System

Abstract

To provide a system capable of streamlining temple operations and passing on the teachings of Buddhism to the next generation.SOLUTION: A system according to an embodiment comprises an acquisition unit, a determination unit, and a provision unit. The acquisition unit acquires operational information indicative of temple activities. The determination unit determines the contents of operational improvements on the basis of the operational information acquired by the acquisition unit. The provision unit provides the contents of operational improvements determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, temple operations were analog, and there was a problem that efficiency was not sufficiently improved.

[0005] The system of the embodiment aims to make temple operations more efficient and pass on Buddhist teachings to the next generation. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a determination unit, and a provision unit. The acquisition unit acquires business information indicating the business of the temple. The determination unit determines the content of improvement of the business based on the business information acquired by the acquisition unit. The provision unit provides the content of improvement determined by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can streamline temple operations and pass on Buddhist teachings to future generations. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system for streamlining temple operations and conveying Buddhist teachings to the next generation. This system acquires business information of each temple, determines the content of improvements to the operations based on the acquired business information, and provides the determined improvement content to the temple and users. Furthermore, the system generates content explaining Buddhist teachings and discovers ideas for events and workshops based on Buddhism. Feedback is collected from believers and the general public, and the collected feedback is analyzed to identify improvements to the platform. This system makes temple operations more efficient and makes it easier to convey Buddhist teachings to the next generation. In addition, by utilizing AI, the digitalization of operations will progress and the digital transformation of the temple industry will accelerate. For example, an acquisition unit acquires business information of each temple, and a decision unit decides the content of improvements to the operations based on the acquired business information. The determined improvement content is provided to the temple and users by a provision unit. Furthermore, a content generation unit generates content explaining Buddhist teachings, and an idea excavation unit discovers ideas for events and workshops based on Buddhism. It also includes a feedback collection section that collects feedback from believers and the general public, which is then analyzed by a feedback analysis section to identify improvements to the platform, so that the system can streamline temple operations and pass on Buddhist teachings to future generations.

[0029] The system according to the embodiment includes an acquisition unit, a determination unit, and a provision unit. The acquisition unit acquires business information indicating the business of the temple. The business information includes, for example, a schedule of a memorial service, information on believers, and income and expenditure reports, but is not limited to such examples. The acquisition unit acquires, for example, business information of each temple from a database. The acquisition unit can also collect business information in real time. For example, the acquisition unit can automatically collect business information using a sensor. The determination unit determines the improvement content of the business based on the business information acquired by the acquisition unit. The improvement content includes, for example, the efficiency of business processes, the improvement of communication with believers, etc., but is not limited to such examples. The determination unit analyzes the business information using, for example, AI, and determines the optimal improvement content. The provision unit provides the improvement content determined by the determination unit to the temple and the user. The provision unit can also provide, for example, a digital worship in a virtual space. The provision unit can also automatically provide the improvement content using, for example, AI. This allows the system to streamline the business of the temple and pass on the teachings of Buddhism to the next generation.

[0030] The acquisition unit acquires business information showing the temple's business. The business information includes, for example, a schedule of a memorial service, information on believers, and income and expenditure reports, but is not limited to such examples. The acquisition unit acquires, for example, business information of each temple from a database. Specifically, the acquisition unit collects data through a dedicated application or web portal for each temple to record its daily business. This allows detailed data such as the schedule of a memorial service, information on believers, and income and expenditure reports to be managed in a centralized manner. Furthermore, the acquisition unit can collect business information in real time. For example, the acquisition unit can automatically collect business information using a sensor. Specifically, cameras and microphones for monitoring the progress of a memorial service, RFID tags and beacons for grasping the attendance status of believers, and accounting software for automating income and expenditure reports are possible. These devices and software transmit data to a central database via the Internet, allowing the acquisition unit to access the data in real time. This allows the acquisition unit to efficiently and accurately collect business information and grasp the entire business of the temple. Furthermore, the acquisition unit can appropriately classify the collected data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the decision unit and the provision unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the acquisition unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The determination unit determines the improvement content of the business based on the business information acquired by the acquisition unit. The improvement content includes, for example, the efficiency of the business process, the improvement of communication with believers, etc., but is not limited to such examples. The determination unit, for example, uses AI to analyze the business information and determines the optimal improvement content. Specifically, the AI ​​uses a machine learning algorithm to analyze past business data and extract patterns and trends. For example, it analyzes the fluctuation of the number of participants in the schedule of the memorial service, the content of the feedback from the believers, the balance of income and expenditure in the income and expenditure report, etc. This allows the AI ​​to identify which part of the business process has room for efficiency and what kind of improvement is needed in communication with the believers. Furthermore, the AI ​​can also analyze feedback from the believers using natural language processing technology and generate specific improvement proposals. For example, it analyzes what kind of impressions the believers have about the memorial service and what kind of requests they have, and proposes the improvement content based on that. In addition, the AI ​​can perform simulations and predict the effect when the proposed improvement content is implemented. As a result, the determination unit can determine the improvement content of the business with a scientific approach based on data and improve the business efficiency of the temple. Furthermore, the determination unit can periodically review the improvement content and aim for continuous improvement. This enables the decision-making department to keep the temple's operations in an optimal state at all times and maintain good relationships with believers.

[0032] The provision unit provides the improvement content determined by the determination unit to the temple and the user. The provision unit can also provide, for example, digital worship in a virtual space. Specifically, the provision unit uses virtual reality (VR) technology to provide an environment in which believers can participate in a temple memorial service from the comfort of their own homes. By wearing a VR headset, believers can access the temple in the virtual space and experience the memorial service in real time. The provision unit can also automatically provide the improvement content using, for example, AI. Specifically, the AI ​​provides customized services according to the believer's individual needs and requests. For example, if a believer is interested in a specific memorial service, the AI ​​automatically provides information about the memorial service and how to participate. In addition, individual recommendations can be made based on the memorial services that the believer has participated in in the past and the feedback that the believer has provided. Furthermore, the provision unit can provide the improvement content in a variety of ways. For example, it can send notifications through a smartphone app or provide information through email or SMS. The provision unit can also provide services that reflect the improvement content when believers visit the temple in person. For example, the reception system can be improved to allow believers to worship smoothly. The Service Department can also collect feedback from believers and evaluate the effectiveness of the improvements. This allows the Service Department to provide optimal services to believers and improve the efficiency of the temple's operations. Furthermore, the Service Department can continually review the improvements and respond flexibly to the needs of believers. This allows the Service Department to improve the efficiency of temple operations and pass on the teachings of Buddhism to the next generation.

[0033] The system includes a content generation unit that generates content that explains the teachings of Buddhism. The content generation unit generates content that explains the teachings of Buddhism. The content includes, for example, basic teachings of Buddhism, history, and meditation methods, but is not limited to such examples. The content generation unit automatically generates content that explains the teachings of Buddhism using, for example, AI. For example, the content generation unit generates text that explains the teachings of Buddhism using a text generation AI (for example, LLM). The content generation unit can also generate video and audio content that explains the teachings of Buddhism using a multimodal generation AI. For example, the content generation unit generates a video that explains the teachings of Buddhism and provides the video to a user. This allows the system to pass on the teachings of Buddhism to the next generation.

[0034] The system includes an idea excavation unit that excavates ideas for events or workshops based on Buddhist teachings. The idea excavation unit excavates ideas for events or workshops based on Buddhist teachings. Ideas include, but are not limited to, meditation sessions, sutra copying sessions, and Buddhist lectures. The idea excavation unit automatically excavates ideas for events or workshops based on Buddhist teachings, for example, using AI. For example, the idea excavation unit excavates ideas for meditation or sutra copying workshops using a generation AI. The idea excavation unit can also excavate ideas for lectures on Buddhism using a generation AI. For example, the idea excavation unit excavates ideas for events based on Buddhist teachings and provides the ideas to temples and users. This allows the system to pass on the teachings of Buddhism to the next generation.

[0035] The system includes a feedback collection unit that collects feedback from followers or the general public. The feedback collection unit collects feedback from followers and the general public. The feedback includes, for example, impressions of an event, opinions on the usability of the system, and the like, but is not limited to such examples. The feedback collection unit automatically collects feedback using, for example, AI. For example, the feedback collection unit collects feedback through an online survey. The feedback collection unit can also collect feedback through social media. For example, the feedback collection unit collects comments and messages on SNS and uses them as feedback. This allows the system to collect feedback from followers and the general public and identify areas for improvement in the system.

[0036] The system includes a feedback analysis unit that analyzes the feedback collected by the feedback collection unit. The feedback analysis unit analyzes the collected feedback. The analysis of the feedback includes, but is not limited to, for example, text mining, sentiment analysis, and the like. The feedback analysis unit automatically analyzes the feedback, for example, using AI. For example, the feedback analysis unit analyzes the content of the feedback using text mining technology. The feedback analysis unit can also analyze the emotional aspects of the feedback using sentiment analysis technology. For example, the feedback analysis unit analyzes the text data of the feedback and classifies positive opinions and negative opinions. This allows the system to analyze the collected feedback and identify improvements to the platform.

[0037] The system includes a business analysis unit that analyzes a business process including the automation of schedule management of a memorial service or communication with believers. The business analysis unit analyzes a business process including the automation of schedule management of a memorial service or communication with believers. The business process includes, for example, schedule management of a memorial service and communication with believers, but is not limited to such examples. The business analysis unit automatically analyzes the business process using, for example, AI. For example, the business analysis unit automatically manages the schedule of a memorial service using a schedule management system. The business analysis unit can also automate communication with believers using a chatbot. For example, the business analysis unit automatically responds to inquiries from believers using a chatbot. This allows the system to analyze the business process and improve the efficiency of business.

[0038] The system includes an efficiency suggestion unit that makes a proposal to reduce the burden on the monk based on the information analyzed by the business analysis unit. The efficiency suggestion unit makes a proposal to reduce the burden on the monk based on the information analyzed by the business analysis unit. The proposal includes, for example, the automation of business, the division of business, etc., but is not limited to such examples. The efficiency suggestion unit automatically makes a proposal to reduce the burden on the monk, for example, using AI. For example, the efficiency suggestion unit proposes the automation of business to reduce the burden on the monk. The efficiency suggestion unit can also propose the division of business to reduce the burden on the monk. For example, the efficiency suggestion unit proposes the division of business to divide the business. In this way, the system can reduce the burden on the monk and improve the efficiency of business.

[0039] The content generation unit can generate content that explains the basic teachings and history of Buddhism and methods of meditation. For example, the content generation unit generates content including a detailed explanation of the basic teachings of Buddhism. For example, the content generation unit explains basic doctrines of Buddhism such as the Four Noble Truths and the Eightfold Path. The content generation unit can also generate content including a concise explanation of the history of Buddhism. For example, the content generation unit explains the origin and history of development of Buddhism. Furthermore, the content generation unit can generate content including specific techniques for methods of meditation. For example, the content generation unit explains how to practice meditation and its effects. In this way, by generating content that explains the basic teachings and history of Buddhism and methods of meditation, the teachings of Buddhism can be passed on to the next generation.

[0040] The idea excavation department can excavate ideas for meditation and copying sutras workshops, and lectures on Buddhism. For example, the idea excavation department excavates ideas for workshops that explain the practice and effects of meditation. For example, the idea excavation department proposes a workshop to learn basic techniques of meditation. The idea excavation department can also excavate ideas for workshops that explain the meaning and methods of copying sutras. For example, the idea excavation department proposes a workshop to calm the mind through the practice of copying sutras. Furthermore, the idea excavation department can excavate ideas for lectures on Buddhist teachings. For example, the idea excavation department proposes an event where an expert will give a lecture on the history and doctrines of Buddhism. In this way, by excavating ideas for meditation and copying sutras workshops, and lectures on Buddhism, the teachings of Buddhism can be passed on to the next generation.

[0041] The feedback collection unit can collect feedback from believers and the general public. For example, the feedback collection unit collects feedback about an event and opinions about the usability of the system. For example, the feedback collection unit collects feedback through an online survey. The feedback collection unit can also collect feedback through social media. For example, the feedback collection unit collects comments and messages on SNS and uses them as feedback. In this way, by collecting feedback from believers and the general public, it is possible to identify areas for improvement in the system.

[0042] The feedback analysis unit can analyze the feedback collected by the feedback collection unit and identify areas for improvement of the platform. The feedback analysis unit can analyze the content of the feedback, for example, using text mining technology. For example, the feedback analysis unit can analyze the text data of the feedback and classify positive opinions and negative opinions. The feedback analysis unit can also analyze the emotional aspects of the feedback using sentiment analysis technology. For example, the feedback analysis unit can analyze the text data of the feedback and calculate a sentiment score. This allows the collected feedback to be analyzed and areas for improvement of the platform to be identified, thereby improving the system.

[0043] The business analysis unit can optimize the analysis algorithm by referring to past business data. The business analysis unit refers to business data such as past memorial service schedules and feedback from believers. For example, the business analysis unit selects an optimal analysis algorithm for a similar problem based on the past business data. The business analysis unit can also refer to past business data and preferentially select successful analysis algorithms. For example, the business analysis unit analyzes past business data and avoids unsuccessful analysis algorithms. In this way, the accuracy of analysis is improved by optimizing the analysis algorithm by referring to past business data.

[0044] The business analysis unit can apply different analysis methods to different types of business. The business analysis unit selects the most suitable analysis method according to the type of business, such as a method for schedule management or a method for communication. For example, for schedule management, the business analysis unit automatically manages the schedule of a memorial service using a calendar system. For communication, the business analysis unit can also automate communication with believers using a chatbot. For example, the business analysis unit automatically responds to inquiries from believers using a chatbot. This improves the accuracy of analysis by applying different analysis methods to different types of business.

[0045] The acquisition unit can analyze the past business history of each temple and select the optimal acquisition method. The acquisition unit analyzes business history such as, for example, past memorial service schedules and feedback from believers. For example, the acquisition unit selects a method for acquiring business information at a specific time period from the past business history. The acquisition unit can also select a method for preferentially acquiring business information at the time of a specific event based on the past business history. For example, the acquisition unit analyzes the past business history and selects the optimal acquisition method for each type of business. In this way, the optimal acquisition method can be selected by analyzing the past business history.

[0046] The acquisition unit can perform filtering based on the current situation of the temple or a specific event when acquiring the business information. For example, the acquisition unit takes into consideration the current situation of the temple and preferentially acquires only important business information. For example, when a specific event is being held, the acquisition unit preferentially acquires business information related to the event. The acquisition unit can also filter unnecessary information based on the current situation and events of the temple to efficiently acquire business information. For example, the acquisition unit preferentially acquires important business information based on information such as the status of memorial services and special events. In this way, important information can be preferentially acquired by filtering based on the current situation of the temple and specific events.

[0047] When acquiring business information, the acquisition unit can preferentially acquire highly relevant information by taking into consideration the geographical location information of the temple. For example, the acquisition unit preferentially acquires nearby event information based on the geographical location information of the temple. For example, the acquisition unit preferentially acquires business information related to the area by taking into consideration the geographical location information of the temple. The acquisition unit can also filter distant information based on the geographical location information of the temple and preferentially acquire nearby information. For example, the acquisition unit preferentially acquires highly relevant business information based on information such as the address of the temple and the characteristics of the area. In this way, highly relevant information can be preferentially acquired by taking into consideration the geographical location information of the temple.

[0048] When acquiring business information, the acquisition unit can analyze the social media activity of the temple and acquire related information. For example, the acquisition unit analyzes the social media activity of the temple and acquires related event information. For example, the acquisition unit acquires feedback from believers based on the social media activity of the temple. The acquisition unit can also analyze the social media activity of the temple and acquire trend information related to the business. For example, the acquisition unit acquires related business information based on information such as the content of posts on social media and the reactions of followers. This makes it possible to efficiently acquire related information by analyzing the social media activity of the temple.

[0049] When deciding the content of business improvement, the decision unit can refer to past improvement history to select the optimal improvement method. The decision unit, for example, refers to data such as past improvement content and improvement results. For example, the decision unit selects the optimal improvement method for a similar problem based on the past improvement history. The decision unit can also refer to the past improvement history and preferentially select successful improvement methods. For example, the decision unit analyzes the past improvement history and avoids unsuccessful improvement methods. In this way, the optimal improvement method can be selected by referring to the past improvement history.

[0050] The decision unit can apply different decision algorithms for each type of business when deciding the business improvement content. The decision unit selects an optimal decision algorithm according to the type of business, such as an algorithm for schedule management or an algorithm for communication. For example, for schedule management, the decision unit automatically manages the schedule of a memorial service using a calendar system. For communication, the decision unit can also automate communication with believers using a chatbot. For example, the decision unit automatically responds to inquiries from believers using a chatbot. In this way, by applying different decision algorithms for each type of business, an optimal improvement method can be determined.

[0051] When deciding the content of business improvement, the decision unit can select the optimal improvement method by taking into consideration the geographical location information of the temple. The decision unit, for example, selects an improvement method suitable for the area based on the geographical location information of the temple. For example, the decision unit selects an improvement method that utilizes nearby resources by taking into consideration the geographical location information of the temple. The decision unit can also select an improvement method that avoids distant resources based on the geographical location information of the temple. For example, the decision unit selects an improvement method suitable for the area based on information such as the address of the temple and the characteristics of the area. In this way, an improvement method suitable for the area can be selected by taking into consideration the geographical location information of the temple.

[0052] When determining the content of business improvement, the decision unit can analyze the social media activity of the temple to decide the improvement method. For example, the decision unit analyzes the social media activity of the temple and decides the improvement method based on feedback from believers. For example, the decision unit decides the improvement method in line with trends based on the social media activity of the temple. The decision unit can also analyze the social media activity of the temple and decide the improvement method according to the needs of believers. For example, the decision unit decides the improvement method based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, it is possible to decide the improvement method according to the needs of believers.

[0053] When providing improvement contents, the providing unit can select the optimal providing method by referring to the past providing history. The providing unit, for example, refers to data such as past providing contents and providing results. For example, the providing unit selects the optimal providing method for a similar problem based on the past providing history. The providing unit can also refer to the past providing history and preferentially select successful providing methods. For example, the providing unit analyzes the past providing history and avoids unsuccessful providing methods. In this way, the optimal providing method can be selected by referring to the past providing history.

[0054] The provision unit can apply different provision methods for each type of business when providing the improvement content. The provision unit selects the optimal provision method according to the type of business, such as a method for schedule management or a method for communication. For example, for schedule management, the provision unit automatically manages the schedule of a memorial service using a calendar system. For communication, the provision unit can also automate communication with believers using a chatbot. For example, the provision unit automatically responds to inquiries from believers using a chatbot. In this way, the optimal provision method can be determined by applying different provision methods for each type of business.

[0055] When providing improvement contents, the providing unit can select the optimal providing method by taking into consideration the geographical location information of the temple. For example, the providing unit selects a providing method suitable for the region based on the geographical location information of the temple. For example, the providing unit selects a providing method that utilizes nearby resources by taking into consideration the geographical location information of the temple. The providing unit can also select a providing method that avoids distant resources based on the geographical location information of the temple. For example, the providing unit selects a providing method suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, a providing method suitable for the region can be selected by taking into consideration the geographical location information of the temple.

[0056] When providing improvement contents, the provision unit can analyze the social media activity of the temple to determine the provision method. For example, the provision unit analyzes the social media activity of the temple and determines the provision method based on feedback from believers. For example, the provision unit determines the provision method in line with trends based on the social media activity of the temple. The provision unit can also analyze the social media activity of the temple and determine the provision method according to the needs of believers. For example, the provision unit determines the provision method based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, it is possible to determine the provision method according to the needs of believers.

[0057] The content generation unit can adjust the level of detail based on the importance of the Buddhist teachings when generating the content. For example, the content generation unit generates content including a detailed explanation for the basic teachings of Buddhism. For example, the content generation unit explains basic Buddhist doctrines such as the Four Noble Truths and the Eightfold Path. The content generation unit can also generate content including a concise explanation for the historical background of Buddhism. For example, the content generation unit explains the origin and history of development of Buddhism. Furthermore, the content generation unit can generate content including a detailed explanation and concrete examples for particularly important parts of the Buddhist teachings. For example, the content generation unit explains important parts of the Buddhist teachings with concrete examples. In this way, appropriate content can be generated by adjusting the level of detail based on the importance of the Buddhist teachings.

[0058] The content generation unit can apply different generation algorithms depending on the category of Buddhist teachings when generating content. For example, when generating content related to meditation, the content generation unit applies an algorithm that enhances the relaxation effect. For example, the content generation unit generates content that explains the practice method and effects of meditation. Furthermore, the content generation unit can apply a visually easy-to-understand algorithm when generating content related to the history of Buddhism. For example, the content generation unit generates content that explains the origin and history of development of Buddhism. Furthermore, the content generation unit can apply an algorithm that includes many concrete examples when generating content related to Buddhist teachings. For example, the content generation unit generates content that explains important parts of Buddhist teachings with concrete examples. In this way, appropriate content can be generated by applying different generation algorithms depending on the category of Buddhist teachings.

[0059] The content generation unit can determine the priority order based on the time of submission of the Buddhist teachings when generating the content. For example, the content generation unit preferentially generates content related to important Buddhist events. For example, the content generation unit generates content related to before and after a specific memorial service. The content generation unit can also preferentially generate seasonal content among the Buddhist teachings. For example, the content generation unit generates content related to seasonal Buddhist events. Furthermore, the content generation unit can also preferentially generate highly relevant content based on the time of submission of the Buddhist teachings. For example, the content generation unit preferentially generates content related to a specific event or event. In this way, by determining the priority order based on the time of submission of the Buddhist teachings, it is possible to provide content at an appropriate time.

[0060] The content generation unit can adjust the order based on the relevance of Buddhist teachings when generating the content. For example, the content generation unit generates content that first explains the basic teachings of Buddhism and then explains the detailed contents. For example, the content generation unit first explains the basic doctrines of Buddhism such as the Four Noble Truths and the Eightfold Path, and then explains the detailed contents with specific examples. The content generation unit can also generate content that successively explains highly relevant contents in Buddhist teachings. For example, the content generation unit successively explains highly relevant contents about the history and doctrines of Buddhism. Furthermore, the content generation unit can also generate content in a visually easy-to-understand order based on the relevance of Buddhist teachings. For example, the content generation unit generates content that explains the contents of Buddhist teachings in a visually easy-to-understand order. Thereby, by adjusting the order based on the relevance of Buddhist teachings, it is possible to provide content in an appropriate order.

[0061] When excavating ideas, the idea excavation section can select the optimal excavation method by referring to past idea history. The idea excavation section refers to data such as ideas and implementation results of past events and workshops. For example, the idea excavation section selects the optimal excavation method for a similar problem based on the past idea history. The idea excavation section can also refer to the past idea history and preferentially excavate successful ideas. For example, the idea excavation section analyzes the past idea history and avoids failed ideas. In this way, the optimal excavation method can be selected by referring to the past idea history.

[0062] The idea excavation unit can apply different excavation methods to each category of Buddhist teachings when excavating ideas. For example, when excavating ideas related to meditation, the idea excavation unit applies a method that enhances the relaxation effect. For example, the idea excavation unit excavates an idea for a workshop that explains the practice method and effects of meditation. In addition, the idea excavation unit can apply a visually easy-to-understand method when excavating ideas related to the history of Buddhism. For example, the idea excavation unit excavates an idea for a workshop that explains the origin and history of development of Buddhism. In addition, the idea excavation unit can apply a method that includes many concrete examples when excavating ideas related to Buddhist teachings. For example, the idea excavation unit excavates an idea for a workshop that explains important parts of Buddhist teachings with concrete examples. In this way, appropriate ideas can be excavated by applying different excavation methods to each category of Buddhist teachings.

[0063] When excavating ideas, the idea excavation unit can select an optimal excavation method by taking into consideration the geographical location information of the temple. For example, the idea excavation unit selects a method for excavating ideas suitable for the region based on the geographical location information of the temple. For example, the idea excavation unit selects a method for excavating ideas that utilize nearby resources by taking into consideration the geographical location information of the temple. The idea excavation unit can also select a method for excavating ideas that avoid distant resources based on the geographical location information of the temple. For example, the idea excavation unit excavates ideas suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, ideas suitable for the region can be excavated by taking into consideration the geographical location information of the temple.

[0064] When discovering ideas, the idea excavation unit can analyze the social media activity of the temple to determine the discovery method. For example, the idea excavation unit analyzes the social media activity of the temple and discovers ideas based on feedback from believers. For example, the idea excavation unit discovers ideas that match trends based on the social media activity of the temple. The idea excavation unit can also analyze the social media activity of the temple and discover ideas that meet the needs of believers. For example, the idea excavation unit discovers ideas based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, ideas that meet the needs of believers can be discovered.

[0065] The feedback collection unit can select the optimal collection method by referring to past feedback history when collecting feedback. The feedback collection unit, for example, refers to data such as past feedback content and feedback results. For example, the feedback collection unit selects the optimal collection method for a similar problem based on the past feedback history. The feedback collection unit can also refer to the past feedback history and preferentially select successful collection methods. For example, the feedback collection unit analyzes the past feedback history and avoids unsuccessful collection methods. In this way, the optimal collection method can be selected by referring to the past feedback history.

[0066] The feedback analysis unit can select the optimal analysis method by referring to past feedback data when analyzing feedback. The feedback analysis unit, for example, refers to data such as past feedback content and feedback results. For example, the feedback analysis unit selects the optimal analysis method for a similar problem based on the past feedback data. The feedback analysis unit can also refer to past feedback data and preferentially select successful analysis methods. For example, the feedback analysis unit analyzes past feedback data and avoids unsuccessful analysis methods. In this way, the optimal analysis method can be selected by referring to past feedback data.

[0067] The feedback analysis unit can apply different analysis methods to each type of feedback when analyzing the feedback. The feedback analysis unit selects an optimal analysis method depending on, for example, the type of feedback. For example, the feedback analysis unit analyzes text feedback using text mining technology. The feedback analysis unit can also analyze emotional feedback using sentiment analysis technology. For example, the feedback analysis unit analyzes text data of the feedback and classifies opinions into positive and negative opinions. This enables appropriate analysis by applying different analysis methods to each type of feedback.

[0068] The feedback analysis unit can select the optimal analysis method by taking into consideration the geographical location information of the temple when analyzing the feedback. The feedback analysis unit, for example, selects an analysis method suitable for the region based on the geographical location information of the temple. For example, the feedback analysis unit selects an analysis method that utilizes nearby resources by taking into consideration the geographical location information of the temple. The feedback analysis unit can also select an analysis method that avoids distant resources based on the geographical location information of the temple. For example, the feedback analysis unit selects an analysis method suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, an analysis method suitable for the region can be selected by taking into consideration the geographical location information of the temple.

[0069] During feedback analysis, the feedback analysis unit can analyze the social media activity of the temple and determine the analysis method. For example, the feedback analysis unit analyzes the social media activity of the temple and determines the analysis method based on feedback from believers. For example, the feedback analysis unit determines the analysis method in line with trends based on the social media activity of the temple. The feedback analysis unit can also analyze the social media activity of the temple and determine the analysis method according to the needs of believers. For example, the feedback analysis unit determines the analysis method based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, it is possible to determine the analysis method according to the needs of believers.

[0070] The business analysis unit can optimize the analysis algorithm by referring to past business data when analyzing a business. The business analysis unit refers to business data such as past memorial service schedules and feedback from believers. For example, the business analysis unit selects an optimal analysis algorithm for a similar problem based on the past business data. The business analysis unit can also refer to past business data and preferentially select successful analysis algorithms. For example, the business analysis unit analyzes past business data and avoids unsuccessful analysis algorithms. In this way, the optimal analysis algorithm can be selected by referring to past business data.

[0071] The business analysis unit can apply different analysis methods to different types of business when analyzing business. The business analysis unit selects the most suitable analysis method according to the type of business, such as a method for schedule management or a method for communication. For example, the business analysis unit automatically manages the schedule of a memorial service using a calendar system for schedule management. In addition, the business analysis unit can automate communication with believers using a chatbot for communication. For example, the business analysis unit automatically responds to inquiries from believers using a chatbot. This makes it possible to perform appropriate analysis by applying different analysis methods to different types of business.

[0072] When analyzing a task, the task analysis unit can select the optimal analysis method by taking into consideration the geographic location information of the temple. For example, the task analysis unit selects an analysis method suitable for the region based on the geographic location information of the temple. For example, the task analysis unit selects an analysis method that utilizes nearby resources by taking into consideration the geographic location information of the temple. The task analysis unit can also select an analysis method that avoids distant resources based on the geographic location information of the temple. For example, the task analysis unit selects an analysis method suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, an analysis method suitable for the region can be selected by taking into consideration the geographic location information of the temple.

[0073] During the business analysis, the business analysis unit can analyze the social media activity of the temple and determine the analysis method. For example, the business analysis unit analyzes the social media activity of the temple and determines the analysis method based on feedback from believers. For example, the business analysis unit determines the analysis method in line with trends based on the social media activity of the temple. The business analysis unit can also analyze the social media activity of the temple and determine the analysis method according to the needs of believers. For example, the business analysis unit determines the analysis method based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, it is possible to determine the analysis method according to the needs of believers.

[0074] When proposing efficiency improvements, the efficiency proposal unit can select the optimal proposal method by referring to past proposal history. The efficiency proposal unit, for example, refers to data such as past proposal contents and proposal results. For example, the efficiency proposal unit selects the optimal proposal method for a similar problem based on the past proposal history. The efficiency proposal unit can also refer to the past proposal history and preferentially select successful proposal methods. For example, the efficiency proposal unit analyzes the past proposal history and avoids unsuccessful proposal methods. In this way, the optimal proposal method can be selected by referring to the past proposal history.

[0075] The efficiency proposal unit can apply different proposal methods to each type of work when proposing efficiency improvements. The efficiency proposal unit selects the optimal proposal method according to the type of work, such as a method for schedule management or a method for communication. For example, the efficiency proposal unit automatically manages the schedule of a memorial service using a calendar system for schedule management. In addition, the efficiency proposal unit can also automate communication with believers using a chatbot for communication. For example, the efficiency proposal unit automatically responds to inquiries from believers using a chatbot. This makes it possible to make appropriate proposals by applying different proposal methods to each type of work.

[0076] When proposing efficiency improvements, the efficiency proposal unit can select the optimal proposal method by taking into consideration the geographical location information of the temple. For example, the efficiency proposal unit selects a proposal method suitable for the region based on the geographical location information of the temple. For example, the efficiency proposal unit selects a proposal method that utilizes nearby resources by taking into consideration the geographical location information of the temple. The efficiency proposal unit can also select a proposal method that avoids distant resources based on the geographical location information of the temple. For example, the efficiency proposal unit selects a proposal method suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, a proposal method suitable for the region can be selected by taking into consideration the geographical location information of the temple.

[0077] When proposing efficiency improvements, the efficiency suggestion unit can analyze the social media activity of the temple to determine the proposal method. For example, the efficiency suggestion unit analyzes the social media activity of the temple and determines the proposal method based on feedback from believers. For example, the efficiency suggestion unit determines a proposal method that matches trends based on the social media activity of the temple. The efficiency suggestion unit can also analyze the social media activity of the temple and determine a proposal method that meets the needs of believers. For example, the efficiency suggestion unit determines a proposal method based on information such as the content of posts on social media and the reactions of followers. In this way, a proposal method that meets the needs of believers can be determined by analyzing the social media activity of the temple.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] In addition to the acquisition unit, the system can include a geographic information acquisition unit that takes into consideration the geographic location information of each temple and preferentially acquires highly relevant information. The geographic information acquisition unit can, for example, preferentially acquire event information held near the temple. The geographic information acquisition unit can also filter out distant information and preferentially acquire nearby information. Furthermore, the geographic information acquisition unit can also preferentially acquire highly relevant business information based on information such as the address of the temple and the characteristics of the area. This makes it possible to efficiently acquire highly relevant information by taking into consideration the geographic location information of the temple.

[0080] In addition to the determination unit, the system can include a past data reference unit that optimizes the analysis algorithm by referring to past business data. The past data reference unit can refer to business data such as past memorial service schedules and feedback from believers. The past data reference unit can also select an optimal analysis algorithm for a similar problem based on the past business data. Furthermore, the past data reference unit can refer to past business data and preferentially select a successful analysis algorithm. This makes it possible to improve the accuracy of the analysis by referring to past business data.

[0081] In addition to the providing unit, the system can include a business-specific providing unit that applies different providing methods to different types of business. The business-specific providing unit can select the optimal providing method according to the type of business, such as a method for schedule management or a method for communication. In addition, the business-specific providing unit can automatically manage the schedule of a memorial service using a calendar system for schedule management. Furthermore, the business-specific providing unit can automate communication with believers using a chatbot for communication. This makes it possible to determine the optimal providing method by applying different providing methods to different types of business.

[0082] In addition to the idea excavation section, the system can include a past idea reference section that refers to past idea history to select the optimal excavation method. The past idea reference section can refer to data such as ideas and implementation results of past events and workshops. The past idea reference section can also select the optimal excavation method for a similar problem based on the past idea history. Furthermore, the past idea reference section can refer to the past idea history to preferentially excavate successful ideas. This makes it possible to select the optimal excavation method by referring to the past idea history.

[0083] In addition to the feedback collection unit, the system can include a social media analysis unit that analyzes the social media activity of the temple and collects related information when collecting feedback. The social media analysis unit can, for example, analyze the social media activity of the temple and collect feedback from believers. The social media analysis unit can also collect trend information related to business based on the social media activity of the temple. Furthermore, the social media analysis unit can also collect related business information based on information such as the content of posts on social media and the reactions of followers. This makes it possible to efficiently collect related information by analyzing the social media activity of the temple.

[0084] The process flow of the first embodiment will be briefly described below.

[0085] Step 1: The acquisition unit acquires business information indicating the business of the temple. The business information includes, for example, a schedule of memorial services, information on believers, and income and expenditure reports. The acquisition unit can acquire business information of each temple from a database, and can also automatically collect business information in real time using a sensor. Step 2: The decision unit decides on the improvement content of the business based on the business information acquired by the acquisition unit. The improvement content includes, for example, making the business process more efficient and improving communication with followers. The decision unit uses AI to analyze the business information and decides on the optimal improvement content. Step 3: The provision unit provides the improvement content determined by the determination unit to the temple and the user. The provision unit can provide digital worship in a virtual space, and can also automatically provide improvement content using AI.

[0086] (Example 2) The system according to the embodiment of the present invention is a system for streamlining temple operations and conveying Buddhist teachings to the next generation. This system acquires business information of each temple, determines the content of improvements to the operations based on the acquired business information, and provides the determined improvement content to the temple and users. Furthermore, the system generates content explaining Buddhist teachings and discovers ideas for events and workshops based on Buddhism. Feedback is collected from believers and the general public, and the collected feedback is analyzed to identify improvements to the platform. This system makes temple operations more efficient and makes it easier to convey Buddhist teachings to the next generation. In addition, by utilizing AI, the digitalization of operations will progress and the digital transformation of the temple industry will accelerate. For example, an acquisition unit acquires business information of each temple, and a decision unit decides the content of improvements to the operations based on the acquired business information. The determined improvement content is provided to the temple and users by a provision unit. Furthermore, a content generation unit generates content explaining Buddhist teachings, and an idea excavation unit discovers ideas for events and workshops based on Buddhism. It also includes a feedback collection section that collects feedback from believers and the general public, which is then analyzed by a feedback analysis section to identify improvements to the platform, so that the system can streamline temple operations and pass on Buddhist teachings to future generations.

[0087] The system according to the embodiment includes an acquisition unit, a determination unit, and a provision unit. The acquisition unit acquires business information indicating the business of the temple. The business information includes, for example, a schedule of a memorial service, information on believers, and income and expenditure reports, but is not limited to such examples. The acquisition unit acquires, for example, business information of each temple from a database. The acquisition unit can also collect business information in real time. For example, the acquisition unit can automatically collect business information using a sensor. The determination unit determines the improvement content of the business based on the business information acquired by the acquisition unit. The improvement content includes, for example, the efficiency of business processes, the improvement of communication with believers, etc., but is not limited to such examples. The determination unit analyzes the business information using, for example, AI, and determines the optimal improvement content. The provision unit provides the improvement content determined by the determination unit to the temple and the user. The provision unit can also provide, for example, a digital worship in a virtual space. The provision unit can also automatically provide the improvement content using, for example, AI. This allows the system to streamline the business of the temple and pass on the teachings of Buddhism to the next generation.

[0088] The acquisition unit acquires business information showing the temple's business. The business information includes, for example, a schedule of a memorial service, information on believers, and income and expenditure reports, but is not limited to such examples. The acquisition unit acquires, for example, business information of each temple from a database. Specifically, the acquisition unit collects data through a dedicated application or web portal for each temple to record its daily business. This allows detailed data such as the schedule of a memorial service, information on believers, and income and expenditure reports to be managed in a centralized manner. Furthermore, the acquisition unit can collect business information in real time. For example, the acquisition unit can automatically collect business information using a sensor. Specifically, cameras and microphones for monitoring the progress of a memorial service, RFID tags and beacons for grasping the attendance status of believers, and accounting software for automating income and expenditure reports are possible. These devices and software transmit data to a central database via the Internet, allowing the acquisition unit to access the data in real time. This allows the acquisition unit to efficiently and accurately collect business information and grasp the entire business of the temple. Furthermore, the acquisition unit can appropriately classify the collected data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the decision unit and the provision unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the acquisition unit to collect data efficiently and effectively and improve the performance of the entire system.

[0089] The determination unit determines the improvement content of the business based on the business information acquired by the acquisition unit. The improvement content includes, for example, the efficiency of the business process, the improvement of communication with believers, etc., but is not limited to such examples. The determination unit, for example, uses AI to analyze the business information and determines the optimal improvement content. Specifically, the AI ​​uses a machine learning algorithm to analyze past business data and extract patterns and trends. For example, it analyzes the fluctuation of the number of participants in the schedule of the memorial service, the content of the feedback from the believers, the balance of income and expenditure in the income and expenditure report, etc. This allows the AI ​​to identify which part of the business process has room for efficiency and what kind of improvement is needed in communication with the believers. Furthermore, the AI ​​can also analyze feedback from the believers using natural language processing technology and generate specific improvement proposals. For example, it analyzes what kind of impressions the believers have about the memorial service and what kind of requests they have, and proposes the improvement content based on that. In addition, the AI ​​can perform simulations and predict the effect when the proposed improvement content is implemented. As a result, the determination unit can determine the improvement content of the business with a scientific approach based on data and improve the business efficiency of the temple. Furthermore, the determination unit can periodically review the improvement content and aim for continuous improvement. This enables the decision-making department to keep the temple's operations in an optimal state at all times and maintain good relationships with believers.

[0090] The provision unit provides the improvement content determined by the determination unit to the temple and the user. The provision unit can also provide, for example, digital worship in a virtual space. Specifically, the provision unit uses virtual reality (VR) technology to provide an environment in which believers can participate in a temple memorial service from the comfort of their own homes. By wearing a VR headset, believers can access the temple in the virtual space and experience the memorial service in real time. The provision unit can also automatically provide the improvement content using, for example, AI. Specifically, the AI ​​provides customized services according to the believer's individual needs and requests. For example, if a believer is interested in a specific memorial service, the AI ​​automatically provides information about the memorial service and how to participate. In addition, individual recommendations can be made based on the memorial services that the believer has participated in in the past and the feedback that the believer has provided. Furthermore, the provision unit can provide the improvement content in a variety of ways. For example, it can send notifications through a smartphone app or provide information through email or SMS. The provision unit can also provide services that reflect the improvement content when believers visit the temple in person. For example, the reception system can be improved to allow believers to worship smoothly. The Service Department can also collect feedback from believers and evaluate the effectiveness of the improvements. This allows the Service Department to provide optimal services to believers and improve the efficiency of the temple's operations. Furthermore, the Service Department can continually review the improvements and respond flexibly to the needs of believers. This allows the Service Department to improve the efficiency of temple operations and pass on the teachings of Buddhism to the next generation.

[0091] The system includes a content generation unit that generates content that explains the teachings of Buddhism. The content generation unit generates content that explains the teachings of Buddhism. The content includes, for example, basic teachings of Buddhism, history, and meditation methods, but is not limited to such examples. The content generation unit automatically generates content that explains the teachings of Buddhism using, for example, AI. For example, the content generation unit generates text that explains the teachings of Buddhism using a text generation AI (for example, LLM). The content generation unit can also generate video and audio content that explains the teachings of Buddhism using a multimodal generation AI. For example, the content generation unit generates a video that explains the teachings of Buddhism and provides the video to a user. This allows the system to pass on the teachings of Buddhism to the next generation.

[0092] The system includes an idea excavation unit that excavates ideas for events or workshops based on Buddhist teachings. The idea excavation unit excavates ideas for events or workshops based on Buddhist teachings. Ideas include, but are not limited to, meditation sessions, sutra copying sessions, and Buddhist lectures. The idea excavation unit automatically excavates ideas for events or workshops based on Buddhist teachings, for example, using AI. For example, the idea excavation unit excavates ideas for meditation or sutra copying workshops using a generation AI. The idea excavation unit can also excavate ideas for lectures on Buddhism using a generation AI. For example, the idea excavation unit excavates ideas for events based on Buddhist teachings and provides the ideas to temples and users. This allows the system to pass on the teachings of Buddhism to the next generation.

[0093] The system includes a feedback collection unit that collects feedback from followers or the general public. The feedback collection unit collects feedback from followers and the general public. The feedback includes, for example, impressions of an event, opinions on the usability of the system, and the like, but is not limited to such examples. The feedback collection unit automatically collects feedback using, for example, AI. For example, the feedback collection unit collects feedback through an online survey. The feedback collection unit can also collect feedback through social media. For example, the feedback collection unit collects comments and messages on SNS and uses them as feedback. This allows the system to collect feedback from followers and the general public and identify areas for improvement in the system.

[0094] The system includes a feedback analysis unit that analyzes the feedback collected by the feedback collection unit. The feedback analysis unit analyzes the collected feedback. The analysis of the feedback includes, but is not limited to, for example, text mining, sentiment analysis, and the like. The feedback analysis unit automatically analyzes the feedback, for example, using AI. For example, the feedback analysis unit analyzes the content of the feedback using text mining technology. The feedback analysis unit can also analyze the emotional aspects of the feedback using sentiment analysis technology. For example, the feedback analysis unit analyzes the text data of the feedback and classifies positive opinions and negative opinions. This allows the system to analyze the collected feedback and identify improvements to the platform.

[0095] The system includes a business analysis unit that analyzes a business process including the automation of schedule management of a memorial service or communication with believers. The business analysis unit analyzes a business process including the automation of schedule management of a memorial service or communication with believers. The business process includes, for example, schedule management of a memorial service and communication with believers, but is not limited to such examples. The business analysis unit automatically analyzes the business process using, for example, AI. For example, the business analysis unit automatically manages the schedule of a memorial service using a schedule management system. The business analysis unit can also automate communication with believers using a chatbot. For example, the business analysis unit automatically responds to inquiries from believers using a chatbot. This allows the system to analyze the business process and improve the efficiency of business.

[0096] The system includes an efficiency suggestion unit that makes a proposal to reduce the burden on the monk based on the information analyzed by the business analysis unit. The efficiency suggestion unit makes a proposal to reduce the burden on the monk based on the information analyzed by the business analysis unit. The proposal includes, for example, the automation of business, the division of business, etc., but is not limited to such examples. The efficiency suggestion unit automatically makes a proposal to reduce the burden on the monk, for example, using AI. For example, the efficiency suggestion unit proposes the automation of business to reduce the burden on the monk. The efficiency suggestion unit can also propose the division of business to reduce the burden on the monk. For example, the efficiency suggestion unit proposes the division of business to divide the business. In this way, the system can reduce the burden on the monk and improve the efficiency of business.

[0097] The content generation unit can generate content that explains the basic teachings and history of Buddhism and methods of meditation. For example, the content generation unit generates content including a detailed explanation of the basic teachings of Buddhism. For example, the content generation unit explains basic doctrines of Buddhism such as the Four Noble Truths and the Eightfold Path. The content generation unit can also generate content including a concise explanation of the history of Buddhism. For example, the content generation unit explains the origin and history of development of Buddhism. Furthermore, the content generation unit can generate content including specific techniques for methods of meditation. For example, the content generation unit explains how to practice meditation and its effects. In this way, by generating content that explains the basic teachings and history of Buddhism and methods of meditation, the teachings of Buddhism can be passed on to the next generation.

[0098] The idea excavation department can excavate ideas for meditation and copying sutras workshops, and lectures on Buddhism. For example, the idea excavation department excavates ideas for workshops that explain the practice and effects of meditation. For example, the idea excavation department proposes a workshop to learn basic techniques of meditation. The idea excavation department can also excavate ideas for workshops that explain the meaning and methods of copying sutras. For example, the idea excavation department proposes a workshop to calm the mind through the practice of copying sutras. Furthermore, the idea excavation department can excavate ideas for lectures on Buddhist teachings. For example, the idea excavation department proposes an event where an expert will give a lecture on the history and doctrines of Buddhism. In this way, by excavating ideas for meditation and copying sutras workshops, and lectures on Buddhism, the teachings of Buddhism can be passed on to the next generation.

[0099] The feedback collection unit can collect feedback from believers and the general public. For example, the feedback collection unit collects feedback about an event and opinions about the usability of the system. For example, the feedback collection unit collects feedback through an online survey. The feedback collection unit can also collect feedback through social media. For example, the feedback collection unit collects comments and messages on SNS and uses them as feedback. In this way, by collecting feedback from believers and the general public, it is possible to identify areas for improvement in the system.

[0100] The feedback analysis unit can analyze the feedback collected by the feedback collection unit and identify areas for improvement of the platform. The feedback analysis unit can analyze the content of the feedback, for example, using text mining technology. For example, the feedback analysis unit can analyze the text data of the feedback and classify positive opinions and negative opinions. The feedback analysis unit can also analyze the emotional aspects of the feedback using sentiment analysis technology. For example, the feedback analysis unit can analyze the text data of the feedback and calculate a sentiment score. This allows the collected feedback to be analyzed and areas for improvement of the platform to be identified, thereby improving the system.

[0101] The business analysis unit can estimate the user's emotion and specifically adjust the analysis method of the business process based on the estimated user's emotion. The business analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the business analysis unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The business analysis unit can also estimate the user's emotion using text analysis technology. For example, the business analysis unit analyzes the user's text data and calculates an emotion score. This allows for more appropriate analysis by adjusting the analysis method of the business process based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0102] The business analysis unit can optimize the analysis algorithm by referring to past business data. The business analysis unit refers to business data such as past memorial service schedules and feedback from believers. For example, the business analysis unit selects an optimal analysis algorithm for a similar problem based on the past business data. The business analysis unit can also refer to past business data and preferentially select successful analysis algorithms. For example, the business analysis unit analyzes past business data and avoids unsuccessful analysis algorithms. In this way, the accuracy of analysis is improved by optimizing the analysis algorithm by referring to past business data.

[0103] The business analysis unit can apply different analysis methods to different types of business. The business analysis unit selects the most suitable analysis method according to the type of business, such as a method for schedule management or a method for communication. For example, for schedule management, the business analysis unit automatically manages the schedule of a memorial service using a calendar system. For communication, the business analysis unit can also automate communication with believers using a chatbot. For example, the business analysis unit automatically responds to inquiries from believers using a chatbot. This improves the accuracy of analysis by applying different analysis methods to different types of business.

[0104] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring business information based on the estimated user's emotion. The acquisition unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the acquisition unit analyzes facial expression data of the user captured by a camera and calculates an emotion score. The acquisition unit can also estimate the user's emotion using text analysis technology. For example, the acquisition unit analyzes text data of the user and calculates an emotion score. This allows the information to be acquired at a more appropriate timing by adjusting the timing of acquiring business information based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] The acquisition unit can analyze the past business history of each temple and select the optimal acquisition method. The acquisition unit analyzes business history such as, for example, past memorial service schedules and feedback from believers. For example, the acquisition unit selects a method for acquiring business information at a specific time period from the past business history. The acquisition unit can also select a method for preferentially acquiring business information at the time of a specific event based on the past business history. For example, the acquisition unit analyzes the past business history and selects the optimal acquisition method for each type of business. In this way, the optimal acquisition method can be selected by analyzing the past business history.

[0106] The acquisition unit can perform filtering based on the current situation of the temple or a specific event when acquiring the business information. For example, the acquisition unit takes into consideration the current situation of the temple and preferentially acquires only important business information. For example, when a specific event is being held, the acquisition unit preferentially acquires business information related to the event. The acquisition unit can also filter unnecessary information based on the current situation and events of the temple to efficiently acquire business information. For example, the acquisition unit preferentially acquires important business information based on information such as the status of memorial services and special events. In this way, important information can be preferentially acquired by filtering based on the current situation of the temple and specific events.

[0107] The acquisition unit can estimate the user's emotion and determine the priority of the business information to be acquired based on the estimated user's emotion. The acquisition unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the acquisition unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The acquisition unit can also estimate the user's emotion using text analysis technology. For example, the acquisition unit analyzes the user's text data and calculates an emotion score. This allows important information to be preferentially acquired by determining the priority of the business information based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] When acquiring business information, the acquisition unit can preferentially acquire highly relevant information by taking into consideration the geographical location information of the temple. For example, the acquisition unit preferentially acquires nearby event information based on the geographical location information of the temple. For example, the acquisition unit preferentially acquires business information related to the area by taking into consideration the geographical location information of the temple. The acquisition unit can also filter distant information based on the geographical location information of the temple and preferentially acquire nearby information. For example, the acquisition unit preferentially acquires highly relevant business information based on information such as the address of the temple and the characteristics of the area. In this way, highly relevant information can be preferentially acquired by taking into consideration the geographical location information of the temple.

[0109] When acquiring business information, the acquisition unit can analyze the social media activity of the temple and acquire related information. For example, the acquisition unit analyzes the social media activity of the temple and acquires related event information. For example, the acquisition unit acquires feedback from believers based on the social media activity of the temple. The acquisition unit can also analyze the social media activity of the temple and acquire trend information related to the business. For example, the acquisition unit acquires related business information based on information such as the content of posts on social media and the reactions of followers. This makes it possible to efficiently acquire related information by analyzing the social media activity of the temple.

[0110] The determination unit can estimate the user's emotion and adjust the method of determining the business improvement content based on the estimated user's emotion. The determination unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the determination unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The determination unit can also estimate the user's emotion using text analysis technology. For example, the determination unit analyzes the user's text data and calculates an emotion score. This allows for more appropriate improvement by adjusting the method of determining the business improvement content based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] When deciding the content of business improvement, the decision unit can refer to past improvement history to select the optimal improvement method. The decision unit, for example, refers to data such as past improvement content and improvement results. For example, the decision unit selects the optimal improvement method for a similar problem based on the past improvement history. The decision unit can also refer to the past improvement history and preferentially select successful improvement methods. For example, the decision unit analyzes the past improvement history and avoids unsuccessful improvement methods. In this way, the optimal improvement method can be selected by referring to the past improvement history.

[0112] The decision unit can apply different decision algorithms for each type of business when deciding the business improvement content. The decision unit selects an optimal decision algorithm according to the type of business, such as an algorithm for schedule management or an algorithm for communication. For example, for schedule management, the decision unit automatically manages the schedule of a memorial service using a calendar system. For communication, the decision unit can also automate communication with believers using a chatbot. For example, the decision unit automatically responds to inquiries from believers using a chatbot. In this way, by applying different decision algorithms for each type of business, an optimal improvement method can be determined.

[0113] The determination unit can estimate the user's emotion and determine the priority of the improvement contents based on the estimated user's emotion. The determination unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the determination unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The determination unit can also estimate the user's emotion using text analysis technology. For example, the determination unit analyzes the user's text data and calculates an emotion score. This allows important improvement contents to be preferentially executed by determining the priority of the improvement contents based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0114] When deciding the content of business improvement, the decision unit can select the optimal improvement method by taking into consideration the geographical location information of the temple. The decision unit, for example, selects an improvement method suitable for the area based on the geographical location information of the temple. For example, the decision unit selects an improvement method that utilizes nearby resources by taking into consideration the geographical location information of the temple. The decision unit can also select an improvement method that avoids distant resources based on the geographical location information of the temple. For example, the decision unit selects an improvement method suitable for the area based on information such as the address of the temple and the characteristics of the area. In this way, an improvement method suitable for the area can be selected by taking into consideration the geographical location information of the temple.

[0115] When determining the content of business improvement, the decision unit can analyze the social media activity of the temple to decide the improvement method. For example, the decision unit analyzes the social media activity of the temple and decides the improvement method based on feedback from believers. For example, the decision unit decides the improvement method in line with trends based on the social media activity of the temple. The decision unit can also analyze the social media activity of the temple and decide the improvement method according to the needs of believers. For example, the decision unit decides the improvement method based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, it is possible to decide the improvement method according to the needs of believers.

[0116] The providing unit can estimate the user's emotion and adjust the method of providing the improvement content based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the providing unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The providing unit can also estimate the user's emotion using text analysis technology. For example, the providing unit analyzes the user's text data and calculates an emotion score. This allows for more appropriate provision by adjusting the method of providing the improvement content based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0117] When providing improvement contents, the providing unit can select the optimal providing method by referring to the past providing history. The providing unit, for example, refers to data such as past providing contents and providing results. For example, the providing unit selects the optimal providing method for a similar problem based on the past providing history. The providing unit can also refer to the past providing history and preferentially select successful providing methods. For example, the providing unit analyzes the past providing history and avoids unsuccessful providing methods. In this way, the optimal providing method can be selected by referring to the past providing history.

[0118] The provision unit can apply different provision methods for each type of business when providing the improvement content. The provision unit selects the optimal provision method according to the type of business, such as a method for schedule management or a method for communication. For example, for schedule management, the provision unit automatically manages the schedule of a memorial service using a calendar system. For communication, the provision unit can also automate communication with believers using a chatbot. For example, the provision unit automatically responds to inquiries from believers using a chatbot. In this way, the optimal provision method can be determined by applying different provision methods for each type of business.

[0119] The providing unit can estimate the user's emotion and determine the priority of the content to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the providing unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The providing unit can also estimate the user's emotion using text analysis technology. For example, the providing unit analyzes the user's text data and calculates an emotion score. This allows important content to be provided preferentially by determining the priority of the content to be provided based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0120] When providing improvement contents, the providing unit can select the optimal providing method by taking into consideration the geographical location information of the temple. For example, the providing unit selects a providing method suitable for the region based on the geographical location information of the temple. For example, the providing unit selects a providing method that utilizes nearby resources by taking into consideration the geographical location information of the temple. The providing unit can also select a providing method that avoids distant resources based on the geographical location information of the temple. For example, the providing unit selects a providing method suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, a providing method suitable for the region can be selected by taking into consideration the geographical location information of the temple.

[0121] When providing improvement contents, the provision unit can analyze the social media activity of the temple to determine the provision method. For example, the provision unit analyzes the social media activity of the temple and determines the provision method based on feedback from believers. For example, the provision unit determines the provision method in line with trends based on the social media activity of the temple. The provision unit can also analyze the social media activity of the temple and determine the provision method according to the needs of believers. For example, the provision unit determines the provision method based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, it is possible to determine the provision method according to the needs of believers.

[0122] The content generation unit can estimate the user's emotion and adjust the content expression method based on the estimated user's emotion. The content generation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the content generation unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The content generation unit can also estimate the user's emotion using text analysis technology. For example, the content generation unit analyzes the user's text data and calculates an emotion score. This allows more appropriate content to be generated by adjusting the content expression method based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0123] The content generation unit can adjust the level of detail based on the importance of the Buddhist teachings when generating the content. For example, the content generation unit generates content including a detailed explanation for the basic teachings of Buddhism. For example, the content generation unit explains basic Buddhist doctrines such as the Four Noble Truths and the Eightfold Path. The content generation unit can also generate content including a concise explanation for the historical background of Buddhism. For example, the content generation unit explains the origin and history of development of Buddhism. Furthermore, the content generation unit can generate content including a detailed explanation and concrete examples for particularly important parts of the Buddhist teachings. For example, the content generation unit explains important parts of the Buddhist teachings with concrete examples. In this way, appropriate content can be generated by adjusting the level of detail based on the importance of the Buddhist teachings.

[0124] The content generation unit can apply different generation algorithms depending on the category of Buddhist teachings when generating content. For example, when generating content related to meditation, the content generation unit applies an algorithm that enhances the relaxation effect. For example, the content generation unit generates content that explains the practice method and effects of meditation. Furthermore, the content generation unit can apply a visually easy-to-understand algorithm when generating content related to the history of Buddhism. For example, the content generation unit generates content that explains the origin and history of development of Buddhism. Furthermore, the content generation unit can apply an algorithm that includes many concrete examples when generating content related to Buddhist teachings. For example, the content generation unit generates content that explains important parts of Buddhist teachings with concrete examples. In this way, appropriate content can be generated by applying different generation algorithms depending on the category of Buddhist teachings.

[0125] The content generation unit can estimate the user's emotion and adjust the length of the content based on the estimated user's emotion. The content generation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the content generation unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The content generation unit can also estimate the user's emotion using text analysis technology. For example, the content generation unit analyzes the user's text data and calculates an emotion score. This allows more appropriate content to be generated by adjusting the length of the content based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0126] The content generation unit can determine the priority order based on the time of submission of the Buddhist teachings when generating the content. For example, the content generation unit preferentially generates content related to important Buddhist events. For example, the content generation unit generates content related to before and after a specific memorial service. The content generation unit can also preferentially generate seasonal content among the Buddhist teachings. For example, the content generation unit generates content related to seasonal Buddhist events. Furthermore, the content generation unit can also preferentially generate highly relevant content based on the time of submission of the Buddhist teachings. For example, the content generation unit preferentially generates content related to a specific event or event. In this way, by determining the priority order based on the time of submission of the Buddhist teachings, it is possible to provide content at an appropriate time.

[0127] The content generation unit can adjust the order based on the relevance of Buddhist teachings when generating the content. For example, the content generation unit generates content that first explains the basic teachings of Buddhism and then explains the detailed contents. For example, the content generation unit first explains the basic doctrines of Buddhism such as the Four Noble Truths and the Eightfold Path, and then explains the detailed contents with specific examples. The content generation unit can also generate content that successively explains highly relevant contents in Buddhist teachings. For example, the content generation unit successively explains highly relevant contents about the history and doctrines of Buddhism. Furthermore, the content generation unit can also generate content in a visually easy-to-understand order based on the relevance of Buddhist teachings. For example, the content generation unit generates content that explains the contents of Buddhist teachings in a visually easy-to-understand order. Thereby, by adjusting the order based on the relevance of Buddhist teachings, it is possible to provide content in an appropriate order.

[0128] The idea excavation unit can estimate the user's emotion and adjust the idea excavation method based on the estimated user's emotion. The idea excavation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the idea excavation unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The idea excavation unit can also estimate the user's emotion using text analysis technology. For example, the idea excavation unit analyzes the user's text data and calculates an emotion score. This allows more appropriate ideas to be excavated by adjusting the idea excavation method based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0129] When excavating ideas, the idea excavation section can select the optimal excavation method by referring to past idea history. The idea excavation section refers to data such as ideas and implementation results of past events and workshops. For example, the idea excavation section selects the optimal excavation method for a similar problem based on the past idea history. The idea excavation section can also refer to the past idea history and preferentially excavate successful ideas. For example, the idea excavation section analyzes the past idea history and avoids failed ideas. In this way, the optimal excavation method can be selected by referring to the past idea history.

[0130] The idea excavation unit can apply different excavation methods to each category of Buddhist teachings when excavating ideas. For example, when excavating ideas related to meditation, the idea excavation unit applies a method that enhances the relaxation effect. For example, the idea excavation unit excavates an idea for a workshop that explains the practice method and effects of meditation. In addition, the idea excavation unit can apply a visually easy-to-understand method when excavating ideas related to the history of Buddhism. For example, the idea excavation unit excavates an idea for a workshop that explains the origin and history of development of Buddhism. In addition, the idea excavation unit can apply a method that includes many concrete examples when excavating ideas related to Buddhist teachings. For example, the idea excavation unit excavates an idea for a workshop that explains important parts of Buddhist teachings with concrete examples. In this way, appropriate ideas can be excavated by applying different excavation methods to each category of Buddhist teachings.

[0131] The idea excavation unit can estimate the user's emotion and determine the priority order of ideas based on the estimated user's emotion. The idea excavation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the idea excavation unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The idea excavation unit can also estimate the user's emotion using text analysis technology. For example, the idea excavation unit analyzes the user's text data and calculates an emotion score. This allows important ideas to be preferentially excavated by determining the priority order of ideas based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0132] When excavating ideas, the idea excavation unit can select an optimal excavation method by taking into consideration the geographical location information of the temple. For example, the idea excavation unit selects a method for excavating ideas suitable for the region based on the geographical location information of the temple. For example, the idea excavation unit selects a method for excavating ideas that utilize nearby resources by taking into consideration the geographical location information of the temple. The idea excavation unit can also select a method for excavating ideas that avoid distant resources based on the geographical location information of the temple. For example, the idea excavation unit excavates ideas suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, ideas suitable for the region can be excavated by taking into consideration the geographical location information of the temple.

[0133] When discovering ideas, the idea excavation unit can analyze the social media activity of the temple to determine the discovery method. For example, the idea excavation unit analyzes the social media activity of the temple and discovers ideas based on feedback from believers. For example, the idea excavation unit discovers ideas that match trends based on the social media activity of the temple. The idea excavation unit can also analyze the social media activity of the temple and discover ideas that meet the needs of believers. For example, the idea excavation unit discovers ideas based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, ideas that meet the needs of believers can be discovered.

[0134] The feedback collection unit can estimate the user's emotion and adjust the feedback collection method based on the estimated user's emotion. The feedback collection unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the feedback collection unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The feedback collection unit can also estimate the user's emotion using text analysis technology. For example, the feedback collection unit analyzes the user's text data and calculates an emotion score. This allows more appropriate feedback to be collected by adjusting the feedback collection method based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0135] The feedback collection unit can select the optimal collection method by referring to past feedback history when collecting feedback. The feedback collection unit, for example, refers to data such as past feedback content and feedback results. For example, the feedback collection unit selects the optimal collection method for a similar problem based on the past feedback history. The feedback collection unit can also refer to the past feedback history and preferentially select successful collection methods. For example, the feedback collection unit analyzes the past feedback history and avoids unsuccessful collection methods. In this way, the optimal collection method can be selected by referring to the past feedback history.

[0136] The feedback analysis unit can estimate the user's emotion and adjust the feedback analysis method based on the estimated user's emotion. The feedback analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the feedback analysis unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The feedback analysis unit can also estimate the user's emotion using text analysis technology. For example, the feedback analysis unit analyzes the user's text data and calculates an emotion score. This allows for more appropriate analysis by adjusting the feedback analysis method based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0137] The feedback analysis unit can select the optimal analysis method by referring to past feedback data when analyzing feedback. The feedback analysis unit, for example, refers to data such as past feedback content and feedback results. For example, the feedback analysis unit selects the optimal analysis method for a similar problem based on the past feedback data. The feedback analysis unit can also refer to past feedback data and preferentially select successful analysis methods. For example, the feedback analysis unit analyzes past feedback data and avoids unsuccessful analysis methods. In this way, the optimal analysis method can be selected by referring to past feedback data.

[0138] The feedback analysis unit can apply different analysis methods to each type of feedback when analyzing the feedback. The feedback analysis unit selects an optimal analysis method depending on, for example, the type of feedback. For example, the feedback analysis unit analyzes text feedback using text mining technology. The feedback analysis unit can also analyze emotional feedback using sentiment analysis technology. For example, the feedback analysis unit analyzes text data of the feedback and classifies opinions into positive and negative opinions. This enables appropriate analysis by applying different analysis methods to each type of feedback.

[0139] The feedback analysis unit can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. The feedback analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the feedback analysis unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The feedback analysis unit can also estimate the user's emotion using text analysis technology. For example, the feedback analysis unit analyzes the user's text data and calculates an emotion score. This allows important feedback to be preferentially analyzed by determining the priority of feedback based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0140] The feedback analysis unit can select the optimal analysis method by taking into consideration the geographical location information of the temple when analyzing the feedback. The feedback analysis unit, for example, selects an analysis method suitable for the region based on the geographical location information of the temple. For example, the feedback analysis unit selects an analysis method that utilizes nearby resources by taking into consideration the geographical location information of the temple. The feedback analysis unit can also select an analysis method that avoids distant resources based on the geographical location information of the temple. For example, the feedback analysis unit selects an analysis method suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, an analysis method suitable for the region can be selected by taking into consideration the geographical location information of the temple.

[0141] During feedback analysis, the feedback analysis unit can analyze the social media activity of the temple and determine the analysis method. For example, the feedback analysis unit analyzes the social media activity of the temple and determines the analysis method based on feedback from believers. For example, the feedback analysis unit determines the analysis method in line with trends based on the social media activity of the temple. The feedback analysis unit can also analyze the social media activity of the temple and determine the analysis method according to the needs of believers. For example, the feedback analysis unit determines the analysis method based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, it is possible to determine the analysis method according to the needs of believers.

[0142] The business analysis unit can estimate the user's emotion and adjust the analysis method of the business process based on the estimated user's emotion. The business analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the business analysis unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The business analysis unit can also estimate the user's emotion using text analysis technology. For example, the business analysis unit analyzes the user's text data and calculates an emotion score. This allows for more appropriate analysis by adjusting the analysis method of the business process based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0143] The business analysis unit can optimize the analysis algorithm by referring to past business data when analyzing a business. The business analysis unit refers to business data such as past memorial service schedules and feedback from believers. For example, the business analysis unit selects an optimal analysis algorithm for a similar problem based on the past business data. The business analysis unit can also refer to past business data and preferentially select successful analysis algorithms. For example, the business analysis unit analyzes past business data and avoids unsuccessful analysis algorithms. In this way, the optimal analysis algorithm can be selected by referring to past business data.

[0144] The business analysis unit can apply different analysis methods to different types of business when analyzing business. The business analysis unit selects the most suitable analysis method according to the type of business, such as a method for schedule management or a method for communication. For example, the business analysis unit automatically manages the schedule of a memorial service using a calendar system for schedule management. In addition, the business analysis unit can automate communication with believers using a chatbot for communication. For example, the business analysis unit automatically responds to inquiries from believers using a chatbot. This makes it possible to perform appropriate analysis by applying different analysis methods to different types of business.

[0145] The business analysis unit can estimate the user's emotion and determine the priority of business processes based on the estimated user's emotion. The business analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the business analysis unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The business analysis unit can also estimate the user's emotion using text analysis technology. For example, the business analysis unit analyzes the user's text data and calculates an emotion score. This allows important business processes to be analyzed preferentially by determining the priority of business processes based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0146] When analyzing a task, the task analysis unit can select the optimal analysis method by taking into consideration the geographic location information of the temple. For example, the task analysis unit selects an analysis method suitable for the region based on the geographic location information of the temple. For example, the task analysis unit selects an analysis method that utilizes nearby resources by taking into consideration the geographic location information of the temple. The task analysis unit can also select an analysis method that avoids distant resources based on the geographic location information of the temple. For example, the task analysis unit selects an analysis method suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, an analysis method suitable for the region can be selected by taking into consideration the geographic location information of the temple.

[0147] During the business analysis, the business analysis unit can analyze the social media activity of the temple and determine the analysis method. For example, the business analysis unit analyzes the social media activity of the temple and determines the analysis method based on feedback from believers. For example, the business analysis unit determines the analysis method in line with trends based on the social media activity of the temple. The business analysis unit can also analyze the social media activity of the temple and determine the analysis method according to the needs of believers. For example, the business analysis unit determines the analysis method based on information such as the content of posts on social media and the reactions of followers. In this way, by analyzing the social media activity of the temple, it is possible to determine the analysis method according to the needs of believers.

[0148] The efficiency improvement suggestion unit can estimate the user's emotion and adjust the efficiency improvement suggestion method based on the estimated user's emotion. The efficiency improvement suggestion unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the efficiency improvement suggestion unit analyzes the user's facial expression data captured by a camera and calculates an emotion score. The efficiency improvement suggestion unit can also estimate the user's emotion using text analysis technology. For example, the efficiency improvement suggestion unit analyzes the user's text data and calculates an emotion score. This allows for more appropriate suggestions by adjusting the efficiency improvement suggestion method based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0149] When proposing efficiency improvements, the efficiency proposal unit can select the optimal proposal method by referring to past proposal history. The efficiency proposal unit, for example, refers to data such as past proposal contents and proposal results. For example, the efficiency proposal unit selects the optimal proposal method for a similar problem based on the past proposal history. The efficiency proposal unit can also refer to the past proposal history and preferentially select successful proposal methods. For example, the efficiency proposal unit analyzes the past proposal history and avoids unsuccessful proposal methods. In this way, the optimal proposal method can be selected by referring to the past proposal history.

[0150] The efficiency proposal unit can apply different proposal methods to each type of work when proposing efficiency improvements. The efficiency proposal unit selects the optimal proposal method according to the type of work, such as a method for schedule management or a method for communication. For example, the efficiency proposal unit automatically manages the schedule of a memorial service using a calendar system for schedule management. In addition, the efficiency proposal unit can also automate communication with believers using a chatbot for communication. For example, the efficiency proposal unit automatically responds to inquiries from believers using a chatbot. This makes it possible to make appropriate proposals by applying different proposal methods to each type of work.

[0151] The efficiency suggestion unit can estimate the user's emotions and determine the priority of efficiency improvement proposals based on the estimated user's emotions. The efficiency suggestion unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the efficiency suggestion unit analyzes facial expression data of the user captured by a camera and calculates an emotion score. The efficiency suggestion unit can also estimate the user's emotions using text analysis technology. For example, the efficiency suggestion unit analyzes text data of the user and calculates an emotion score. This allows important proposals to be prioritized by determining the priority of efficiency improvement proposals based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0152] When proposing efficiency improvements, the efficiency proposal unit can select the optimal proposal method by taking into consideration the geographical location information of the temple. For example, the efficiency proposal unit selects a proposal method suitable for the region based on the geographical location information of the temple. For example, the efficiency proposal unit selects a proposal method that utilizes nearby resources by taking into consideration the geographical location information of the temple. The efficiency proposal unit can also select a proposal method that avoids distant resources based on the geographical location information of the temple. For example, the efficiency proposal unit selects a proposal method suitable for the region based on information such as the address of the temple and the characteristics of the region. In this way, a proposal method suitable for the region can be selected by taking into consideration the geographical location information of the temple.

[0153] When proposing efficiency improvements, the efficiency suggestion unit can analyze the social media activity of the temple to determine the proposal method. For example, the efficiency suggestion unit analyzes the social media activity of the temple and determines the proposal method based on feedback from believers. For example, the efficiency suggestion unit determines a proposal method that matches trends based on the social media activity of the temple. The efficiency suggestion unit can also analyze the social media activity of the temple and determine a proposal method that meets the needs of believers. For example, the efficiency suggestion unit determines a proposal method based on information such as the content of posts on social media and the reactions of followers. In this way, a proposal method that meets the needs of believers can be determined by analyzing the social media activity of the temple.

[0154] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0155] In addition to the acquisition unit, the determination unit, and the provision unit, the system can include an emotion adjustment unit that estimates the user's emotion and adjusts the content of improvements to the work based on the estimated emotion. For example, when the user is feeling stressed, the emotion adjustment unit can make a suggestion to reduce the burden of the work. Also, when the user has positive emotions, the emotion adjustment unit can make a more proactive suggestion for improvement. Furthermore, the emotion adjustment unit can adjust the priority of the improvement content based on the user's emotion. This enables flexible work improvement according to the user's emotion.

[0156] In addition to the content generation unit, the system may include an emotion expression adjustment unit that estimates the user's emotion and adjusts the way the content is expressed based on the estimated emotion. For example, the emotion expression adjustment unit may generate content in a gentle tone when the user feels like relaxing. Also, when the user feels like concentrating, the emotion expression adjustment unit may generate content including more detailed and specialized content. Furthermore, the emotion expression adjustment unit may adjust the length and format of the content based on the user's emotion. This makes it possible to provide optimal content according to the user's emotion.

[0157] In addition to the idea mining unit, the system can include an emotion idea adjustment unit that estimates the user's emotion and adjusts the idea of ​​an event or workshop based on the estimated emotion. For example, if the user feels like relaxing, the emotion idea adjustment unit can suggest a meditation or yoga workshop. Also, if the user feels like learning, the emotion idea adjustment unit can suggest a lecture on the history or doctrine of Buddhism. Furthermore, the emotion idea adjustment unit can adjust the format and content of the event based on the user's emotion. This makes it possible to provide the most suitable event or workshop according to the user's emotion.

[0158] In addition to the feedback collection unit, the system may include an emotion feedback adjustment unit that estimates the user's emotion and adjusts the feedback collection method based on the estimated emotion. For example, the emotion feedback adjustment unit may collect feedback in the form of a simple questionnaire when the user is feeling stressed. Also, when the user has positive emotions, the emotion feedback adjustment unit may request detailed feedback. Furthermore, the emotion feedback adjustment unit may adjust the timing of feedback collection based on the user's emotion. This enables optimal feedback collection according to the user's emotion.

[0159] In addition to the feedback analysis unit, the system may include a sentiment analysis adjustment unit that estimates the user's sentiment and adjusts the feedback analysis method based on the estimated sentiment. For example, when the user has negative sentiment, the sentiment analysis adjustment unit can carefully analyze the content of the feedback and identify areas for improvement. Also, when the user has positive sentiment, the sentiment analysis adjustment unit can proactively incorporate the content of the feedback. Furthermore, the sentiment analysis adjustment unit can adjust the priority of the feedback based on the user's sentiment. This enables optimal feedback analysis according to the user's sentiment.

[0160] In addition to the acquisition unit, the system can include a geographic information acquisition unit that takes into consideration the geographic location information of each temple and preferentially acquires highly relevant information. The geographic information acquisition unit can, for example, preferentially acquire event information held near the temple. The geographic information acquisition unit can also filter out distant information and preferentially acquire nearby information. Furthermore, the geographic information acquisition unit can also preferentially acquire highly relevant business information based on information such as the address of the temple and the characteristics of the area. This makes it possible to efficiently acquire highly relevant information by taking into consideration the geographic location information of the temple.

[0161] In addition to the determination unit, the system can include a past data reference unit that optimizes the analysis algorithm by referring to past business data. The past data reference unit can refer to business data such as past memorial service schedules and feedback from believers. The past data reference unit can also select an optimal analysis algorithm for a similar problem based on the past business data. Furthermore, the past data reference unit can refer to past business data and preferentially select a successful analysis algorithm. This makes it possible to improve the accuracy of the analysis by referring to past business data.

[0162] In addition to the providing unit, the system can include a business-specific providing unit that applies different providing methods to different types of business. The business-specific providing unit can select the optimal providing method according to the type of business, such as a method for schedule management or a method for communication. In addition, the business-specific providing unit can automatically manage the schedule of a memorial service using a calendar system for schedule management. Furthermore, the business-specific providing unit can automate communication with believers using a chatbot for communication. This makes it possible to determine the optimal providing method by applying different providing methods to different types of business.

[0163] In addition to the idea excavation section, the system can include a past idea reference section that refers to past idea history to select the optimal excavation method. The past idea reference section can refer to data such as ideas and implementation results of past events and workshops. The past idea reference section can also select the optimal excavation method for a similar problem based on the past idea history. Furthermore, the past idea reference section can refer to the past idea history to preferentially excavate successful ideas. This makes it possible to select the optimal excavation method by referring to the past idea history.

[0164] In addition to the feedback collection unit, the system can include a social media analysis unit that analyzes the social media activity of the temple and collects related information when collecting feedback. The social media analysis unit can, for example, analyze the social media activity of the temple and collect feedback from believers. The social media analysis unit can also collect trend information related to business based on the social media activity of the temple. Furthermore, the social media analysis unit can also collect related business information based on information such as the content of posts on social media and the reactions of followers. This makes it possible to efficiently collect related information by analyzing the social media activity of the temple.

[0165] The process flow of the second embodiment will be briefly described below.

[0166] Step 1: The acquisition unit acquires business information indicating the business of the temple. The business information includes, for example, a schedule of memorial services, information on believers, and income and expenditure reports. The acquisition unit can acquire business information of each temple from a database, and can also automatically collect business information in real time using a sensor. Step 2: The decision unit decides on the improvement content of the business based on the business information acquired by the acquisition unit. The improvement content includes, for example, making the business process more efficient and improving communication with followers. The decision unit uses AI to analyze the business information and decides on the optimal improvement content. Step 3: The provision unit provides the improvement content determined by the determination unit to the temple and the user. The provision unit can provide digital worship in a virtual space, and can also automatically provide improvement content using AI.

[0167] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0169] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0170] For example, the acquisition unit can acquire business information of the temple using the camera 42 and microphone 38B of the smart device 14. For example, the determination unit analyzes the business information acquired by the specific processing unit 290 of the data processing device 12 and determines the improvement content of the business. For example, the provision unit provides the improvement content determined by the control unit 46A of the smart device 14 to the temple and the user. For example, the content generation unit generates content explaining the teachings of Buddhism by the specific processing unit 290 of the data processing device 12. For example, the idea excavation unit excavates ideas for events and workshops based on Buddhism using the control unit 46A of the smart device 14. For example, the feedback collection unit collects feedback from believers and the general public using the camera 42 and microphone 38B of the smart device 14. For example, the feedback analysis unit analyzes the feedback collected by the specific processing unit 290 of the data processing device 12. For example, the business analysis unit analyzes business processes including schedule management of memorial services and automation of communication with believers by the specific processing unit 290 of the data processing device 12. For example, the efficiency suggestion unit makes suggestions to reduce the burden on the priest based on the information analyzed by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0171] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0172] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0173] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0178] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0181] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0183] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0185] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0186] For example, the acquisition unit can acquire business information of the temple using the camera 42 and microphone 238 of the smart glasses 214. For example, the determination unit analyzes the business information acquired by the specific processing unit 290 of the data processing device 12 and determines the improvement content of the business. For example, the provision unit provides the improvement content determined by the control unit 46A of the smart glasses 214 to the temple and the user. For example, the content generation unit generates content explaining the teachings of Buddhism by the specific processing unit 290 of the data processing device 12. For example, the idea excavation unit excavates ideas for events and workshops based on Buddhism using the control unit 46A of the smart glasses 214. For example, the feedback collection unit collects feedback from believers and the general public using the camera 42 and microphone 238 of the smart glasses 214. For example, the feedback analysis unit analyzes the feedback collected by the specific processing unit 290 of the data processing device 12. For example, the business analysis unit analyzes business processes including schedule management of memorial services and automation of communication with believers by the specific processing unit 290 of the data processing device 12. For example, the efficiency suggestion unit makes suggestions to reduce the burden on the priest based on the information analyzed by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0187] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0188] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0189] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0190] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0191] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0192] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0193] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0194] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0195] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0197] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0198] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0199] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0200] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0201] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0202] For example, the acquisition unit can acquire business information of the temple using the camera 42 and microphone 238 of the headset type terminal 314. For example, the determination unit analyzes the business information acquired by the specific processing unit 290 of the data processing device 12 and determines the improvement content of the business. For example, the provision unit provides the improvement content determined using the control unit 46A of the headset type terminal 314 to the temple and the user. For example, the content generation unit generates content explaining the teachings of Buddhism by the specific processing unit 290 of the data processing device 12. For example, the idea excavation unit excavates ideas for events and workshops based on Buddhism using the control unit 46A of the headset type terminal 314. For example, the feedback collection unit collects feedback from believers and the general public using the camera 42 and microphone 238 of the headset type terminal 314. For example, the feedback analysis unit analyzes the feedback collected by the specific processing unit 290 of the data processing device 12. For example, the business analysis unit analyzes business processes including schedule management of memorial services and automation of communication with believers by the specific processing unit 290 of the data processing device 12. For example, the efficiency suggestion unit makes suggestions to reduce the burden on the priest based on the information analyzed by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0203] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0204] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0205] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0206] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0207] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0209] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0210] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0211] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0212] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0213] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0214] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0215] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0216] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0217] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0218] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0219] For example, the acquisition unit can acquire business information of the temple using the camera 42 and microphone 238 of the robot 414. For example, the determination unit analyzes the business information acquired by the specific processing unit 290 of the data processing device 12 and determines the improvement content of the business. For example, the provision unit provides the improvement content determined by the control unit 46A of the robot 414 to the temple and the user. For example, the content generation unit generates content explaining the teachings of Buddhism by the specific processing unit 290 of the data processing device 12. For example, the idea excavation unit excavates ideas for events and workshops based on Buddhism using the control unit 46A of the robot 414. For example, the feedback collection unit collects feedback from believers and the general public using the camera 42 and microphone 238 of the robot 414. For example, the feedback analysis unit analyzes the feedback collected by the specific processing unit 290 of the data processing device 12. For example, the business analysis unit analyzes business processes including schedule management of memorial services and automation of communication with believers by the specific processing unit 290 of the data processing device 12. For example, the efficiency suggestion unit makes suggestions to reduce the burden on the priest based on the information analyzed by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0220] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0221] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0222] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0223] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0224] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0225] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0226] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0227] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0228] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0229] In addition, 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 upon request from the data processing device 12.

[0230] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0231] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0232] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0233] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0234] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0235] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0236] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0237] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0238] (Appendix 1) An acquisition unit that acquires business information indicating the business of the temple; a decision unit that decides on an improvement content of the business based on the business information acquired by the acquisition unit; a providing unit that provides the improvement content determined by the determining unit. A system characterized by: (Appendix 2) Equipped with a content generation unit that generates content explaining Buddhist teachings 2. The system of claim 1, (Appendix 3) Have an idea-generating department to generate ideas for events or workshops based on Buddhist teachings 2. The system of claim 1, (Appendix 4) Having a feedback collection section to collect feedback from followers or the general public 2. The system of claim 1, (Appendix 5) A feedback analysis unit that analyzes the feedback collected by the feedback collection unit. 5. The system of claim 4. (Appendix 6) Equipped with a business analysis department that analyzes business processes, including the schedule management of memorial services and the automation of communication with believers 2. The system of claim 1, (Appendix 7) An efficiency suggestion unit is provided that makes suggestions to reduce the burden on monks based on the information analyzed by the business analysis unit. 7. The system of claim 6, (Appendix 8) The content generation unit Generate content that explains the basic teachings, history, and meditation techniques of Buddhism 3. The system of claim 2, (Appendix 9) The idea mining section includes: Discover ideas for meditation and sutra-copying workshops and talks on Buddhism 4. The system of claim 3. (Appendix 10) The feedback collection unit: Collect feedback from followers and the general public 5. The system of claim 4. (Appendix 11) The feedback analysis unit: Analyzing the feedback collected by the feedback collection unit to identify improvements to the platform. 6. The system of claim 5, (Appendix 12) The business analysis unit, Infer user emotions and specifically adjust the analysis method of business processes based on the inferred user emotions. 7. The system of claim 6, (Appendix 13) The business analysis unit, Optimize analysis algorithms by referencing past business data 7. The system of claim 6, (Appendix 14) The business analysis unit, Apply different analysis methods to different types of work 7. The system of claim 6, (Appendix 15) The acquisition unit is To estimate a user's emotion and adjust the timing of acquiring business information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The acquisition unit is Analyze the past business history of each temple and select the most appropriate acquisition method 2. The system of claim 1, (Appendix 17) The acquisition unit is Filter business information based on the temple's current status or specific events 2. The system of claim 1, (Appendix 18) The acquisition unit is To estimate a user's emotion and determine the priority of business information to be acquired based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The acquisition unit is When obtaining business information, the temple's geographic location information is taken into consideration to obtain the most relevant information. 2. The system of claim 1, (Appendix 20) The acquisition unit is When acquiring business information, analyze the temple's social media activity to acquire relevant information 2. The system of claim 1, (Appendix 21) The determination unit is To estimate a user's emotions and adjust a method for determining business improvement contents based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The determination unit is When deciding on business improvement content, refer to past improvement history to select the optimal improvement method 2. The system of claim 1, (Appendix 23) The determination unit is When deciding on business improvement content, apply different decision algorithms for each type of business. 2. The system of claim 1, (Appendix 24) The determination unit is Estimate user sentiment and prioritize improvements based on the estimated user sentiment 2. The system of claim 1, (Appendix 25) The determination unit is When deciding on business improvement measures, the geographic location of the temple is taken into consideration to select the most appropriate improvement method. 2. The system of claim 1, (Appendix 26) The determination unit is When deciding on business improvements, analyze the temple's social media activity to determine how to improve. 2. The system of claim 1, (Appendix 27) The providing unit is Inferring user sentiment and adjusting how improvements are provided based on the inferred user sentiment 2. The system of claim 1, (Appendix 28) The providing unit is When providing improvements, select the optimal delivery method by referring to past delivery history 2. The system of claim 1, (Appendix 29) The providing unit is When providing improvements, apply different delivery methods for different types of work 2. The system of claim 1, (Appendix 30) The providing unit is Inferring user emotions and prioritizing content to be provided based on the estimated user emotions 2. The system of claim 1, (Appendix 31) The providing unit is When providing improvements, the optimal method of providing them will be selected taking into account the geographic location of the temple. 2. The system of claim 1, (Appendix 32) The providing unit is When providing improvements, analyze the temple's social media activity to determine how to provide them 2. The system of claim 1, (Appendix 33) The content generation unit Inferring user emotions and adjusting content presentation based on the estimated user emotions 3. The system of claim 2, (Appendix 34) The content generation unit When generating content, adjust the level of detail based on the importance of Buddhist teachings. 3. The system of claim 2, (Appendix 35) The content generation unit When generating content, different generation algorithms are applied depending on the category of Buddhist teachings. 3. The system of claim 2, (Appendix 36) The content generation unit Inferring user emotion and adjusting the length of content based on the estimated user emotion 3. The system of claim 2, (Appendix 37) The content generation unit When generating content, prioritize Buddhist teachings based on when they were submitted. 3. The system of claim 2, (Appendix 38) The content generation unit When generating content, adjust the order based on relevance to Buddhist teachings. 3. The system of claim 2, (Appendix 39) The idea mining section includes: Infer user emotions and adjust idea mining methods based on the inferred user emotions 4. The system of claim 3. (Appendix 40) The idea mining section includes: When generating ideas, select the best method by referring to past idea history. 4. The system of claim 3. (Appendix 41) The idea mining section includes: When mining ideas, apply different mining techniques to different categories of Buddhist teachings. 4. The system of claim 3. (Appendix 42) The idea mining section includes: Infer user sentiment and prioritize ideas based on the inferred user sentiment 4. The system of claim 3. (Appendix 43) The idea mining section includes: When generating ideas, select the optimal method of discovery by taking into account the geographic location of the temple. 4. The system of claim 3. (Appendix 44) The idea mining section includes: When generating ideas, analyze the temple’s social media activity to determine how to generate ideas. 4. The system of claim 3. (Appendix 45) The feedback collection unit: Infer user sentiment and adjust feedback collection methods based on the inferred user sentiment 5. The system of claim 4. (Appendix 46) The feedback collection unit: When collecting feedback, choose the best method by looking at past feedback history 5. The system of claim 4. (Appendix 47) The feedback analysis unit: Infer user sentiment and adjust the feedback analysis method based on the inferred user sentiment 6. The system of claim 5, (Appendix 48) The feedback analysis unit: When analyzing feedback, select the most appropriate analysis method by referring to past feedback data. 6. The system of claim 5, (Appendix 49) The feedback analysis unit: When analyzing feedback, apply different analysis methods to different types of feedback 6. The system of claim 5, (Appendix 50) The feedback analysis unit: Estimate user emotions and prioritize feedback based on the estimated user emotions 6. The system of claim 5, (Appendix 51) The feedback analysis unit: When analyzing feedback, select the most appropriate analysis method taking into account the geographic location of the temple. 6. The system of claim 5, (Appendix 52) The feedback analysis unit: During feedback analysis, analyze the temple’s social media activity to determine how to analyze it. 6. The system of claim 5, (Appendix 53) The business analysis unit, Inferring user emotions and adjusting the analysis method of business processes based on the inferred user emotions 7. The system of claim 6, (Appendix 54) The business analysis unit, When analyzing business processes, optimize the analysis algorithm by referencing past business data 7. The system of claim 6, (Appendix 55) The business analysis unit, When analyzing business processes, apply different analytical methods to each type of business process. 7. The system of claim 6, (Appendix 56) The business analysis unit, Infer user emotions and prioritize business processes based on the inferred user emotions 7. The system of claim 6, (Appendix 57) The business analysis unit, When analyzing business operations, select the most appropriate analysis method taking into account the geographic location information of the temple. 7. The system of claim 6, (Appendix 58) The business analysis unit, During business analysis, analyze the temple’s social media activity to determine the analysis method. 7. The system of claim 6, (Appendix 59) The efficiency suggestion unit, Estimate the user's emotions and adjust the efficiency suggestion method based on the estimated user emotions 8. The system of claim 7, (Appendix 60) The efficiency suggestion unit, When proposing efficiency improvements, select the optimal proposal method by referring to past proposal history 8. The system of claim 7, (Appendix 61) The efficiency suggestion unit, When proposing efficiency improvements, apply different proposal methods to each type of business. 8. The system of claim 7, (Appendix 62) The efficiency suggestion unit, Estimate user emotions and prioritize efficiency suggestions based on the estimated user emotions 8. The system of claim 7, (Appendix 63) The efficiency suggestion unit, When proposing efficiency improvements, the optimal proposal method is selected taking into account the geographic location information of the temple. 8. The system of claim 7, (Appendix 64) The efficiency suggestion unit, When proposing efficiency improvements, analyze the temple's social media activity to determine the method of proposal 8. The system of claim 7, [Explanation of symbols]

[0239] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires business information indicating the business of the temple; a decision unit that decides on an improvement content of the business based on the business information acquired by the acquisition unit; a providing unit that provides the improvement content determined by the determining unit. A system characterized by:

2. Equipped with a content generation unit that generates content explaining Buddhist teachings 2. The system of claim 1 .

3. Have an idea-generating department to generate ideas for events or workshops based on Buddhist teachings 2. The system of claim 1 .

4. Having a feedback collection section to collect feedback from followers or the general public 2. The system of claim 1 .

5. A feedback analysis unit that analyzes the feedback collected by the feedback collection unit.

5. The system of claim 4.

6. Equipped with a business analysis department that analyzes business processes, including the schedule management of memorial services and the automation of communication with believers 2. The system of claim 1 .

7. An efficiency suggestion unit is provided that makes suggestions to reduce the burden on monks based on the information analyzed by the business analysis unit.

7. The system of claim 6.

8. The content generation unit Generate content that explains the basic teachings, history, and meditation techniques of Buddhism 3. The system of claim 2.

9. The idea discovery section includes: Discover ideas for meditation and sutra-copying workshops and talks on Buddhism 4. The system of claim 3.

10. The feedback collection unit: Collect feedback from followers and the general public 5. The system of claim 4.

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