system

Abstract

The system according to the embodiment aims to improve the efficiency of creating and publishing job advertisements and reduce costs. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a video generation unit, and a provision unit. The reception unit inputs job information. The generation unit analyzes the information input by the reception unit and generates the content of a job advertisement. The video generation unit creates a job advertisement video based on the advertisement copy generated by the generation unit. The provision unit publishes the job advertisement video created by the video generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of taking a lot of time and money to create and publish job advertisements.

[0005] The system according to the embodiment aims to improve the efficiency of creating and publishing job advertisements and reduce costs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a video generation unit, and a provision unit. The reception unit inputs job information. The generation unit analyzes the information input by the reception unit and generates the content of a job advertisement. The video generation unit creates a job advertisement video based on the advertisement copy generated by the generation unit. The provision unit publishes the job advertisement video created by the video generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of creating and publishing job advertisements and reduce costs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automatic job advertisement creation system according to an embodiment of the present invention uses a generation AI and a video generation AI to automatically create optimal job advertisements. This automatic job advertisement creation system inputs job information, analyzes the input job information, and generates optimal job advertisement content. The video generation AI creates a job advertisement video based on the generated ad copy. Finally, the generated job advertisement video is reviewed, modified as necessary, and published. This system automates the creation of job advertisements and reduces costs. For example, a user inputs job information, such as the job type, required skills, work location, and salary. This information is then input into the generation AI. The generation AI then analyzes the input job information and generates optimal job advertisement content. The generation AI generates effective ad copy based on past job advertisement data and market trends. For example, the ad copy may include a catchy slogan that is likely to attract job seekers' attention and a description of an attractive work environment. The video generation AI then creates a job advertisement video based on the generated ad copy. The video generation AI then generates a visually appealing video based on the generated ad copy. For example, the video generation AI generates a video that includes workplace scenes and interviews with employees. Finally, the generated job advertisement video is checked and any necessary corrections are made. Once corrections are complete, the job advertisement is published. This system automates the creation of job advertisements, thereby reducing costs. As a result, the automatic job advertisement creation system automates the creation of job advertisements, thereby reducing costs.

[0029] According to an embodiment, the automatic recruitment advertisement creation system includes a reception unit, a generation unit, a video generation unit, and a provision unit. The reception unit receives recruitment information input by a user. The recruitment information input by the user includes, but is not limited to, the type of job offered, required skills, work location, and salary. The reception unit receives input from the user, for example, via a web form. The reception unit can also support multiple input methods, such as voice input and text input. The generation unit uses a generation AI to analyze the recruitment information input by the reception unit and generate optimal recruitment advertisement content. The generation unit generates effective advertising copy, for example, based on past recruitment advertisement data and market trends. For example, the generation AI receives a prompt such as, "Please generate optimal advertising copy based on this recruitment information," and generates the advertising copy. The generation unit uses the generation AI to generate advertising copy that includes a catchy slogan that is likely to attract job seekers' attention and a description of an attractive work environment. The video generation unit creates a recruitment advertisement video based on the advertising copy generated by the generation unit. The video generation unit generates a visually appealing video based on, for example, the generated advertising copy. The video generation unit generates a video that includes, for example, workplace scenes and interviews with working employees. The video generation unit uses a generation AI to generate a video scenario based on the advertising copy and creates a video based on the scenario. The provision unit checks the job advertisement video created by the video generation unit and makes corrections as necessary. The provision unit, for example, plays the generated video and checks its content. If there is a problem with the content of the video, the provision unit can make corrections. The provision unit publishes the job advertisement video after corrections have been completed. The provision unit, for example, publishes the job advertisement video on a website or social media platform. As a result, the automatic job advertisement creation system according to the embodiment can automate the creation of job advertisements and reduce costs.

[0030] The generation unit can analyze past job advertisement data or market trends to generate effective advertising copy. The generation unit, for example, analyzes past job advertisement data to generate effective advertising copy. For example, the generation unit collects text data from past job advertisements and uses text analysis technology to extract patterns for effective advertising copy. The generation unit can also analyze market trends to generate effective advertising copy. For example, the generation unit collects market data such as industry growth rates and job seeker trends and generates effective advertising copy using data mining technology. This allows the generation unit to generate effective advertising copy based on past data and market trends. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past job advertisement data and market trends into the generation AI and cause the generation AI to generate effective advertising copy.

[0031] The video generation unit can generate a visually excellent video based on the generated advertising copy. The video generation unit, for example, generates a visually appealing video based on the generated advertising copy. For example, the video generation unit generates a video including workplace scenes and interviews with working employees based on the generated advertising copy. The video generation unit can also generate a video using visually excellent designs and effects based on the generated advertising copy. For example, the video generation unit generates a video using visually impactful images and animations based on the generated advertising copy. This allows the video generation unit to generate a visually appealing video. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the generated advertising copy to a generation AI and cause the generation AI to generate a visually excellent video.

[0032] The video generation unit can generate videos including workplace scenes or interviews with working employees. The video generation unit generates, for example, videos including workplace scenes. For example, the video generation unit captures workplace scenes such as the interior of the office or the work environment and incorporates them into the video. The video generation unit can also generate videos including interviews with working employees. For example, the video generation unit captures interviews with employees and incorporates the employees' voices and facial expressions into the video. This allows the video generation unit to generate videos including workplace scenes and employee interviews. Some or all of the above-described processing in the video generation unit may be performed using, or without, a generation AI. For example, the video generation unit can input video data of workplace scenes or employee interviews into the generation AI and have the generation AI generate the video.

[0033] The providing unit can check the generated video of the job advertisement and make corrections as necessary. The providing unit, for example, plays the generated video of the job advertisement and checks the content. For example, the providing unit checks the content while playing the video and makes corrections if there are any problems. The providing unit can also correct text and video using a video editing function. For example, the providing unit can correct text in the video or cut out part of the video. This allows the providing unit to check the generated video and make corrections as necessary. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the generated video into a generation AI and have the generation AI perform corrections to the video.

[0034] The providing unit can publish the job advertisement video after the revisions are complete. The providing unit, for example, publishes the job advertisement video after the revisions are complete on a website or social media platform. For example, the providing unit can post the job advertisement video on the company's official website or on social media accounts. The providing unit can also post the job advertisement video on a job information site. For example, the providing unit uploads the job advertisement video to a job information site and makes it available to job seekers. This allows the providing unit to publish the job advertisement video after the revisions are complete. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the job advertisement video after the revisions are complete into the generation AI and cause the generation AI to publish the video.

[0035] The reception unit can analyze the user's past job posting input history and select an appropriate input method. The reception unit, for example, analyzes the user's past job posting input history and selects the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically complete information previously input by the user. Furthermore, the reception unit can also suggest an optimal input order based on the user's past input history. This allows the reception unit to select the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0036] The reception unit can filter job information based on the user's current industry trends or the company's needs when inputting the job information. For example, the reception unit can automatically suggest required skills and qualifications based on current industry trends. The reception unit can also filter required experience and abilities based on the company's needs. Furthermore, the reception unit can optimize the job information based on the latest market trends. This allows the reception unit to input optimal job information by filtering based on industry trends and the company's needs. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data related to industry trends and company needs into the generation AI and have the generation AI perform the filtering.

[0037] When inputting job information, the reception unit can prioritize inputting highly relevant information based on the user's geographical location information. The reception unit, for example, prioritizes inputting highly relevant information taking into account the user's geographical location information. For example, the reception unit prioritizes inputting nearby job information based on the user's current location. The reception unit can also prioritize inputting information related to commuting time and transportation based on the user's workplace. Furthermore, the reception unit can prioritize inputting region-specific job information based on the user's geographical location information. This allows the reception unit to prioritize inputting highly relevant information taking into account the geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant information.

[0038] The reception unit can analyze the user's social media activity and input related information when entering job information. The reception unit, for example, analyzes the user's social media activity and inputs related information. For example, the reception unit can input related skills and experience based on the user's social media activity. The reception unit can also input recommender information based on the user's social media network. Furthermore, the reception unit can input related job information based on the user's social media interests. This allows the reception unit to analyze social media activity and input related information. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related information.

[0039] The generation unit can adjust the level of detail of the ad copy based on the importance of the job information at the time of generation. The generation unit adjusts the level of detail of the ad copy based on, for example, the importance of the job information. For example, the generation unit generates ad copy including a detailed description for important job information. The generation unit can also generate concise ad copy for general job information. Furthermore, the generation unit can generate ad copy that focuses on the main points for urgent job information. This allows the generation unit to adjust the level of detail of the ad copy based on the importance of the job information. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input job information importance data into the generation AI and cause the generation AI to adjust the level of detail of the ad copy.

[0040] The generation unit can apply different generation algorithms depending on the category of the job information during generation. For example, the generation unit applies different generation algorithms depending on the category of the job information. For example, in the case of a job information for a technical position, the generation unit generates advertising copy that emphasizes technical skills and experience. In addition, in the case of a job information for an office worker, the generation unit can generate advertising copy that emphasizes communication skills and organizational ability. Furthermore, in the case of a job information for a sales position, the generation unit can generate advertising copy that emphasizes sales performance and customer service skills. This allows the generation unit to apply different generation algorithms depending on the category of the job information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input job information category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0041] The generation unit can determine the priority of ad copy based on the submission date of the job information at the time of generation. The generation unit determines the priority of ad copy based on, for example, the submission date of the job information. For example, the generation unit generates ad copy as a top priority for urgent job information. The generation unit can also generate ad copy later for job information that was submitted earlier. Furthermore, the generation unit can also generate ad copy preferentially for job information that is soon to be submitted. This allows the generation unit to determine the priority of ad copy based on the submission date of the job information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission date of the job information into the generation AI and have the generation AI determine the priority of ad copy.

[0042] The generation unit can adjust the order of ad copy based on the relevance of the job information during generation. The generation unit, for example, adjusts the order of ad copy based on the relevance of the job information. For example, if the relevance of the job information is high, the generation unit generates ad copy first. Also, if the relevance of the job information is low, the generation unit can generate ad copy later. Furthermore, the generation unit can adjust the order of generated ad copy according to the relevance of the job information. This allows the generation unit to adjust the order of ad copy based on the relevance of the job information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input relevance data of the job information into the generation AI and cause the generation AI to adjust the order of the ad copy.

[0043] When generating a video, the video generation unit can adjust the level of detail of the video based on the importance of the job information. The video generation unit adjusts the level of detail of the video based on, for example, the importance of the job information. For example, in the case of important job information, the video generation unit generates a video including a detailed explanation. In addition, in the case of general job information, the video generation unit can also generate a concise video. Furthermore, in the case of urgent job information, the video generation unit can also generate a video that focuses on the main points. In this way, the video generation unit can adjust the level of detail of the video based on the importance of the job information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input importance data of the job information into the generation AI and cause the generation AI to adjust the level of detail of the video.

[0044] When generating a video, the video generation unit can apply different generation algorithms depending on the category of the job information. For example, the video generation unit applies different generation algorithms depending on the category of the job information. For example, in the case of job information for a technical position, the video generation unit generates a video that emphasizes technical skills and experience. In addition, in the case of job information for an office position, the video generation unit can also generate a video that emphasizes communication skills and organizational ability. Furthermore, in the case of job information for a sales position, the video generation unit can also generate a video that emphasizes sales performance and customer service skills. This allows the video generation unit to apply different generation algorithms depending on the category of the job information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input job information category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0045] When generating videos, the video generation unit can determine the priority of videos based on the submission date of the job information. The video generation unit determines the priority of videos based on, for example, the submission date of the job information. For example, the video generation unit generates videos with the highest priority for urgent job information. The video generation unit can also generate videos later for job information that was submitted earlier. Furthermore, the video generation unit can also generate videos with priority for job information that is soon to be submitted. This allows the video generation unit to determine the priority of videos based on the submission date of the job information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input data on the submission date of job information into the generation AI and have the generation AI determine the priority of videos.

[0046] The video generation unit can adjust the order of the videos based on the relevance of the job information when generating the videos. The video generation unit adjusts the order of the videos based on, for example, the relevance of the job information. For example, if the relevance of the job information is high, the video generation unit generates the video first. Furthermore, if the relevance of the job information is low, the video generation unit can also generate the video later. Furthermore, the video generation unit can adjust the order of the videos according to the relevance of the job information. This allows the video generation unit to adjust the order of the videos based on the relevance of the job information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input relevance data of the job information into the generation AI and cause the generation AI to adjust the order of the videos.

[0047] When checking a video, the providing unit can select the optimal verification method by referring to the user's past verification history. The providing unit, for example, selects the optimal verification method by referring to the user's past verification history. For example, the providing unit preferentially suggests verification methods that the user has used in the past. The providing unit can also suggest the optimal verification procedure based on the user's past verification history. Furthermore, the providing unit can also suggest an efficient verification method based on the user's past verification history. This allows the providing unit to select the optimal verification method based on the past verification history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past verification history data into the generation AI and cause the generation AI to select the optimal verification method.

[0048] The providing unit may filter the video based on the user's current industry trends or the company's needs when reviewing the video. For example, the providing unit may provide a verification procedure that highlights important points based on current industry trends. The providing unit may also provide a procedure for verifying required skills and qualifications based on the company's needs. Furthermore, the providing unit may provide a verification procedure that optimizes job information based on the latest market trends. This allows the providing unit to provide an optimal verification procedure by filtering based on industry trends and the company's needs. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input data related to industry trends and company needs into the generation AI and have the generation AI perform the filtering.

[0049] The providing unit can prioritize checking highly relevant videos in consideration of the user's geographical location information when checking videos. For example, the providing unit prioritizes checking highly relevant videos in consideration of the user's geographical location information when checking videos. For example, the providing unit prioritizes checking videos containing nearby job information based on the user's current location. The providing unit can also prioritize checking videos containing information about commuting times and transportation methods based on the user's workplace. Furthermore, the providing unit can prioritize checking videos containing region-specific job information based on the user's geographical location information. This allows the providing unit to prioritize checking highly relevant videos in consideration of the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant videos.

[0050] When checking videos, the providing unit can analyze the user's social media activity and check related videos. The providing unit, for example, analyzes the user's social media activity and checks related videos. For example, the providing unit can check videos containing related skills and experience based on the user's social media activity. The providing unit can also check videos containing recommender information based on the user's social media network. Furthermore, the providing unit can check videos containing related job information based on the user's social media interests. This allows the providing unit to analyze social media activity and check related videos. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to select related videos.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can have an interactive chatbot function that guides the user through the entry of job information based on the user's input. For example, the reception unit can provide real-time feedback on the information entered by the user and complete necessary information. The reception unit can also support the user's entry by presenting appropriate questions when the user has difficulty entering information. Furthermore, the reception unit can record the user's input history and refer to it the next time the user enters information. This allows the reception unit to improve the user's input experience.

[0053] The generation unit can automatically generate profiles of target job seekers based on the content of the job information. For example, the generation unit creates a profile of an ideal job seeker based on the skills and experience described in the job information. The generation unit can also analyze past recruitment data and adjust the profile based on successful recruitment cases. Furthermore, the generation unit can optimize the content of job advertisements based on the generated profile. This allows the generation unit to create more effective job advertisements.

[0054] The video generation unit can create interactive video content based on the generated advertising copy. For example, the video generation unit can generate a video in which the viewer can experience different scenarios by selecting options within the video. The video generation unit can also change the content of the video in real time according to the viewer's reaction. Furthermore, the video generation unit can also have a function to measure the interest level of job seekers by having the viewer answer questions while watching the video. This allows the video generation unit to increase viewer engagement.

[0055] The video generation unit can create virtual reality (VR) content based on the generated advertising copy. For example, the video generation unit can recreate a workplace as a 3D model, allowing job seekers to virtually visit the workplace using a VR headset. The video generation unit can also enable job seekers to watch interviews with employees in a VR environment. Furthermore, the video generation unit can provide an interactive VR experience that allows job seekers to explore specific areas of the workplace and obtain detailed information. This allows the video generation unit to provide job seekers with a more realistic workplace experience.

[0056] The providing unit may have a function of collecting user feedback in real time when reviewing the generated job advertisement video. For example, the providing unit may provide an interface that allows the user to input comments while watching the video. The providing unit may also automatically list corrections to the video based on the user feedback. Furthermore, the providing unit may analyze the user feedback, identify common problems, and suggest corrections. This allows the providing unit to improve the quality of the video more quickly and efficiently.

[0057] When publishing the revised job advertisement video, the provider can distribute the video in a format optimized for each publishing platform. For example, the provider exports the video in a format suitable for social media platforms such as YouTube (registered trademark) and Facebook (registered trademark). The provider can also provide the video in a format suitable for the company's official website. Furthermore, the provider can upload the video in a format suitable for a job information site. This allows the provider to provide videos optimized for each platform.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit accepts job information entered by the user. The job information entered by the user includes, for example, the type of job being recruited, the required skills, the work location, and the salary. The reception unit accepts input from the user through a web form, and can also support multiple input methods, such as voice input and text input. Step 2: The generation unit uses the generation AI to analyze the job information entered by the reception unit and generate the optimal job advertisement content. The generation unit generates effective ad copy based on past job advertisement data and market trends. The generation AI receives the prompt, "Please generate the optimal ad copy based on this job information," and generates ad copy that includes a catchy slogan that will attract job seekers' interest and a description of an attractive work environment. Step 3: The video generation unit creates a video of the recruitment advertisement based on the advertisement copy generated by the generation unit. The video generation unit generates a visually appealing video based on the generated advertisement copy. For example, it generates a video that includes scenes from the workplace and interviews with employees, and uses a generative AI to generate a video scenario based on the advertisement copy, and then creates a video based on that scenario. Step 4: The Producer reviews the job advertisement video created by the Video Creator and makes any necessary corrections. The Producer plays the generated video and checks its content. If there are any problems with the video content, they make corrections and publish the revised job advertisement video on the website or social media platform.

[0060] (Example 2) The automatic job advertisement creation system according to an embodiment of the present invention uses a generation AI and a video generation AI to automatically create optimal job advertisements. This automatic job advertisement creation system inputs job information, analyzes the input job information, and generates optimal job advertisement content. The video generation AI creates a job advertisement video based on the generated ad copy. Finally, the generated job advertisement video is reviewed, modified as necessary, and published. This system automates the creation of job advertisements and reduces costs. For example, a user inputs job information, such as the job type, required skills, work location, and salary. This information is then input into the generation AI. The generation AI then analyzes the input job information and generates optimal job advertisement content. The generation AI generates effective ad copy based on past job advertisement data and market trends. For example, the ad copy may include a catchy slogan that is likely to attract job seekers' attention and a description of an attractive work environment. The video generation AI then creates a job advertisement video based on the generated ad copy. The video generation AI then generates a visually appealing video based on the generated ad copy. For example, the video generation AI generates a video that includes workplace scenes and interviews with employees. Finally, the generated job advertisement video is checked and any necessary corrections are made. Once corrections are complete, the job advertisement is published. This system automates the creation of job advertisements, thereby reducing costs. As a result, the automatic job advertisement creation system automates the creation of job advertisements, thereby reducing costs.

[0061] According to an embodiment, the automatic recruitment advertisement creation system includes a reception unit, a generation unit, a video generation unit, and a provision unit. The reception unit receives recruitment information input by a user. The recruitment information input by the user includes, but is not limited to, the type of job offered, required skills, work location, and salary. The reception unit receives input from the user, for example, via a web form. The reception unit can also support multiple input methods, such as voice input and text input. The generation unit uses a generation AI to analyze the recruitment information input by the reception unit and generate optimal recruitment advertisement content. The generation unit generates effective advertising copy, for example, based on past recruitment advertisement data and market trends. For example, the generation AI receives a prompt such as, "Please generate optimal advertising copy based on this recruitment information," and generates the advertising copy. The generation unit uses the generation AI to generate advertising copy that includes a catchy slogan that is likely to attract job seekers' attention and a description of an attractive work environment. The video generation unit creates a recruitment advertisement video based on the advertising copy generated by the generation unit. The video generation unit generates a visually appealing video based on, for example, the generated advertising copy. The video generation unit generates a video that includes, for example, workplace scenes and interviews with working employees. The video generation unit uses a generation AI to generate a video scenario based on the advertising copy and creates a video based on the scenario. The provision unit checks the job advertisement video created by the video generation unit and makes corrections as necessary. The provision unit, for example, plays the generated video and checks its content. If there is a problem with the content of the video, the provision unit can make corrections. The provision unit publishes the job advertisement video after corrections have been completed. The provision unit, for example, publishes the job advertisement video on a website or social media platform. As a result, the automatic job advertisement creation system according to the embodiment can automate the creation of job advertisements and reduce costs.

[0062] The generation unit can analyze past job advertisement data or market trends to generate effective advertising copy. The generation unit, for example, analyzes past job advertisement data to generate effective advertising copy. For example, the generation unit collects text data from past job advertisements and uses text analysis technology to extract patterns for effective advertising copy. The generation unit can also analyze market trends to generate effective advertising copy. For example, the generation unit collects market data such as industry growth rates and job seeker trends and generates effective advertising copy using data mining technology. This allows the generation unit to generate effective advertising copy based on past data and market trends. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past job advertisement data and market trends into the generation AI and cause the generation AI to generate effective advertising copy.

[0063] The video generation unit can generate a visually excellent video based on the generated advertising copy. The video generation unit, for example, generates a visually appealing video based on the generated advertising copy. For example, the video generation unit generates a video including workplace scenes and interviews with working employees based on the generated advertising copy. The video generation unit can also generate a video using visually excellent designs and effects based on the generated advertising copy. For example, the video generation unit generates a video using visually impactful images and animations based on the generated advertising copy. This allows the video generation unit to generate a visually appealing video. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the generated advertising copy to a generation AI and cause the generation AI to generate a visually excellent video.

[0064] The video generation unit can generate videos including workplace scenes or interviews with working employees. The video generation unit generates, for example, videos including workplace scenes. For example, the video generation unit captures workplace scenes such as the interior of the office or the work environment and incorporates them into the video. The video generation unit can also generate videos including interviews with working employees. For example, the video generation unit captures interviews with employees and incorporates the employees' voices and facial expressions into the video. This allows the video generation unit to generate videos including workplace scenes and employee interviews. Some or all of the above-described processing in the video generation unit may be performed using, or without, a generation AI. For example, the video generation unit can input video data of workplace scenes or employee interviews into the generation AI and have the generation AI generate the video.

[0065] The providing unit can check the generated video of the job advertisement and make corrections as necessary. The providing unit, for example, plays the generated video of the job advertisement and checks the content. For example, the providing unit checks the content while playing the video and makes corrections if there are any problems. The providing unit can also correct text and video using a video editing function. For example, the providing unit can correct text in the video or cut out part of the video. This allows the providing unit to check the generated video and make corrections as necessary. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the generated video into a generation AI and have the generation AI perform corrections to the video.

[0066] The providing unit can publish the job advertisement video after the revisions are complete. The providing unit, for example, publishes the job advertisement video after the revisions are complete on a website or social media platform. For example, the providing unit can post the job advertisement video on the company's official website or on social media accounts. The providing unit can also post the job advertisement video on a job information site. For example, the providing unit uploads the job advertisement video to a job information site and makes it available to job seekers. This allows the providing unit to publish the job advertisement video after the revisions are complete. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the job advertisement video after the revisions are complete into the generation AI and cause the generation AI to publish the video.

[0067] The reception unit can estimate the user's emotions and adjust the timing of inputting job information based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of inputting job information based on the estimated emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input job information during a time when the user can relax. Furthermore, if the user is concentrating, the reception unit can also prompt the user to input job information at that time. Furthermore, if the user is tired, the reception unit can also prompt the user to input job information after a break. This allows the reception unit to adjust the input timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0068] The reception unit can analyze the user's past job posting input history and select an appropriate input method. The reception unit, for example, analyzes the user's past job posting input history and selects the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically complete information previously input by the user. Furthermore, the reception unit can also suggest an optimal input order based on the user's past input history. This allows the reception unit to select the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0069] The reception unit can filter job information based on the user's current industry trends or the company's needs when inputting the job information. For example, the reception unit can automatically suggest required skills and qualifications based on current industry trends. The reception unit can also filter required experience and abilities based on the company's needs. Furthermore, the reception unit can optimize the job information based on the latest market trends. This allows the reception unit to input optimal job information by filtering based on industry trends and the company's needs. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data related to industry trends and company needs into the generation AI and have the generation AI perform the filtering.

[0070] The reception unit can estimate the user's emotions and determine the priority of job listings to be entered based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of job listings based on the estimated emotions. For example, if the user is anxious, the reception unit may prompt the user to enter important information first. The reception unit may also prompt the user to enter detailed information if the user is relaxed. Furthermore, if the user is tired, the reception unit may prompt the user to start with simple information. This allows the reception unit to determine the priority of job listings to be entered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using the generation AI, for example, or without the generation AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0071] When inputting job information, the reception unit can prioritize inputting highly relevant information based on the user's geographical location information. The reception unit, for example, prioritizes inputting highly relevant information taking into account the user's geographical location information. For example, the reception unit prioritizes inputting nearby job information based on the user's current location. The reception unit can also prioritize inputting information related to commuting time and transportation based on the user's workplace. Furthermore, the reception unit can prioritize inputting region-specific job information based on the user's geographical location information. This allows the reception unit to prioritize inputting highly relevant information taking into account the geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant information.

[0072] The reception unit can analyze the user's social media activity and input related information when entering job information. The reception unit, for example, analyzes the user's social media activity and inputs related information. For example, the reception unit can input related skills and experience based on the user's social media activity. The reception unit can also input recommender information based on the user's social media network. Furthermore, the reception unit can input related job information based on the user's social media interests. This allows the reception unit to analyze social media activity and input related information. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related information.

[0073] The generation unit can estimate a user's emotions and adjust the way the ad copy is expressed based on the estimated user emotions. For example, the generation unit can estimate a user's emotions and adjust the way the ad copy is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate ad copy using soft language. If the user is in a hurry, the generation unit can also generate ad copy that is concise and to the point. Furthermore, if the user is excited, the generation unit can generate ad copy using visually stimulating language. This allows the generation unit to adjust the way the ad copy is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the way the ad copy is expressed.

[0074] The generation unit can adjust the level of detail of the ad copy based on the importance of the job information at the time of generation. The generation unit adjusts the level of detail of the ad copy based on, for example, the importance of the job information. For example, the generation unit generates ad copy including a detailed description for important job information. The generation unit can also generate concise ad copy for general job information. Furthermore, the generation unit can generate ad copy that focuses on the main points for urgent job information. This allows the generation unit to adjust the level of detail of the ad copy based on the importance of the job information. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input job information importance data into the generation AI and cause the generation AI to adjust the level of detail of the ad copy.

[0075] The generation unit can apply different generation algorithms depending on the category of the job information during generation. For example, the generation unit applies different generation algorithms depending on the category of the job information. For example, in the case of a job information for a technical position, the generation unit generates advertising copy that emphasizes technical skills and experience. In addition, in the case of a job information for an office worker, the generation unit can generate advertising copy that emphasizes communication skills and organizational ability. Furthermore, in the case of a job information for a sales position, the generation unit can generate advertising copy that emphasizes sales performance and customer service skills. This allows the generation unit to apply different generation algorithms depending on the category of the job information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input job information category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0076] The generation unit can estimate a user's emotions and adjust the length of the ad copy based on the estimated user emotions. For example, the generation unit can estimate a user's emotions and adjust the length of the ad copy based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point ad copy. Furthermore, if the user is relaxed, the generation unit can generate longer ad copy with detailed explanations. Furthermore, if the user is excited, the generation unit can generate ad copy using visually stimulating expressions. This allows the generation unit to adjust the length of the ad copy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the length of the ad copy.

[0077] The generation unit can determine the priority of ad copy based on the submission date of the job information at the time of generation. The generation unit determines the priority of ad copy based on, for example, the submission date of the job information. For example, the generation unit generates ad copy as a top priority for urgent job information. The generation unit can also generate ad copy later for job information that was submitted earlier. Furthermore, the generation unit can also generate ad copy preferentially for job information that is soon to be submitted. This allows the generation unit to determine the priority of ad copy based on the submission date of the job information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission date of the job information into the generation AI and have the generation AI determine the priority of ad copy.

[0078] The generation unit can adjust the order of ad copy based on the relevance of the job information during generation. The generation unit, for example, adjusts the order of ad copy based on the relevance of the job information. For example, if the relevance of the job information is high, the generation unit generates ad copy first. Also, if the relevance of the job information is low, the generation unit can generate ad copy later. Furthermore, the generation unit can adjust the order of generated ad copy according to the relevance of the job information. This allows the generation unit to adjust the order of ad copy based on the relevance of the job information. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input relevance data of the job information into the generation AI and cause the generation AI to adjust the order of the ad copy.

[0079] The video generation unit can estimate the user's emotions and adjust the video presentation method based on the estimated user emotions. For example, the video generation unit can estimate the user's emotions and adjust the video presentation method based on the estimated emotions. For example, if the user is relaxed, the video generation unit can generate a video using soft colors and music. If the user is in a hurry, the video generation unit can also generate a concise and to-the-point video. Furthermore, if the user is excited, the video generation unit can also generate a video with visually stimulating effects. This allows the video generation unit to adjust the video presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the video generation unit can be performed using, for example, the generation AI. For example, the video generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the video presentation method.

[0080] When generating a video, the video generation unit can adjust the level of detail of the video based on the importance of the job information. The video generation unit adjusts the level of detail of the video based on, for example, the importance of the job information. For example, in the case of important job information, the video generation unit generates a video including a detailed explanation. In addition, in the case of general job information, the video generation unit can also generate a concise video. Furthermore, in the case of urgent job information, the video generation unit can also generate a video that focuses on the main points. In this way, the video generation unit can adjust the level of detail of the video based on the importance of the job information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input importance data of the job information into the generation AI and cause the generation AI to adjust the level of detail of the video.

[0081] When generating a video, the video generation unit can apply different generation algorithms depending on the category of the job information. For example, the video generation unit applies different generation algorithms depending on the category of the job information. For example, in the case of job information for a technical position, the video generation unit generates a video that emphasizes technical skills and experience. In addition, in the case of job information for an office position, the video generation unit can also generate a video that emphasizes communication skills and organizational ability. Furthermore, in the case of job information for a sales position, the video generation unit can also generate a video that emphasizes sales performance and customer service skills. This allows the video generation unit to apply different generation algorithms depending on the category of the job information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input job information category data into the generation AI and cause the generation AI to apply different generation algorithms.

[0082] The video generation unit can estimate the user's emotion and adjust the length of the video based on the estimated user's emotion. The video generation unit, for example, estimates the user's emotion and adjusts the length of the video based on the estimated emotion. For example, if the user is in a hurry, the video generation unit can generate a short, to-the-point video. Furthermore, if the user is relaxed, the video generation unit can generate a longer video with detailed explanations. Furthermore, if the user is excited, the video generation unit can generate a video with visually stimulating effects. This allows the video generation unit to adjust the length of the video according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the video generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the video generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the video.

[0083] When generating videos, the video generation unit can determine the priority of videos based on the submission date of the job information. The video generation unit determines the priority of videos based on, for example, the submission date of the job information. For example, the video generation unit generates videos with the highest priority for urgent job information. The video generation unit can also generate videos later for job information that was submitted earlier. Furthermore, the video generation unit can also generate videos with priority for job information that is soon to be submitted. This allows the video generation unit to determine the priority of videos based on the submission date of the job information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input data on the submission date of job information into the generation AI and have the generation AI determine the priority of videos.

[0084] The video generation unit can adjust the order of the videos based on the relevance of the job information when generating the videos. The video generation unit adjusts the order of the videos based on, for example, the relevance of the job information. For example, if the relevance of the job information is high, the video generation unit generates the video first. Furthermore, if the relevance of the job information is low, the video generation unit can also generate the video later. Furthermore, the video generation unit can adjust the order of the videos according to the relevance of the job information. This allows the video generation unit to adjust the order of the videos based on the relevance of the job information. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input relevance data of the job information into the generation AI and cause the generation AI to adjust the order of the videos.

[0085] The providing unit can estimate the user's emotions and adjust the video viewing method based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the video viewing method based on the estimated emotions. For example, the providing unit can provide detailed viewing instructions when the user is relaxed. The providing unit can also provide concise viewing instructions when the user is in a hurry. Furthermore, the providing unit can provide visually stimulating viewing instructions when the user is excited. This allows the providing unit to adjust the video viewing method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the video viewing method.

[0086] When checking a video, the providing unit can select the optimal verification method by referring to the user's past verification history. The providing unit, for example, selects the optimal verification method by referring to the user's past verification history. For example, the providing unit preferentially suggests verification methods that the user has used in the past. The providing unit can also suggest the optimal verification procedure based on the user's past verification history. Furthermore, the providing unit can also suggest an efficient verification method based on the user's past verification history. This allows the providing unit to select the optimal verification method based on the past verification history. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past verification history data into the generation AI and cause the generation AI to select the optimal verification method.

[0087] The providing unit may filter the video based on the user's current industry trends or the company's needs when reviewing the video. For example, the providing unit may provide a verification procedure that highlights important points based on current industry trends. The providing unit may also provide a procedure for verifying required skills and qualifications based on the company's needs. Furthermore, the providing unit may provide a verification procedure that optimizes job information based on the latest market trends. This allows the providing unit to provide an optimal verification procedure by filtering based on industry trends and the company's needs. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input data related to industry trends and company needs into the generation AI and have the generation AI perform the filtering.

[0088] The providing unit can estimate the user's emotions and determine the priority of video revisions based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of video revisions based on the estimated emotions. For example, if the user is in a hurry, the providing unit can prioritize important revisions. Furthermore, if the user is relaxed, the providing unit can also perform detailed revisions. Furthermore, if the user is excited, the providing unit can also perform visually stimulating revisions. This allows the providing unit to determine the priority of video revisions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of video revisions.

[0089] The providing unit can prioritize checking highly relevant videos in consideration of the user's geographical location information when checking videos. For example, the providing unit prioritizes checking highly relevant videos in consideration of the user's geographical location information when checking videos. For example, the providing unit prioritizes checking videos containing nearby job information based on the user's current location. The providing unit can also prioritize checking videos containing information about commuting times and transportation methods based on the user's workplace. Furthermore, the providing unit can prioritize checking videos containing region-specific job information based on the user's geographical location information. This allows the providing unit to prioritize checking highly relevant videos in consideration of the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant videos.

[0090] When checking videos, the providing unit can analyze the user's social media activity and check related videos. The providing unit, for example, analyzes the user's social media activity and checks related videos. For example, the providing unit can check videos containing related skills and experience based on the user's social media activity. The providing unit can also check videos containing recommender information based on the user's social media network. Furthermore, the providing unit can check videos containing related job information based on the user's social media interests. This allows the providing unit to analyze social media activity and check related videos. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to select related videos. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, video generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives job information from a user using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the job information using a generation AI to generate optimal advertising copy. The video generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a video of the job advertisement based on the generated advertising copy. The provision unit checks the generated video using the output device 40 of the smart device 14, makes corrections as necessary, and publishes the job advertisement. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, video generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives job information from a user using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the job information using a generation AI to generate optimal advertising copy. The video generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a video of the job advertisement based on the generated advertising copy. The provision unit checks the generated video using the speaker 240 of the smart glasses 214, makes corrections as necessary, and publishes the job advertisement. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, video generation unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit receives job information from a user using the microphone 238 of the headset terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the job information using a generation AI to generate optimal advertising copy. The video generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a video of the job advertisement based on the generated advertising copy. The provision unit checks the generated video using the display 343 of the headset terminal 314, makes corrections as necessary, and publishes the job advertisement. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, video generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives job information from a user using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the job information using a generation AI to generate optimal advertising copy. The video generation unit is realized by the specific processing unit 290 of the data processing device 12, and creates a video of the job advertisement based on the generated advertising copy. The provision unit checks the video generated using the speaker 240 of the robot 414, makes corrections as necessary, and publishes the job advertisement.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The reception unit can have an interactive chatbot function that guides the user through the entry of job information based on the user's input. For example, the reception unit can provide real-time feedback on the information entered by the user and complete necessary information. The reception unit can also support the user's entry by presenting appropriate questions when the user has difficulty entering information. Furthermore, the reception unit can record the user's input history and refer to it the next time the user enters information. This allows the reception unit to improve the user's input experience.

[0093] The generation unit can automatically generate profiles of target job seekers based on the content of the job information. For example, the generation unit creates a profile of an ideal job seeker based on the skills and experience described in the job information. The generation unit can also analyze past recruitment data and adjust the profile based on successful recruitment cases. Furthermore, the generation unit can optimize the content of job advertisements based on the generated profile. This allows the generation unit to create more effective job advertisements.

[0094] The video generation unit can create interactive video content based on the generated advertising copy. For example, the video generation unit can generate a video in which the viewer can experience different scenarios by selecting options within the video. The video generation unit can also change the content of the video in real time according to the viewer's reaction. Furthermore, the video generation unit can also have a function to measure the interest level of job seekers by having the viewer answer questions while watching the video. This allows the video generation unit to increase viewer engagement.

[0095] The video generation unit can create virtual reality (VR) content based on the generated advertising copy. For example, the video generation unit can recreate a workplace as a 3D model, allowing job seekers to virtually visit the workplace using a VR headset. The video generation unit can also enable job seekers to watch interviews with employees in a VR environment. Furthermore, the video generation unit can provide an interactive VR experience that allows job seekers to explore specific areas of the workplace and obtain detailed information. This allows the video generation unit to provide job seekers with a more realistic workplace experience.

[0096] The providing unit may have a function of collecting user feedback in real time when reviewing the generated job advertisement video. For example, the providing unit may provide an interface that allows the user to input comments while watching the video. The providing unit may also automatically list corrections to the video based on the user feedback. Furthermore, the providing unit may analyze the user feedback, identify common problems, and suggest corrections. This allows the providing unit to improve the quality of the video more quickly and efficiently.

[0097] When publishing the revised job advertisement video, the provider can distribute the video in a format optimized for each publishing platform. For example, the provider can export the video in a format suitable for social media platforms such as YouTube and Facebook. The provider can also provide the video in a format suitable for the company's official website. Furthermore, the provider can upload the video in a format suitable for a job information site. This allows the provider to provide videos optimized for each platform.

[0098] The reception unit can estimate the user's emotions and provide assistance in entering job information based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can provide a template to simplify the input. Also, if the user is relaxed, the reception unit can provide a guide to encourage detailed input. Furthermore, if the user is concentrating, the reception unit can suggest a shortcut to efficiently proceed with input. In this way, the reception unit can provide input assistance according to the user's emotions.

[0099] The generation unit can estimate the user's emotions and adjust the tone of the advertisement copy based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate advertisement copy with a friendly tone. If the user is in a hurry, the generation unit can also generate advertisement copy with a concise and direct tone. Furthermore, if the user is excited, the generation unit can also generate advertisement copy with an energetic tone. In this way, the generation unit can adjust the tone of the advertisement copy according to the user's emotions.

[0100] The video generation unit can estimate the user's emotion and adjust the audio narration of the video based on the estimated emotion. For example, the video generation unit can use a calm audio narration when the user is relaxed. The video generation unit can also use a fast-paced audio narration when the user is in a hurry. Furthermore, the video generation unit can use an energetic audio narration when the user is excited. In this way, the video generation unit can adjust the audio narration according to the user's emotion.

[0101] The providing unit can estimate the user's emotions and make video correction suggestions based on the estimated emotions. For example, when the user is relaxed, the providing unit can make detailed correction suggestions. When the user is in a hurry, the providing unit can also make concise correction suggestions. Furthermore, when the user is excited, the providing unit can also make visually stimulating correction suggestions. This allows the providing unit to make appropriate correction suggestions according to the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit accepts job information entered by the user. The job information entered by the user includes, for example, the type of job being recruited, the required skills, the work location, and the salary. The reception unit accepts input from the user through a web form, and can also support multiple input methods, such as voice input and text input. Step 2: The generation unit uses the generation AI to analyze the job information entered by the reception unit and generate the optimal job advertisement content. The generation unit generates effective ad copy based on past job advertisement data and market trends. The generation AI receives the prompt, "Please generate the optimal ad copy based on this job information," and generates ad copy that includes a catchy slogan that will attract job seekers' interest and a description of an attractive work environment. Step 3: The video generation unit creates a video of the recruitment advertisement based on the advertisement copy generated by the generation unit. The video generation unit generates a visually appealing video based on the generated advertisement copy. For example, it generates a video that includes scenes from the workplace and interviews with employees, and uses a generative AI to generate a video scenario based on the advertisement copy, and then creates a video based on that scenario. Step 4: The Producer reviews the job advertisement video created by the Video Creator and makes any necessary corrections. The Producer plays the generated video and checks its content. If there are any problems with the video content, they make corrections and publish the revised job advertisement video on the website or social media platform.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where job information is entered; a generation unit that analyzes the information input by the reception unit and generates content of the job advertisement; a video generation unit that generates a video of the recruitment advertisement based on the advertisement copy generated by the generation unit; a providing unit that publishes the video of the job advertisement created by the video generating unit. A system characterized by:

2. The generation unit Analyze past job ad data or market trends to generate effective ad copy The system of claim 1 .

3. The video generation unit Generate visually appealing videos based on generated ad copy The system of claim 1 .

4. The video generation unit Generate videos that include workplace scenes or interviews with employees The system of claim 1 .

5. The providing unit Review the generated job ad video and make any necessary adjustments The system of claim 1 .

6. The providing unit Publish the revised job ad video The system of claim 1 .

7. The reception unit Estimate user emotions and adjust the timing of job postings based on the estimated user emotions The system of claim 1 .

8. The reception unit Analyze the user's past job information entry history and select the appropriate entry method The system of claim 1 .

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