System

Abstract

An object of a system according to an embodiment is to make a process of creating a company newsletter efficient and to automate the process.SOLUTION: A system includes a news gathering destination selection part, a news gathering execution part, an imaging part, an article creation part, and a check part. The news gathering destination selecting block selects a news gathering destination. The news gathering execution block makes an interview with the news gathering destination selected by the news gathering destination selection block. The photographing section takes a photograph on the basis of the contents collected by the news gathering execution section. The article creation block creates an article on the basis of the information obtained by the news gathering execution block and the imaging block. The check part checks the article created by the article creation part before release.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of creating company newsletters was time-consuming and labor-intensive, making it difficult to do efficiently.

[0005] The system according to the embodiment aims to streamline and automate the process of creating an in-house newsletter. [Means for solving the problem]

[0006] The system according to the embodiment comprises a news gathering selection unit, a news gathering implementation unit, a photography unit, an article creation unit, and a checking unit. The news gathering selection unit selects news gathering locations. The news gathering implementation unit conducts interviews with news gathering locations selected by the news gathering selection unit. The photography unit takes photographs based on the content of the news gathering conducted by the news gathering implementation unit. The article creation unit creates articles based on information obtained by the news gathering implementation unit and the photography unit. The checking unit checks articles created by the article creation unit before they are released. [Effects of the Invention]

[0007] The system according to the embodiment can streamline and automate the process of creating an in-house newsletter. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The in-house newsletter automation system according to the embodiment of the present invention is a system that uses AI to automate everything from selecting interview subjects to conducting interviews, taking photos, compiling articles, and checking them before release. As a result, the in-house newsletter automation system can release articles every day and share information within the company quickly and efficiently.

[0029] An in-house newsletter automation system according to an embodiment includes a reporting subject selection unit, a reporting implementation unit, a photography unit, an article creation unit, and a checking unit. The reporting subject selection unit selects reporting subjects. For example, the generation AI analyzes an internal database or the contents of past in-house newsletters to identify noteworthy reporting subjects. The generation AI can also select reporting subjects by analyzing project progress and employee activities. The generation AI can also select reporting subjects based on prompts containing instructions for selecting reporting subjects. The reporting implementation unit conducts interviews with the reporting subjects selected by the reporting subject selection unit. For example, the generation AI conducts interviews with the selected reporting subjects, generates questions, and records the responses. The generation AI can also ask questions such as, "Tell me about your ongoing projects," and record the responses as text data. The generation AI can also conduct interviews in a dialogue format and automatically record the questions and answers. The photography unit takes photos based on the content of the interviews conducted by the reporting implementation unit. For example, the generation AI operates a camera to take pictures of interview subjects or project sites, and releases the shutter at the appropriate time. The generation AI can also automatically take photos of interview subjects and use them in articles. The generation AI can also take photos of the subject at the appropriate time. The article creation department creates articles based on information obtained by the interview implementation department and photography department. For example, the generation AI creates articles based on the interview content and photos taken, organizes the interview content, and summarizes it in an easy-to-read format. The generation AI can also automatically adjust the article structure and writing style to generate highly polished articles. The generation AI can also automatically organize the content of articles and summarize them. The checking department checks articles created by the article creation department before release. For example, the generation AI checks completed articles before release, checking for typos and consistency of content. The generation AI can also make corrections as necessary to maintain the quality of the article. The generation AI can also automatically check and correct the content of articles. As a result, the in-house newsletter automation system according to the embodiment can release articles every day, enabling quick and efficient information sharing within the company.For example, the output unit can display the grading results to students and teachers via a web or mobile application, print the results using a printer if students or teachers prefer paper feedback, or send the results via email, providing quick feedback by sending the results directly to students and parents.

[0030] The interviewee selection unit can analyze internal social media or chat logs and identify interviewees from unofficial sources. For example, the generation AI analyzes internal social media or chat logs to identify noteworthy interviewees from informal communications between employees. For example, it analyzes chats in which project progress or new ideas are discussed and selects them as interviewees. The interviewee selection unit can also analyze internal social media or chat logs to identify interviewees based on employee activities. The generation AI can also identify interviewees from unofficial sources and select them as interviewees. This allows for the collection of more diverse information by identifying interviewees from unofficial sources.

[0031] The interview target selection unit can analyze employee schedules or calendars and automatically suggest the optimal timing for interviews. For example, the generation AI in the interview target selection unit can analyze employee schedules and calendars and automatically suggest the time slot when interview candidates are least busy. For example, scheduling interviews to avoid scheduled meetings or business trips. The interview target selection unit can also analyze employee schedules and calendars and suggest the most efficient timing for interview candidates. The generation AI in the interview target selection unit can also suggest the optimal timing for interviews based on employee schedules and calendars. This improves the efficiency of interviews by suggesting the optimal timing for interviews.

[0032] The interview subject selection department can analyze external industry news or trend information and propose new interview themes by linking them to internal interview subjects. For example, the interview subject selection department uses a generation AI to analyze external industry news and trend information and propose new interview themes by linking them to internal interview subjects. For example, the interview subject selection department can select an internal project related to a technology or trend that is attracting attention in the industry as an interview subject. The interview subject selection department can also use a generation AI to analyze external industry news and trend information and propose new interview themes by linking them to internal interview subjects. The interview subject selection department can also use a generation AI to propose interview themes based on external industry news and trend information. This allows the content of articles to be more diverse by proposing new interview themes based on external industry news and trend information.

[0033] The interview subject selection department can select interview subjects cross-functionally to promote collaboration between different departments within the company. For example, the interview subject selection department selects interview subjects cross-functionally so that the generation AI can promote collaboration between different departments within the company. For example, the interview subject selection department selects a project that is being carried out jointly by the technology department and the marketing department as an interview subject. The interview subject selection department can also select interview subjects so that the generation AI can promote collaboration between different departments within the company. The interview subject selection department can also select interview subjects based on collaboration between different departments so that the generation AI can promote collaboration between different departments. This strengthens internal collaboration by promoting collaboration between different departments.

[0034] The interview implementation department can automatically generate more in-depth questions based on past response data during an interview. For example, the generation AI in the interview can refer to past response data during an interview and automatically generate more in-depth questions. For example, the generation AI can ask more detailed questions about a topic that was touched on in the previous interview. The interview implementation department can also have the generation AI generate questions based on past response data during an interview. The interview implementation department can also have the generation AI analyze past response data and generate more in-depth questions. In this way, more detailed information can be collected by generating more in-depth questions based on past response data.

[0035] The interview implementation unit can translate the interview audio data in real time, enabling multilingual interviews. For example, the interview implementation unit can have a generation AI translate the interview audio data in real time, enabling multilingual interviews. For example, an interview conducted in English can be translated into Japanese and instantly displayed. The interview implementation unit can also have a generation AI translate the interview audio data in real time, enabling it to be in other languages. The interview implementation unit can also have a generation AI translate the interview audio data in real time, enabling multilingual interviews based on the audio data. This allows multilingual interviews to be conducted internationally.

[0036] The interview implementation department can summarize the contents of the interview in real time and provide immediate feedback. For example, the interview implementation department can have a generation AI summarize the contents of the interview in real time and provide immediate feedback. For example, a summary is generated immediately after the interview is completed and provided to the subject. The interview implementation department can also have a generation AI summarize the contents of the interview in real time and provide feedback. The interview implementation department can also have a generation AI generate a summary based on the contents of the interview and provide immediate feedback. This allows the interview contents to be summarized immediately and feedback to be provided, improving the efficiency of interviews.

[0037] The shooting unit can analyze the movements or facial expressions of the subject and automatically determine the best moment to take a photo. For example, the generation AI in the shooting unit can analyze the movements and facial expressions of the subject in real time and automatically determine the best moment to take a photo. For example, capturing the moment of a smile or an important gesture. The shooting unit can also analyze the movements and facial expressions of the subject and determine the best moment to take a photo. The generation AI in the shooting unit can also analyze the movements and facial expressions in real time and determine the best moment to take a photo. This allows for better photos to be taken by automatically determining the best moment to take a photo.

[0038] The shooting unit can adjust the amount of light or color temperature of the shooting environment in real time to take the optimal photo. For example, the generation AI of the shooting unit analyzes the amount of light and color temperature of the shooting environment in real time and automatically adjusts to the optimal settings. For example, it changes the camera settings to suit the indoor lighting conditions. The shooting unit can also adjust the amount of light and color temperature of the shooting environment in real time to take the optimal photo. The generation AI of the shooting unit can also adjust the shooting settings based on the amount of light and color temperature. This allows the optimal photo to be taken by adjusting the amount of light and color temperature in real time.

[0039] The photography unit can operate a drone and automatically take aerial or wide-angle shots. For example, the generation AI can operate the drone to automatically take aerial or wide-angle shots. For example, taking aerial shots of a panoramic view of an in-house event. The photography unit can also operate a drone with the generation AI to take aerial or wide-angle shots. The photography unit can also use the generation AI to take photos based on the drone. This makes it possible to use a drone to take photos from perspectives that cannot be captured with a normal camera.

[0040] The photography unit can edit the photos taken in real time, making them instantly usable. For example, the photography unit can edit photos taken by the generation AI in real time, making them instantly usable. For example, it can automatically perform color correction and cropping. The photography unit can also edit the photos taken by the generation AI in real time, making them instantly usable. The photography unit can also edit based on the photos taken by the generation AI. This allows for the efficiency of article creation to be improved by instantly editing the photos taken.

[0041] The article creation unit can analyze past article data and automatically suggest the optimal article structure or writing style. In the article creation unit, for example, a generation AI analyzes past article data and automatically suggests the optimal article structure and writing style. For example, a new article is created based on the patterns of past successful articles. The article creation unit can also analyze past article data and suggest an article structure and writing style. The article creation unit can also have a generation AI analyze past article data and suggest an article structure and writing style based on past article data. This improves the quality of articles by suggesting the optimal article structure and writing style based on past article data.

[0042] The article creation unit can proofread the content of the article in real time and immediately correct any typos or grammatical errors. For example, the article creation unit allows the generation AI to proofread the content of the article in real time and immediately correct any typos or grammatical errors. For example, proofreading is performed automatically while the article is being created. The article creation unit can also allow the generation AI to proofread the content of the article in real time and correct any typos or grammatical errors. The article creation unit can also allow the generation AI to proofread based on the content of the article. In this way, the quality of the article is improved by proofreading the content of the article in real time.

[0043] The article creation department can automatically translate articles into multiple languages ​​to accommodate international readers. For example, the generation AI can automatically translate articles into multiple languages ​​to accommodate international readers. For example, translating an English article into Japanese or French. The article creation department can also automatically translate articles into multiple languages ​​to accommodate other languages. The article creation department can also use the generation AI to support multiple languages ​​based on the article. This allows articles to be translated into multiple languages ​​to accommodate international readers.

[0044] The article creation unit can convert the content of the article into a visual note or infographic to make it easier to understand visually. For example, the article creation unit can have a generation AI convert the content of the article into a visual note or infographic to make it easier to understand visually. For example, the main points of the article can be shown in diagrams or graphs. The article creation unit can also have a generation AI convert the content of the article into a visual note or infographic to make it easier to understand visually. The article creation unit can also have a generation AI create a visual note or infographic based on the content of the article. This makes the content of the article easier to understand visually, thereby deepening the reader's understanding.

[0045] The checking unit can evaluate the content of an article from multiple perspectives and suggest revisions to eliminate bias or prejudice. For example, the checking unit can have the generation AI evaluate the content of an article from multiple perspectives and suggest revisions to eliminate bias or prejudice. For example, the checking unit can adjust the content of the article so that it is not biased towards a particular perspective. The checking unit can also have the generation AI evaluate the content of an article from multiple perspectives and suggest revisions. The checking unit can also have the generation AI suggest revisions to eliminate bias or prejudice based on the content of the article. This makes it possible to provide fair and reliable articles by eliminating bias and prejudice.

[0046] The checking unit can check the content of articles from a legal perspective and make corrections to prevent compliance violations. For example, the generation AI can check the content of articles from a legal perspective and make corrections to prevent compliance violations. For example, the generation AI can automatically detect issues related to copyright or privacy and suggest corrections. The checking unit can also have the generation AI check the content of articles from a legal perspective and make corrections. The checking unit can also have the generation AI make corrections based on the content of articles to prevent compliance violations. In this way, compliance violations can be prevented by checking from a legal perspective.

[0047] The checking department can automatically request that the content of an article be reviewed by experts in different industries and obtain multifaceted feedback. For example, the generation AI can automatically request that the content of an article be reviewed by experts in different industries and obtain multifaceted feedback. For example, opinions can be collected from experts in technical, legal, and marketing fields. The generation AI can also request that the content of an article be reviewed by experts in different industries and obtain feedback. The generation AI can also request a review based on the content of the article and obtain feedback. This allows for multifaceted feedback to be obtained, thereby improving the quality of the article.

[0048] The checking unit can simulate the content of an article in real time and predict reactions after its release. For example, the generation AI in the checking unit can simulate the content of an article in real time and predict reactions after its release. For example, it can predict readers' emotional reactions and comment trends. The checking unit can also simulate the content of an article in real time and predict reactions. The generation AI can also predict reactions after its release based on the content of the article. This allows the content of the article to be optimized in advance by predicting reactions after its release.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The interview subject selection department can select interview subjects cross-functionally to promote collaboration between different departments within the company. For example, the generation AI selects interview subjects cross-functionally to promote collaboration between different departments within the company. For example, a project being jointly carried out by the technology department and the marketing department is selected as an interview subject. The interview subject selection department can also select interview subjects so that the generation AI can promote collaboration between different departments within the company. The interview subject selection department can also select interview subjects so that the generation AI can promote collaboration between different departments. This strengthens internal collaboration by promoting collaboration between different departments.

[0051] The reporting department can automatically generate more in-depth questions based on past response data during an interview. For example, the generation AI can refer to past response data during an interview and automatically generate more in-depth questions. For example, asking more detailed questions about a topic that was touched on in the previous interview. The reporting department can also have the generation AI generate questions based on past response data during an interview. The reporting department can also have the generation AI analyze past response data and generate more in-depth questions. In this way, more detailed information can be collected by generating more in-depth questions based on past response data.

[0052] The interview implementation unit can translate the interview audio data in real time, enabling multilingual interviews. For example, the generation AI can translate the interview audio data in real time, enabling multilingual interviews. For example, an interview conducted in English can be translated into Japanese and instantly displayed. The interview implementation unit can also have the generation AI translate the interview audio data in real time, enabling it to be in other languages. The interview implementation unit can also have the generation AI translate the interview audio data in real time, enabling multilingual interviews based on the audio data. This allows multilingual interviews to be conducted internationally.

[0053] The photography unit can operate a drone and automatically take aerial or wide-angle shots. For example, the generation AI can operate a drone and automatically take aerial or wide-angle shots. For example, taking aerial shots of a panoramic view of an in-house event. The photography unit can also operate a drone with the generation AI and take aerial or wide-angle shots. The photography unit can also use the generation AI to take photos based on the drone. This makes it possible to use a drone to take photos from perspectives that cannot be captured with a normal camera.

[0054] The shooting unit can adjust the light intensity or color temperature of the shooting environment in real time to take the optimal photo. For example, the generation AI analyzes the light intensity and color temperature of the shooting environment in real time and automatically adjusts to the optimal settings. For example, it changes the camera settings to suit the indoor lighting conditions. The shooting unit can also adjust the light intensity and color temperature of the shooting environment in real time to take the optimal photo. The shooting unit can also adjust the shooting settings based on the light intensity and color temperature. This allows the optimal photo to be taken by adjusting the light intensity and color temperature in real time.

[0055] The article creation department can convert the content of an article into a visual note or infographic to make it easier to understand visually. For example, the generation AI can convert the content of an article into a visual note or infographic to make it easier to understand visually. For example, the main points of the article can be shown in diagrams or graphs. The article creation department can also convert the content of an article into a visual note or infographic to make it easier to understand visually. The article creation department can also have the generation AI create a visual note or infographic based on the content of the article. This makes the content of the article easier to understand visually, thereby deepening the reader's understanding.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The interviewee selection unit selects interviewees. For example, the generation AI analyzes the contents of internal company databases and past company newsletters to identify noteworthy interviewees. The generation AI can also select interviewees by analyzing project progress and employee activities. Furthermore, the generation AI can select interviewees based on prompts that include instructions for selecting interviewees. Step 2: The interview implementation department conducts interviews with the interviewees selected by the interviewee selection department. For example, the generation AI conducts interviews with the selected interviewees, generates questions, and records the interviewees' answers. The generation AI can also ask questions such as, "Tell me about your ongoing projects," and record the interviewees' answers as text data. Furthermore, the generation AI can also conduct interviews in a dialogue format and automatically record the questions and answers. Step 3: The photography department takes photos based on the content of the interviews conducted by the interview implementation department. For example, the generation AI operates the camera to photograph the people being interviewed or the project site, and releases the shutter at the appropriate time. The generation AI can also automatically take photos of the interview subject and use them in the article. Furthermore, the generation AI can also take photos of the subject at the appropriate time. Step 4: The article creation department creates an article based on the information obtained by the interview execution department and the photography department. For example, the generation AI creates an article based on the interview content and the photos taken, organizes the interview content, and summarizes it in an easy-to-read format. The generation AI can also automatically adjust the article's structure and writing style to generate a highly complete article. Furthermore, the generation AI can automatically organize the content of the article and summarize it into an article. Step 5: The Checking Department checks the articles created by the Article Creation Department before they are released. For example, the Generation AI checks the completed article before release, checking for typos and consistency of content. The Generation AI can also make corrections as necessary to maintain the quality of the article. Furthermore, the Generation AI can automatically check and correct the content of the article.

[0058] (Example 2) The in-house newsletter automation system according to the embodiment of the present invention is a system that uses AI to automate everything from selecting interview subjects to conducting interviews, taking photos, compiling articles, and checking them before release. As a result, the in-house newsletter automation system can release articles every day and share information within the company quickly and efficiently.

[0059] An in-house newsletter automation system according to an embodiment includes a reporting subject selection unit, a reporting implementation unit, a photography unit, an article creation unit, and a checking unit. The reporting subject selection unit selects reporting subjects. For example, the generation AI analyzes an internal database or the contents of past in-house newsletters to identify noteworthy reporting subjects. The generation AI can also select reporting subjects by analyzing project progress and employee activities. The generation AI can also select reporting subjects based on prompts containing instructions for selecting reporting subjects. The reporting implementation unit conducts interviews with the reporting subjects selected by the reporting subject selection unit. For example, the generation AI conducts interviews with the selected reporting subjects, generates questions, and records the responses. The generation AI can also ask questions such as, "Tell me about your ongoing projects," and record the responses as text data. The generation AI can also conduct interviews in a dialogue format and automatically record the questions and answers. The photography unit takes photos based on the content of the interviews conducted by the reporting implementation unit. For example, the generation AI operates a camera to take pictures of interview subjects or project sites, and releases the shutter at the appropriate time. The generation AI can also automatically take photos of interview subjects and use them in articles. The generation AI can also take photos of the subject at the appropriate time. The article creation department creates articles based on information obtained by the interview implementation department and photography department. For example, the generation AI creates articles based on the interview content and photos taken, organizes the interview content, and summarizes it in an easy-to-read format. The generation AI can also automatically adjust the article structure and writing style to generate highly polished articles. The generation AI can also automatically organize the content of articles and summarize them. The checking department checks articles created by the article creation department before release. For example, the generation AI checks completed articles before release, checking for typos and consistency of content. The generation AI can also make corrections as necessary to maintain the quality of the article. The generation AI can also automatically check and correct the content of articles. As a result, the in-house newsletter automation system according to the embodiment can release articles every day, enabling quick and efficient information sharing within the company.For example, the output unit can display the grading results to students and teachers via a web or mobile application, print the results using a printer if students or teachers prefer paper feedback, or send the results via email, providing quick feedback by sending the results directly to students and parents.

[0060] The interviewee selection unit can analyze internal social media or chat logs and identify interviewees from unofficial sources. For example, the generation AI analyzes internal social media or chat logs to identify noteworthy interviewees from informal communications between employees. For example, it analyzes chats in which project progress or new ideas are discussed and selects them as interviewees. The interviewee selection unit can also analyze internal social media or chat logs to identify interviewees based on employee activities. The generation AI can also identify interviewees from unofficial sources and select them as interviewees. This allows for the collection of more diverse information by identifying interviewees from unofficial sources.

[0061] The interview target selection unit can analyze employee schedules or calendars and automatically suggest the optimal timing for interviews. For example, the generation AI in the interview target selection unit can analyze employee schedules and calendars and automatically suggest the time slot when interview candidates are least busy. For example, scheduling interviews to avoid scheduled meetings or business trips. The interview target selection unit can also analyze employee schedules and calendars and suggest the most efficient timing for interview candidates. The generation AI in the interview target selection unit can also suggest the optimal timing for interviews based on employee schedules and calendars. This improves the efficiency of interviews by suggesting the optimal timing for interviews.

[0062] The interviewee selection unit can use the emotion estimation function to analyze the emotional state of employees and prioritize selecting employees with positive emotions as interviewees. The interviewee selection unit, for example, uses the emotion estimation function to analyze the emotional state of employees and prioritize selecting employees with positive emotions as interviewees. For example, an employee who has received high evaluations for their recent work is selected as an interviewee. The interviewee selection unit can also use the emotion estimation function to analyze the emotional state of employees and identify employees with positive emotions. The interviewee selection unit can also use the emotion estimation function to select interviewees based on the emotional state of employees. In this way, by prioritizing interviews with employees with positive emotions, the content of the article becomes more appealing.

[0063] The interview subject selection department can analyze external industry news or trend information and propose new interview themes by linking them to internal interview subjects. For example, the interview subject selection department uses a generation AI to analyze external industry news and trend information and propose new interview themes by linking them to internal interview subjects. For example, the interview subject selection department can select an internal project related to a technology or trend that is attracting attention in the industry as an interview subject. The interview subject selection department can also use a generation AI to analyze external industry news and trend information and propose new interview themes by linking them to internal interview subjects. The interview subject selection department can also use a generation AI to propose interview themes based on external industry news and trend information. This allows the content of articles to be more diverse by proposing new interview themes based on external industry news and trend information.

[0064] The interview subject selection department can select interview subjects cross-functionally to promote collaboration between different departments within the company. For example, the interview subject selection department selects interview subjects cross-functionally so that the generation AI can promote collaboration between different departments within the company. For example, the interview subject selection department selects a project that is being carried out jointly by the technology department and the marketing department as an interview subject. The interview subject selection department can also select interview subjects so that the generation AI can promote collaboration between different departments within the company. The interview subject selection department can also select interview subjects based on collaboration between different departments so that the generation AI can promote collaboration between different departments. This strengthens internal collaboration by promoting collaboration between different departments.

[0065] The interview subject selection unit can use the emotion estimation function to monitor the emotional state of employees in real time and dynamically change interview subjects according to changes in emotions. The interview subject selection unit can, for example, use the emotion estimation function to monitor the emotional state of employees in real time and dynamically change interview subjects according to changes in emotions. For example, it selects employees whose positive emotions have increased as interview subjects. The interview subject selection unit can also use the emotion estimation function to monitor the emotional state of employees in real time and dynamically change interview subjects. The interview subject selection unit can also use the emotion estimation function to dynamically change interview subjects based on the emotional state of employees. This makes it possible to conduct more appropriate interviews by dynamically changing interview subjects according to changes in emotions.

[0066] The interview implementation department can automatically generate more in-depth questions based on past response data during an interview. For example, the generation AI in the interview can refer to past response data during an interview and automatically generate more in-depth questions. For example, the generation AI can ask more detailed questions about a topic that was touched on in the previous interview. The interview implementation department can also have the generation AI generate questions based on past response data during an interview. The interview implementation department can also have the generation AI analyze past response data and generate more in-depth questions. In this way, more detailed information can be collected by generating more in-depth questions based on past response data.

[0067] The interview implementation unit can perform sentiment analysis of answers in real time during the interview and generate follow-up questions according to the sentiment. In the interview implementation unit, for example, the generation AI can perform sentiment analysis of answers in real time during the interview and generate follow-up questions according to the sentiment. For example, more detailed questions can be asked for answers with a strong positive sentiment. The interview implementation unit can also perform sentiment analysis of answers in real time during the interview and generate follow-up questions. The interview implementation unit can also perform sentiment analysis of answers in real time during the interview and generate questions according to the sentiment. In this way, the generation AI can perform sentiment analysis of answers in real time during the interview and generate questions according to the sentiment. The quality of the interview can be improved by generating follow-up questions according to the sentiment.

[0068] The interview implementation unit can use the emotion estimation function to analyze the emotional state of the interviewee and automatically generate a dialogue to relax them. The interview implementation unit, for example, uses the emotion estimation function to analyze the emotional state of the interviewee and automatically generate a dialogue to relax them. For example, it can provide a light topic to relax a nervous interviewee. The interview implementation unit can also use the emotion estimation function to analyze the emotional state of the interviewee and generate a dialogue to relax them. The interview implementation unit can also use the emotion estimation function to generate a dialogue based on the emotional state of the interviewee. This can relax the interviewee and elicit more natural answers.

[0069] The interview implementation unit can translate the interview audio data in real time, enabling multilingual interviews. For example, the interview implementation unit can have a generation AI translate the interview audio data in real time, enabling multilingual interviews. For example, an interview conducted in English can be translated into Japanese and instantly displayed. The interview implementation unit can also have a generation AI translate the interview audio data in real time, enabling it to be in other languages. The interview implementation unit can also have a generation AI translate the interview audio data in real time, enabling multilingual interviews based on the audio data. This allows multilingual interviews to be conducted internationally.

[0070] The interview implementation department can summarize the contents of the interview in real time and provide immediate feedback. For example, the interview implementation department can have a generation AI summarize the contents of the interview in real time and provide immediate feedback. For example, a summary is generated immediately after the interview is completed and provided to the subject. The interview implementation department can also have a generation AI summarize the contents of the interview in real time and provide feedback. The interview implementation department can also have a generation AI generate a summary based on the contents of the interview and provide immediate feedback. This allows the interview contents to be summarized immediately and feedback to be provided, improving the efficiency of interviews.

[0071] The interview implementation unit can use the emotion estimation function to monitor the emotional state of the interviewee in real time and optimize the progress of the interview. The interview implementation unit, for example, uses the emotion estimation function to monitor the emotional state of the interviewee in real time and optimize the progress of the interview. For example, if the interviewee is nervous, the interview implementation unit can ask questions to help the interviewee relax. The interview implementation unit can also use the emotion estimation function to monitor the emotional state of the interviewee in real time and adjust the progress of the interview. The interview implementation unit can also use the emotion estimation function to optimize the progress of the interview based on the emotional state of the interviewee. This allows for more effective interviews by optimizing the progress of the interview.

[0072] The shooting unit can analyze the movements or facial expressions of the subject and automatically determine the best moment to take a photo. For example, the generation AI in the shooting unit can analyze the movements and facial expressions of the subject in real time and automatically determine the best moment to take a photo. For example, capturing the moment of a smile or an important gesture. The shooting unit can also analyze the movements and facial expressions of the subject and determine the best moment to take a photo. The generation AI in the shooting unit can also analyze the movements and facial expressions in real time and determine the best moment to take a photo. This allows for better photos to be taken by automatically determining the best moment to take a photo.

[0073] The shooting unit can adjust the amount of light or color temperature of the shooting environment in real time to take the optimal photo. For example, the generation AI of the shooting unit analyzes the amount of light and color temperature of the shooting environment in real time and automatically adjusts to the optimal settings. For example, it changes the camera settings to suit the indoor lighting conditions. The shooting unit can also adjust the amount of light and color temperature of the shooting environment in real time to take the optimal photo. The generation AI of the shooting unit can also adjust the shooting settings based on the amount of light and color temperature. This allows the optimal photo to be taken by adjusting the amount of light and color temperature in real time.

[0074] The photographing unit can use the emotion estimation function to analyze the emotional state of the photographed subject and automatically generate instructions to relax the subject. The photographing unit, for example, uses the emotion estimation function to analyze the emotional state of the photographed subject and automatically generate instructions to relax the subject. For example, the photographing unit can suggest poses or facial expressions to relax a tense subject. The photographing unit can also use the emotion estimation function to analyze the emotional state of the photographed subject and generate instructions to relax the subject. The photographing unit can also use the emotion estimation function to generate instructions based on the emotional state of the photographed subject. This allows the photographed subject to relax, resulting in a more natural-looking photo.

[0075] The photography unit can operate a drone and automatically take aerial or wide-angle shots. For example, the generation AI can operate the drone to automatically take aerial or wide-angle shots. For example, taking aerial shots of a panoramic view of an in-house event. The photography unit can also operate a drone with the generation AI to take aerial or wide-angle shots. The photography unit can also use the generation AI to take photos based on the drone. This makes it possible to use a drone to take photos from perspectives that cannot be captured with a normal camera.

[0076] The photography unit can edit the photos taken in real time, making them instantly usable. For example, the photography unit can edit photos taken by the generation AI in real time, making them instantly usable. For example, it can automatically perform color correction and cropping. The photography unit can also edit the photos taken by the generation AI in real time, making them instantly usable. The photography unit can also edit based on the photos taken by the generation AI. This allows for the efficiency of article creation to be improved by instantly editing the photos taken.

[0077] The photographing unit can use the emotion estimation function to monitor the emotional state of the person being photographed in real time and dynamically adjust the optimal photographing timing. The photographing unit, for example, can use the emotion estimation function to monitor the emotional state of the person being photographed in real time and dynamically adjust the optimal photographing timing. For example, to capture a moment when the person is relaxed. The photographing unit can also use the emotion estimation function to monitor the emotional state of the person being photographed in real time and adjust the photographing timing. The photographing unit can also use the emotion estimation function to adjust the photographing timing based on the emotional state of the person being photographed. This allows for dynamic adjustment of the optimal photographing timing to take better photos.

[0078] The article creation unit can analyze past article data and automatically suggest the optimal article structure or writing style. In the article creation unit, for example, a generation AI analyzes past article data and automatically suggests the optimal article structure and writing style. For example, a new article is created based on the patterns of past successful articles. The article creation unit can also analyze past article data and suggest an article structure and writing style. The article creation unit can also have a generation AI analyze past article data and suggest an article structure and writing style based on past article data. This improves the quality of articles by suggesting the optimal article structure and writing style based on past article data.

[0079] The article creation unit can proofread the content of the article in real time and immediately correct any typos or grammatical errors. For example, the article creation unit allows the generation AI to proofread the content of the article in real time and immediately correct any typos or grammatical errors. For example, proofreading is performed automatically while the article is being created. The article creation unit can also allow the generation AI to proofread the content of the article in real time and correct any typos or grammatical errors. The article creation unit can also allow the generation AI to proofread based on the content of the article. In this way, the quality of the article is improved by proofreading the content of the article in real time.

[0080] The article creation unit can use the emotion estimation function to analyze the emotional impact that the content of the article has on the reader and automatically adjust the optimal expression. The article creation unit, for example, uses the emotion estimation function to analyze the emotional impact that the content of the article has on the reader and automatically adjust the optimal expression. For example, changing the expression to one that elicits positive emotions. The article creation unit can also use the emotion estimation function to analyze the emotional impact that the content of the article has on the reader and adjust the expression. The article creation unit can also use the emotion estimation function to adjust the expression based on the content of the article. In this way, the effectiveness of the article is improved by analyzing the emotional impact that the content of the article has on the reader and adjusting the optimal expression.

[0081] The article creation department can automatically translate articles into multiple languages ​​to accommodate international readers. For example, the generation AI can automatically translate articles into multiple languages ​​to accommodate international readers. For example, translating an English article into Japanese or French. The article creation department can also automatically translate articles into multiple languages ​​to accommodate other languages. The article creation department can also use the generation AI to support multiple languages ​​based on the article. This allows articles to be translated into multiple languages ​​to accommodate international readers.

[0082] The article creation unit can convert the content of the article into a visual note or infographic to make it easier to understand visually. For example, the article creation unit can have a generation AI convert the content of the article into a visual note or infographic to make it easier to understand visually. For example, the main points of the article can be shown in diagrams or graphs. The article creation unit can also have a generation AI convert the content of the article into a visual note or infographic to make it easier to understand visually. The article creation unit can also have a generation AI create a visual note or infographic based on the content of the article. This makes the content of the article easier to understand visually, thereby deepening the reader's understanding.

[0083] The article creation unit can use the emotion estimation function to monitor the emotional impact of the article content on the reader in real time and dynamically adjust the optimal expression. The article creation unit, for example, uses the emotion estimation function to monitor the emotional impact of the article content on the reader in real time and dynamically adjust the optimal expression. For example, the article creation unit changes the tone of the article according to the reader's emotional reaction. The article creation unit can also use the emotion estimation function to monitor the emotional impact of the article content on the reader in real time and adjust the expression. The article creation unit can also use the emotion estimation function to dynamically adjust the expression based on the article content. In this way, the effectiveness of the article is improved by monitoring the emotional impact on the reader in real time and dynamically adjusting the optimal expression.

[0084] The checking unit can evaluate the content of an article from multiple perspectives and suggest revisions to eliminate bias or prejudice. For example, the checking unit can have the generation AI evaluate the content of an article from multiple perspectives and suggest revisions to eliminate bias or prejudice. For example, the checking unit can adjust the content of the article so that it is not biased towards a particular perspective. The checking unit can also have the generation AI evaluate the content of an article from multiple perspectives and suggest revisions. The checking unit can also have the generation AI suggest revisions to eliminate bias or prejudice based on the content of the article. This makes it possible to provide fair and reliable articles by eliminating bias and prejudice.

[0085] The checking unit can check the content of articles from a legal perspective and make corrections to prevent compliance violations. For example, the generation AI can check the content of articles from a legal perspective and make corrections to prevent compliance violations. For example, the generation AI can automatically detect issues related to copyright or privacy and suggest corrections. The checking unit can also have the generation AI check the content of articles from a legal perspective and make corrections. The checking unit can also have the generation AI make corrections based on the content of articles to prevent compliance violations. In this way, compliance violations can be prevented by checking from a legal perspective.

[0086] The checking unit can use the emotion estimation function to analyze the emotional impact that the content of an article has on a reader and make corrections to eliminate any negative impact. The checking unit, for example, uses the emotion estimation function to analyze the emotional impact that the content of an article has on a reader and make corrections to eliminate any negative impact. For example, it corrects expressions that make readers feel uncomfortable. The checking unit can also use the emotion estimation function to analyze the emotional impact that the content of an article has on a reader and make corrections. The checking unit can also use the emotion estimation function to make corrections to eliminate any negative impact based on the content of the article. This can eliminate any negative impact and provide better articles to readers.

[0087] The checking department can automatically request that the content of an article be reviewed by experts in different industries and obtain multifaceted feedback. For example, the generation AI can automatically request that the content of an article be reviewed by experts in different industries and obtain multifaceted feedback. For example, opinions can be collected from experts in technical, legal, and marketing fields. The generation AI can also request that the content of an article be reviewed by experts in different industries and obtain feedback. The generation AI can also request a review based on the content of the article and obtain feedback. This allows for multifaceted feedback to be obtained, thereby improving the quality of the article.

[0088] The checking unit can simulate the content of an article in real time and predict reactions after its release. For example, the generation AI in the checking unit can simulate the content of an article in real time and predict reactions after its release. For example, it can predict readers' emotional reactions and comment trends. The checking unit can also simulate the content of an article in real time and predict reactions. The generation AI can also predict reactions after its release based on the content of the article. This allows the content of the article to be optimized in advance by predicting reactions after its release.

[0089] The checking unit uses the emotion estimation function to monitor the emotional impact of the article content on readers in real time and dynamically make optimal revisions. The checking unit, for example, uses the emotion estimation function to monitor the emotional impact of the article content on readers in real time and dynamically make optimal revisions. For example, the content of the article is adjusted according to the reader's emotional reaction. The checking unit can also use the emotion estimation function to monitor the emotional impact of the article content on readers in real time and make revisions. The checking unit can also use the emotion estimation function to dynamically make revisions based on the article content. In this way, the effectiveness of the article is improved by monitoring the emotional impact on readers in real time and dynamically making optimal revisions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The interview subject selection department can select interview subjects cross-functionally to promote collaboration between different departments within the company. For example, the generation AI selects interview subjects cross-functionally to promote collaboration between different departments within the company. For example, a project being jointly carried out by the technology department and the marketing department is selected as an interview subject. The interview subject selection department can also select interview subjects so that the generation AI can promote collaboration between different departments within the company. The interview subject selection department can also select interview subjects so that the generation AI can promote collaboration between different departments. This strengthens internal collaboration by promoting collaboration between different departments.

[0092] The reporting department can automatically generate more in-depth questions based on past response data during an interview. For example, the generation AI can refer to past response data during an interview and automatically generate more in-depth questions. For example, asking more detailed questions about a topic that was touched on in the previous interview. The reporting department can also have the generation AI generate questions based on past response data during an interview. The reporting department can also have the generation AI analyze past response data and generate more in-depth questions. In this way, more detailed information can be collected by generating more in-depth questions based on past response data.

[0093] The interview implementation department can perform sentiment analysis of answers in real time during the interview and generate follow-up questions according to the sentiment. For example, the generation AI can perform sentiment analysis of answers in real time during the interview and generate follow-up questions according to the sentiment. For example, more detailed questions can be asked for answers with a strong positive sentiment. The interview implementation department can also have the generation AI perform sentiment analysis during the interview and generate follow-up questions. The interview implementation department can also have the generation AI perform sentiment analysis in real time during the interview and generate questions according to the sentiment. This improves the quality of the interview by generating follow-up questions according to the sentiment.

[0094] The interview implementation unit can translate the interview audio data in real time, enabling multilingual interviews. For example, the generation AI can translate the interview audio data in real time, enabling multilingual interviews. For example, an interview conducted in English can be translated into Japanese and instantly displayed. The interview implementation unit can also have the generation AI translate the interview audio data in real time, enabling it to be in other languages. The interview implementation unit can also have the generation AI translate the interview audio data in real time, enabling multilingual interviews based on the audio data. This allows multilingual interviews to be conducted internationally.

[0095] The interview implementation unit can use the emotion estimation function to analyze the emotional state of the interviewee and automatically generate a dialogue to relax them. For example, the emotion estimation function can be used to analyze the emotional state of the interviewee and automatically generate a dialogue to relax them. For example, the emotion estimation function can be used to provide a light topic to relax a nervous interviewee. The interview implementation unit can also use the emotion estimation function to analyze the emotional state of the interviewee and generate a dialogue to relax them. The interview implementation unit can also use the emotion estimation function to generate a dialogue based on the emotional state of the interviewee. This can relax the interviewee and elicit more natural answers.

[0096] The photography unit can operate a drone and automatically take aerial or wide-angle shots. For example, the generation AI can operate a drone and automatically take aerial or wide-angle shots. For example, taking aerial shots of a panoramic view of an in-house event. The photography unit can also operate a drone with the generation AI and take aerial or wide-angle shots. The photography unit can also use the generation AI to take photos based on the drone. This makes it possible to use a drone to take photos from perspectives that cannot be captured with a normal camera.

[0097] The photographing unit can use the emotion estimation function to analyze the emotional state of the person being photographed and automatically generate instructions to relax them. For example, the emotion estimation function can be used to analyze the emotional state of the person being photographed and automatically generate instructions to relax them. For example, the emotion estimation function can be used to suggest poses or facial expressions to relax a nervous person. The photographing unit can also use the emotion estimation function to analyze the emotional state of the person being photographed and generate instructions to relax them. The photographing unit can also use the emotion estimation function to generate instructions based on the emotional state of the person being photographed. This can relax the person being photographed, allowing for more natural-looking photos to be taken.

[0098] The shooting unit can adjust the light intensity or color temperature of the shooting environment in real time to take the optimal photo. For example, the generation AI analyzes the light intensity and color temperature of the shooting environment in real time and automatically adjusts to the optimal settings. For example, it changes the camera settings to suit the indoor lighting conditions. The shooting unit can also adjust the light intensity and color temperature of the shooting environment in real time to take the optimal photo. The shooting unit can also adjust the shooting settings based on the light intensity and color temperature. This allows the optimal photo to be taken by adjusting the light intensity and color temperature in real time.

[0099] The article creation unit can use the emotion estimation function to analyze the emotional impact that the content of the article has on the reader and automatically adjust the optimal expression. For example, the emotion estimation function can be used to analyze the emotional impact that the content of the article has on the reader and automatically adjust the optimal expression. For example, the expression can be changed to one that elicits positive emotions. The article creation unit can also use the emotion estimation function to analyze the emotional impact that the content of the article has on the reader and adjust the expression. The article creation unit can also use the emotion estimation function to adjust the expression based on the content of the article. In this way, the effectiveness of the article can be improved by analyzing the emotional impact that the content of the article has on the reader and adjusting the optimal expression.

[0100] The article creation department can convert the content of an article into a visual note or infographic to make it easier to understand visually. For example, the generation AI can convert the content of an article into a visual note or infographic to make it easier to understand visually. For example, the main points of the article can be shown in diagrams or graphs. The article creation department can also convert the content of an article into a visual note or infographic to make it easier to understand visually. The article creation department can also have the generation AI create a visual note or infographic based on the content of the article. This makes the content of the article easier to understand visually, thereby deepening the reader's understanding.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The interviewee selection unit selects interviewees. For example, the generation AI analyzes the contents of internal company databases and past company newsletters to identify noteworthy interviewees. The generation AI can also select interviewees by analyzing project progress and employee activities. Furthermore, the generation AI can select interviewees based on prompts that include instructions for selecting interviewees. Step 2: The interview implementation department conducts interviews with the interviewees selected by the interviewee selection department. For example, the generation AI conducts interviews with the selected interviewees, generates questions, and records the interviewees' answers. The generation AI can also ask questions such as, "Tell me about your ongoing projects," and record the interviewees' answers as text data. Furthermore, the generation AI can also conduct interviews in a dialogue format and automatically record the questions and answers. Step 3: The photography department takes photos based on the content of the interviews conducted by the interview implementation department. For example, the generation AI operates the camera to photograph the people being interviewed or the project site, and releases the shutter at the appropriate time. The generation AI can also automatically take photos of the interview subject and use them in the article. Furthermore, the generation AI can also take photos of the subject at the appropriate time. Step 4: The article creation department creates an article based on the information obtained by the interview execution department and the photography department. For example, the generation AI creates an article based on the interview content and the photos taken, organizes the interview content, and summarizes it in an easy-to-read format. The generation AI can also automatically adjust the article's structure and writing style to generate a highly complete article. Furthermore, the generation AI can automatically organize the content of the article and summarize it into an article. Step 5: The Checking Department checks the articles created by the Article Creation Department before they are released. For example, the Generation AI checks the completed article before release, checking for typos and consistency of content. The Generation AI can also make corrections as necessary to maintain the quality of the article. Furthermore, the Generation AI can automatically check and correct the content of the article.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 reporting location selection department that selects reporting locations; a reporting implementation unit that conducts interviews with the reporting targets selected by the reporting target selection unit; a photography unit that takes photographs based on the content of the interview conducted by the interview implementation unit; an article writing unit that writes an article based on the information obtained by the reporting unit and the photography unit; a check unit that checks the article created by the article creation unit before release. A system characterized by:

2. The interview destination selection unit Analyze internal social media or chat logs to identify interviewees from unofficial sources 2. The system of claim 1.

3. The interview destination selection unit Analyze external industry news or trend information and propose new coverage topics by linking them to internal sources.

2. The system of claim 1.

4. The reporting department During interviews, more in-depth questions are automatically generated based on past response data.

2. The system of claim 1.

5. The imaging unit is Analyzes the subject's movements and facial expressions to automatically determine the best moment to take a photo.

2. The system of claim 1.

6. The article creation unit Analyzes past article data and automatically suggests optimal article structure or writing style 2. The system of claim 1.

7. The checking unit Evaluate the content of an article from multiple perspectives and suggest revisions to eliminate bias or prejudice 2. The system of claim 1.

8. The interview destination selection unit Analyze employees' emotional state and prioritize interviewees who have positive emotions 2. The system of claim 1.

Images