system

Abstract

The system according to the embodiment aims to efficiently organize collected information and create high-quality articles in a short amount of time. [Solution] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information gathered through interviews. The analysis unit analyzes the information input by the reception unit and extracts important points. The generation unit creates a draft of an article based on the important points extracted by the analysis unit. The provision unit provides the draft created by the generation unit to a reporter or writer and accepts instructions for corrections or additions.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently organize collected information and create high-quality articles in a short amount of time.

[0005] The system according to the embodiment aims to efficiently organize collected information and create high-quality articles in a short amount of time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information gathered through interviews. The analysis unit analyzes the information input by the reception unit and extracts important points. The generation unit creates a draft of an article based on the important points extracted by the analysis unit. The provision unit provides the draft created by the generation unit to a reporter or writer and accepts instructions for corrections or additions. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently organize collected information and create high-quality articles in a short amount of 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) An information organization and article writing support system according to an embodiment of the present invention utilizes generative AI to support reporters and writers in the process of gathering and writing information, from organizing information to drafting articles. In this system, reporters and writers input information they have gathered into the tool, and the generative AI analyzes this information, extracts key points, and creates a draft article. For example, when a reporter or writer uploads audio data, notes, and related materials from an interview, the generative AI automatically organizes the key points of the information using natural language processing technology. Next, based on the key points extracted by the generative AI, the system generates natural-sounding sentences while taking into account the article's structure and writing style. For example, the system can improve the overall flow of the article by quoting the content of the interview. Furthermore, the generative AI provides the reporter or writer with a draft article and accepts corrections and additional instructions. The reporter or writer makes necessary corrections based on the draft created by the generative AI to complete the final article. This tool allows reporters and writers to efficiently organize large amounts of information and create high-quality articles in a short amount of time. This reduces the reporter's burden and improves the quality of the articles. This allows the information organization and article creation support system to efficiently organize the information gathered by reporters and writers, enabling high-quality articles to be created in a short amount of time.

[0029] An information organization and article writing support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information gathered by reporters or writers. The information gathered by reporters or writers includes, but is not limited to, audio data of interviews, notes, and related materials. The reception unit can input audio data in MP3 or WAV format, for example. The reception unit can also input handwritten notes and digital notes. The reception unit can also input related materials such as PDF files, web links, and books. The analysis unit uses a generation AI to analyze the information input by the reception unit and extract important points. The analysis unit automatically organizes the main points of the information using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit divides sentences into words using morphological analysis, analyzes the structure of the sentence using grammatical analysis, and understands the meaning of the sentence using semantic analysis. The generation unit creates a draft article based on the important points extracted by the analysis unit. The generation unit generates natural-looking sentences while taking into consideration, for example, the structure and style of the article. The generation unit can generate natural-looking sentences while taking into consideration, for example, the structure and style of the article using a text generation AI (e.g., LLM). The generation unit can also generate natural-looking sentences while taking into consideration, for example, the structure and style of the article using a multimodal generation AI. The provision unit provides the draft created by the generation unit to a reporter or writer and accepts instructions for corrections and additions. The provision unit provides, for example, an interface through which the reporter or writer can input instructions for corrections and additions. The interface includes a user interface and a graphical user interface. For example, the provision unit enables the reporter or writer to input instructions for corrections and additions using the user interface. The provision unit can also enable the reporter or writer to input instructions for corrections and additions using a graphical user interface. As a result, the information organization and article creation support system according to the embodiment can efficiently organize information gathered by a reporter or writer and create high-quality articles in a short period of time.

[0030] The reception unit can input audio data, memos, related materials, etc. The reception unit can input audio data in MP3 or WAV format, for example. The reception unit can also input handwritten memos and digital memos. For example, handwritten memos can be scanned and converted into digital data, which can then be input to the reception unit. The reception unit can also input related materials such as PDF files, web links, and books. For example, the reception unit can upload related materials and convert them into a format that is easy for the generation AI to analyze. This allows information in a variety of formats to be input to the reception unit. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input audio data to the generation AI and have the generation AI analyze the audio data.

[0031] The analysis unit can automatically organize the main points of information using natural language processing technology. The analysis unit automatically organizes the main points of information using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. For example, the analysis unit divides a sentence into words using morphological analysis, analyzes the structure of the sentence using grammatical analysis, and understands the meaning of the sentence using semantic analysis. The analysis unit can also automatically organize the main points of information using a generative AI. For example, the generative AI extracts the main points of the information and organizes important points. This enables efficient information analysis by automatically organizing the main points of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the analysis unit can input information to the generative AI and have the generative AI extract the main points of the information.

[0032] The generation unit can generate natural-looking sentences while taking into account the structure and style of the article. The generation unit can generate natural-looking sentences while taking into account the structure and style of the article, for example. The generation unit can generate natural-looking sentences while taking into account the structure and style of the article using a text generation AI (e.g., LLM). The generation unit can also generate natural-looking sentences while taking into account the structure and style of the article using a multimodal generation AI. For example, the generation unit can organize the flow of the entire article while quoting the content of an interview. The generation unit can also extract and summarize important parts of the sentence using the generation AI. This allows for the generation of natural-looking sentences, resulting in the creation of high-quality articles. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input prompts to the generation AI to generate natural-looking sentences while taking into account the structure and style of the article, and create a draft of the article based on the generated sentences.

[0033] The providing unit can provide an interface through which a reporter or writer can input instructions for corrections or additions. The providing unit provides, for example, an interface through which a reporter or writer can input instructions for corrections or additions. The interface includes a user interface, a graphical user interface, and the like. For example, the providing unit enables a reporter or writer to input instructions for corrections or additions using a user interface. The providing unit can also enable a reporter or writer to input instructions for corrections or additions using a graphical user interface. This makes it easier to revise articles by providing an interface through which a reporter or writer can input instructions for corrections or additions. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input an interface through which a reporter or writer can input instructions for corrections or additions to the generation AI, and accept instructions for corrections or additions based on the generated interface.

[0034] The reception unit can analyze the reporter's past reporting history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the reporter has used in the past. The reception unit can also recommend input methods related to a specific topic based on the reporter's past reporting history. Furthermore, the reception unit can prioritize and suggest input devices (smartphones, tablets, etc.) that the reporter has frequently used in the past. This enables efficient information input by selecting the optimal input method based on the reporter's past reporting history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the reporter's past reporting history data into the generation AI and have the generation AI select the optimal input method.

[0035] When inputting information, the reception unit can filter the information based on the reporter's current project or area of ​​interest. For example, the reception unit prioritizes inputting only information related to the project the reporter is currently working on. The reception unit can also filter and input highly relevant information based on the reporter's area of ​​interest. Furthermore, the reception unit can also prioritize inputting related information based on topics in which the reporter has shown interest in the past. This allows highly relevant information to be input efficiently by filtering information based on the reporter's current project or area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the reporter's project or area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0036] When inputting information, the reception unit can prioritize inputting highly relevant information by taking into account the geographical location information of the reporter. For example, the reception unit prioritizes inputting information related to the reporter's current location. Furthermore, when the reporter is at a location where the reporter is reporting, the reception unit can also prioritize inputting information related to that location. Furthermore, when the reporter is traveling, the reception unit can also prioritize inputting highly relevant information based on the reporter's current location. In this way, by inputting information taking into account the geographical location information of the reporter, highly relevant information can be input efficiently. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the reporter's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0037] When inputting information, the reception unit can analyze the reporter's social media activity and input related information. For example, the reception unit can input related information based on information shared by the reporter on social media. The reception unit can also analyze the content of posts from accounts the reporter follows and input related information. Furthermore, the reception unit can input related information based on topics the reporter has previously mentioned on social media. This enables efficient information input by inputting related information based on the reporter's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the reporter's social media activity data into the generation AI and have the generation AI input related information.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the information. This enables efficient information analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, in the case of a news article, the analysis unit can apply an algorithm that performs a quick analysis. In addition, in the case of a scientific paper, the analysis unit can also apply an algorithm that performs a detailed analysis. Furthermore, in the case of an entertainment article, the analysis unit can apply an algorithm that performs a visually easy-to-understand analysis. This enables efficient information analysis by applying an analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply different analysis algorithms.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of information with an approaching deadline. The analysis unit can also postpone analysis of information with a more distant submission date. Furthermore, the analysis unit can adjust the priority of analysis according to the submission time. This enables efficient information analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information submission time data into the generation AI and have the generation AI determine the analysis priority.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the information. This enables efficient information analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0042] The generation unit can adjust the level of detail of the draft based on the importance of the article when generating the draft. For example, the generation unit generates a detailed draft for an article with high importance. The generation unit can also generate a concise draft for an article with low importance. Furthermore, the generation unit can adjust the level of detail of the draft according to the importance of the article. This enables efficient draft creation by adjusting the level of detail of the draft according to the importance of the article. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input article importance data into the generation AI and cause the generation AI to adjust the level of detail of the draft.

[0043] The generation unit can apply different generation algorithms depending on the article category when generating a draft. For example, in the case of a news article, the generation unit can apply an algorithm that performs quick generation. In addition, in the case of a scientific paper, the generation unit can also apply an algorithm that performs detailed generation. Furthermore, in the case of an entertainment article, the generation unit can also apply an algorithm that performs visually easy-to-understand generation. This enables efficient draft creation by applying a generation algorithm depending on the article category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input article category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0044] When generating drafts, the generation unit can determine the priority of the drafts based on the submission dates of the articles. For example, the generation unit prioritizes the generation of articles with upcoming deadlines. The generation unit can also postpone the generation of articles with more distant submission dates. Furthermore, the generation unit can adjust the priority of the drafts according to the submission dates. This enables efficient draft creation by determining the priority of the drafts based on the submission dates of the articles. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input article submission date data into the generation AI and have the generation AI determine the priority of the drafts.

[0045] The generation unit can adjust the order of the drafts based on the relevance of the articles when generating the drafts. For example, the generation unit generates highly relevant articles with priority. The generation unit can also postpone generation of less relevant articles. Furthermore, the generation unit can adjust the order of the drafts according to the relevance of the articles. This enables efficient draft creation by adjusting the order of the drafts based on the relevance of the articles. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input article relevance data into the generation AI and cause the generation AI to adjust the order of the drafts.

[0046] When displaying the interface, the providing unit can select the optimal display method by referring to the reporter's past operation history. For example, the providing unit can prioritize providing display methods that the reporter has used in the past. The providing unit can also suggest the most efficient display method based on the reporter's past operation history. Furthermore, the providing unit can also provide interface designs that the reporter has preferred in the past. This enables efficient information provision by selecting the optimal display method based on the reporter's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the reporter's operation history data into the generation AI and have the generation AI select the optimal display method.

[0047] When displaying the interface, the providing unit can select the optimal display method by taking into account the reporter's device information. For example, if the reporter is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the reporter is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the reporter is using a desktop, the providing unit can also provide a display method that allows efficient operation of multiple windows. This enables efficient information provision by selecting the optimal display method based on the reporter's device information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the reporter's device information into the generation AI and have the generation AI select the optimal display method.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] When a reporter or writer enters information they have gathered, the reception department can automatically convert the audio data into text using voice recognition technology. For example, when a reporter uploads audio data from an interview, the reception department uses voice recognition technology to convert the audio into text and sends it to the analysis department. The reception department can also scan handwritten notes and convert them into text using OCR technology. Furthermore, the reception department can analyze PDF files of related materials and extract text data. This allows the reception department to efficiently convert information in a variety of formats into text and provide it to the analysis department.

[0050] The analysis unit can have a function to evaluate the reliability of input information. For example, the analysis unit evaluates the reliability of the information source and prioritizes analysis of highly reliable information. The analysis unit can also check the consistency of the information and issue a warning if there is a contradiction. Furthermore, the analysis unit can check the source of the information and warn about information where the source is not clearly stated. This allows the analysis unit to create articles based on highly reliable information.

[0051] The generator can support multiple style guides when generating draft articles. For example, the generator can generate articles based on different style guides, such as AP style, Chicago style, and MLA style. The generator can also generate articles based on a style guide customized by a user. Furthermore, the generator can support a style guide for a specific media outlet and generate articles appropriate for that media outlet. This allows the generator to efficiently generate articles that support a variety of style guides.

[0052] The delivery unit can provide a real-time collaborative editing function when providing the generated draft to reporters and writers. For example, the delivery unit provides an interface that allows multiple reporters and writers to edit the draft simultaneously. The delivery unit can also save the editing history and track changes. Furthermore, the delivery unit can provide a comment function that allows reporters and writers to provide feedback on the draft. This allows the delivery unit to support efficient collaborative editing and produce high-quality articles.

[0053] The providing unit may be equipped with a voice input function when a reporter or writer inputs corrections or additional instructions. For example, the providing unit may provide an interface that allows a reporter or writer to input corrections or additional instructions by voice. The providing unit may also convert the voice instructions into text using voice recognition technology and send it to the generating unit. Furthermore, the providing unit may save a history of voice instructions so that the history can be checked later. This allows the providing unit to utilize voice input to support efficient corrections and additional instructions.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception desk inputs the information gathered by reporters and writers. This information includes interview audio data, notes, and related materials. The reception desk can input audio data in MP3 or WAV format, as well as related materials such as handwritten notes, digital notes, PDF files, web links, and books. Step 2: The analysis unit uses generative AI to analyze the information entered by the reception unit and extract important points. The analysis unit automatically organizes the main points of the information using natural language processing technology. Specifically, it performs morphological analysis, grammatical analysis, and semantic analysis, divides the sentence into words, analyzes the structure of the sentence, and understands the meaning of the sentence. Step 3: The generator creates a draft of the article based on the key points extracted by the analyzer. The generator uses text generation AI (e.g., LLM) and multimodal generation AI to generate natural-sounding sentences while taking into account the article's structure and style. Step 4: The providing unit provides the draft created by the generating unit to the reporter or writer and accepts instructions for corrections and additions. The providing unit provides an interface through which the reporter or writer can input instructions for corrections and additions. The interface includes a user interface and a graphical user interface.

[0056] (Example 2) An information organization and article writing support system according to an embodiment of the present invention utilizes generative AI to support reporters and writers in the process of gathering and writing information, from organizing information to drafting articles. In this system, reporters and writers input information they have gathered into the tool, and the generative AI analyzes this information, extracts key points, and creates a draft article. For example, when a reporter or writer uploads audio data, notes, and related materials from an interview, the generative AI automatically organizes the key points of the information using natural language processing technology. Next, based on the key points extracted by the generative AI, the system generates natural-sounding sentences while taking into account the article's structure and writing style. For example, the system can improve the overall flow of the article by quoting the content of the interview. Furthermore, the generative AI provides the reporter or writer with a draft article and accepts corrections and additional instructions. The reporter or writer makes necessary corrections based on the draft created by the generative AI to complete the final article. This tool allows reporters and writers to efficiently organize large amounts of information and create high-quality articles in a short amount of time. This reduces the reporter's burden and improves the quality of the articles. This allows the information organization and article creation support system to efficiently organize the information gathered by reporters and writers, enabling high-quality articles to be created in a short amount of time.

[0057] An information organization and article writing support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information gathered by reporters or writers. The information gathered by reporters or writers includes, but is not limited to, audio data of interviews, notes, and related materials. The reception unit can input audio data in MP3 or WAV format, for example. The reception unit can also input handwritten notes and digital notes. The reception unit can also input related materials such as PDF files, web links, and books. The analysis unit uses a generation AI to analyze the information input by the reception unit and extract important points. The analysis unit automatically organizes the main points of the information using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit divides sentences into words using morphological analysis, analyzes the structure of the sentence using grammatical analysis, and understands the meaning of the sentence using semantic analysis. The generation unit creates a draft article based on the important points extracted by the analysis unit. The generation unit generates natural-looking sentences while taking into consideration, for example, the structure and style of the article. The generation unit can generate natural-looking sentences while taking into consideration, for example, the structure and style of the article using a text generation AI (e.g., LLM). The generation unit can also generate natural-looking sentences while taking into consideration, for example, the structure and style of the article using a multimodal generation AI. The provision unit provides the draft created by the generation unit to a reporter or writer and accepts instructions for corrections and additions. The provision unit provides, for example, an interface through which the reporter or writer can input instructions for corrections and additions. The interface includes a user interface and a graphical user interface. For example, the provision unit enables the reporter or writer to input instructions for corrections and additions using the user interface. The provision unit can also enable the reporter or writer to input instructions for corrections and additions using a graphical user interface. As a result, the information organization and article creation support system according to the embodiment can efficiently organize information gathered by a reporter or writer and create high-quality articles in a short period of time.

[0058] The reception unit can input audio data, memos, related materials, etc. The reception unit can input audio data in MP3 or WAV format, for example. The reception unit can also input handwritten memos and digital memos. For example, handwritten memos can be scanned and converted into digital data, which can then be input to the reception unit. The reception unit can also input related materials such as PDF files, web links, and books. For example, the reception unit can upload related materials and convert them into a format that is easy for the generation AI to analyze. This allows information in a variety of formats to be input to the reception unit. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input audio data to the generation AI and have the generation AI analyze the audio data.

[0059] The analysis unit can automatically organize the main points of information using natural language processing technology. The analysis unit automatically organizes the main points of information using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, semantic analysis, etc. For example, the analysis unit divides a sentence into words using morphological analysis, analyzes the structure of the sentence using grammatical analysis, and understands the meaning of the sentence using semantic analysis. The analysis unit can also automatically organize the main points of information using a generative AI. For example, the generative AI extracts the main points of the information and organizes important points. This enables efficient information analysis by automatically organizing the main points of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the analysis unit can input information to the generative AI and have the generative AI extract the main points of the information.

[0060] The generation unit can generate natural-looking sentences while taking into account the structure and style of the article. The generation unit can generate natural-looking sentences while taking into account the structure and style of the article, for example. The generation unit can generate natural-looking sentences while taking into account the structure and style of the article using a text generation AI (e.g., LLM). The generation unit can also generate natural-looking sentences while taking into account the structure and style of the article using a multimodal generation AI. For example, the generation unit can organize the flow of the entire article while quoting the content of an interview. The generation unit can also extract and summarize important parts of the sentence using the generation AI. This allows for the generation of natural-looking sentences, resulting in the creation of high-quality articles. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input prompts to the generation AI to generate natural-looking sentences while taking into account the structure and style of the article, and create a draft of the article based on the generated sentences.

[0061] The providing unit can provide an interface through which a reporter or writer can input instructions for corrections or additions. The providing unit provides, for example, an interface through which a reporter or writer can input instructions for corrections or additions. The interface includes a user interface, a graphical user interface, and the like. For example, the providing unit enables a reporter or writer to input instructions for corrections or additions using a user interface. The providing unit can also enable a reporter or writer to input instructions for corrections or additions using a graphical user interface. This makes it easier to revise articles by providing an interface through which a reporter or writer can input instructions for corrections or additions. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input an interface through which a reporter or writer can input instructions for corrections or additions to the generation AI, and accept instructions for corrections or additions based on the generated interface.

[0062] The reception unit can estimate the reporter's emotions and adjust the timing of information input based on the estimated emotions. For example, if the reporter is feeling stressed, the reception unit can have the generation AI delay the input timing, providing a relaxing environment. Furthermore, if the reporter is concentrating, the reception unit can have the generation AI speed up the input timing, allowing the reporter to input information efficiently. Furthermore, if the reporter is tired, the reception unit can have the generation AI adjust the input timing, allowing the reporter to input information while taking breaks. This allows for efficient information input by adjusting the information input timing according to the reporter's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the reporter's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0063] The reception unit can analyze the reporter's past reporting history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the reporter has used in the past. The reception unit can also recommend input methods related to a specific topic based on the reporter's past reporting history. Furthermore, the reception unit can prioritize and suggest input devices (smartphones, tablets, etc.) that the reporter has frequently used in the past. This enables efficient information input by selecting the optimal input method based on the reporter's past reporting history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the reporter's past reporting history data into the generation AI and have the generation AI select the optimal input method.

[0064] When inputting information, the reception unit can filter the information based on the reporter's current project or area of ​​interest. For example, the reception unit prioritizes inputting only information related to the project the reporter is currently working on. The reception unit can also filter and input highly relevant information based on the reporter's area of ​​interest. Furthermore, the reception unit can also prioritize inputting related information based on topics in which the reporter has shown interest in the past. This allows highly relevant information to be input efficiently by filtering information based on the reporter's current project or area of ​​interest. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the reporter's project or area of ​​interest into the generation AI and have the generation AI perform the filtering.

[0065] The reception unit can estimate the reporter's emotions and determine the priority of information to be input based on the estimated emotions. For example, if the reporter is stressed, the reception unit can postpone less important information and prioritize input of important information. The reception unit can also prioritize input of detailed information when the reporter is relaxed. Furthermore, the reception unit can prioritize input of the most important information when the reporter is in a hurry. This enables efficient information input by determining the priority of information according to the reporter's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the reporter's emotion data into the generation AI and have the generation AI determine the priority of information.

[0066] When inputting information, the reception unit can prioritize inputting highly relevant information by taking into account the geographical location information of the reporter. For example, the reception unit prioritizes inputting information related to the reporter's current location. Furthermore, when the reporter is at a location where the reporter is reporting, the reception unit can also prioritize inputting information related to that location. Furthermore, when the reporter is traveling, the reception unit can also prioritize inputting highly relevant information based on the reporter's current location. In this way, by inputting information taking into account the geographical location information of the reporter, highly relevant information can be input efficiently. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the reporter's geographical location information to the generation AI and cause the generation AI to prioritize input of highly relevant information.

[0067] When inputting information, the reception unit can analyze the reporter's social media activity and input related information. For example, the reception unit can input related information based on information shared by the reporter on social media. The reception unit can also analyze the content of posts from accounts the reporter follows and input related information. Furthermore, the reception unit can input related information based on topics the reporter has previously mentioned on social media. This enables efficient information input by inputting related information based on the reporter's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the reporter's social media activity data into the generation AI and have the generation AI input related information.

[0068] The analysis unit can estimate the reporter's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the reporter is relaxed, the analysis unit can provide detailed analysis results. If the reporter is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the reporter is stressed, the analysis unit can also provide visually easy-to-understand analysis results. This enables efficient information analysis by adjusting the way the analysis is presented according to the reporter's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the reporter's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0069] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the information. This enables efficient information analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0070] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, in the case of a news article, the analysis unit can apply an algorithm that performs a quick analysis. In addition, in the case of a scientific paper, the analysis unit can also apply an algorithm that performs a detailed analysis. Furthermore, in the case of an entertainment article, the analysis unit can apply an algorithm that performs a visually easy-to-understand analysis. This enables efficient information analysis by applying an analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply different analysis algorithms.

[0071] The analysis unit can estimate the reporter's emotions and adjust the length of the analysis based on the estimated reporter's emotions. For example, if the reporter is in a hurry, the analysis unit can provide a short, concise analysis result. If the reporter is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the reporter is stressed, the analysis unit can also provide a visually easy-to-understand analysis result. This enables efficient information analysis by adjusting the length of the analysis according to the reporter's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the reporter's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0072] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of information with an approaching deadline. The analysis unit can also postpone analysis of information with a more distant submission date. Furthermore, the analysis unit can adjust the priority of analysis according to the submission time. This enables efficient information analysis by determining the priority of analysis based on the time of submission of information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information submission time data into the generation AI and have the generation AI determine the analysis priority.

[0073] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the information. This enables efficient information analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0074] The generation unit can estimate the reporter's emotions and adjust the expression style of the draft based on the estimated emotions. For example, if the reporter is relaxed, the generation unit can generate a draft using detailed expressions. If the reporter is in a hurry, the generation unit can also generate a draft using concise expressions. Furthermore, if the reporter is stressed, the generation unit can generate a draft using visually easy-to-understand expressions. This enables efficient draft creation by adjusting the expression style of the draft according to the reporter's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the reporter's emotion data into the generation AI and have the generation AI adjust the expression style of the draft.

[0075] The generation unit can adjust the level of detail of the draft based on the importance of the article when generating the draft. For example, the generation unit generates a detailed draft for an article with high importance. The generation unit can also generate a concise draft for an article with low importance. Furthermore, the generation unit can adjust the level of detail of the draft according to the importance of the article. This enables efficient draft creation by adjusting the level of detail of the draft according to the importance of the article. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input article importance data into the generation AI and cause the generation AI to adjust the level of detail of the draft.

[0076] The generation unit can apply different generation algorithms depending on the article category when generating a draft. For example, in the case of a news article, the generation unit can apply an algorithm that performs quick generation. In addition, in the case of a scientific paper, the generation unit can also apply an algorithm that performs detailed generation. Furthermore, in the case of an entertainment article, the generation unit can also apply an algorithm that performs visually easy-to-understand generation. This enables efficient draft creation by applying a generation algorithm depending on the article category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input article category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0077] The generation unit can estimate the reporter's emotions and adjust the length of the draft based on the estimated emotions. For example, if the reporter is in a hurry, the generation unit can generate a short, concise draft. If the reporter is relaxed, the generation unit can also generate a longer draft with detailed explanations. Furthermore, if the reporter is stressed, the generation unit can also generate a visually easy-to-understand draft. This allows for efficient draft creation by adjusting the length of the draft according to the reporter's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input the reporter's emotion data into the generation AI and have the generation AI adjust the length of the draft.

[0078] When generating drafts, the generation unit can determine the priority of the drafts based on the submission dates of the articles. For example, the generation unit prioritizes the generation of articles with upcoming deadlines. The generation unit can also postpone the generation of articles with more distant submission dates. Furthermore, the generation unit can adjust the priority of the drafts according to the submission dates. This enables efficient draft creation by determining the priority of the drafts based on the submission dates of the articles. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input article submission date data into the generation AI and have the generation AI determine the priority of the drafts.

[0079] The generation unit can adjust the order of the drafts based on the relevance of the articles when generating the drafts. For example, the generation unit generates highly relevant articles with priority. The generation unit can also postpone generation of less relevant articles. Furthermore, the generation unit can adjust the order of the drafts according to the relevance of the articles. This enables efficient draft creation by adjusting the order of the drafts based on the relevance of the articles. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input article relevance data into the generation AI and cause the generation AI to adjust the order of the drafts.

[0080] The providing unit can estimate the reporter's emotions and adjust the interface display method based on the estimated reporter's emotions. For example, if the reporter is nervous, the providing unit can provide an interface with subdued colors. Furthermore, if the reporter is relaxed, the providing unit can provide an interface with bright colors. Furthermore, if the reporter is tired, the providing unit can provide a simple, highly visible interface. This enables efficient information provision by adjusting the interface display method according to the reporter's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the reporter's emotion data into the generation AI and have the generation AI adjust the interface display method.

[0081] When displaying the interface, the providing unit can select the optimal display method by referring to the reporter's past operation history. For example, the providing unit can prioritize providing display methods that the reporter has used in the past. The providing unit can also suggest the most efficient display method based on the reporter's past operation history. Furthermore, the providing unit can also provide interface designs that the reporter has preferred in the past. This enables efficient information provision by selecting the optimal display method based on the reporter's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the reporter's operation history data into the generation AI and have the generation AI select the optimal display method.

[0082] The providing unit can estimate the reporter's emotions and adjust the interface operation procedures based on the estimated reporter's emotions. For example, if the reporter is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the reporter is relaxed, the providing unit can also provide detailed operation procedures. Furthermore, if the reporter is in a hurry, the providing unit can also provide procedures that allow for quick operation. This enables efficient information provision by adjusting the interface operation procedures according to the reporter's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the reporter's emotion data into the generation AI and have the generation AI adjust the interface operation procedures.

[0083] When displaying the interface, the providing unit can select the optimal display method by taking into account the reporter's device information. For example, if the reporter is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the reporter is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the reporter is using a desktop, the providing unit can also provide a display method that allows efficient operation of multiple windows. This enables efficient information provision by selecting the optimal display method based on the reporter's device information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the reporter's device information into the generation AI and have the generation AI select the optimal display method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs information gathered by a reporter or writer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generation AI to extract important points. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a draft of an article based on the extracted points. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the draft to the reporter or writer and accepts instructions for corrections and additions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs information gathered by a reporter or writer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generation AI to extract important points. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a draft of an article based on the extracted points. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the draft to the reporter or writer and accepts instructions for corrections and additions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and inputs information gathered by a reporter or writer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generation AI to extract important points. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a draft of an article based on the extracted points. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the draft to the reporter or writer and accepts instructions for corrections and additions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs information gathered by a reporter or writer. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using a generation AI to extract important points. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a draft of an article based on the extracted points. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the draft to the reporter or writer and accepts instructions for corrections and additions.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] When a reporter or writer enters information they have gathered, the reception department can automatically convert the audio data into text using voice recognition technology. For example, when a reporter uploads audio data from an interview, the reception department uses voice recognition technology to convert the audio into text and sends it to the analysis department. The reception department can also scan handwritten notes and convert them into text using OCR technology. Furthermore, the reception department can analyze PDF files of related materials and extract text data. This allows the reception department to efficiently convert information in a variety of formats into text and provide it to the analysis department.

[0086] The analysis unit can have a function to evaluate the reliability of input information. For example, the analysis unit evaluates the reliability of the information source and prioritizes analysis of highly reliable information. The analysis unit can also check the consistency of the information and issue a warning if there is a contradiction. Furthermore, the analysis unit can check the source of the information and warn about information where the source is not clearly stated. This allows the analysis unit to create articles based on highly reliable information.

[0087] The generator can support multiple style guides when generating draft articles. For example, the generator can generate articles based on different style guides, such as AP style, Chicago style, and MLA style. The generator can also generate articles based on a style guide customized by a user. Furthermore, the generator can support a style guide for a specific media outlet and generate articles appropriate for that media outlet. This allows the generator to efficiently generate articles that support a variety of style guides.

[0088] The delivery unit can provide a real-time collaborative editing function when providing the generated draft to reporters and writers. For example, the delivery unit provides an interface that allows multiple reporters and writers to edit the draft simultaneously. The delivery unit can also save the editing history and track changes. Furthermore, the delivery unit can provide a comment function that allows reporters and writers to provide feedback on the draft. This allows the delivery unit to support efficient collaborative editing and produce high-quality articles.

[0089] The providing unit may be equipped with a voice input function when a reporter or writer inputs corrections or additional instructions. For example, the providing unit may provide an interface that allows a reporter or writer to input corrections or additional instructions by voice. The providing unit may also convert the voice instructions into text using voice recognition technology and send it to the generating unit. Furthermore, the providing unit may save a history of voice instructions so that the history can be checked later. This allows the providing unit to utilize voice input to support efficient corrections and additional instructions.

[0090] The reception unit can estimate the reporter's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the reporter is feeling stressed, the reception unit can provide an interface with a simple and calm design. If the reporter is relaxed, the reception unit can provide a colorful and interactive interface. Furthermore, if the reporter is tired, the reception unit can provide an interface with high visibility. In this way, the reception unit can provide an optimal interface according to the reporter's emotions and support efficient information input.

[0091] The analysis unit can estimate the reporter's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the reporter is relaxed, detailed analysis results can be provided. If the reporter is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the reporter is stressed, analysis results can be presented using easy-to-understand graphs and charts. This allows the analysis unit to provide optimal analysis results according to the reporter's emotions and support efficient information analysis.

[0092] The generator can estimate the reporter's emotions and adjust the tone of the draft based on the estimated emotions. For example, if the reporter is relaxed, the generator can generate a draft with a friendly tone. If the reporter is in a hurry, the generator can generate a draft with a concise and direct tone. Furthermore, if the reporter is stressed, the generator can generate a draft with a calm and objective tone. This allows the generator to provide a draft with an optimal tone according to the reporter's emotions and support efficient article writing.

[0093] The feedback unit can estimate the reporter's emotions and adjust the feedback method based on the estimated reporter's emotions. For example, if the reporter is nervous, the feedback unit can provide gentler feedback. If the reporter is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the reporter is tired, the feedback unit can provide concise and to-the-point feedback. This allows the feedback unit to provide optimal feedback according to the reporter's emotions and support efficient article revisions.

[0094] The information providing unit can estimate the reporter's emotions and provide interface customization options based on the estimated emotions. For example, if the reporter is relaxed, the information providing unit can provide options to freely customize the interface color and layout. If the reporter is in a hurry, the information providing unit can provide options that allow for quick operation with default settings. Furthermore, if the reporter is stressed, the information providing unit can provide simple and intuitive customization options. This allows the information providing unit to provide an optimal interface according to the reporter's emotions and support efficient information provision.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The reception desk inputs the information gathered by reporters and writers. This information includes interview audio data, notes, and related materials. The reception desk can input audio data in MP3 or WAV format, as well as related materials such as handwritten notes, digital notes, PDF files, web links, and books. Step 2: The analysis unit uses generative AI to analyze the information entered by the reception unit and extract important points. The analysis unit automatically organizes the main points of the information using natural language processing technology. Specifically, it performs morphological analysis, grammatical analysis, and semantic analysis, divides the sentence into words, analyzes the structure of the sentence, and understands the meaning of the sentence. Step 3: The generator creates a draft of the article based on the key points extracted by the analyzer. The generator uses text generation AI (e.g., LLM) and multimodal generation AI to generate natural-sounding sentences while taking into account the article's structure and style. Step 4: The providing unit provides the draft created by the generating unit to the reporter or writer and accepts instructions for corrections and additions. The providing unit provides an interface through which the reporter or writer can input instructions for corrections and additions. The interface includes a user interface and a graphical user interface.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

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

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

[0144] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

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

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where the collected information is entered, an analysis unit that analyzes the information input by the reception unit and extracts important points; a generation unit that creates a draft of an article based on the important points extracted by the analysis unit; a providing unit that provides the draft created by the generating unit to a reporter or writer and receives instructions for corrections or additions. A system characterized by:

2. The reception unit Input audio data, notes, related materials, etc. The system of claim 1 .

3. The analysis unit Automatically organize the main points of information using natural language processing technology The system of claim 1 .

4. The generation unit Generate natural sentences while taking into account the structure and style of the article The system of claim 1 .

5. The providing unit Provide an interface that allows reporters and writers to enter corrections and additional instructions The system of claim 1 .

6. The reception unit Estimate the reporter's emotions and adjust the timing of information input based on the estimated emotions of the reporter. The system of claim 1 .

7. The reception unit Analyze the reporter's past interview history and select the optimal input method The system of claim 1 .

8. The reception unit As information is entered, it can be filtered based on the reporter's current projects or areas of interest. The system of claim 1 .

Images