system

Abstract

The system according to this embodiment aims to expedite the project approval process. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a sharing unit, and a collection unit. The reception unit receives recorded presentation videos from the project owner. The generation unit analyzes the videos received by the reception unit and organizes the criteria for obtaining approval from each relevant department. The sharing unit shares the criteria organized by the generation unit on the cloud and clarifies the approval / rejection points for each relevant department. The collection unit collects the approval / rejection results from each relevant department in real time and provides them to the project owner.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to align the schedules of all relevant parties in the project approval process and quick decision-making cannot be achieved.

[0005] The system according to the embodiment aims to speed up the project approval process.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a sharing unit, and a collection unit. The reception unit receives a presentation video recorded by the project owner. The generation unit analyzes the video received by the reception unit and organizes the criteria for obtaining approval from each relevant department. The sharing unit shares the criteria organized by the generation unit on the cloud and clarifies the approval / rejection points for each relevant department. The collection unit collects the approval / rejection results from each relevant department in real time and provides them to the project owner. [Effects of the Invention]

[0007] The system according to this embodiment can expedite the project approval process. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The decision support system according to an embodiment of the present invention is a system that utilizes generative AI to support rapid decision-making for projects. In this decision support system, the project owner pre-records a presentation video (e.g., a keynote presentation video), and based on this, the generative AI organizes criteria for obtaining implementation approval from relevant departments and shares them on the cloud. By clarifying the approval / rejection points for each relevant department and obtaining approval / rejection from the heads of each relevant department to obtain evidence, the system dramatically speeds up necessary decision-making. For example, the project owner records a presentation video explaining the project overview. This video explains in detail the project's objectives, progress, and the roles of each department. Next, this video is input into the generative AI, which analyzes the content of the video and organizes criteria for obtaining implementation approval from each relevant department. The generative AI clarifies the necessary approval points for each department and shares them on the cloud. The heads of each relevant department make approval / rejection decisions based on the criteria presented on the cloud. The generative AI collects the approval / rejection results from each department in real time and provides them to the project owner. This allows the project owner to centrally manage the approval status of each department and enables rapid decision-making. Furthermore, the generating AI pre-defines potential points of contention in the criteria and presents them to each department. This allows for proactive mediation of disagreements between departments and supports rapid decision-making. This mechanism ensures smooth project progress, enabling quick decision-making without delays in task completion deadlines. Additionally, by correctly structuring the approval process with relevant departments, the project can proceed efficiently without unnecessary delays. In this way, the decision support system can facilitate rapid decision-making in projects.

[0029] The decision support system according to this embodiment comprises a reception unit, a generation unit, a sharing unit, and a collection unit. The reception unit receives a presentation video recorded by the project owner. The presentation video recorded by the project owner may include, for example, the project's objectives, progress, and the roles of each department, but is not limited to such examples. The reception unit may, for example, upload the video recorded by the project owner to the cloud. The reception unit can also directly input the video recorded by the project owner into the system. Furthermore, the reception unit can convert the video recorded by the project owner into a format that is easy to analyze. For example, the reception unit converts the video file into an appropriate format that is easy for the generation AI to analyze. The generation unit uses the generation AI to analyze the video input by the reception unit and organize the criteria for obtaining approval from each relevant department. For example, the generation AI analyzes the content of the video and clarifies the approval points required for each department. The generation unit can also share the criteria organized by the generation AI based on the video content on the cloud. Furthermore, the generation unit can pre-define points on the criteria where doubts may arise using the generation AI and present this information to each department. For example, the generation unit can use the generation AI to analyze the video content and present technical issues or business risks in a tangible way. The sharing unit shares the criteria organized by the generation unit on the cloud, clarifying the approval / rejection points for each relevant department. For example, the sharing unit can upload the criteria to the cloud and make them accessible to the heads of each department. The sharing unit can also update the approval / rejection results for each department on the cloud in real time. In addition, the sharing unit can notify the project owner of the approval / rejection results for each department. For example, the sharing unit updates the approval / rejection results for each department on the cloud in real time, allowing the project owner to stay informed of the latest approval status. The collection unit collects the approval / rejection results for each relevant department in real time and provides them to the project owner. For example, the collection unit collects the approval / rejection results for each department on the cloud and notifies the project owner.Furthermore, the data collection unit can centrally manage the approval / rejection results from each department, enabling the project owner to make decisions quickly. In addition, the data collection unit can analyze the approval / rejection results from each department and provide feedback to the project owner. For example, the data collection unit can analyze the approval / rejection results from each department, identify delays or problems in approval, and report them to the project owner. This allows the decision support system, according to the embodiment, to quickly obtain approval from each relevant department based on videos recorded by the project owner.

[0030] The reception desk inputs presentation videos recorded by the project owner. These videos may include, but are not limited to, the project's objectives, progress, and the roles of each department. The reception desk can, for example, upload the videos recorded by the project owner to the cloud. Alternatively, the reception desk can directly input the videos recorded by the project owner into the system. Furthermore, the reception desk can convert the videos recorded by the project owner into a format that is easy to analyze. For example, the reception desk converts video files to an appropriate format that is easy for the generating AI to analyze. Specifically, the reception desk converts video files to common formats such as MP4 or AVI so that the generating AI can analyze them efficiently. It also adjusts the video's resolution and frame rate so that the generating AI can accurately extract the necessary information. In addition, the reception desk has a function to convert the audio data of the video into text, making it easier for the generating AI to analyze the audio data. For example, it uses speech recognition technology to convert the audio in the video into text data, and the generating AI performs analysis based on the text data. This allows the reception desk to efficiently input videos recorded by the project owner and convert them into a format that is easy for the generating AI to analyze.

[0031] The generation unit uses a generation AI to analyze videos submitted by the reception unit and organize criteria for obtaining approval from each relevant department. For example, the generation unit uses the generation AI to analyze the video content and clarify the approval points required for each department. Specifically, the generation AI analyzes the audio and video data within the video and extracts information such as the project's objectives, progress, and the roles of each department. The generation AI uses natural language processing technology to convert the audio data within the video into text data and extract important information. It also uses image recognition technology to analyze the video data within the video and grasp the project's progress and the roles of each department. Furthermore, based on the extracted information, the generation AI organizes the approval points required for each department and creates criteria. For example, it indicates to the technical department that technical problems need to be resolved and to the business department that business risks need to be assessed. The generation unit can also share the criteria organized by the generation AI based on the video content on the cloud. In addition, the generation unit can pre-verify points where doubts may arise in the criteria and present that information to each department. For example, the generation unit uses AI to analyze the video content and present technical issues and business risks in a verbalized form. This allows the generation unit to efficiently organize the criteria for obtaining approval from each relevant department and ensure the smooth progress of the project.

[0032] The shared department shares the criteria organized by the generation department on the cloud and clarifies the approval / rejection points for each relevant department. For example, the shared department uploads the criteria to the cloud and makes them accessible to the heads of each department. Specifically, the shared department uses a cloud storage service to upload the criteria created by the generation department and makes them accessible to the heads of each department. The shared department can also update the approval / rejection results for each department on the cloud in real time. For example, when the head of each department enters the approval / rejection results for the criteria, the shared department updates the results on the cloud in real time, allowing other departments and project owners to understand the latest status. Furthermore, the shared department can also notify the project owner of the approval / rejection results for each department. For example, the shared department updates the approval / rejection results for each department on the cloud in real time, allowing the project owner to understand the latest approval status. The shared department also has a notification function and can notify the project owner of the approval / rejection results via email or push notification. In this way, the shared department can efficiently share the criteria organized by the generation department and quickly provide the project owner with the approval / rejection results for each department.

[0033] The data collection unit collects approval / rejection results from each relevant department in real time and provides them to the project owner. For example, the data collection unit collects approval / rejection results from each department on the cloud and notifies the project owner. Specifically, the data collection unit accesses a database on the cloud to collect approval / rejection results from each department. The data collection unit can also centrally manage the approval / rejection results from each department, enabling the project owner to make quick decisions. For example, the data collection unit centrally manages the approval / rejection results from each department, allowing the project owner to grasp the overall situation at a glance. Furthermore, the data collection unit can analyze the approval / rejection results from each department and provide feedback to the project owner. For example, the data collection unit analyzes the approval / rejection results from each department, identifies delays in approval and problems, and reports them to the project owner. The data collection unit can also make suggestions to improve the progress of the project based on the feedback. In this way, the data collection unit can efficiently collect approval / rejection results from each relevant department and provide them to the project owner, thereby supporting quick decision-making.

[0034] The generation unit can pre-define points where doubts may arise on the criteria and present this information to each department. For example, the generation unit's generation AI analyzes the content of a video and presents technical issues and business risks in language. The generation unit's generation AI analyzes the content of a video and identifies points where doubts may arise for each department. The generation unit can also have the generation AI verbalize points where doubts may arise and present them to each department. Furthermore, the generation unit can pre-adjust disagreements between departments based on the points where doubts may arise. For example, the generation unit can pre-adjust disagreements by having the generation AI verbalize technical issues and present them to each department. This allows for pre-adjustment of disagreements between departments and supports rapid decision-making.

[0035] The shared system can update the approval / rejection results for each department in real time on the cloud. For example, the shared system can upload criteria to the cloud and make them accessible to the heads of each department. The shared system can also update the approval / rejection results for each department in real time on the cloud. Furthermore, the shared system can notify the project owner of the approval / rejection results for each department. For example, the shared system can update the approval / rejection results for each department in real time on the cloud, allowing the project owner to understand the latest approval status. This allows the project owner to understand the latest approval status by updating the approval / rejection results for each department in real time. Some or all of the above processes in the shared system may be performed using AI, for example, or not using AI. For example, the shared system can update the approval / rejection results using an AI model that updates the approval / rejection results for each department in real time on the cloud.

[0036] The data collection unit can notify the project owner of the approval / rejection results for each department. For example, the data collection unit can collect the approval / rejection results for each department on the cloud and notify the project owner. The data collection unit can also centrally manage the approval / rejection results for each department, enabling the project owner to make quick decisions. Furthermore, the data collection unit can analyze the approval / rejection results for each department and provide feedback to the project owner. For example, the data collection unit can analyze the approval / rejection results for each department, identify delays or problems in approval, and report them to the project owner. This allows the project owner to quickly understand the approval / rejection results for each department. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can notify the project owner of the approval / rejection results using an AI model that collects the approval / rejection results for each department on the cloud and notifies the project owner of the results.

[0037] The reception department can analyze the project owner's past video submission history and select the optimal reception method. For example, the reception department can analyze the submission times of videos previously submitted by the project owner and propose the optimal reception time. The reception department can also analyze the project owner's past submission frequency and propose the optimal reception frequency. Furthermore, the reception department can analyze the project owner's past submission methods (online, offline, etc.) and propose the optimal reception method. For example, the reception department can analyze the submission times of videos previously submitted by the project owner and propose the optimal reception time. This allows for the selection of the optimal reception method based on past history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the project owner's past video submission history into a generating AI and have the generating AI select the optimal reception method.

[0038] The reception unit can filter videos upon receipt based on project progress and importance. For example, the reception unit can prioritize receiving videos of high importance according to project progress. The reception unit can also filter and receive important videos based on project importance. Furthermore, the reception unit can filter and not receive unnecessary videos based on project progress and importance. For example, the reception unit can prioritize receiving videos of high importance according to project progress. This ensures that important videos are prioritized according to project progress and importance. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input project progress and importance into a generating AI and have the generating AI perform video filtering.

[0039] The reception desk can prioritize receiving videos that are highly relevant, taking into account the project owner's geographical location information. For example, if the project owner is nearby, the reception desk will prioritize receiving highly relevant videos. Conversely, if the project owner is far away, the reception desk can postpone receiving less relevant videos. Furthermore, the reception desk can also prioritize receiving the most suitable videos, taking into account the project owner's geographical location information. For example, the reception desk will prioritize receiving highly relevant videos, taking into account the project owner's geographical location information. This ensures that the reception desk receives the most suitable videos, taking into account the project owner's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the project owner's geographical location information into a generating AI and have the generating AI select highly relevant videos.

[0040] The reception department can analyze the project owner's social media activity when receiving videos and accept relevant videos. For example, the reception department can analyze the project owner's social media activity and prioritize accepting relevant videos. The reception department can also analyze the project owner's social media activity and postpone accepting less relevant videos. Furthermore, the reception department can analyze the project owner's social media activity and prioritize accepting the most suitable videos. For example, the reception department can analyze the project owner's social media activity and prioritize accepting relevant videos. This allows the reception department to accept relevant videos based on the project owner's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the project owner's social media activity into a generating AI and have the generating AI select relevant videos.

[0041] The generation unit can adjust the level of detail of the criteria based on the importance of the project when generating the criteria. For example, if the project is highly important, the generation unit will generate detailed criteria. Conversely, if the project is less important, the generation unit can also generate concise criteria. Furthermore, the generation unit can generate criteria with an appropriate level of detail depending on the project's importance. For example, the generation unit will generate detailed criteria based on the project's importance. This allows for adjustment of the level of detail of the criteria according to the project's importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the criteria.

[0042] The generation unit can apply different generation algorithms according to the role of each department when generating criteria. For example, the generation unit can apply the optimal generation algorithm according to the role of each department. The generation unit can also apply different generation algorithms according to the role of each department. Furthermore, the generation unit can apply an appropriate generation algorithm according to the role of each department. For example, the generation unit can apply the optimal generation algorithm according to the role of each department. This ensures that the optimal generation algorithm is applied according to the role of each department. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input role data for each department into a generation AI and have the generation AI execute the application of the generation algorithm.

[0043] The generation unit can determine the priority of criteria based on the project submission date when generating criteria. For example, if the project submission date is approaching, the generation unit will generate high-priority criteria. Conversely, if the project submission date is far off, the generation unit can also generate low-priority criteria. Furthermore, the generation unit can generate criteria with appropriate priority levels depending on the project submission date. For example, the generation unit will generate high-priority criteria based on the project submission date. This allows for adjustment of criterion priority according to the project submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project submission date data into a generation AI and have the generation AI determine the priority of criteria.

[0044] The generation unit can adjust the order of criteria based on project relevance during criterion generation. For example, if the projects are highly relevant, the generation unit will generate important criteria first. Conversely, if the projects are less relevant, the generation unit can generate less important criteria later. Furthermore, the generation unit can generate criteria in an appropriate order according to project relevance. For example, the generation unit generates important criteria first based on project relevance. This allows the order of criteria to be adjusted according to project relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project relevance data into a generation AI and have the generation AI perform the adjustment of the criterion order.

[0045] The sharing unit can select the optimal sharing method by referring to the past approval history of each department when sharing criteria. For example, the sharing unit can select the optimal sharing method by referring to the past approval history of each department. The sharing unit can also select an appropriate sharing method by referring to the past approval history of each department. Furthermore, the sharing unit can also select the optimal sharing method by referring to the past approval history of each department. For example, the sharing unit can select the optimal sharing method by referring to the past approval history of each department. This allows the optimal sharing method to be selected based on the past approval history of each department. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without using AI. For example, the sharing unit can input the past approval history data of each department into a generating AI and have the generating AI perform the selection of the optimal sharing method.

[0046] The shared section can apply different sharing algorithms depending on the role of each department when criteria are shared. For example, the shared section can apply the optimal sharing algorithm depending on the role of each department. The shared section can also apply different sharing algorithms depending on the role of each department. Furthermore, the shared section can also apply an appropriate sharing algorithm depending on the role of each department. For example, the shared section can apply the optimal sharing algorithm depending on the role of each department. This ensures that the optimal sharing algorithm is applied according to the role of each department. Some or all of the above processing in the shared section may be performed using AI, for example, or without AI. For example, the shared section can input role data for each department into a generating AI and have the generating AI execute the application of the sharing algorithm.

[0047] The sharing unit can select the optimal sharing method when criteria are shared, taking into account the geographical location information of each department. For example, the sharing unit can select the optimal sharing method by considering the geographical location information of each department. The sharing unit can also select an appropriate sharing method by considering the geographical location information of each department. Furthermore, the sharing unit can also select the optimal sharing method by considering the geographical location information of each department. For example, the sharing unit can select the optimal sharing method by considering the geographical location information of each department. This allows the optimal sharing method to be selected based on the geographical location information of each department. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without using AI. For example, the sharing unit can input geographical location data of each department into a generating AI and have the generating AI perform the selection of the optimal sharing method.

[0048] The sharing unit can analyze each department's social media activity and adjust the sharing method when criteria are shared. For example, the sharing unit can analyze each department's social media activity and select the optimal sharing method. The sharing unit can also analyze each department's social media activity and select an appropriate sharing method. Furthermore, the sharing unit can analyze each department's social media activity and select the optimal sharing method. For example, the sharing unit can analyze each department's social media activity and select the optimal sharing method. This allows the optimal sharing method to be selected based on each department's social media activity. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input each department's social media activity data into a generating AI and have the generating AI perform the adjustment of the sharing method.

[0049] The collection unit can select the optimal collection method by referring to the past approval / rejection history of each department when collecting approval / rejection results. For example, the collection unit can select the optimal collection method by referring to the past approval / rejection history of each department. The collection unit can also select an appropriate collection method by referring to the past approval / rejection history of each department. Furthermore, the collection unit can also select the optimal collection method by referring to the past approval / rejection history of each department. For example, the collection unit can select the optimal collection method by referring to the past approval / rejection history of each department. This allows the collection unit to select the optimal collection method based on the past approval / rejection history of each department. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the past approval / rejection history data of each department into a generating AI and have the generating AI select the optimal collection method.

[0050] The collection unit can apply different collection algorithms depending on the role of each department when collecting approval / rejection results. For example, the collection unit can apply the optimal collection algorithm depending on the role of each department. The collection unit can also apply different collection algorithms depending on the role of each department. Furthermore, the collection unit can also apply an appropriate collection algorithm depending on the role of each department. For example, the collection unit can apply the optimal collection algorithm depending on the role of each department. This ensures that the optimal collection algorithm is applied according to the role of each department. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the role data of each department into a generating AI and have the generating AI execute the application of the collection algorithm.

[0051] The collection unit can select the optimal collection method when collecting approval / rejection results, taking into account the geographical location information of each department. For example, the collection unit can select the optimal collection method by taking into account the geographical location information of each department. The collection unit can also select an appropriate collection method by taking into account the geographical location information of each department. Furthermore, the collection unit can also select the optimal collection method by taking into account the geographical location information of each department. For example, the collection unit can select the optimal collection method by taking into account the geographical location information of each department. This allows the collection unit to select the optimal collection method based on the geographical location information of each department. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input geographical location data of each department into a generating AI and have the generating AI select the optimal collection method.

[0052] The data collection unit can analyze the social media activities of each department and adjust the collection method when collecting approval / rejection results. For example, the data collection unit can analyze the social media activities of each department and select the optimal collection method. The data collection unit can also analyze the social media activities of each department and select an appropriate collection method. Furthermore, the data collection unit can analyze the social media activities of each department and select the optimal collection method. For example, the data collection unit can analyze the social media activities of each department and select the optimal collection method. This allows the optimal collection method to be selected based on the social media activities of each department. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data from each department into a generating AI and have the generating AI perform the adjustment of the collection method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception department can automatically summarize the content of videos recorded by project owners and send the summary to the generation department. For example, the reception department can analyze the video content, extract key points, and create a summary. The reception department can also send the summary to the generation department for reference when organizing criteria. Furthermore, the reception department can share the summary on the cloud, allowing department heads to review it. This enables project owners to efficiently understand the content of their recorded videos and supports rapid decision-making.

[0055] The generation unit can automatically suggest similar criteria by referencing data from past projects when organizing criteria. For example, the generation unit can search the past project database and extract criteria from similar projects. It can also organize the criteria for a new project based on the extracted criteria. Furthermore, the generation unit can evaluate the effectiveness of criteria based on past project data and suggest the optimal criteria. This allows for efficient criteria organization by leveraging knowledge from past projects.

[0056] The shared system can automatically send reminders to the heads of each department when criteria are shared. For example, the shared system can send a reminder if approval or rejection is not made after a certain period has passed since the criteria were shared. The shared system can also adjust the frequency of reminders to send them at appropriate times. Furthermore, the shared system can customize the content of the reminders and send individual reminders to the heads of each department. This encourages the heads of each department to approve or reject the criteria promptly.

[0057] The data collection unit can be equipped with a function to evaluate the reliability of the approval / rejection results when collecting them from each department. For example, the data collection unit can analyze the approval / rejection results from each department and identify results with low reliability. The data collection unit can also perform additional verification on results with low reliability. Furthermore, the data collection unit can notify the project owner of the reliability evaluation results so that they can be used as a reference for decision-making. This ensures the reliability of the approval / rejection results and supports accurate decision-making.

[0058] The reception desk can analyze the project owner's past video submission history and suggest the most suitable video format. For example, it can analyze the format of videos the project owner has submitted in the past and suggest the optimal format. The reception desk can also suggest the appropriate video length and level of detail based on the project owner's past submission history. Furthermore, the reception desk can suggest the most suitable submission method based on the project owner's past submission history. This allows for the selection of the most suitable video submission method based on past history.

[0059] The reception system can automatically filter video content based on project progress and importance. For example, it can prioritize receiving high-priority content based on project progress. It can also filter out important content based on project importance. Furthermore, it can filter out unnecessary content and not accept it based on project progress and importance. This ensures that important content is prioritized according to project progress and importance.

[0060] The reception desk can prioritize receiving videos based on their relevance, taking into account the project owner's geographical location. For example, if the project owner is nearby, it will prioritize receiving highly relevant content. Conversely, if the project owner is far away, it can postpone receiving less relevant content. Furthermore, it can prioritize receiving the most suitable content, taking the project owner's geographical location into consideration.

[0061] The reception desk can analyze the project owner's social media activity when receiving videos and prioritize receiving relevant content. For example, it can analyze the project owner's social media activity and prioritize receiving relevant content. It can also analyze the project owner's social media activity and postpone less relevant content. Furthermore, it can analyze the project owner's social media activity and prioritize receiving the most relevant content. In this way, relevant content can be received based on the project owner's social media activity.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk inputs the presentation video recorded by the project owner (e.g., keynote presentation video). The video recorded by the project owner includes the project objectives, progress, and the roles of each department. The reception desk can upload the video to the cloud or input it directly into the system. They can also convert the video into a format that is easy to analyze. Step 2: The generation unit uses a generation AI to analyze the video input by the reception unit and organize the criteria for obtaining approval from each relevant department. The generation unit analyzes the content of the video and clarifies the approval points required for each department. The generation AI also verbalizes any points where doubts may arise regarding the criteria and presents them to each department. Step 3: The sharing department shares the criteria organized by the generation department on the cloud and clarifies the approval / rejection points for each relevant department. The sharing department uploads the criteria to the cloud and makes them accessible to the heads of each department. In addition, the approval / rejection results for each department are updated in real time on the cloud and notified to the project owner. Step 4: The collection unit collects approval / rejection results from each relevant department in real time and provides them to the project owner. The collection unit collects approval / rejection results from each department on the cloud and notifies the project owner. It also centrally manages the approval / rejection results from each department to enable the project owner to make quick decisions. Furthermore, it analyzes the approval / rejection results from each department, identifies delays in approval and problems, and reports them to the project owner.

[0064] (Example of form 2) The decision support system according to an embodiment of the present invention is a system that utilizes generative AI to support rapid decision-making for projects. In this decision support system, the project owner pre-records a presentation video (e.g., a keynote presentation video), and based on this, the generative AI organizes criteria for obtaining implementation approval from relevant departments and shares them on the cloud. By clarifying the approval / rejection points for each relevant department and obtaining approval / rejection from the heads of each relevant department to obtain evidence, the system dramatically speeds up necessary decision-making. For example, the project owner records a presentation video explaining the project overview. This video explains in detail the project's objectives, progress, and the roles of each department. Next, this video is input into the generative AI, which analyzes the content of the video and organizes criteria for obtaining implementation approval from each relevant department. The generative AI clarifies the necessary approval points for each department and shares them on the cloud. The heads of each relevant department make approval / rejection decisions based on the criteria presented on the cloud. The generative AI collects the approval / rejection results from each department in real time and provides them to the project owner. This allows the project owner to centrally manage the approval status of each department and enables rapid decision-making. Furthermore, the generating AI pre-defines potential points of contention in the criteria and presents them to each department. This allows for proactive mediation of disagreements between departments and supports rapid decision-making. This mechanism ensures smooth project progress, enabling quick decision-making without delays in task completion deadlines. Additionally, by correctly structuring the approval process with relevant departments, the project can proceed efficiently without unnecessary delays. In this way, the decision support system can facilitate rapid decision-making in projects.

[0065] The decision support system according to this embodiment comprises a reception unit, a generation unit, a sharing unit, and a collection unit. The reception unit receives a presentation video recorded by the project owner. The presentation video recorded by the project owner may include, for example, the project's objectives, progress, and the roles of each department, but is not limited to such examples. The reception unit may, for example, upload the video recorded by the project owner to the cloud. The reception unit can also directly input the video recorded by the project owner into the system. Furthermore, the reception unit can convert the video recorded by the project owner into a format that is easy to analyze. For example, the reception unit converts the video file into an appropriate format that is easy for the generation AI to analyze. The generation unit uses the generation AI to analyze the video input by the reception unit and organize the criteria for obtaining approval from each relevant department. For example, the generation AI analyzes the content of the video and clarifies the approval points required for each department. The generation unit can also share the criteria organized by the generation AI based on the video content on the cloud. Furthermore, the generation unit can pre-define points on the criteria where doubts may arise using the generation AI and present this information to each department. For example, the generation unit can use the generation AI to analyze the video content and present technical issues or business risks in a tangible way. The sharing unit shares the criteria organized by the generation unit on the cloud, clarifying the approval / rejection points for each relevant department. For example, the sharing unit can upload the criteria to the cloud and make them accessible to the heads of each department. The sharing unit can also update the approval / rejection results for each department on the cloud in real time. In addition, the sharing unit can notify the project owner of the approval / rejection results for each department. For example, the sharing unit updates the approval / rejection results for each department on the cloud in real time, allowing the project owner to stay informed of the latest approval status. The collection unit collects the approval / rejection results for each relevant department in real time and provides them to the project owner. For example, the collection unit collects the approval / rejection results for each department on the cloud and notifies the project owner.Furthermore, the data collection unit can centrally manage the approval / rejection results from each department, enabling the project owner to make decisions quickly. In addition, the data collection unit can analyze the approval / rejection results from each department and provide feedback to the project owner. For example, the data collection unit can analyze the approval / rejection results from each department, identify delays or problems in approval, and report them to the project owner. This allows the decision support system, according to the embodiment, to quickly obtain approval from each relevant department based on videos recorded by the project owner.

[0066] The reception desk inputs presentation videos recorded by the project owner. These videos may include, but are not limited to, the project's objectives, progress, and the roles of each department. The reception desk can, for example, upload the videos recorded by the project owner to the cloud. Alternatively, the reception desk can directly input the videos recorded by the project owner into the system. Furthermore, the reception desk can convert the videos recorded by the project owner into a format that is easy to analyze. For example, the reception desk converts video files to an appropriate format that is easy for the generating AI to analyze. Specifically, the reception desk converts video files to common formats such as MP4 or AVI so that the generating AI can analyze them efficiently. It also adjusts the video's resolution and frame rate so that the generating AI can accurately extract the necessary information. In addition, the reception desk has a function to convert the audio data of the video into text, making it easier for the generating AI to analyze the audio data. For example, it uses speech recognition technology to convert the audio in the video into text data, and the generating AI performs analysis based on the text data. This allows the reception desk to efficiently input videos recorded by the project owner and convert them into a format that is easy for the generating AI to analyze.

[0067] The generation unit uses a generation AI to analyze videos submitted by the reception unit and organize criteria for obtaining approval from each relevant department. For example, the generation unit uses the generation AI to analyze the video content and clarify the approval points required for each department. Specifically, the generation AI analyzes the audio and video data within the video and extracts information such as the project's objectives, progress, and the roles of each department. The generation AI uses natural language processing technology to convert the audio data within the video into text data and extract important information. It also uses image recognition technology to analyze the video data within the video and grasp the project's progress and the roles of each department. Furthermore, based on the extracted information, the generation AI organizes the approval points required for each department and creates criteria. For example, it indicates to the technical department that technical problems need to be resolved and to the business department that business risks need to be assessed. The generation unit can also share the criteria organized by the generation AI based on the video content on the cloud. In addition, the generation unit can pre-verify points where doubts may arise in the criteria and present that information to each department. For example, the generation unit uses AI to analyze the video content and present technical issues and business risks in a verbalized form. This allows the generation unit to efficiently organize the criteria for obtaining approval from each relevant department and ensure the smooth progress of the project.

[0068] The shared department shares the criteria organized by the generation department on the cloud and clarifies the approval / rejection points for each relevant department. For example, the shared department uploads the criteria to the cloud and makes them accessible to the heads of each department. Specifically, the shared department uses a cloud storage service to upload the criteria created by the generation department and makes them accessible to the heads of each department. The shared department can also update the approval / rejection results for each department on the cloud in real time. For example, when the head of each department enters the approval / rejection results for the criteria, the shared department updates the results on the cloud in real time, allowing other departments and project owners to understand the latest status. Furthermore, the shared department can also notify the project owner of the approval / rejection results for each department. For example, the shared department updates the approval / rejection results for each department on the cloud in real time, allowing the project owner to understand the latest approval status. The shared department also has a notification function and can notify the project owner of the approval / rejection results via email or push notification. In this way, the shared department can efficiently share the criteria organized by the generation department and quickly provide the project owner with the approval / rejection results for each department.

[0069] The data collection unit collects approval / rejection results from each relevant department in real time and provides them to the project owner. For example, the data collection unit collects approval / rejection results from each department on the cloud and notifies the project owner. Specifically, the data collection unit accesses a database on the cloud to collect approval / rejection results from each department. The data collection unit can also centrally manage the approval / rejection results from each department, enabling the project owner to make quick decisions. For example, the data collection unit centrally manages the approval / rejection results from each department, allowing the project owner to grasp the overall situation at a glance. Furthermore, the data collection unit can analyze the approval / rejection results from each department and provide feedback to the project owner. For example, the data collection unit analyzes the approval / rejection results from each department, identifies delays in approval and problems, and reports them to the project owner. The data collection unit can also make suggestions to improve the progress of the project based on the feedback. In this way, the data collection unit can efficiently collect approval / rejection results from each relevant department and provide them to the project owner, thereby supporting quick decision-making.

[0070] The generation unit can pre-define points where doubts may arise on the criteria and present this information to each department. For example, the generation unit's generation AI analyzes the content of a video and presents technical issues and business risks in language. The generation unit's generation AI analyzes the content of a video and identifies points where doubts may arise for each department. The generation unit can also have the generation AI verbalize points where doubts may arise and present them to each department. Furthermore, the generation unit can pre-adjust disagreements between departments based on the points where doubts may arise. For example, the generation unit can pre-adjust disagreements by having the generation AI verbalize technical issues and present them to each department. This allows for pre-adjustment of disagreements between departments and supports rapid decision-making.

[0071] The shared system can update the approval / rejection results for each department in real time on the cloud. For example, the shared system can upload criteria to the cloud and make them accessible to the heads of each department. The shared system can also update the approval / rejection results for each department in real time on the cloud. Furthermore, the shared system can notify the project owner of the approval / rejection results for each department. For example, the shared system can update the approval / rejection results for each department in real time on the cloud, allowing the project owner to understand the latest approval status. This allows the project owner to understand the latest approval status by updating the approval / rejection results for each department in real time. Some or all of the above processes in the shared system may be performed using AI, for example, or not using AI. For example, the shared system can update the approval / rejection results using an AI model that updates the approval / rejection results for each department in real time on the cloud.

[0072] The data collection unit can notify the project owner of the approval / rejection results for each department. For example, the data collection unit can collect the approval / rejection results for each department on the cloud and notify the project owner. The data collection unit can also centrally manage the approval / rejection results for each department, enabling the project owner to make quick decisions. Furthermore, the data collection unit can analyze the approval / rejection results for each department and provide feedback to the project owner. For example, the data collection unit can analyze the approval / rejection results for each department, identify delays or problems in approval, and report them to the project owner. This allows the project owner to quickly understand the approval / rejection results for each department. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can notify the project owner of the approval / rejection results using an AI model that collects the approval / rejection results for each department on the cloud and notifies the project owner of the results.

[0073] The reception desk can estimate the user's emotions and adjust the video submission timing based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can delay the submission timing to allow the user to submit the video in a relaxed state. Conversely, if the user is in a hurry, the reception desk can advance the submission timing to allow for quicker video submission. Furthermore, if the user is concentrating, the reception desk can adjust the submission timing to ensure the video is submitted at the optimal time. For example, the reception desk estimates the user's emotions and adjusts the submission timing to allow for video submission in a relaxed state. This allows for video submission at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception department can analyze the project owner's past video submission history and select the optimal reception method. For example, the reception department can analyze the submission times of videos previously submitted by the project owner and propose the optimal reception time. The reception department can also analyze the project owner's past submission frequency and propose the optimal reception frequency. Furthermore, the reception department can analyze the project owner's past submission methods (online, offline, etc.) and propose the optimal reception method. For example, the reception department can analyze the submission times of videos previously submitted by the project owner and propose the optimal reception time. This allows for the selection of the optimal reception method based on past history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the project owner's past video submission history into a generating AI and have the generating AI select the optimal reception method.

[0075] The reception unit can filter videos upon receipt based on project progress and importance. For example, the reception unit can prioritize receiving videos of high importance according to project progress. The reception unit can also filter and receive important videos based on project importance. Furthermore, the reception unit can filter and not receive unnecessary videos based on project progress and importance. For example, the reception unit can prioritize receiving videos of high importance according to project progress. This ensures that important videos are prioritized according to project progress and importance. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input project progress and importance into a generating AI and have the generating AI perform video filtering.

[0076] The reception desk can estimate the user's emotions and determine the priority of videos to be received based on the estimated emotions. For example, if the user is stressed, the reception desk can lower the priority so that the user can submit the video in a relaxed state. Conversely, if the user is in a hurry, the reception desk can raise the priority so that the video can be submitted quickly. Furthermore, if the user is focused, the reception desk can adjust the priority so that the video can be submitted at the optimal time. For example, the reception desk can estimate the user's emotions and adjust the priority so that the user can submit the video in a relaxed state. This allows for the prioritization of videos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception desk can prioritize receiving videos that are highly relevant, taking into account the project owner's geographical location information. For example, if the project owner is nearby, the reception desk will prioritize receiving highly relevant videos. Conversely, if the project owner is far away, the reception desk can postpone receiving less relevant videos. Furthermore, the reception desk can also prioritize receiving the most suitable videos, taking into account the project owner's geographical location information. For example, the reception desk will prioritize receiving highly relevant videos, taking into account the project owner's geographical location information. This ensures that the reception desk receives the most suitable videos, taking into account the project owner's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the project owner's geographical location information into a generating AI and have the generating AI select highly relevant videos.

[0078] The reception department can analyze the project owner's social media activity when receiving videos and accept relevant videos. For example, the reception department can analyze the project owner's social media activity and prioritize accepting relevant videos. The reception department can also analyze the project owner's social media activity and postpone accepting less relevant videos. Furthermore, the reception department can analyze the project owner's social media activity and prioritize accepting the most suitable videos. For example, the reception department can analyze the project owner's social media activity and prioritize accepting relevant videos. This allows the reception department to accept relevant videos based on the project owner's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the project owner's social media activity into a generating AI and have the generating AI select relevant videos.

[0079] The generation unit can estimate the user's emotions and adjust the way the criteria are expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit will use a soft expression. It can also use a concise expression if the user is in a hurry. Furthermore, it can use an emphasized expression if the user is excited. For example, the generation unit estimates the user's emotions and uses an easily understandable expression when the user is relaxed. This allows the expression of the criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the expression of the criteria.

[0080] The generation unit can adjust the level of detail of the criteria based on the importance of the project when generating the criteria. For example, if the project is highly important, the generation unit will generate detailed criteria. Conversely, if the project is less important, the generation unit can also generate concise criteria. Furthermore, the generation unit can generate criteria with an appropriate level of detail depending on the project's importance. For example, the generation unit will generate detailed criteria based on the project's importance. This allows for adjustment of the level of detail of the criteria according to the project's importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the criteria.

[0081] The generation unit can apply different generation algorithms according to the role of each department when generating criteria. For example, the generation unit can apply the optimal generation algorithm according to the role of each department. The generation unit can also apply different generation algorithms according to the role of each department. Furthermore, the generation unit can apply an appropriate generation algorithm according to the role of each department. For example, the generation unit can apply the optimal generation algorithm according to the role of each department. This ensures that the optimal generation algorithm is applied according to the role of each department. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input role data for each department into a generation AI and have the generation AI execute the application of the generation algorithm.

[0082] The generation unit can estimate the user's emotions and adjust the length of the criteria based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate longer criteria. It can also generate shorter criteria if the user is in a hurry. Furthermore, if the user is excited, the generation unit can generate criteria of a moderate length. For example, the generation unit can estimate the user's emotions and generate longer criteria that are easy to understand when the user is relaxed. This allows the length of the criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the criteria.

[0083] The generation unit can determine the priority of criteria based on the project submission date when generating criteria. For example, if the project submission date is approaching, the generation unit will generate high-priority criteria. Conversely, if the project submission date is far off, the generation unit can also generate low-priority criteria. Furthermore, the generation unit can generate criteria with appropriate priority levels depending on the project submission date. For example, the generation unit will generate high-priority criteria based on the project submission date. This allows for adjustment of criterion priority according to the project submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project submission date data into a generation AI and have the generation AI determine the priority of criteria.

[0084] The generation unit can adjust the order of criteria based on project relevance during criterion generation. For example, if the projects are highly relevant, the generation unit will generate important criteria first. Conversely, if the projects are less relevant, the generation unit can generate less important criteria later. Furthermore, the generation unit can generate criteria in an appropriate order according to project relevance. For example, the generation unit generates important criteria first based on project relevance. This allows the order of criteria to be adjusted according to project relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project relevance data into a generation AI and have the generation AI perform the adjustment of the criterion order.

[0085] The sharing unit can estimate the user's emotions and adjust the way criteria are shared based on the estimated emotions. For example, if the user is relaxed, the sharing unit can use a gentle sharing method. It can also use a concise sharing method if the user is in a hurry. Furthermore, it can use an emphatic sharing method if the user is excited. For example, the sharing unit can estimate the user's emotions and use an easy-to-understand sharing method when the user is relaxed. This allows the sharing method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI, or not. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI adjust the way criteria are shared.

[0086] The sharing unit can select the optimal sharing method by referring to the past approval history of each department when sharing criteria. For example, the sharing unit can select the optimal sharing method by referring to the past approval history of each department. The sharing unit can also select an appropriate sharing method by referring to the past approval history of each department. Furthermore, the sharing unit can also select the optimal sharing method by referring to the past approval history of each department. For example, the sharing unit can select the optimal sharing method by referring to the past approval history of each department. This allows the optimal sharing method to be selected based on the past approval history of each department. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without using AI. For example, the sharing unit can input the past approval history data of each department into a generating AI and have the generating AI perform the selection of the optimal sharing method.

[0087] The shared section can apply different sharing algorithms depending on the role of each department when criteria are shared. For example, the shared section can apply the optimal sharing algorithm depending on the role of each department. The shared section can also apply different sharing algorithms depending on the role of each department. Furthermore, the shared section can also apply an appropriate sharing algorithm depending on the role of each department. For example, the shared section can apply the optimal sharing algorithm depending on the role of each department. This ensures that the optimal sharing algorithm is applied according to the role of each department. Some or all of the above processing in the shared section may be performed using AI, for example, or without AI. For example, the shared section can input role data for each department into a generating AI and have the generating AI execute the application of the sharing algorithm.

[0088] The sharing unit can estimate the user's emotions and adjust the frequency of sharing criteria based on the estimated emotions. For example, if the user is relaxed, the sharing unit can lower the sharing frequency. Conversely, if the user is in a hurry, the sharing unit can increase the sharing frequency. Furthermore, if the user is excited, the sharing unit can set an appropriate sharing frequency. For example, the sharing unit estimates the user's emotions and shares criteria at a frequency that is easy to understand when the user is relaxed. This allows the sharing frequency of criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of criterion sharing.

[0089] The sharing unit can select the optimal sharing method when criteria are shared, taking into account the geographical location information of each department. For example, the sharing unit can select the optimal sharing method by considering the geographical location information of each department. The sharing unit can also select an appropriate sharing method by considering the geographical location information of each department. Furthermore, the sharing unit can also select the optimal sharing method by considering the geographical location information of each department. For example, the sharing unit can select the optimal sharing method by considering the geographical location information of each department. This allows the optimal sharing method to be selected based on the geographical location information of each department. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without using AI. For example, the sharing unit can input geographical location data of each department into a generating AI and have the generating AI perform the selection of the optimal sharing method.

[0090] The sharing unit can analyze each department's social media activity and adjust the sharing method when criteria are shared. For example, the sharing unit can analyze each department's social media activity and select the optimal sharing method. The sharing unit can also analyze each department's social media activity and select an appropriate sharing method. Furthermore, the sharing unit can analyze each department's social media activity and select the optimal sharing method. For example, the sharing unit can analyze each department's social media activity and select the optimal sharing method. This allows the optimal sharing method to be selected based on each department's social media activity. Some or all of the above processing in the sharing unit may be performed using AI, for example, or without AI. For example, the sharing unit can input each department's social media activity data into a generating AI and have the generating AI perform the adjustment of the sharing method.

[0091] The data collection unit can estimate the user's emotions and adjust the method of collecting affirmation / denial results based on the estimated emotions. For example, if the user is relaxed, the data collection unit can use a gentle collection method. It can also use a concise collection method if the user is in a hurry. Furthermore, it can use an emphasized collection method if the user is agitated. For example, the data collection unit can estimate the user's emotions and use an easy-to-understand collection method when the user is relaxed. This allows the method of collecting affirmation / denial results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the method of collecting affirmation / denial results.

[0092] The collection unit can select the optimal collection method by referring to the past approval / rejection history of each department when collecting approval / rejection results. For example, the collection unit can select the optimal collection method by referring to the past approval / rejection history of each department. The collection unit can also select an appropriate collection method by referring to the past approval / rejection history of each department. Furthermore, the collection unit can also select the optimal collection method by referring to the past approval / rejection history of each department. For example, the collection unit can select the optimal collection method by referring to the past approval / rejection history of each department. This allows the collection unit to select the optimal collection method based on the past approval / rejection history of each department. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the past approval / rejection history data of each department into a generating AI and have the generating AI select the optimal collection method.

[0093] The collection unit can apply different collection algorithms depending on the role of each department when collecting approval / rejection results. For example, the collection unit can apply the optimal collection algorithm depending on the role of each department. The collection unit can also apply different collection algorithms depending on the role of each department. Furthermore, the collection unit can also apply an appropriate collection algorithm depending on the role of each department. For example, the collection unit can apply the optimal collection algorithm depending on the role of each department. This ensures that the optimal collection algorithm is applied according to the role of each department. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input the role data of each department into a generating AI and have the generating AI execute the application of the collection algorithm.

[0094] The data collection unit can estimate the user's emotions and adjust the frequency of collecting affirmation / denial results based on the estimated emotions. For example, the data collection unit can lower the collection frequency when the user is relaxed. It can also increase the collection frequency when the user is in a hurry. Furthermore, it can set an appropriate collection frequency when the user is excited. For example, the data collection unit estimates the user's emotions and collects affirmation / denial results at a frequency that is easy to understand when the user is relaxed. This allows the collection frequency of affirmation / denial results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of collecting affirmation / denial results.

[0095] The collection unit can select the optimal collection method when collecting approval / rejection results, taking into account the geographical location information of each department. For example, the collection unit can select the optimal collection method by taking into account the geographical location information of each department. The collection unit can also select an appropriate collection method by taking into account the geographical location information of each department. Furthermore, the collection unit can also select the optimal collection method by taking into account the geographical location information of each department. For example, the collection unit can select the optimal collection method by taking into account the geographical location information of each department. This allows the collection unit to select the optimal collection method based on the geographical location information of each department. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input geographical location data of each department into a generating AI and have the generating AI select the optimal collection method.

[0096] The data collection unit can analyze the social media activities of each department and adjust the collection method when collecting approval / rejection results. For example, the data collection unit can analyze the social media activities of each department and select the optimal collection method. The data collection unit can also analyze the social media activities of each department and select an appropriate collection method. Furthermore, the data collection unit can analyze the social media activities of each department and select the optimal collection method. For example, the data collection unit can analyze the social media activities of each department and select the optimal collection method. This allows the optimal collection method to be selected based on the social media activities of each department. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data from each department into a generating AI and have the generating AI perform the adjustment of the collection method.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception department can automatically summarize the content of videos recorded by project owners and send the summary to the generation department. For example, the reception department can analyze the video content, extract key points, and create a summary. The reception department can also send the summary to the generation department for reference when organizing criteria. Furthermore, the reception department can share the summary on the cloud, allowing department heads to review it. This enables project owners to efficiently understand the content of their recorded videos and supports rapid decision-making.

[0099] The generation unit can automatically suggest similar criteria by referencing data from past projects when organizing criteria. For example, the generation unit can search the past project database and extract criteria from similar projects. It can also organize the criteria for a new project based on the extracted criteria. Furthermore, the generation unit can evaluate the effectiveness of criteria based on past project data and suggest the optimal criteria. This allows for efficient criteria organization by leveraging knowledge from past projects.

[0100] The shared system can automatically send reminders to the heads of each department when criteria are shared. For example, the shared system can send a reminder if approval or rejection is not made after a certain period has passed since the criteria were shared. The shared system can also adjust the frequency of reminders to send them at appropriate times. Furthermore, the shared system can customize the content of the reminders and send individual reminders to the heads of each department. This encourages the heads of each department to approve or reject the criteria promptly.

[0101] The data collection unit can be equipped with a function to evaluate the reliability of the approval / rejection results when collecting them from each department. For example, the data collection unit can analyze the approval / rejection results from each department and identify results with low reliability. The data collection unit can also perform additional verification on results with low reliability. Furthermore, the data collection unit can notify the project owner of the reliability evaluation results so that they can be used as a reference for decision-making. This ensures the reliability of the approval / rejection results and supports accurate decision-making.

[0102] The reception system can estimate the user's emotions and automatically edit the video content based on that estimation. For example, if the user is feeling stressed, the video content can be edited to be concise and easy to submit. If the user is relaxed, it can be edited to include more detailed content. Furthermore, if the user is focused, it can be edited to highlight important points. This allows the system to provide optimal video content tailored to the user's emotions and support efficient decision-making.

[0103] The reception desk can analyze the project owner's past video submission history and suggest the most suitable video format. For example, it can analyze the format of videos the project owner has submitted in the past and suggest the optimal format. The reception desk can also suggest the appropriate video length and level of detail based on the project owner's past submission history. Furthermore, the reception desk can suggest the most suitable submission method based on the project owner's past submission history. This allows for the selection of the most suitable video submission method based on past history.

[0104] The reception system can automatically filter video content based on project progress and importance. For example, it can prioritize receiving high-priority content based on project progress. It can also filter out important content based on project importance. Furthermore, it can filter out unnecessary content and not accept it based on project progress and importance. This ensures that important content is prioritized according to project progress and importance.

[0105] The reception system can estimate the user's emotions and automatically summarize the video content based on those emotions. For example, if the user is stressed, it can create a concise summary of the video content to make it easier to submit. If the user is relaxed, it can create a summary that includes more detailed information. Furthermore, if the user is focused, it can summarize by highlighting key points. This allows the system to provide the most appropriate video content according to the user's emotions, supporting efficient decision-making.

[0106] The reception desk can prioritize receiving videos based on their relevance, taking into account the project owner's geographical location. For example, if the project owner is nearby, it will prioritize receiving highly relevant content. Conversely, if the project owner is far away, it can postpone receiving less relevant content. Furthermore, it can prioritize receiving the most suitable content, taking the project owner's geographical location into consideration.

[0107] The reception desk can analyze the project owner's social media activity when receiving videos and prioritize receiving relevant content. For example, it can analyze the project owner's social media activity and prioritize receiving relevant content. It can also analyze the project owner's social media activity and postpone less relevant content. Furthermore, it can analyze the project owner's social media activity and prioritize receiving the most relevant content. In this way, relevant content can be received based on the project owner's social media activity.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception desk inputs the presentation video recorded by the project owner (e.g., keynote presentation video). The video recorded by the project owner includes the project objectives, progress, and the roles of each department. The reception desk can upload the video to the cloud or input it directly into the system. They can also convert the video into a format that is easy to analyze. Step 2: The generation unit uses a generation AI to analyze the video input by the reception unit and organize the criteria for obtaining approval from each relevant department. The generation unit analyzes the content of the video and clarifies the approval points required for each department. The generation AI also verbalizes any points where doubts may arise regarding the criteria and presents them to each department. Step 3: The sharing department shares the criteria organized by the generation department on the cloud and clarifies the approval / rejection points for each relevant department. The sharing department uploads the criteria to the cloud and makes them accessible to the heads of each department. In addition, the approval / rejection results for each department are updated in real time on the cloud and notified to the project owner. Step 4: The collection unit collects approval / rejection results from each relevant department in real time and provides them to the project owner. The collection unit collects approval / rejection results from each department on the cloud and notifies the project owner. It also centrally manages the approval / rejection results from each department to enable the project owner to make quick decisions. Furthermore, it analyzes the approval / rejection results from each department, identifies delays in approval and problems, and reports them to the project owner.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the reception unit, generation unit, sharing unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and uploads videos recorded by the project owner to the cloud. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the videos using generation AI and organizes the criteria. The sharing unit is implemented by the control unit 46A of the smart device 14 and shares the criteria on the cloud. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects the approval / rejection results from each department in real time and provides them to the project owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, generation unit, sharing unit, and collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which uploads videos recorded by the project owner to the cloud. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the videos using generation AI and organizes the criteria. The sharing unit is implemented by the control unit 46A of the smart glasses 214, which shares the criteria on the cloud. The collection unit is implemented by the identification processing unit 290 of the data processing unit 12, which collects the approval / rejection results from each department in real time and provides them to the project owner. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the reception unit, generation unit, sharing unit, and collection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which uploads videos recorded by the project owner to the cloud. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the videos using generation AI and organizes the criteria. The sharing unit is implemented by, for example, the control unit 46A of the headset terminal 314, which shares the criteria on the cloud. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which collects the approval / rejection results from each department in real time and provides them to the project owner. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, generation unit, sharing unit, and collection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which uploads videos recorded by the project owner to the cloud. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the videos using generation AI and organizes the criteria. The sharing unit is implemented by, for example, the control unit 46A of the robot 414, which shares the criteria on the cloud. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which collects the approval / rejection results for each department in real time and provides them to the project owner. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The reception desk receives the presentation video recorded by the project owner, A generation unit analyzes the video input by the reception unit and organizes the criteria for obtaining implementation approval from each relevant department, The criteria organized by the generation unit are shared on the cloud, and the sharing unit clarifies the approval / rejection points for each relevant department. It includes a collection unit that collects approval / rejection results from each relevant department in real time and provides them to the project owner. A system characterized by the following features. (Note 2) The generating unit is Points that may raise questions regarding the criteria should be articulated in advance, and their content should be presented for each department. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned shared portion is, The approval / rejection results for each department are updated in real time on the cloud. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The project owner will be notified of the approval / rejection results for each department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of video submissions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the project owner's past video submission history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving videos, filtering is performed based on the project's progress and importance. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and prioritizes the videos to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When accepting video submissions, the project owner's geographical location information will be taken into consideration to prioritize submissions of videos that are highly relevant. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving video submissions, the system analyzes the project owner's social media activity and accepts relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts how the criteria are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating criteria, adjust the level of detail of the criteria based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating criteria, different generation algorithms are applied according to the role of each department. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system estimates the user's emotions and adjusts the length of the criteria based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating criteria, prioritize the criteria based on the project submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating criteria, adjust the order of criteria based on project relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned shared portion is, It estimates the user's emotions and adjusts how criteria are shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned shared portion is, When sharing criteria, refer to the past approval history of each department to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned shared portion is, When sharing criteria, different sharing algorithms are applied according to the role of each department. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned shared portion is, It estimates the user's emotions and adjusts the frequency of sharing criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned shared portion is, When sharing criteria, the optimal sharing method will be selected considering the geographical location information of each department. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned shared portion is, When sharing criteria, we analyze each department's social media activity and adjust the sharing method accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is We estimate the user's emotions and adjust the method of collecting acceptance / rejection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When collecting approval / rejection results, the most suitable collection method will be selected by referring to the past approval / rejection history of each department. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is When collecting approval / rejection results, different collection algorithms are applied according to the role of each department. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of collecting affirmative / negative results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned collection unit is When collecting approval / rejection results, the most suitable collection method will be selected considering the geographical location information of each department. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned collection unit is When collecting approval / rejection results, we analyze the social media activity of each department and adjust the collection method accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk receives the presentation video recorded by the project owner, A generation unit analyzes the video input by the reception unit and organizes the criteria for obtaining implementation approval from each relevant department, The criteria organized by the generation unit are shared on the cloud, and the sharing unit clarifies the approval / rejection points for each relevant department. It includes a collection unit that collects approval / rejection results from each relevant department in real time and provides them to the project owner. A system characterized by the following features.

2. The generating unit is Points that may raise questions regarding the criteria should be articulated in advance, and their content should be presented for each department. The system according to feature 1.

3. The aforementioned shared portion is, The approval / rejection results for each department are updated in real time on the cloud. The system according to feature 1.

4. The aforementioned collection unit is The project owner will be notified of the approval / rejection results for each department. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of video submissions based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the project owner's past video submission history to select the most suitable submission method. The system according to feature 1.

7. The aforementioned reception unit is When receiving videos, filtering is performed based on the project's progress and importance. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of videos to accept based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When accepting video submissions, the project owner's geographical location information will be taken into consideration to prioritize submissions of videos that are highly relevant. The system according to feature 1.

10. The aforementioned reception unit is When receiving video submissions, the system analyzes the project owner's social media activity and accepts relevant videos. The system according to feature 1.

Images