review unit analyzes the user's social media activity history to re-review highly relevant materials. For example, the re-review unit analyzes the user's posts and reactions to identify relevant materials. This allows for efficient re-reviewing of highly relevant materials by analyzing the user's social media activity. Some or all of the above-described processing in the re-review unit may be performed using, or without, AI. For example, the re-review unit may input social media data into a generation AI and cause the generation AI to identify highly relevant materials.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit can analyze the user's past review history and select the optimal reception method. For example, the reception unit analyzes the time periods during which the user frequently submitted documents in the past and receives documents during those time periods. The reception unit can also analyze the types of documents the user has submitted in the past and prioritize receiving similar documents. Furthermore, the reception unit selects the optimal reception method (online, offline, etc.) from the user's past submission history. This allows the optimal reception method to be selected by analyzing the user's past document submission history, enabling efficient document reception.

[0054] The analysis unit can determine the priority of analysis based on the time of submission of documents. For example, documents with an upcoming submission deadline can be analyzed first. Documents with a distant deadline can also be postponed. Furthermore, the analysis priority is determined according to the time of submission. This allows for efficient analysis by determining the priority of analysis based on the time of submission of documents.

[0055] The reception unit can prioritize receiving highly relevant materials by taking into account the user's geographical location information. For example, if the user is in a specific area, materials related to that area can be prioritized. Also, if the user is on a business trip, materials related to the business trip destination can be prioritized. Furthermore, if the user is at home, materials that are easily accessible from home can be prioritized. In this way, by taking into account the user's geographical location information, highly relevant materials can be efficiently received.

[0056] The analysis unit can apply different analysis algorithms depending on the category of the document. For example, a technical analysis algorithm can be applied to technical documents. An analysis algorithm specialized for marketing can be applied to marketing documents. Furthermore, an analysis algorithm specialized for financial analysis can be applied to financial documents. In this way, by applying different analysis algorithms depending on the category of the document, more appropriate analysis results can be provided.

[0057] The proposal department can determine the priority of proposals based on the time of submission of materials. For example, materials with an upcoming submission deadline can be given priority. Materials with a distant submission deadline can also be postponed. Furthermore, the priority of proposals can be determined according to the time of submission. This allows efficient proposals to be made by determining the priority of proposals based on the time of submission of materials.

[0058] The re-review unit can select the optimal re-review method by referring to the user's past review history. For example, it can analyze the trends in reviews the user has made in the past and select the optimal re-review method. It can also focus re-reviews on specific problems based on the user's past review history. Furthermore, it determines the priority of re-reviews based on the user's past review history. This allows the optimal re-review method to be selected by referring to the user's past review history, enabling efficient re-reviews.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives presentation materials from users. Presentation materials created by users include slides, documents, videos, etc. The reception unit receives the materials uploaded by users and inputs them into the system. The reception unit can also automatically receive materials sent by users via email, or retrieve materials from cloud storage services. Step 2: The analysis unit analyzes the materials received by the reception unit. The analysis unit performs grammatical analysis, content analysis, visual analysis, etc. Grammatical analysis detects grammatical errors in the materials, and content analysis evaluates the consistency and logical flow of the materials. Visual analysis detects problems with the layout and design of the materials. Step 3: The suggestion unit makes correction suggestions based on the results of the analysis by the analysis unit. The suggestion unit makes suggestions to correct grammatical errors, improve the content, and change the visual layout. Step 4: The re-reviewing department reviews the revised material proposed by the proposing department again. The re-reviewing department re-analyzes the revised material for grammar, re-checks the content, and performs a visual evaluation.

[0061] (Example 2) A presentation material review system according to an embodiment of the present invention automatically reviews presentation materials created by a user and proposes revisions. In this system, presentation materials created by a user are input into an AI, which analyzes the content of the materials and detects grammatical and phrasal errors, logical inconsistencies, and visual layout issues. The AI ​​then proposes revisions based on the detected issues. Furthermore, the AI ​​re-reviews the revised materials and proposes additional revisions as needed. This improves the quality of the materials and reduces the number of reviews and revisions required. This system reduces the labor required for creating and reviewing presentation materials, thereby improving internal efficiency. For example, the reduction in the time required for creating materials and the reduction in the number of reviews allows for more time to focus on other tasks. Furthermore, the improvement in the quality of materials facilitates smoother internal and external communication. This allows the presentation material review system to efficiently review users' presentation materials and propose revisions, thereby reducing labor costs.

[0062] A presentation material review system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a re-review unit. The reception unit accepts presentation materials from a user. Presentation materials created by a user include, but are not limited to, slides, documents, and videos. The reception unit, for example, receives presentation materials uploaded by a user and inputs them into the system. The reception unit can also automatically receive materials sent by a user via email. The reception unit can also obtain materials from a cloud storage service. The analysis unit analyzes the materials accepted by the reception unit. The analysis unit performs, for example, grammatical analysis, content analysis, and visual analysis. The grammatical analysis detects grammatical errors in the materials. For example, the analysis unit identifies grammatical errors based on grammatical rules. The content analysis evaluates the consistency and logical flow of the content of the materials. For example, the analysis unit checks whether each section of the materials is logically connected. The visual analysis detects problems with the layout and design of the materials. For example, the analysis unit identifies inconsistencies in font size and color usage. The suggestion unit makes correction suggestions based on the results of the analysis by the analysis unit. The suggestion unit, for example, makes suggestions to correct grammatical errors. For example, the suggestion unit presents specific correction suggestions for correcting grammatical errors. The suggestion unit makes suggestions to improve the content. For example, the suggestion unit makes suggestions to improve the logical flow. The suggestion unit makes suggestions to change the visual layout. For example, the suggestion unit suggests unifying the font size and color usage. The re-review unit re-reviews the material after the corrections proposed by the suggestion unit. For example, the re-review unit re-grammatically analyzes the material after the corrections to check for additional grammatical errors. The re-review unit re-checks the content. For example, the re-review unit re-evaluates the logical consistency of the material after the corrections. The re-review unit performs a visual evaluation. For example, the re-review unit re-checks the layout and design of the material after the corrections. As a result, the presentation material review system according to the embodiment efficiently reviews a user's presentation materials and suggests corrections, thereby reducing labor hours.

[0063] The analysis unit can detect grammatical or phrasal errors, logical inconsistencies, and visual layout problems. The analysis unit, for example, performs grammatical analysis and detects grammatical errors. For example, the analysis unit identifies grammatical errors based on grammatical rules. The analysis unit can also detect expression errors. For example, the analysis unit identifies inappropriate expressions based on a style guide. The analysis unit also evaluates logical consistency. For example, the analysis unit checks whether each section of the document is logically connected. The analysis unit also detects visual layout problems. For example, the analysis unit identifies inconsistencies in font size or color usage. This improves the quality of the document by automatically detecting grammatical or phrasal errors, logical inconsistencies, and visual layout problems in the document. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can use an AI model that uses natural language processing technology for grammatical analysis.

[0064] The suggestion unit can make correction suggestions based on the detected problems. The suggestion unit, for example, makes suggestions to correct grammatical errors. For example, the suggestion unit presents specific correction suggestions for correcting grammatical errors. The suggestion unit can also make suggestions to correct expression errors. For example, the suggestion unit makes suggestions to correct inappropriate expressions based on a style guide. The suggestion unit can also make suggestions to improve logical consistency. For example, the suggestion unit can make suggestions to correct each section of the document so that it is logically connected. The suggestion unit can also make suggestions to change the visual layout. For example, the suggestion unit can suggest standardizing font sizes and colors. This allows for efficient correction of the document by making correction suggestions based on the detected problems. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can use an AI model that uses natural language processing technology to make correction suggestions for grammatical errors.

[0065] The re-review unit can review the revised material again and make additional correction suggestions. For example, the re-review unit can perform grammatical analysis on the revised material again to check for additional grammatical errors. For example, the re-review unit can identify grammatical errors based on grammatical rules. The re-review unit can also re-check the content. For example, the re-review unit can re-evaluate the logical consistency of the revised material. Furthermore, the re-review unit can perform visual evaluation. For example, the re-review unit can re-check the layout and design of the revised material. This allows the revised material to be reviewed again and additional correction suggestions made as necessary, thereby further improving the quality of the material. Some or all of the above-mentioned processing in the re-review unit can be performed using, for example, AI, or can be performed without using AI. For example, the re-review unit can use an AI model that uses natural language processing technology for grammatical analysis.

[0066] The suggestion unit may make suggestions for correcting grammatical errors. For example, the suggestion unit may present specific suggestions for correcting grammatical errors. For example, the suggestion unit may identify grammatical errors based on grammatical rules and suggest how to correct them. The suggestion unit may also refer to a style guide for correcting grammatical errors. For example, the suggestion unit may make suggestions for correcting improper grammar based on the style guide. As a result, the suggestions for correcting grammatical errors improve the grammatical accuracy of the material. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may use an AI model that uses natural language processing technology to make correction suggestions for grammatical errors.

[0067] The suggestion unit can make suggestions to change expressions to more specific expressions. For example, the suggestion unit can make suggestions to change ambiguous expressions to more specific expressions. For example, the suggestion unit can make suggestions to modify ambiguous expressions based on specific examples or data. The suggestion unit can also suggest the use of technical terms. For example, the suggestion unit can suggest replacing general expressions with technical terms. This makes suggestions to change expressions to more specific expressions, thereby improving the expressiveness of the material. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use an AI model that uses natural language processing technology to change ambiguous expressions to more specific expressions.

[0068] The suggestion unit can make suggestions for unifying the slide layout. For example, the suggestion unit can suggest unifying fonts. For example, the suggestion unit can suggest using the same font for all slides in a document. The suggestion unit can also suggest unifying color usage. For example, the suggestion unit can suggest using the same color palette for all slides in a document. The suggestion unit can also suggest balancing the layout. For example, the suggestion unit can suggest evenly arranging text and images within a slide. This makes suggestions for unifying the slide layout, thereby improving the visual consistency of the document. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can use an AI model that uses image analysis technology to unify the slide layout.

[0069] The presentation material review system further includes a reception unit that analyzes a user's emotions and adjusts the timing of receiving materials based on the analyzed user emotions. For example, if the user is feeling stressed, the reception unit delays receiving materials to allow the user time to relax. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit immediately receives the materials and begins reviewing them promptly. For example, the reception unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the reception unit prioritizes receiving materials and reviews them promptly. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This reduces user stress and enables efficient material reception by adjusting the timing of receiving materials based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0070] The reception unit can analyze the user's past document submission history and select the optimal reception method. For example, the reception unit analyzes the time periods during which the user frequently submitted documents in the past and receives documents during those time periods. For example, the reception unit stores the user's submission history in a database and analyzes the pattern of submission time periods. The reception unit can also analyze the types of documents the user has previously submitted and prioritize receiving similar documents. For example, the reception unit identifies document categories from the user's submission history and prioritizes receiving documents of the same category. Furthermore, the reception unit selects the optimal reception method (online, offline, etc.) based on the user's past submission history. For example, based on the user's submission history, the reception unit prioritizes online reception if there are many online submissions, and prioritizes in-person reception if there are many in-person submissions. This allows the optimal reception method to be selected by analyzing the user's past document submission history, enabling efficient document reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input submission history data into the generation AI and have the generation AI select the optimal reception method.

[0071] When receiving materials, the reception unit can filter the materials based on the user's current project or area of ​​interest. For example, the reception unit prioritizes receiving materials related to the user's ongoing project. For example, the reception unit stores the user's project information in a database and identifies related materials. The reception unit can also filter and receive highly relevant materials based on the user's area of ​​interest. For example, the reception unit acquires the user's area of ​​interest from profile information and prioritizes receiving related materials. Furthermore, the reception unit prioritizes receiving necessary materials according to the progress of the user's project. For example, the reception unit monitors the progress of the project in real time and identifies necessary materials. This allows for efficient reception of highly relevant materials by filtering materials based on the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input project information and area of ​​interest data into a generation AI and have the generation AI filter highly relevant materials.

[0072] The reception unit can analyze the user's emotions and determine the priority of materials to be received based on the analyzed user emotions. For example, if the user is feeling stressed, the reception unit postpones less important materials and prioritizes more important materials. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit receives all materials equally. For example, the reception unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the reception unit prioritizes materials that are more urgent. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the reception unit to prioritize materials based on the user's emotions, thereby prioritizing important materials. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] When accepting materials, the reception unit can prioritize accepting highly relevant materials by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting materials related to that area. For example, the reception unit acquires the user's geographical location information from GPS data and identifies relevant materials. Furthermore, if the user is on a business trip, the reception unit can prioritize accepting materials related to the user's business trip destination. For example, the reception unit stores the user's business trip destination information in a database and identifies relevant materials. Furthermore, if the user is at home, the reception unit prioritizes accepting materials that are easily accessible from home. For example, the reception unit acquires geographical location information from the user's IP address and identifies relevant materials. This allows for efficient acceptance of highly relevant materials by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit may input geographical location information data to a generation AI and cause the generation AI to identify highly relevant materials.

[0074] The reception unit can analyze the user's social media activity and receive relevant materials when receiving the materials. The reception unit, for example, prioritizes receiving relevant materials based on information shared by the user on social media. For example, the reception unit can analyze the user's social media account and identify the shared information. The reception unit can also receive materials related to topics the user follows on social media. For example, the reception unit can store the user's followed topics in a database and identify relevant materials. The reception unit can also analyze the user's social media activity history and receive highly relevant materials. For example, the reception unit can analyze the user's posts and reactions to identify relevant materials. This allows the user's social media activity to be analyzed and highly relevant materials to be efficiently received. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input social media data into a generation AI and cause the generation AI to identify highly relevant materials.

[0075] The analysis unit can analyze the user's emotions and adjust the presentation of the analysis based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit provides a concise analysis result that focuses on the main points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the analysis result to be easily understood by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the document. For example, the analysis unit performs a detailed analysis on documents with high importance. For example, the analysis unit evaluates the importance of the document based on the progress of the project and the user's needs. The analysis unit can also perform a brief analysis on documents with low importance. For example, the analysis unit adjusts the depth of the analysis based on the importance of the document. Furthermore, the analysis unit determines the priority of the analysis based on the importance of the document. For example, the analysis unit prioritizes analysis of documents with high importance and postpones analysis of documents with low importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the document. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the document. For example, the analysis unit applies a technical analysis algorithm to technical documents. For example, the analysis unit uses an algorithm that analyzes the content of the technical documents from a professional perspective. The analysis unit can also apply an analysis algorithm specialized for marketing to marketing documents. For example, the analysis unit uses an algorithm that analyzes the content of the marketing documents from a market analysis perspective. Furthermore, the analysis unit applies an analysis algorithm specialized for financial analysis to financial documents. For example, the analysis unit uses an algorithm that analyzes the content of the financial documents based on financial indicators. In this way, by applying different analysis algorithms depending on the category of the document, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input document category data into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0078] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, the analysis unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit provides an analysis result with visually stimulating effects. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the analysis unit to adjust the length of the analysis based on the user's emotions, thereby providing optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0079] During analysis, the analysis unit can determine the analysis priority based on the submission date of the documents. For example, the analysis unit prioritizes analysis of documents with an upcoming submission deadline. For example, the analysis unit stores the submission deadline of the documents in a database and identifies documents with an upcoming submission deadline. The analysis unit can also postpone documents with a distant submission deadline. For example, the analysis unit adjusts the analysis priority based on the submission deadline. Furthermore, the analysis unit determines the analysis priority according to the submission date. For example, the analysis unit determines the order of analysis based on the submission date or submission time. This enables efficient analysis by determining the analysis priority based on the submission date of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission date data to the generation AI and have the generation AI determine the analysis priority.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the materials. For example, the analysis unit prioritizes analysis of highly relevant materials. For example, the analysis unit evaluates the similarity of the themes or contents of the materials and identifies highly relevant materials. The analysis unit can also postpone less relevant materials. For example, the analysis unit adjusts the order of analysis based on the relevance of the materials. Furthermore, the analysis unit determines the priority of analysis according to the relevance of the materials. For example, the analysis unit determines the order of analysis based on thematic agreement or content similarity. This enables efficient analysis by adjusting the order of analysis based on the relevance of the materials. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the materials to the generation AI and have the generation AI adjust the order of analysis.

[0081] The suggestion unit can analyze the user's emotions and adjust the way suggestions are presented based on the analyzed user's emotions. For example, if the user is nervous, the suggestion unit provides simple, highly visible suggestions. For example, the suggestion unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit provides detailed suggestions. For example, the suggestion unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the suggestion unit provides concise suggestions that focus on the main points. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the suggestion unit to adjust the way suggestions are presented based on the user's emotions, thereby providing suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0082] The suggestion unit can adjust the level of detail of the proposal based on the importance of the document when making a proposal. For example, the suggestion unit makes a detailed proposal for a document with a high level of importance. For example, the suggestion unit evaluates the importance of the document based on the progress of the project and the user's needs. The suggestion unit can also make a concise proposal for a document with a low level of importance. For example, the suggestion unit adjusts the depth of the proposal based on the importance of the document. Furthermore, the suggestion unit determines the priority of the proposal based on the importance of the document. For example, the suggestion unit prioritizes the proposal of a document with a high level of importance and postpones the proposal of a document with a low level of importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the document. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data on the importance of the document to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0083] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the document. For example, the proposal unit applies a technical proposal algorithm to technical documents. For example, the proposal unit uses an algorithm that analyzes the content of the technical documents from a professional perspective and makes appropriate proposals. The proposal unit can also apply a proposal algorithm specialized for marketing to marketing documents. For example, the proposal unit analyzes the content of the marketing documents from a market analysis perspective and uses an algorithm that makes appropriate proposals. Furthermore, the proposal unit applies a proposal algorithm specialized for financial analysis to financial documents. For example, the proposal unit analyzes the content of the financial documents based on financial indicators and uses an algorithm that makes appropriate proposals. In this way, by applying different proposal algorithms depending on the category of the document, more appropriate proposals can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input document category data into a generation AI and cause the generation AI to select an appropriate proposal algorithm.

[0084] The suggestion unit can analyze the user's emotions and adjust the length of the suggestions based on the analyzed user emotions. For example, if the user is in a hurry, the suggestion unit provides short and to-the-point suggestions. For example, the suggestion unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the suggestion unit provides detailed suggestions. For example, the suggestion unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is excited, the suggestion unit provides suggestions with visually stimulating effects. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the length of the suggestions to be adjusted based on the user's emotions, thereby providing optimal suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0085] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the document. For example, the proposal unit prioritizes proposals based on the upcoming submission deadline. For example, the proposal unit stores the submission deadline of the document in a database and identifies the document with the upcoming submission deadline. The proposal unit can also postpone documents with a distant submission deadline. For example, the proposal unit adjusts the priority of the proposal based on the submission deadline. Furthermore, the proposal unit determines the priority of the proposal according to the submission date. For example, the proposal unit determines the order of the proposals based on the submission date or submission time. This enables efficient proposals by determining the priority of the proposals based on the submission date of the document. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input submission date data into a generation AI and have the generation AI determine the priority of the proposals.

[0086] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the materials. For example, the suggestion unit prioritizes the proposal of highly relevant materials. For example, the suggestion unit evaluates the similarity of the themes or contents of the materials and identifies highly relevant materials. The suggestion unit can also postpone less relevant materials. For example, the suggestion unit adjusts the order of proposals based on the relevance of the materials. Furthermore, the suggestion unit determines the priority of proposals according to the relevance of the materials. For example, the suggestion unit determines the order of proposals based on thematic agreement or content similarity. This enables efficient proposals by adjusting the order of proposals based on the relevance of the materials. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input relevance data of the materials to a generation AI and cause the generation AI to adjust the order of proposals.

[0087] The re-review unit can analyze the user's emotions and adjust the re-review method based on the analyzed user's emotions. For example, if the user is nervous, the re-review unit provides a simple, highly visible re-review result. For example, the re-review unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the re-review unit provides a detailed re-review result. For example, the re-review unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the re-review unit provides a concise re-review result that focuses on the main points. For example, the re-review unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the re-review method to be adjusted based on the user's emotions, thereby providing a re-review result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the review unit may be performed using AI, or may be performed without using AI. For example, the review unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0088] During a re-review, the re-review unit can select an optimal re-review method by referring to the user's past review history. The re-review unit, for example, analyzes the trends of reviews the user has made in the past and selects the optimal re-review method. For example, the re-review unit stores the user's past review history in a database and analyzes the review trends. The re-review unit can also focus the re-review on specific issues based on the user's past review history. For example, the re-review unit identifies specific issues based on the user's past review history and adjusts the level of detail of the re-review. Furthermore, the re-review unit determines the priority of the re-review based on the user's past review history. For example, the re-review unit prioritizes important re-reviews based on the past review history. This allows the optimal re-review method to be selected by referring to the user's past review history, enabling efficient re-review. Some or all of the above-described processing in the re-review unit may be performed using, or without, AI. For example, the re-review unit can input past review history data into a generation AI and have the generation AI select an optimal re-review method.

[0089] During re-review, the re-review unit can customize the re-review method based on the user's current project status. For example, the re-review unit may focus on re-reviewing materials related to the user's ongoing project. For example, the re-review unit may store the user's project information in a database and identify related materials. The re-review unit may also customize the re-review method according to the progress of the user's project. For example, the re-review unit may monitor the project progress in real time and adjust the re-review method. Furthermore, the re-review unit may adjust the order of re-reviews based on the user's project priority. For example, the re-review unit may prioritize important re-reviews based on the project priority. This enables efficient re-reviews by customizing the re-review method based on the user's current project status. Some or all of the above-described processing in the re-review unit may be performed using, or without, AI. For example, the re-review unit may input project information data into a generation AI and cause the generation AI to customize the re-review method.

[0090] The re-review unit can analyze the user's emotions and determine the priority of re-reviews based on the analyzed user's emotions. For example, if the user is feeling stressed, the re-review unit postpones less important re-reviews and prioritizes more important re-reviews. For example, the re-review unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the re-review unit performs all re-reviews equally. For example, the re-review unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the re-review unit prioritizes more urgent re-reviews. For example, the re-review unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the priority of re-reviews to be determined based on the user's emotions, thereby prioritizing important re-reviews. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the review unit may be performed using AI, or may be performed without using AI. For example, the review unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0091] During re-review, the re-review unit can select the optimal re-review method by taking into account the user's geographic location information. For example, if the user is in a specific area, the re-review unit prioritizes re-reviewing materials related to that area. For example, the re-review unit may obtain the user's geographic location information from GPS data and identify relevant materials. Furthermore, if the user is on a business trip, the re-review unit may prioritize re-reviewing materials related to the user's business trip destination. For example, the re-review unit may store the user's business trip destination information in a database and identify relevant materials. Furthermore, if the user is at home, the re-review unit prioritizes re-reviewing materials that are easily accessible from home. For example, the re-review unit may obtain geographic location information from the user's IP address and identify relevant materials. This allows for the selection of the optimal re-review method by taking the user's geographic location information into account, enabling efficient re-review. Some or all of the above-described processing in the re-review unit may be performed using, or without, AI. For example, the re-review unit may input geographic location information data into a generation AI and cause the generation AI to select the optimal re-review method.

[0092] During re-review, the re-review unit can analyze the user's social media activity and suggest a re-review method. The re-review unit, for example, prioritizes re-reviewing relevant materials based on information shared by the user on social media. For example, the re-review unit analyzes the user's social media account to identify the shared information. The re-review unit can also re-review materials related to topics the user follows on social media. For example, the re-review unit stores the user's followed topics in a database to identify relevant materials. Furthermore, the re-review unit analyzes the user's social media activity history to re-review highly relevant materials. For example, the re-review unit analyzes the user's posts and reactions to identify relevant materials. This allows for efficient re-reviewing of highly relevant materials by analyzing the user's social media activity. Some or all of the above-described processing in the re-review unit may be performed using, or without, AI. For example, the re-review unit may input social media data into a generation AI and cause the generation AI to identify highly relevant materials. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and re-review unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can accept presentation materials from a user via the control unit 46A of the smart device 14. The analysis unit performs grammatical analysis, content analysis, and visual analysis of the materials via the specific processing unit 290 of the data processing device 12. The suggestion unit makes revision suggestions via the specific processing unit 290 of the data processing device 12, and the re-review unit re-reviews the revised materials. Furthermore, the reception unit, which has an emotion analysis function, estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14 and adjusts the timing of accepting the materials. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and re-review unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can accept presentation materials from a user via the control unit 46A of the smart glasses 214. The analysis unit performs grammatical analysis, content analysis, and visual analysis of the materials via the specific processing unit 290 of the data processing device 12. The suggestion unit makes correction suggestions via the specific processing unit 290 of the data processing device 12, and the re-review unit re-reviews the corrected materials. Furthermore, the reception unit, which has an emotion analysis function, estimates the user's emotions using the camera 42 and microphone 238 of the smart glasses 214 and adjusts the timing of accepting the materials. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and re-review unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can accept presentation materials from a user via the control unit 46A of the headset terminal 314. The analysis unit performs grammatical analysis, content analysis, and visual analysis of the materials via the specific processing unit 290 of the data processing device 12. The suggestion unit makes correction suggestions via the specific processing unit 290 of the data processing device 12, and the re-review unit reviews the corrected materials again. Furthermore, the reception unit, which has an emotion analysis function, estimates the user's emotions using the camera 42 and microphone 238 of the headset terminal 314 and adjusts the timing of accepting the materials. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and re-review unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can accept presentation materials from a user via the control unit 46A of the robot 414. The analysis unit performs grammatical analysis, content analysis, and visual analysis of the materials via the specific processing unit 290 of the data processing device 12. The suggestion unit makes correction suggestions via the specific processing unit 290 of the data processing device 12, and the re-review unit reviews the corrected materials again. Furthermore, the reception unit, which has an emotion analysis function, estimates the user's emotions using the camera 42 and microphone 238 of the robot 414 and adjusts the timing of accepting the materials.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can analyze the user's past review history and select the optimal reception method. For example, the reception unit analyzes the time periods during which the user frequently submitted documents in the past and receives documents during those time periods. The reception unit can also analyze the types of documents the user has submitted in the past and prioritize receiving similar documents. Furthermore, the reception unit selects the optimal reception method (online, offline, etc.) from the user's past submission history. This allows the optimal reception method to be selected by analyzing the user's past document submission history, enabling efficient document reception.

[0095] The analysis unit can determine the priority of analysis based on the time of submission of documents. For example, documents with an upcoming submission deadline can be analyzed first. Documents with a distant deadline can also be postponed. Furthermore, the analysis priority is determined according to the time of submission. This allows for efficient analysis by determining the priority of analysis based on the time of submission of documents.

[0096] The suggestion unit can analyze the user's emotions and adjust the way suggestions are expressed based on the analyzed user's emotions. For example, if the user is nervous, a simple, highly visible suggestion is provided. If the user is relaxed, a detailed suggestion is provided. If the user is in a hurry, a concise suggestion that focuses on the main points is provided. In this way, by adjusting the way suggestions are expressed based on the user's emotions, it is possible to provide suggestions that are easy for the user to understand.

[0097] The re-review unit can analyze the user's emotions and adjust the re-review method based on the analyzed user's emotions. For example, if the user is nervous, a simple and highly visible re-review result is provided. If the user is relaxed, a detailed re-review result is provided. Furthermore, if the user is in a hurry, a concise re-review result that focuses on the main points is provided. In this way, by adjusting the re-review method based on the user's emotions, it is possible to provide a re-review result that is easy for the user to understand.

[0098] The reception unit can prioritize receiving highly relevant materials by taking into account the user's geographical location information. For example, if the user is in a specific area, materials related to that area can be prioritized. Also, if the user is on a business trip, materials related to the business trip destination can be prioritized. Furthermore, if the user is at home, materials that are easily accessible from home can be prioritized. In this way, by taking into account the user's geographical location information, highly relevant materials can be efficiently received.

[0099] The analysis unit can apply different analysis algorithms depending on the category of the document. For example, a technical analysis algorithm can be applied to technical documents. An analysis algorithm specialized for marketing can be applied to marketing documents. Furthermore, an analysis algorithm specialized for financial analysis can be applied to financial documents. In this way, by applying different analysis algorithms depending on the category of the document, more appropriate analysis results can be provided.

[0100] The proposal department can determine the priority of proposals based on the time of submission of materials. For example, materials with an upcoming submission deadline can be given priority. Materials with a distant submission deadline can also be postponed. Furthermore, the priority of proposals can be determined according to the time of submission. This allows efficient proposals to be made by determining the priority of proposals based on the time of submission of materials.

[0101] The re-review unit can select the optimal re-review method by referring to the user's past review history. For example, it can analyze the trends in reviews the user has made in the past and select the optimal re-review method. It can also focus re-reviews on specific problems based on the user's past review history. Furthermore, it determines the priority of re-reviews based on the user's past review history. This allows the optimal re-review method to be selected by referring to the user's past review history, enabling efficient re-reviews.

[0102] The suggestion unit can analyze the user's emotions and adjust the length of the suggestions based on the analyzed user's emotions. For example, if the user is in a hurry, a short and to-the-point suggestion is provided. If the user is relaxed, a detailed suggestion is provided. If the user is excited, a suggestion with a visually stimulating effect is provided. In this way, by adjusting the length of the suggestion based on the user's emotions, the most suitable suggestion for the user can be provided.

[0103] The re-review unit can analyze the user's emotions and determine the priority of re-reviews based on the analyzed user's emotions. For example, if the user is feeling stressed, re-reviews with low importance are postponed and re-reviews with high importance are given priority. Also, if the user is relaxed, all re-reviews are performed equally. Furthermore, if the user is in a hurry, re-reviews with high urgency are given priority. In this way, by determining the priority of re-reviews based on the user's emotions, important re-reviews can be given priority.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives presentation materials from users. Presentation materials created by users include slides, documents, videos, etc. The reception unit receives the materials uploaded by users and inputs them into the system. The reception unit can also automatically receive materials sent by users via email, or retrieve materials from cloud storage services. Step 2: The analysis unit analyzes the materials received by the reception unit. The analysis unit performs grammatical analysis, content analysis, visual analysis, etc. Grammatical analysis detects grammatical errors in the materials, and content analysis evaluates the consistency and logical flow of the materials. Visual analysis detects problems with the layout and design of the materials. Step 3: The suggestion unit makes correction suggestions based on the results of the analysis by the analysis unit. The suggestion unit makes suggestions to correct grammatical errors, improve the content, and change the visual layout. Step 4: The re-reviewing department reviews the revised material proposed by the proposing department again. The re-reviewing department re-analyzes the revised material for grammar, re-checks the content, and performs a visual evaluation.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives presentation materials from a user; an analysis unit that analyzes the materials accepted by the acceptance unit; a suggestion unit that makes a correction suggestion based on the result of the analysis by the analysis unit; a re-review unit that re-reviews the revised material proposed by the proposal unit. A system characterized by:

2. The analysis unit Detect grammatical or phrasing errors, logical consistency, and visual layout issues 2. The system of claim 1.

3. The proposal unit Providing fix suggestions based on detected issues 2. The system of claim 1.

4. The re-review unit Review the revised materials again and make additional revision suggestions 2. The system of claim 1.

5. The proposal unit Makes suggestions to fix grammar errors 2. The system of claim 1.

6. The proposal unit Propose a more specific change to the wording 2. The system of claim 1.

7. The proposal unit Propose a unified slide layout 2. The system of claim 1.

8. The reception unit Analyze user emotions and adjust the timing of receiving materials based on the analyzed user emotions 2. The system of claim 1.

9. The reception unit Analyze the user's past submission history and select the optimal reception method 2. The system of claim 1.

10. The reception unit Filtering materials at the time of submission based on the user's current projects and areas of interest 2. The system of claim 1.

Images