System

Abstract

An object of the system according to the embodiment is to shorten the production time while improving the quality of the sentence.SOLUTION: A system according to an embodiment includes a sentence analysis unit, a correction proposal unit, a configuration proposal unit, and a generation unit. The sentence analysis unit analyzes a sentence input by a user. The correction proposal unit makes a correction proposal of grammar or style on the basis of the sentence analyzed by the sentence analysis unit. The configuration proposal unit analyzes the configuration of the entire text corrected and proposed by the correction proposal unit, and makes a proposal for a more logical and readable configuration. The generation unit generates the specialized content based on the configuration proposed by the configuration proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to simultaneously improve the quality of text and shorten production time.

[0005] The system according to the embodiment aims to improve the quality of writing while shortening the production time. [Means for solving the problem]

[0006] The system according to the embodiment includes a text analysis unit, a correction suggestion unit, a structure suggestion unit, and a generation unit. The text analysis unit analyzes text input by a user. The correction suggestion unit suggests grammar and style corrections based on the text analyzed by the text analysis unit. The structure suggestion unit analyzes the overall structure of the text for which corrections have been suggested by the correction suggestion unit and makes suggestions to make the structure more logical and easy to read. The generation unit generates specialized content based on the structure suggested by the structure suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the quality of writing while shortening the production time. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The document creation support system according to the embodiment of the present invention is a system that refines the structure and expression of text and quickly generates high-quality specialized content, thereby enabling the document creation support system to efficiently create high-quality text.

[0029] A document creation support system according to an embodiment includes a text analysis unit, a correction suggestion unit, a structure suggestion unit, and a generation unit. The text analysis unit analyzes a text input by a user. For example, the text analysis unit analyzes the text using morphological analysis. The text analysis unit can also analyze the grammatical structure of the text using grammatical analysis. The text analysis unit can also analyze the meaning of the text using semantic analysis. The correction suggestion unit suggests grammar and style corrections based on the text analyzed by the text analysis unit. For example, the correction suggestion unit performs a grammar check and suggests corrections to grammatical errors. The correction suggestion unit can also suggest style improvements based on style guidelines. The correction suggestion unit can also learn a user's past writing history and suggest corrections optimized for each individual user. The structure suggestion unit analyzes the overall structure of the text for which corrections have been suggested by the correction suggestion unit and makes suggestions to make the text more logical and easy to read. For example, the structure suggestion unit suggests a logical paragraph structure. The structure suggestion unit can also make suggestions to improve the flow of information. The structure suggestion unit can also learn writing structures of different genres and formats and propose structures optimized for specific genres and formats. The generation unit generates high-quality specialized content based on the structures proposed by the structure suggestion unit. For example, the generation unit automatically references the latest research papers and specialized books and generates high-quality specialized content based on them. The generation unit can also build a knowledge base specialized in a specific field of expertise and generate specialized content by utilizing that knowledge. The generation unit can also use an emotion estimation function to generate specialized content based on the user's emotional state and provide content that is likely to resonate emotionally. This allows the document creation support system according to the embodiment to efficiently create high-quality writing. For example, writers can create high-quality articles in a short amount of time, students can improve the quality of their papers and reports, and professionals can improve the quality of their business documents and presentation materials.

[0030] The correction suggestion unit can learn the user's past writing history and make grammar corrections and style improvement suggestions that are optimized for each individual user. For example, the correction suggestion unit uses a generation AI to analyze the user's past writing history and identify frequently used grammatical mistakes and style habits. For example, it makes suggestions to simplify redundant expressions that the user often uses. The correction suggestion unit can also learn specific grammatical mistakes and style improvements based on the user's past writing history and make suggestions that are optimized for each individual user. For example, it collects data on documents that the user has created in the past and makes correction suggestions based on that data. This makes it possible to learn the user's past writing history and make suggestions that are optimized for each individual user, thereby enabling more effective writing support.

[0031] The revision suggestion unit can automatically identify different writing styles and tones and make revision suggestions that match the specific writing style and tone. For example, the revision suggestion unit analyzes the writing style and tone of the text input by the generation AI and makes appropriate revision suggestions. For example, it suggests a formal writing style for academic papers and a persuasive tone for marketing materials. The revision suggestion unit can also automatically identify different writing styles and tones and make revision suggestions that match the specific writing style and tone. For example, it suggests changing a formal writing style to an informal writing style. The revision suggestion unit can also make appropriate revision suggestions based on the results of the style and tone identification. This enables the user to create text that meets their intentions by automatically identifying different writing styles and tones and making revision suggestions that match the specific writing style and tone.

[0032] The correction suggestion unit can make grammar corrections and style improvement suggestions in different languages ​​to support multilingual writing. For example, the correction suggestion unit uses a generation AI to make grammar corrections in different languages ​​to support multilingual writing. For example, grammar corrections are automatically made in English, Japanese, French, etc. The correction suggestion unit can also make style improvement suggestions in different languages. For example, it can suggest a style suitable for English business documents. The correction suggestion unit can also make grammar corrections and style improvement suggestions in different languages ​​to support multilingual writing. As a result, by making grammar corrections and style improvement suggestions in different languages, multilingual writing becomes possible.

[0033] The correction suggestion unit can analyze the voice input and make grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion. The correction suggestion unit, for example, analyzes the user's voice input using a generation AI and makes grammatical corrections simultaneously with the voice-to-text conversion. For example, it automatically corrects grammatical errors in the voice-input sentence. The correction suggestion unit can also analyze the voice input and make style improvement suggestions simultaneously with the voice-to-text conversion. For example, it makes suggestions to improve the style of the voice-input sentence. The correction suggestion unit can also analyze the voice input and make grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion. This enables efficient writing by analyzing the voice input and making grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion.

[0034] The structure proposal unit can analyze the logical structure of a sentence, automatically detect logical leaps and contradictions, and propose revisions. For example, the structure proposal unit uses a generative AI to analyze the logical structure of a sentence and automatically detect logical leaps and contradictions. For example, it proposes revisions when there is a contradiction in the context. The structure proposal unit can also analyze the logical structure of a sentence, automatically detect logical leaps and contradictions, and propose revisions. For example, it detects a lack of logical consistency and proposes revisions. The structure proposal unit can also analyze the logical structure of a sentence, automatically detect logical leaps and contradictions, and propose revisions. In this way, by analyzing the logical structure of a sentence, automatically detecting logical leaps and contradictions, and proposing revisions, it becomes possible to create logically consistent sentences.

[0035] The structure suggestion unit can learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats. For example, the generative AI in the structure suggestion unit learns the structure of sentences in different genres and formats and proposes structures optimized for specific genres and formats. For example, it proposes a logical structure for academic papers and an emotional structure for essays. The structure suggestion unit can also learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats. For example, it proposes a technical structure for technical documents and a business structure for business documents. The structure suggestion unit can also learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats. In this way, by learning the structure of sentences in different genres and formats and proposing structures optimized for specific genres and formats, it becomes possible to create text that meets the user's purpose.

[0036] The structure proposal unit can propose text structures that are compatible with different media formats. For example, the generation AI in the structure proposal unit proposes text structures that are compatible with different media formats. For example, it proposes an easy-to-read structure for blog articles and a logical structure for reports. The structure proposal unit can also propose text structures that are compatible with different media formats. For example, it proposes a visually easy-to-understand structure for presentation materials. The structure proposal unit can also propose text structures that are compatible with different media formats. In this way, by proposing text structures that are compatible with different media formats, it becomes possible for the user to create text that meets their purpose.

[0037] The structure suggestion unit can receive real-time feedback from the user and dynamically adjust the sentence structure based on that. In the structure suggestion unit, for example, a generative AI receives real-time feedback from the user and dynamically adjusts the sentence structure based on that. For example, it immediately corrects parts pointed out by the user. The structure suggestion unit can also receive real-time feedback from the user and dynamically adjust the sentence structure based on that. For example, it makes a suggestion to change the order of paragraphs based on the user's feedback. The structure suggestion unit can also receive real-time feedback from the user and dynamically adjust the sentence structure based on that. In this way, by receiving real-time feedback from the user and dynamically adjusting the sentence structure based on that, it becomes possible to create sentences that are in line with the user's intentions.

[0038] The generation unit can automatically refer to the latest research papers and specialized books and generate high-quality specialized content based on them. For example, the generation unit uses a generative AI to automatically collect the latest research papers and analyze their contents to generate high-quality specialized content. For example, it generates articles about the latest trends in AI technology. The generation unit can also automatically refer to the latest research papers and specialized books and generate high-quality specialized content based on them. For example, it generates specialized articles based on academic papers. The generation unit can also automatically refer to the latest research papers and specialized books and generate high-quality specialized content based on them. In this way, by automatically referring to the latest research papers and specialized books and generating high-quality specialized content based on them, it is possible to provide specialized content that always contains the latest information.

[0039] The generation unit can build a knowledge base specialized in a specific field of expertise and generate specialized content by utilizing that knowledge. For example, the generation unit uses a generative AI to build a knowledge base specialized in a specific field of expertise and generate high-quality specialized content based on that knowledge. For example, the generation unit generates articles based on a knowledge base in the legal field. The generation unit can also build a knowledge base specialized in a specific field of expertise and generate specialized content by utilizing that knowledge. For example, the generation unit generates specialized articles based on a knowledge base in the medical field. The generation unit can also build a knowledge base specialized in a specific field of expertise and generate specialized content by utilizing that knowledge. In this way, by building a knowledge base specialized in a specific field of expertise and generating specialized content by utilizing that knowledge, highly specialized content can be provided.

[0040] The generation unit can integrate knowledge from different specialized fields to generate crossover specialized content. For example, the generation AI can integrate knowledge from different specialized fields to generate crossover specialized content. For example, it can generate an article that combines medical and technical knowledge. The generation unit can also integrate knowledge from different specialized fields to generate crossover specialized content. For example, it can generate a specialized article that combines knowledge of economics and sociology. The generation unit can also integrate knowledge from different specialized fields to generate crossover specialized content. In this way, by integrating knowledge from different specialized fields to generate crossover specialized content, it is possible to provide specialized content from a wide range of perspectives.

[0041] The generation unit can learn the user's past history of creating specialized content and generate specialized content optimized for each individual user. For example, the generation unit uses a generation AI to analyze the user's past history of creating specialized content and generate specialized content optimized for each individual user. For example, the generation unit generates articles based on themes that the user often writes about. The generation unit can also learn the user's past history of creating specialized content and generate specialized content optimized for each individual user. For example, the generation unit generates articles optimized for specific themes based on the user's past history of creating specialized content. The generation unit can also learn the user's past history of creating specialized content and generate specialized content optimized for each individual user. In this way, by learning the user's past history of creating specialized content and generating specialized content optimized for each individual user, specialized content that meets the user's needs can be provided.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The document creation support system can also include an information suggestion unit that learns the user's past search history and automatically suggests related information. For example, it can suggest related articles or materials based on keywords the user has previously searched for. The information suggestion unit can also analyze the user's search history and provide the latest information on a specific topic. This allows the user to quickly obtain the information they need and improve the efficiency of their document creation.

[0044] The correction suggestion unit can analyze the user's input speed and typing pattern and make correction suggestions in real time. For example, if the user is typing quickly, it can immediately suggest corrections for typos. It can also analyze typing patterns and predict specific mistakes and make correction suggestions. This allows the user to receive correction suggestions immediately while typing, improving the accuracy and efficiency of writing.

[0045] The correction suggestion unit can make grammar corrections and style improvement suggestions in different languages ​​to support multilingual writing. For example, it can automatically make grammar corrections in English, Japanese, French, etc. The correction suggestion unit can also make style improvement suggestions in different languages. For example, it can suggest a style suitable for English business documents. This makes it possible to create multilingual writing by making grammar corrections and style improvement suggestions in different languages.

[0046] The correction suggestion unit can analyze the voice input and make grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion. For example, it can automatically correct grammatical errors in the voice-input sentence. It can also analyze the voice input and make style improvement suggestions simultaneously with the voice-to-text conversion. This allows for efficient writing by analyzing the voice input and making grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion.

[0047] The structure suggestion unit can analyze the logical structure of a sentence, automatically detect logical jumps and contradictions, and suggest revisions. For example, it can suggest revisions when there is a contradiction in the context. It can also detect lack of logical consistency and suggest revisions. This makes it possible to create logically consistent sentences by analyzing the logical structure of a sentence, automatically detecting logical jumps and contradictions, and suggesting revisions.

[0048] The structure suggestion unit can learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats. For example, it can propose a logical structure for academic papers and an emotional structure for essays. It can also propose a technical structure for technical documents and a business structure for business documents. This allows the system to learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats, enabling users to create texts that meet their goals.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The sentence analysis unit analyzes the sentence entered by the user. For example, it analyzes the sentence using morphological analysis, analyzes the grammatical structure of the sentence using grammatical analysis, and analyzes the meaning of the sentence using semantic analysis. Step 2: The correction suggestion unit makes suggestions for grammar and style corrections based on the text analyzed by the text analysis unit. For example, it checks grammar and makes suggestions for correcting grammatical errors, and suggests style improvements based on style guidelines. It can also learn the user's past writing history and make correction suggestions optimized for each individual user. Step 3: The structure suggestion unit analyzes the overall structure of the text suggested by the revision suggestion unit and makes suggestions to make the structure more logical and readable. For example, it suggests logical paragraph structures and improves the flow of information. It can also learn the structure of sentences in different genres and formats and make structure suggestions optimized for specific genres and formats. Step 4: The generation unit generates high-quality specialized content based on the structure proposed by the structure proposal unit. For example, the generation unit automatically references the latest research papers and specialized books and generates high-quality specialized content based on them. It is also possible to build a knowledge base specialized in a specific field and use that knowledge to generate specialized content. Furthermore, it is possible to use an emotion estimation function to generate specialized content based on the user's emotional state and provide content that is likely to resonate emotionally.

[0051] (Example 2) The document creation support system according to the embodiment of the present invention is a system that refines the structure and expression of text and quickly generates high-quality specialized content, thereby enabling the document creation support system to efficiently create high-quality text.

[0052] A document creation support system according to an embodiment includes a text analysis unit, a correction suggestion unit, a structure suggestion unit, and a generation unit. The text analysis unit analyzes a text input by a user. For example, the text analysis unit analyzes the text using morphological analysis. The text analysis unit can also analyze the grammatical structure of the text using grammatical analysis. The text analysis unit can also analyze the meaning of the text using semantic analysis. The correction suggestion unit suggests grammar and style corrections based on the text analyzed by the text analysis unit. For example, the correction suggestion unit performs a grammar check and suggests corrections to grammatical errors. The correction suggestion unit can also suggest style improvements based on style guidelines. The correction suggestion unit can also learn a user's past writing history and suggest corrections optimized for each individual user. The structure suggestion unit analyzes the overall structure of the text for which corrections have been suggested by the correction suggestion unit and makes suggestions to make the text more logical and easy to read. For example, the structure suggestion unit suggests a logical paragraph structure. The structure suggestion unit can also make suggestions to improve the flow of information. The structure suggestion unit can also learn writing structures of different genres and formats and propose structures optimized for specific genres and formats. The generation unit generates high-quality specialized content based on the structures proposed by the structure suggestion unit. For example, the generation unit automatically references the latest research papers and specialized books and generates high-quality specialized content based on them. The generation unit can also build a knowledge base specialized in a specific field of expertise and generate specialized content by utilizing that knowledge. The generation unit can also use an emotion estimation function to generate specialized content based on the user's emotional state and provide content that is likely to resonate emotionally. This allows the document creation support system according to the embodiment to efficiently create high-quality writing. For example, writers can create high-quality articles in a short amount of time, students can improve the quality of their papers and reports, and professionals can improve the quality of their business documents and presentation materials.

[0053] The correction suggestion unit can learn the user's past writing history and make grammar corrections and style improvement suggestions that are optimized for each individual user. For example, the correction suggestion unit uses a generation AI to analyze the user's past writing history and identify frequently used grammatical mistakes and style habits. For example, it makes suggestions to simplify redundant expressions that the user often uses. The correction suggestion unit can also learn specific grammatical mistakes and style improvements based on the user's past writing history and make suggestions that are optimized for each individual user. For example, it collects data on documents that the user has created in the past and makes correction suggestions based on that data. This makes it possible to learn the user's past writing history and make suggestions that are optimized for each individual user, thereby enabling more effective writing support.

[0054] The revision suggestion unit can automatically identify different writing styles and tones and make revision suggestions that match the specific writing style and tone. For example, the revision suggestion unit analyzes the writing style and tone of the text input by the generation AI and makes appropriate revision suggestions. For example, it suggests a formal writing style for academic papers and a persuasive tone for marketing materials. The revision suggestion unit can also automatically identify different writing styles and tones and make revision suggestions that match the specific writing style and tone. For example, it suggests changing a formal writing style to an informal writing style. The revision suggestion unit can also make appropriate revision suggestions based on the results of the style and tone identification. This enables the user to create text that meets their intentions by automatically identifying different writing styles and tones and making revision suggestions that match the specific writing style and tone.

[0055] The correction suggestion unit can use the emotion estimation function to make grammatical corrections and style improvement suggestions according to the emotional state of the user. For example, the correction suggestion unit can use the emotion estimation function to analyze the emotional tone of a sentence input by the user and make appropriate correction suggestions. For example, the correction suggestion unit can suggest expressions that elicit positive emotions. The correction suggestion unit can also use the emotion estimation function to make grammatical corrections and style improvement suggestions according to the emotional state of the user. For example, the correction suggestion unit can analyze the emotional tone of a sentence input by the user and suggest replacing negative expressions with positive expressions. The correction suggestion unit can also use the emotion estimation function to make correction suggestions according to the emotional state of the user. As a result, by using the emotion estimation function to make suggestions according to the emotional state of the user, it becomes possible to create sentences that are in line with the user's intentions.

[0056] The correction suggestion unit can make grammar corrections and style improvement suggestions in different languages ​​to support multilingual writing. For example, the correction suggestion unit uses a generation AI to make grammar corrections in different languages ​​to support multilingual writing. For example, grammar corrections are automatically made in English, Japanese, French, etc. The correction suggestion unit can also make style improvement suggestions in different languages. For example, it can suggest a style suitable for English business documents. The correction suggestion unit can also make grammar corrections and style improvement suggestions in different languages ​​to support multilingual writing. As a result, by making grammar corrections and style improvement suggestions in different languages, multilingual writing becomes possible.

[0057] The correction suggestion unit can analyze the voice input and make grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion. The correction suggestion unit, for example, analyzes the user's voice input using a generation AI and makes grammatical corrections simultaneously with the voice-to-text conversion. For example, it automatically corrects grammatical errors in the voice-input sentence. The correction suggestion unit can also analyze the voice input and make style improvement suggestions simultaneously with the voice-to-text conversion. For example, it makes suggestions to improve the style of the voice-input sentence. The correction suggestion unit can also analyze the voice input and make grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion. This enables efficient writing by analyzing the voice input and making grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion.

[0058] The correction suggestion unit can use the emotion estimation function to analyze the emotional tone of a sentence input by the user and make correction suggestions that elicit positive emotions. The correction suggestion unit, for example, uses the emotion estimation function to analyze the emotional tone of a sentence input by the user and make correction suggestions that elicit positive emotions. For example, it makes a suggestion to replace a negative expression with a positive expression. The correction suggestion unit can also use the emotion estimation function to analyze the emotional tone of a sentence input by the user and make correction suggestions that elicit positive emotions. For example, it analyzes the emotional tone and suggests expressions that elicit positive emotions. The correction suggestion unit can also use the emotion estimation function to analyze the emotional tone of a sentence input by the user and make correction suggestions that elicit positive emotions. In this way, by using the emotion estimation function to analyze the emotional tone of a sentence input by the user and making correction suggestions that elicit positive emotions, it is possible to create sentences that are easy for readers to empathize with.

[0059] The structure proposal unit can analyze the logical structure of a sentence, automatically detect logical leaps and contradictions, and propose revisions. For example, the structure proposal unit uses a generative AI to analyze the logical structure of a sentence and automatically detect logical leaps and contradictions. For example, it proposes revisions when there is a contradiction in the context. The structure proposal unit can also analyze the logical structure of a sentence, automatically detect logical leaps and contradictions, and propose revisions. For example, it detects a lack of logical consistency and proposes revisions. The structure proposal unit can also analyze the logical structure of a sentence, automatically detect logical leaps and contradictions, and propose revisions. In this way, by analyzing the logical structure of a sentence, automatically detecting logical leaps and contradictions, and proposing revisions, it becomes possible to create logically consistent sentences.

[0060] The structure suggestion unit can learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats. For example, the generative AI in the structure suggestion unit learns the structure of sentences in different genres and formats and proposes structures optimized for specific genres and formats. For example, it proposes a logical structure for academic papers and an emotional structure for essays. The structure suggestion unit can also learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats. For example, it proposes a technical structure for technical documents and a business structure for business documents. The structure suggestion unit can also learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats. In this way, by learning the structure of sentences in different genres and formats and proposing structures optimized for specific genres and formats, it becomes possible to create text that meets the user's purpose.

[0061] The structure proposal unit can propose text structures that are compatible with different media formats. For example, the generation AI in the structure proposal unit proposes text structures that are compatible with different media formats. For example, it proposes an easy-to-read structure for blog articles and a logical structure for reports. The structure proposal unit can also propose text structures that are compatible with different media formats. For example, it proposes a visually easy-to-understand structure for presentation materials. The structure proposal unit can also propose text structures that are compatible with different media formats. In this way, by proposing text structures that are compatible with different media formats, it becomes possible for the user to create text that meets their purpose.

[0062] The structure suggestion unit can receive real-time feedback from the user and dynamically adjust the sentence structure based on that. In the structure suggestion unit, for example, a generative AI receives real-time feedback from the user and dynamically adjusts the sentence structure based on that. For example, it immediately corrects parts pointed out by the user. The structure suggestion unit can also receive real-time feedback from the user and dynamically adjust the sentence structure based on that. For example, it makes a suggestion to change the order of paragraphs based on the user's feedback. The structure suggestion unit can also receive real-time feedback from the user and dynamically adjust the sentence structure based on that. In this way, by receiving real-time feedback from the user and dynamically adjusting the sentence structure based on that, it becomes possible to create sentences that are in line with the user's intentions.

[0063] The composition proposal unit can use the emotion estimation function to identify a composition that the user most emotionally empathizes with and make sentence suggestions based on that composition. The composition proposal unit, for example, can use the emotion estimation function to identify a composition that the user most emotionally empathizes with and make sentence suggestions based on that composition. For example, the composition proposal unit can make a suggestion to bring a part that the user wants to emotionally emphasize to the front. The composition proposal unit can also use the emotion estimation function to identify a composition that the user most emotionally empathizes with and make sentence suggestions based on that composition. For example, the composition proposal unit can analyze emotional tone and suggest a composition that is likely to be emotionally empathized with. The composition proposal unit can also use the emotion estimation function to identify a composition that the user most emotionally empathizes with and make sentence suggestions based on that composition. In this way, by using the emotion estimation function to identify a composition that the user most emotionally empathizes with and making sentence suggestions based on that composition, it is possible to create sentences that are likely to be emotionally empathized with.

[0064] The generation unit can automatically refer to the latest research papers and specialized books and generate high-quality specialized content based on them. For example, the generation unit uses a generative AI to automatically collect the latest research papers and analyze their contents to generate high-quality specialized content. For example, it generates articles about the latest trends in AI technology. The generation unit can also automatically refer to the latest research papers and specialized books and generate high-quality specialized content based on them. For example, it generates specialized articles based on academic papers. The generation unit can also automatically refer to the latest research papers and specialized books and generate high-quality specialized content based on them. In this way, by automatically referring to the latest research papers and specialized books and generating high-quality specialized content based on them, it is possible to provide specialized content that always contains the latest information.

[0065] The generation unit can build a knowledge base specialized in a specific field of expertise and generate specialized content by utilizing that knowledge. For example, the generation unit uses a generative AI to build a knowledge base specialized in a specific field of expertise and generate high-quality specialized content based on that knowledge. For example, the generation unit generates articles based on a knowledge base in the legal field. The generation unit can also build a knowledge base specialized in a specific field of expertise and generate specialized content by utilizing that knowledge. For example, the generation unit generates specialized articles based on a knowledge base in the medical field. The generation unit can also build a knowledge base specialized in a specific field of expertise and generate specialized content by utilizing that knowledge. In this way, by building a knowledge base specialized in a specific field of expertise and generating specialized content by utilizing that knowledge, highly specialized content can be provided.

[0066] The generation unit uses the emotion estimation function to generate specialized content according to the emotional state of the user, thereby providing content that is easy to empathize with emotionally. The generation unit, for example, uses the emotion estimation function to generate specialized content according to the emotional state of the user. For example, an article is generated using expressions that elicit positive emotions. The generation unit also uses the emotion estimation function to generate specialized content according to the emotional state of the user, thereby providing content that is easy to empathize with emotionally. For example, emotional tone is analyzed to provide content that is easy to empathize with emotionally. The generation unit also uses the emotion estimation function to generate specialized content according to the emotional state of the user, thereby providing content that is easy to empathize with emotionally. In this way, specialized content that is easy to empathize with by using the emotion estimation function to generate specialized content according to the emotional state of the user and providing content that is easy to empathize with emotionally can be provided.

[0067] The generation unit can integrate knowledge from different specialized fields to generate crossover specialized content. For example, the generation AI can integrate knowledge from different specialized fields to generate crossover specialized content. For example, it can generate an article that combines medical and technical knowledge. The generation unit can also integrate knowledge from different specialized fields to generate crossover specialized content. For example, it can generate a specialized article that combines knowledge of economics and sociology. The generation unit can also integrate knowledge from different specialized fields to generate crossover specialized content. In this way, by integrating knowledge from different specialized fields to generate crossover specialized content, it is possible to provide specialized content from a wide range of perspectives.

[0068] The generation unit can learn the user's past history of creating specialized content and generate specialized content optimized for each individual user. For example, the generation unit uses a generation AI to analyze the user's past history of creating specialized content and generate specialized content optimized for each individual user. For example, the generation unit generates articles based on themes that the user often writes about. The generation unit can also learn the user's past history of creating specialized content and generate specialized content optimized for each individual user. For example, the generation unit generates articles optimized for specific themes based on the user's past history of creating specialized content. The generation unit can also learn the user's past history of creating specialized content and generate specialized content optimized for each individual user. In this way, by learning the user's past history of creating specialized content and generating specialized content optimized for each individual user, specialized content that meets the user's needs can be provided.

[0069] The generation unit can use the emotion estimation function to identify a specialized theme that the user most emotionally empathizes with and generate specialized content based on that theme. For example, the generation unit can use the emotion estimation function to identify a specialized theme that the user most emotionally empathizes with and generate specialized content based on that theme. For example, the generation unit can generate an article based on a theme that is likely to be emotionally empathized with. The generation unit can also use the emotion estimation function to identify a specialized theme that the user most emotionally empathizes with and generate specialized content based on that theme. For example, the generation unit can analyze emotional tone and generate an article based on a theme that is likely to be emotionally empathized with. The generation unit can also use the emotion estimation function to identify a specialized theme that the user most emotionally empathizes with and generate specialized content based on that theme. In this way, by using the emotion estimation function to identify a specialized theme that the user most emotionally empathizes with and generating specialized content based on that theme, specialized content that is likely to be emotionally empathized with can be provided.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The document creation support system can also include an information suggestion unit that learns the user's past search history and automatically suggests related information. For example, it can suggest related articles or materials based on keywords the user has previously searched for. The information suggestion unit can also analyze the user's search history and provide the latest information on a specific topic. This allows the user to quickly obtain the information they need and improve the efficiency of their document creation.

[0072] The correction suggestion unit can analyze the user's input speed and typing pattern and make correction suggestions in real time. For example, if the user is typing quickly, it can immediately suggest corrections for typos. It can also analyze typing patterns and predict specific mistakes and make correction suggestions. This allows the user to receive correction suggestions immediately while typing, improving the accuracy and efficiency of writing.

[0073] The correction suggestion unit can use the emotion estimation function to suggest corrections to style and tone according to the user's emotional state. For example, if the user is feeling stressed, the correction suggestion unit can suggest a relaxed tone. Also, if the user is excited, the correction suggestion unit can suggest a calm tone. This allows the user to write more appropriate sentences by suggesting corrections to style and tone according to the user's emotional state.

[0074] The correction suggestion unit can use the emotion estimation function to make grammar corrections and style improvement suggestions according to the user's emotional state. For example, if the user is sad, the correction suggestion unit can suggest encouraging expressions. Also, if the user is angry, the correction suggestion unit can suggest calm expressions. In this way, by using the emotion estimation function to make suggestions according to the user's emotional state, it becomes possible to create sentences that are in line with the user's intentions.

[0075] The correction suggestion unit can make grammar corrections and style improvement suggestions in different languages ​​to support multilingual writing. For example, it can automatically make grammar corrections in English, Japanese, French, etc. The correction suggestion unit can also make style improvement suggestions in different languages. For example, it can suggest a style suitable for English business documents. This makes it possible to create multilingual writing by making grammar corrections and style improvement suggestions in different languages.

[0076] The correction suggestion unit can analyze the voice input and make grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion. For example, it can automatically correct grammatical errors in the voice-input sentence. It can also analyze the voice input and make style improvement suggestions simultaneously with the voice-to-text conversion. This allows for efficient writing by analyzing the voice input and making grammatical corrections and style improvement suggestions simultaneously with the voice-to-text conversion.

[0077] The correction suggestion unit can use the emotion estimation function to analyze the emotional tone of a sentence entered by a user and make correction suggestions that will elicit positive emotions. For example, it can make suggestions to replace negative expressions with positive ones. It can also analyze the emotional tone and suggest expressions that will elicit positive emotions. In this way, by using the emotion estimation function to analyze the emotional tone of a sentence entered by a user and making correction suggestions that will elicit positive emotions, it becomes possible to create sentences that are easy for readers to empathize with.

[0078] The structure suggestion unit can analyze the logical structure of a sentence, automatically detect logical jumps and contradictions, and suggest revisions. For example, it can suggest revisions when there is a contradiction in the context. It can also detect lack of logical consistency and suggest revisions. This makes it possible to create logically consistent sentences by analyzing the logical structure of a sentence, automatically detecting logical jumps and contradictions, and suggesting revisions.

[0079] The structure suggestion unit can learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats. For example, it can propose a logical structure for academic papers and an emotional structure for essays. It can also propose a technical structure for technical documents and a business structure for business documents. This allows the system to learn the structure of sentences in different genres and formats and propose structures optimized for specific genres and formats, enabling users to create texts that meet their goals.

[0080] The structure suggestion unit can use the emotion estimation function to identify the structure that the user most emotionally empathizes with and make sentence suggestions based on that structure. For example, it can suggest moving parts that the user wants to emphasize emotionally to the front. It can also analyze emotional tone and suggest structures that are likely to be emotionally empathized with. This makes it possible to create sentences that are likely to be emotionally empathized with by using the emotion estimation function to identify the structure that the user most emotionally empathizes with and making sentence suggestions based on that structure.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The sentence analysis unit analyzes the sentence entered by the user. For example, it analyzes the sentence using morphological analysis, analyzes the grammatical structure of the sentence using grammatical analysis, and analyzes the meaning of the sentence using semantic analysis. Step 2: The correction suggestion unit makes suggestions for grammar and style corrections based on the text analyzed by the text analysis unit. For example, it checks grammar and makes suggestions for correcting grammatical errors, and suggests style improvements based on style guidelines. It can also learn the user's past writing history and make correction suggestions optimized for each individual user. Step 3: The structure suggestion unit analyzes the overall structure of the text suggested by the revision suggestion unit and makes suggestions to make the structure more logical and readable. For example, it suggests logical paragraph structures and improves the flow of information. It can also learn the structure of sentences in different genres and formats and make structure suggestions optimized for specific genres and formats. Step 4: The generation unit generates high-quality specialized content based on the structure proposed by the structure proposal unit. For example, the generation unit automatically references the latest research papers and specialized books and generates high-quality specialized content based on them. It is also possible to build a knowledge base specialized in a specific field and use that knowledge to generate specialized content. Furthermore, it is possible to use an emotion estimation function to generate specialized content based on the user's emotional state and provide content that is likely to resonate emotionally.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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 text analysis unit that analyzes text entered by a user; a correction suggestion unit that suggests corrections to grammar and style based on the sentence analyzed by the sentence analysis unit; a structure suggestion unit that analyzes the structure of the entire text suggested by the correction suggestion unit and makes suggestions to make the structure more logical and easy to read; a generation unit that generates specialized content based on the configuration proposed by the configuration proposal unit. A system characterized by:

2. The modification suggestion unit It learns the user's past writing history and makes suggestions for grammar corrections and style improvements that are optimized for each individual user.

2. The system of claim 1.

3. The modification suggestion unit Support multilingual writing by providing grammar corrections and style improvement suggestions in different languages 2. The system of claim 1.

4. The configuration proposal unit Analyzing the logical structure of the text, automatically detecting the logical jumps and contradictions, and making correction suggestions 2. The system of claim 1.

5. The generation unit Automatically references the latest research papers and specialist books and generates high-quality specialist content based on them 2. The system of claim 1.

6. The modification suggestion unit Providing grammar corrections and style improvements based on the user's emotional state 2. The system of claim 1.

7. The configuration proposal unit Proposing sentence structures according to the user's emotional state 2. The system of claim 1.

8. The generation unit To generate specialized content according to the emotional state of the user and provide content that is likely to resonate with the user emotionally.

2. The system of claim 1.

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