system

Abstract

To provide a system.SOLUTION: A system includes: means for inputting a total floor area, a building age, and a location as basic information on a facility; means for generating a repair plan by generating a prompt sentence to input into a generation AI model and inputting the prompt sentence to the generation AI model on the basis of the basic information and a general schedule of repair works; and means for monitoring, in real time, the progress of the repair plan and updating the repair plan as necessary.SELECTED DRAWING: Figure 1

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] The shortage of on-site personnel of building managers and the complication of repair work due to building aging make it difficult to improve the efficiency of overall management operations.

Means for Solving the Problems

[0005] Provide a system that automatically formulates a repair plan by simply inputting basic information and supports from the process management of the plan to the ordering business. Furthermore, based on the basic information, the plan is automatically updated based on IoT sensor data, and when carrying out construction work, it performs from the creation of an RFP for contractors, question-and-answer to the validity evaluation of the estimated amount. Thereby, the efficiency of building management operations is realized.

Brief Description of the Drawings

[0006] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17] It is a sequence diagram showing the processing flow of the data processing system in Example 1 of Form Example 1 when combined with an emotion engine. [Figure 18] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] It is a sequence diagram showing the processing flow of the data processing system in Example 2 of Form Example 2 when combined with an emotion engine. [Figure 20] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] It is a sequence diagram showing the processing flow of the data processing system in Example 3 of Form Example 3 when combined with an emotion engine. [Figure 22] It is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine.

Embodiments for Carrying Out the Invention

[0007] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the language used in the following description will be explained.

[0009] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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 (registered trademark)), etc.

[0010] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, the numbered 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), or magnetic tapes, etc.

[0012] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0014] [First Embodiment]

[0015] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0016] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0019] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] Embodiments of the present invention include means for inputting basic building information, such as total floor area, year of construction, and other relevant information. This information can be input directly by, for example, the building manager, or obtained from an existing building management system.

[0029] "Example of form 2"

[0030] Furthermore, an embodiment of the present invention includes means for automatically formulating a repair plan based on the above basic information. Specifically, it automatically generates a repair plan based on the basic information of the building and a general schedule for repair work.

[0031] "Example of form 3"

[0032] Furthermore, the embodiment of the present invention includes means to support everything from planning and process management to ordering. Specifically, it manages the progress of repair work and supports ordering as needed.

[0033] "Example of form 4"

[0034] Furthermore, the embodiment of the present invention includes means for automatically updating the plan based on IoT sensor data, using basic information as a basis. Specifically, it analyzes data acquired from IoT sensors within a building and automatically updates the repair plan. For example, if an IoT sensor detects that a part of the building is deteriorating, the repair of that part is added to the plan.

[0035] "Example of form 5"

[0036] Furthermore, the embodiment of the present invention includes means for creating an RFP for contractors, answering questions, and evaluating the appropriateness of the estimated costs when carrying out construction work. Specifically, it creates an RFP based on the details of the repair work and responds to questions from contractors. It also evaluates the estimates submitted by contractors and determines their appropriateness.

[0037] The following describes the processing flow for each example of the form.

[0038] "Example of form 1"

[0039] Step 1: Enter basic building information, such as total floor area, year of construction, and building code. This information can be entered directly by the building manager or retrieved from an existing building management system.

[0040] "Example of form 2"

[0041] Step 1: Enter the basic information of the building.

[0042] Step 2: Automatically create a repair plan based on the basic information above. Specifically, the system automatically generates a repair plan based on the building's basic information and a general schedule for repair work.

[0043] "Example of form 3"

[0044] Step 1: Develop a repair plan.

[0045] Step 2: Implement project schedule management.

[0046] Step 3: Provide support for ordering as needed.

[0047] "Example of form 4"

[0048] Step 1: Enter the building's basic information and create a repair plan.

[0049] Step 2: Obtain data from IoT sensors within the building.

[0050] Step 3: Analyze the acquired data and automatically update the repair plan. For example, if an IoT sensor detects that a part of the building is deteriorating, add the repair of that part to the plan.

[0051] "Example of form 5"

[0052] Step 1: Create an RFP based on the details of the repair work.

[0053] Step 2: Respond to questions from the vendor.

[0054] Step 3: Evaluate the quotes submitted by the contractors and determine their validity.

[0055] (Example 1)

[0056] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] Traditional building management systems had problems such as requiring manual input of basic building information and consuming a lot of time and effort when formulating repair plans. Furthermore, retrieving information from existing management systems and formatting the data was cumbersome, making efficient planning difficult. In addition, the generation of prompt messages using AI models was not automated, placing a significant burden on users.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for receiving said basic information and storing it in a database; means for obtaining additional information from an existing management system; means for formatting the obtained information and converting it into the required format; means for inputting prompt sentences into a generating AI model; means for automatically formulating a repair plan; and means for supporting everything from planning process management to ordering. This makes it possible to automate the entire process from inputting basic building information to formulating a repair plan and generating prompt sentences using a generating AI model.

[0060] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0061] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0062] "Intelligence" refers to information and data related to the intelligent management and operation of a building.

[0063] "Basic information" refers to fundamental data such as the total floor area of ​​a building, its age, and its intellectual property.

[0064] A "database" is a system for systematically organizing and storing information.

[0065] A "management system" is a general term for the software and hardware used for the operation and maintenance of a building.

[0066] "Additional information" refers to building data other than basic information, including energy consumption data and maintenance history.

[0067] "Formatting" refers to the process of converting acquired data into the required format, making it easier to use.

[0068] A "generative AI model" is a model that uses artificial intelligence to perform a specific task.

[0069] A "prompt" refers to an instruction or question that is input into a generative AI model.

[0070] A "repair plan" refers to planning the schedule and details of repairs and maintenance for a building.

[0071] "Process management" refers to managing each stage in the execution of a repair plan.

[0072] "Ordering operations" refers to the process of ordering materials and services necessary for repairs and maintenance.

[0073] A "Request for Proposal" is a document used to request specific proposals from vendors.

[0074] "Question and answer session" refers to the process of answering questions from vendors based on the Request for Proposal (RFP).

[0075] "Evaluating the appropriateness of the quoted price" refers to assessing whether the quoted price submitted by the contractor is reasonable.

[0076] This invention is a system that takes basic building information as input, formulates a repair plan, and generates prompt messages using a generation AI model. A specific embodiment of this system is described below.

[0077] First, the user accesses the web application provided by the server using a device such as a PC or tablet. They open a web browser and enter basic information such as the building's total floor area, age, and intelligence level. For example, the user might enter "Total floor area: 5000 square meters," "Age: 10 years," and "Intelligence level: High."

[0078] Next, the server receives the basic building information entered by the user. The received information is stored in a database server (e.g., MySQL®, PostgreSQL). The server executes SQL queries against the database and inserts the information into the appropriate tables.

[0079] Furthermore, the server retrieves additional building information through the APIs of existing management systems. This includes, for example, building energy consumption data and maintenance history. The server sends API requests and receives the data returned as responses.

[0080] The server then formats the received additional information and converts it to the required format. For example, it might parse JSON data, extract the necessary fields, and format them. The server uses data processing scripts (e.g., Python, JavaScript®) to perform this process.

[0081] Finally, the server generates prompts to input into the AI ​​model based on the formatted information. For example, it might generate a prompt such as, "Please enter the basic information of a building with a total floor area of ​​5,000 square meters, an age of 10 years, and high intelligence." The server inputs this prompt into the AI ​​model and receives a response from the model.

[0082] As a concrete example, the following prompt sentence can be input into the generation AI model.

[0083] Example of a prompt:

[0084] "Please enter the basic information for a building with a total floor area of ​​5,000 square meters, built 10 years ago, and with high intelligence."

[0085] This system enables the complete automation of the entire process, from inputting basic building information and formulating repair plans to generating prompt messages using a generation AI model. This reduces the burden on users and enables efficient building management.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The user enters the building's basic information.

[0089] Users access a web application provided by the server using a device such as a PC or tablet. They open a web browser and enter basic information such as the building's total floor area, age, and intelligence level. The entered information is sent to the server. Specifically, the user enters "Total floor area: 5000 square meters," "Age: 10 years," and "Intelligence level: High" into the form and clicks the submit button.

[0090] Input: Basic information such as the total floor area of ​​the building, the year it was built, and its location.

[0091] Output: Basic information sent to the server

[0092] Step 2:

[0093] The server receives the entered information and saves it to the database.

[0094] The server receives basic building information entered by the user. The received information is stored in a database server (e.g., MySQL, PostgreSQL). The server executes an SQL query against the database and inserts the information into the appropriate table. Specifically, the server executes the SQL query "INSERT INTO buildings (area, age, intelligence) VALUES (5000, 10, 'high');".

[0095] Input: Basic information submitted by the user

[0096] Output: Basic information stored in the database

[0097] Step 3:

[0098] The server retrieves additional information from the existing management system.

[0099] The server retrieves additional building information through the existing management system's API. This includes, for example, building energy consumption data and maintenance history. The server sends an API request and receives the data returned as a response. Specifically, the server sends an API request called "GET / buildings / 12345 / additional_info".

[0100] Input: API Request

[0101] Output: Additional information obtained

[0102] Step 4:

[0103] The server formats the acquired information and converts it into the required format.

[0104] The server formats the received additional information and converts it to the required format. For example, it parses JSON data, extracts the necessary fields, and formats them. The server uses a data processing script (e.g., Python, JavaScript) to perform this process. Specifically, the server executes "json.loads(response_data)" to extract the necessary fields.

[0105] Input: Additional information obtained

[0106] Output: Formatted data

[0107] Step 5:

[0108] The server inputs prompt messages into the generated AI model.

[0109] The server generates prompt statements to input into the AI ​​model based on the formatted information. For example, it might generate a prompt statement like, "Please enter the basic information of a building with a total floor area of ​​5000 square meters, an age of 10 years, and high intelligence." The server inputs this prompt statement into the AI ​​model and receives a response from the model. Specifically, the server performs the process of "inputting a prompt statement into the AI ​​model and receiving a response."

[0110] Input: Formatted data

[0111] Output: Response from the generative AI model

[0112] (Application Example 1)

[0113] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0114] Traditional building management systems supported the planning, scheduling, and ordering of repairs based on basic building information, but lacked security risk assessment, real-time security alert generation, and specific security countermeasures proposals. Therefore, comprehensively managing building safety was difficult, and a rapid response to security risks was required.

[0115] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0116] In this invention, the server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for evaluating security risks based on the basic information; means for generating real-time security alerts based on the security risks; and means for proposing security measures based on the security risks. This enables not only the formulation of repair plans, process management, and ordering support based on the building's basic information, but also the evaluation of security risks, the generation of real-time security alerts, and the proposal of specific security measures.

[0117] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0118] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0119] "Intelligence" refers to information about the building's location and surrounding environment.

[0120] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0121] A "repair plan" is a plan that outlines the schedule and details for systematically carrying out repairs and maintenance on a building.

[0122] "Process management" refers to managing the progress of work based on the repair plan.

[0123] "Ordering operations" refer to the process of ordering materials and services necessary for repairs and maintenance.

[0124] "Security risk" refers to potential safety threats or dangers to a building.

[0125] A "real-time security alert" is a warning that is immediately sent when a security risk occurs.

[0126] "Security measures" refer to specific means and methods for mitigating or eliminating security risks.

[0127] As an embodiment of this invention, a building management system is constructed that inputs basic information such as total floor area, building age, and intelligence, and based on this information, formulates a repair plan and supports process management and ordering operations. In addition, a function is added to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[0128] The server provides a means for inputting basic information such as total floor area, building age, and other relevant data. This basic information can be entered directly by the building manager or obtained from an existing building management system. Based on the entered basic information, the server automatically creates a repair plan and supports everything from project management to ordering.

[0129] Furthermore, the server is equipped with a means to assess security risks based on basic information. The security risk assessment is performed based on information such as the building's age, total floor area, and location. Based on the assessment results, the server generates real-time security alerts and notifies the building manager.

[0130] The server also provides a means to suggest specific security measures based on security risks. This allows building managers to quickly implement appropriate security measures.

[0131] The hardware used includes devices such as smartphones and tablets. The software used will be Python, JSON, and the datetime module. This will enable efficient management of basic building information, assessment of security risks, and implementation of appropriate countermeasures.

[0132] As a concrete example, a building manager uses a smartphone to input information about a building with a total floor area of ​​12,000 square meters, built in 1980, and located in a high-crime area. Based on this information, the server assesses the security risks and determines that the risk is high. A real-time security alert is generated and notified to the building manager. Furthermore, the server proposes specific countermeasures such as installing an advanced monitoring system and increasing security personnel.

[0133] Examples of prompt statements include the following:

[0134] "Please enter the basic information about the building. Enter the total floor area, year of construction, and location."

[0135] In this way, in addition to supporting the development of repair plans, process management, and ordering operations based on the building's basic information, it becomes possible to assess security risks, generate real-time security alerts, and propose specific security measures.

[0136] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0137] Step 1:

[0138] The user uses a terminal to input basic building information (total floor area, year of construction, etc.).

[0139] Input: Basic information such as total floor area, year of construction, and structural integrity.

[0140] Output: The entered basic information is sent to the server.

[0141] Specific operation: The user opens the application on their smartphone or tablet and enters information such as total floor area, year of construction, and other details into the input form. Once the input is complete, they press the submit button to send the information to the server.

[0142] Step 2:

[0143] The server saves the basic information it receives and automatically creates a repair plan.

[0144] Input: Basic information submitted by the user

[0145] Output: Results of the repair plan

[0146] Specific operation: The server saves the received basic information to the database. Then, it automatically generates a repair plan based on the saved information and saves the plan's contents to the database.

[0147] Step 3:

[0148] The server supports the process management and ordering of repair plans.

[0149] Input: Results of the repair plan

[0150] Output: Process management schedule and order list

[0151] Specific operation: Based on the repair plan, the server creates a schedule for each stage and generates an order list for necessary materials and services. This information is then communicated to the building manager.

[0152] Step 4:

[0153] The server assesses security risks based on basic information.

[0154] Input: Basic information submitted by the user

[0155] Output: Security risk assessment results

[0156] Specific operation: The server analyzes basic information and assesses security risks by considering factors such as the building's age, total floor area, and location. The assessment results are calculated as a risk score.

[0157] Step 5:

[0158] The server generates real-time security alerts based on the security risk assessment results.

[0159] Input: Security risk assessment results

[0160] Output: Security alert notification

[0161] Specific operation: When the risk score exceeds a certain threshold, the server generates a real-time security alert and notifies the building administrator's terminal.

[0162] Step 6:

[0163] The server proposes specific security measures based on the security risk assessment results.

[0164] Input: Security risk assessment results

[0165] Output: Security countermeasures proposal

[0166] Specific operation: The server proposes appropriate security measures based on the risk score. For example, specific measures such as installing an advanced monitoring system or increasing security personnel may be proposed. The proposed measures are notified to the building manager.

[0167] In this way, in addition to supporting the development of repair plans, process management, and ordering operations based on the building's basic information, it becomes possible to assess security risks, generate real-time security alerts, and propose specific security measures.

[0168] (Example 2)

[0169] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] Conventional repair planning systems required manual creation of repair plans based on basic building information, which was time-consuming and labor-intensive. Furthermore, updating or modifying the plans was also done manually, resulting in inefficiency and inaccuracies. Additionally, there was a lack of easy ways for users to review the generated repair plans.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for sending prompt messages to a generating AI model to generate a repair plan; means for saving the generated repair plan to a database; and means for users to request a repair plan and display it on a terminal. This enables the automatic generation and efficient management of repair plans.

[0173] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0174] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0175] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[0176] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0177] A "repair plan" refers to a plan outlining the schedule and details of repair work for a building.

[0178] A "generative AI model" refers to a model that uses artificial intelligence to generate data.

[0179] A "prompt statement" refers to an instruction given to a generative AI model.

[0180] A "database" refers to a system for systematically storing and managing data.

[0181] A "server" refers to a computer that provides data and services over a network.

[0182] "Terminal" refers to a device used by a user to operate something (such as a personal computer, tablet, or smartphone).

[0183] A "user" refers to a person who uses the system.

[0184] "Process management" refers to managing the progress of repair work.

[0185] "Ordering operations" refers to the process of ordering materials and services necessary for repair work.

[0186] "RFP" is an abbreviation for Request for Proposal, which refers to a document requesting proposals from vendors.

[0187] "Question and answer session" refers to questions and answers regarding proposals or plans.

[0188] "Evaluating the appropriateness of the quoted price" refers to assessing whether the submitted estimate is reasonable or not.

[0189] This invention is a system for automatically planning and managing building repairs. The system consists of three main elements: a server, terminals, and users.

[0190] Server Role

[0191] The server receives basic building information and stores it in a database. This basic information includes total floor area, age of the building, and intelligence. Based on this information, the server sends prompt messages to a generating AI model to generate a repair plan. The generated repair plan is then stored in the database again.

[0192] The server uses the following hardware and software:

[0193] High-performance database servers (e.g., MySQL, PostgreSQL)

[0194] Generative AI models (e.g., GPT-4(registered trademark))

[0195] Terminal role

[0196] The terminal provides an interface for users to input basic building information and view repair plans. When a user requests a repair plan by operating the terminal, the terminal sends a request to the server. Upon receiving the repair plan from the server, the terminal displays its contents to the user.

[0197] The device uses the following hardware and software:

[0198] PCs, tablets, smartphones

[0199] Web browser (e.g., Google Chrome, Mozilla Firefox)

[0200] User roles

[0201] The user enters basic building information using a terminal. Once the input is complete, the user requests the generation of a repair plan. The user then reviews the generated repair plan and requests modifications or additions as needed.

[0202] Specific example

[0203] The user enters the following information into the terminal's form: "Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago". The terminal sends this information to the server, which then sends the following prompt to the generated AI model:

[0204] "The basic information about the building is as follows: Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior walls repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[0205] The AI ​​model generates a repair plan based on this prompt and sends it back to the server. The server saves the generated repair plan to a database and sends it to the terminal when requested by the user. The terminal displays the received repair plan to the user.

[0206] In this way, users can easily plan and manage building maintenance.

[0207] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0208] Step 1:

[0209] The user enters the building's basic information.

[0210] The user inputs basic building information through the terminal interface. Specifically, they input information such as total floor area, year of construction, and structural integrity. The input information is temporarily stored in the terminal's memory. An example of input data is: "Structure: Reinforced concrete, Year of construction: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago."

[0211] Step 2:

[0212] The terminal sends the entered information to the server.

[0213] The terminal sends basic information entered by the user to the server using an HTTP request. The data sent is in JSON format, which the server receives and parses. The input data is temporarily stored in the server's memory.

[0214] Step 3:

[0215] The server stores basic information in a database.

[0216] The server stores the received basic information in a database. Specifically, it uses a database management system such as MySQL or PostgreSQL. Storing this information in a database makes it accessible for later processing.

[0217] Step 4:

[0218] The server sends a prompt message to the generated AI model.

[0219] The server generates prompts based on the stored basic information. The generated prompts are then sent to the AI ​​model. An example of a prompt is: "The basic information of the building is as follows: Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior walls repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[0220] Step 5:

[0221] The generative AI model generates the repair plan.

[0222] The generation AI model generates a repair plan based on the received prompt text. The generated repair plan is sent back to the server. An example of the output data is a plan that states, "The next repair will be to repaint the exterior walls in two years, and then waterproof the roof five years later."

[0223] Step 6:

[0224] The server saves the generated repair plan to the database.

[0225] The server stores the repair plans received from the generated AI models in a database. This allows users to review and modify the repair plans later.

[0226] Step 7:

[0227] The user requests a repair plan.

[0228] The user requests to view the repair plan using their device. Specifically, they click the "View Repair Plan" button on the device's interface. The request is made using an HTTP GET request.

[0229] Step 8:

[0230] The terminal requests a repair plan from the server.

[0231] The terminal, upon receiving a user request, requests the server to retrieve the repair plan. The server retrieves the repair plan from the database and sends it to the terminal. The data sent is in JSON format.

[0232] Step 9:

[0233] The server sends the repair plan to the terminal.

[0234] The server retrieves the repair plan from the database and sends it to the terminal. The data sent is in JSON format, and the terminal receives and parses it.

[0235] Step 10:

[0236] The device displays the repair plan to the user.

[0237] The terminal displays the received repair plan to the user. Specifically, it displays the details of the repair plan in a web browser. The user can review the displayed repair plan and make requests for modifications or additions as needed.

[0238] (Application Example 2)

[0239] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0240] Conventional repair planning systems struggled to automatically generate repair plans based on basic information about building and factory equipment, and they could not monitor the progress of the plans in real time or update them as needed. As a result, the accuracy and efficiency of repair plans decreased, leading to significant effort and cost in maintaining the equipment.

[0241] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0242] In this invention, the server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for automatically generating a repair plan for each piece of equipment based on the basic information of the equipment and the general schedule of repair work; means for monitoring the progress of the repair plan in real time and updating the plan as necessary; and means for generating a repair plan using a generated AI model. This makes it possible to improve the accuracy and efficiency of repair plans and reduce the effort and cost involved in the maintenance and management of equipment.

[0243] "Total floor area" is a term that refers to the sum of the floor areas of each floor of a building.

[0244] "Years since construction" is a term that refers to the number of years that have passed since a building was constructed.

[0245] "Intelligence" is a term that refers to the knowledge and information necessary for managing buildings and facilities.

[0246] "Basic information" is a term that refers to fundamental data about a building and its facilities, such as total floor area, year of construction, and structural integrity.

[0247] A "repair plan" is a term that refers to planning the schedule and details of repair work for buildings and facilities.

[0248] "Process management" is a term that refers to the means of managing the progress of repair work and ensuring that it proceeds according to plan.

[0249] "Ordering operations" refers to the process of ordering materials and services necessary for repair work from external contractors.

[0250] "Equipment" is a term that refers to machinery and devices installed inside buildings such as factories and office buildings.

[0251] "Repair work" is a term that refers to construction work to repair or improve malfunctions or deterioration of buildings and equipment.

[0252] "General schedule" refers to a standard schedule that is generally applied in repair work.

[0253] "Automatic generation" is a term that refers to a system automatically creating plans and data without human intervention.

[0254] "Progress status" is a term that refers to information indicating how far along the repair work plan is.

[0255] "Real-time" is a term that refers to reflecting the current situation immediately.

[0256] A "generative AI model" is a term that refers to a model that uses artificial intelligence to analyze data and generate plans and predictions.

[0257] The system for implementing this invention operates in cooperation with three parties: a server, a terminal, and a user. The server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for automatically generating a repair plan for each piece of equipment based on the basic information of the equipment and the general schedule of the repair work; means for monitoring the progress of the repair plan in real time and updating the plan as needed; and means for generating a repair plan using a generated AI model.

[0258] The server implements programs using programming languages ​​such as Python to perform data input, analysis, plan generation, and progress monitoring. Specifically, the server acquires basic equipment information and calculates the next repair date based on a typical repair schedule. The calculation results are output in JSON format for user review. The server also monitors progress in real time and updates the plan as needed.

[0259] The terminal provides an interface for users to access the server, enter basic information, and check repair plans. The terminal communicates with the server via a web browser or dedicated application, allowing users to input and retrieve necessary information.

[0260] Users access the server via their terminal and input basic equipment information. This information is sent to the server, which automatically generates a repair plan based on it. The generated repair plan can be viewed by the user via their terminal. Furthermore, users can monitor the progress in real time and update the plan as needed.

[0261] As a specific example, Equipment 1 was installed on January 1, 2020, is frequently used, and has been repaired in the past on January 1, 2021 and January 1, 2022. In this case, the next repair date would be January 1, 2023. The user enters this information into the terminal, and the server calculates the next repair date based on that information and generates a plan.

[0262] Examples of prompts to input into a generative AI model are as follows:

[0263] Based on the basic information of the equipment and the general repair schedule, please calculate the next repair date. The following is an example of input data.

[0264] Basic information about the equipment:

[0265] {

[0266] "Equipment 1": {"Installation Date": "2020-01-01", "Frequency of Use": "High", "Past Repair History": ["2021-01-01", "2022-01-01"]},

[0267] "Equipment 2": {"Installation Date": "2019-01-01", "Usage Frequency": "Medium", "Past Repair History": ["2020-01-01"]}

[0268] }

[0269] General schedule of repair work:

[0270] {

[0271] "High": {"cycle": 1},

[0272] "Medium": {"cycle": 2},

[0273] "Low": {"cycle": 3}

[0274] }

[0275] The flow of the specific process in Application Example 2 will be described using FIG. 14.

[0276] Step 1:

[0277] The user accesses the server through the terminal and inputs the basic information of the facility (floor area, years since construction, rationality, installation date, usage frequency, past repair history, etc.). The input information is sent to the server.

[0278] Input: Basic information of the facility

[0279] Output: Basic information data sent to the server

[0280] Step 2:

[0281] [[ID=5I]]Based on the received basic information, the server refers to the general schedule of repair work and calculates the next repair date. Specifically, it applies the repair cycle according to the usage frequency and calculates the next repair date from the past repair history.

[0282] Input: Basic information data, general schedule of repair work

[0283] Output: Next repair date

[0284] Step 3:

[0285] The server automatically generates a repair plan based on the calculated next repair date. The generated repair plan is saved in JSON format for user review.

[0286] Input: Next repair date

[0287] Output: Repair plan (JSON format)

[0288] Step 4:

[0289] Users can access the server through their terminal and review the generated repair plan. They can modify or update the repair plan as needed.

[0290] Input: Repair plan (JSON format)

[0291] Output: Revised and updated repair plan

[0292] Step 5:

[0293] The server monitors the progress of the repair plan in real time. Using IoT sensor data, it monitors the condition of the equipment and verifies that the plan is progressing as scheduled. It automatically updates the plan as needed.

[0294] Input: IoT sensor data, repair plan

[0295] Output: Updated repair plan

[0296] Step 6:

[0297] The server uses a generative AI model to optimize the repair plan. Specifically, it analyzes past data and the current situation to propose the optimal repair schedule.

[0298] Input: Basic information data, repair plan, IoT sensor data

[0299] Output: Optimized repair plan

[0300] Step 7:

[0301] The user checks the optimized repair plan through the terminal and determines the final repair schedule. The determined repair schedule is saved in the server and transferred to execution.

[0302] Input: Optimized repair plan

[0303] Output: Determined repair schedule

[0304] (Example 3)

[0305] Next, Example 3 of Form Example 3 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart device 14 is referred to as a "terminal".

[0306] In a conventional repair work management system, the formulation of repair plans, the management of progress, and the support of ordering operations are often performed manually, resulting in low efficiency. In addition, since processes such as automatic plan updates using sensor data, creation of proposal requests for contractors, question-and-answer sessions, and evaluation of the validity of estimated amounts are not integrated, overall management is cumbersome. This causes problems such as delays in the progress of repair work and increases in costs.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following respective means.

[0308] In this invention, the server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from planning process management to ordering; means for using a database for managing the progress of repair work; means for acquiring and analyzing data from sensors within the building; means for automatically updating the repair plan; means for creating requests for proposals for contractors and responding to questions; and means for evaluating estimates submitted by contractors and determining their validity. This makes it possible to efficiently carry out a series of processes from planning and managing the progress of repair work to ordering and evaluating estimates.

[0309] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0310] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0311] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[0312] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0313] A "repair plan" refers to planning the details and schedule of repair work for a building.

[0314] "Process management" refers to managing the progress of each stage of repair work.

[0315] "Ordering operations" refers to the process of ordering materials and services necessary for repair work from contractors.

[0316] A "database" refers to a system used to store information about the progress of repair work and other related information.

[0317] A "sensor" refers to a device used to acquire environmental data (e.g., temperature, humidity) within a building.

[0318] A "Request for Proposal (RFP)" is a document that details the repair work and requests proposals from contractors.

[0319] "Question and answer session" refers to the process of answering questions from vendors.

[0320] "Evaluating the appropriateness of the estimated price" refers to assessing whether the price quoted by the contractor is reasonable.

[0321] Modes for carrying out the invention

[0322] This invention is a system for efficiently carrying out repair work, from planning and progress management to ordering and cost estimation. A specific embodiment of this system is described below.

[0323] System Configuration

[0324] The server implements the system using the following hardware and software.

[0325] Hardware: High-performance server machine (e.g., Intel Xeon processor, 64GB RAM, 1TB SSD)

[0326] Software: Database management systems (e.g., MySQL), sensor management APIs, generative AI models (e.g., GPT-3®)

[0327] A terminal is a device used by a user to access the system and uses the following hardware and software:

[0328] Hardware: Personal computers, tablets, smartphones

[0329] Software: Web browser

[0330] System operation

[0331] The server first provides a means for inputting basic information such as total floor area, year of construction, and other relevant data. This allows the user to input fundamental data about the building. Next, the server has a means for automatically formulating a repair plan based on this basic information. This allows the user to efficiently create a repair plan.

[0332] The server provides the means to support everything from planning and process management to ordering. Specifically, it uses a database to manage the progress of repair work and updates the progress of each stage of the work in real time. The server also has the means to acquire and analyze data from sensors within the building. This allows the repair plan to be automatically updated based on the information obtained when sensors detect deterioration in a part of the building.

[0333] Furthermore, the server creates Requests for Proposals (RFPs) for vendors and provides a means to respond to their questions. Using a generative AI model, it generates appropriate answers to vendor questions and sends them to the vendors. The server also has a means to evaluate the quotes submitted by vendors and determine their validity. This allows the user to review the evaluation results of the quotes and make a final decision.

[0334] Specific example

[0335] For example, when a user is creating a building repair plan, an IoT sensor detects that a part of the building is deteriorating. Based on this information, the server automatically updates the repair plan and supports the necessary ordering process.

[0336] Example of a prompt:

[0337] "Please describe a system that automatically updates building maintenance plans based on data from IoT sensors and supports necessary ordering tasks."

[0338] This system enables efficient construction management by allowing users to check the progress of repair work in real time and automatically performing necessary ordering tasks. The flow of specific processing in Example 3 will be explained using Figure 15.

[0339] Step 1:

[0340] Entering basic information

[0341] Users input basic information such as total floor area, building age, and other details using a terminal. The server receives this input data and stores it in a database. This input data is used as foundational data necessary for developing repair plans. Specifically, the user enters information into a web form and clicks the submit button, which sends the data to the server.

[0342] Step 2:

[0343] Planning of repairs

[0344] The server automatically generates a repair plan based on stored basic information. The server considers past repair data and building characteristics to generate an optimal repair schedule. Input data is basic information, and output data is the repair plan. Specifically, the server uses an algorithm to analyze the data and generate the repair plan.

[0345] Step 3:

[0346] Progress Management

[0347] The server uses a database to manage the progress of repair work. Users can check the progress using a terminal. Input data is information about the progress of the work, and output data is a progress report. Specifically, the server periodically updates the database to provide users with the latest progress information.

[0348] Step 4:

[0349] Acquisition and analysis of sensor data

[0350] The server acquires data from sensors within the building and analyzes it in real time. The input data is environmental data acquired from the sensors, and the output data is the analysis results. Specifically, the server uses a sensor management API to acquire data and executes algorithms to detect anomalies.

[0351] Step 5:

[0352] Renewal of repair plan

[0353] The server automatically updates the repair plan based on the analysis results of sensor data. The input data is the analysis results of the sensor data, and the output data is the updated repair plan. Specifically, if an anomaly is detected, the server recalculates the repair plan based on that information and updates the database.

[0354] Step 6:

[0355] Preparation of Request for Proposal and Q&A

[0356] The server automatically generates a Request for Proposal (RFP) based on the details of the repair work. Users can review the RFP using a terminal and modify it as needed. The input data is the details of the repair work, and the output data is the RFP. Specifically, the server generates the RFP using a generation AI model and provides it to the user.

[0357] Step 7:

[0358] Estimate Evaluation

[0359] The server evaluates quotes submitted by vendors and determines their validity. The input data is the vendor's quote, and the output data is the evaluation result. Specifically, the server evaluates whether the quoted amount is appropriate based on past data and notifies the user.

[0360] (Application Example 3)

[0361] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0362] Conventional repair management systems supported the creation of repair plans, process management, and ordering operations based on basic information, but struggled to respond to real-time changes in the factory environment. Furthermore, manual processes were required for creating RFPs for contractors, handling inquiries, and evaluating the validity of quoted prices, necessitating efficient operation. This resulted in problems such as delays in updating repair plans and insufficient evaluation of estimates.

[0363] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0364] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for collecting data from various sensors in the factory; means for analyzing the collected data in real time and identifying areas that require repair; means for automatically updating the repair plan based on the analysis results; means for automatically generating an RFP based on the details of the repair work; means for providing an automatic response function to questions from contractors; and means for evaluating submitted estimates and determining their validity. This enables efficient updating of the repair plan and communication with contractors in response to real-time changes in the factory environment.

[0365] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0366] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0367] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[0368] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0369] A "repair plan" is a document that outlines the details and schedule of repair work to a building.

[0370] "Process management" refers to managing the progress of repair work.

[0371] "Ordering operations" refer to the tasks of ordering materials and services necessary for repair work.

[0372] A "sensor" is a device that detects physical phenomena and outputs them as data.

[0373] "Data collection" refers to gathering information from sensors and other sources.

[0374] "Real-time analysis" means analyzing collected data immediately.

[0375] An "RFP" is a request for proposals that details the repair work.

[0376] The "automatic response function" is a function that automatically answers questions from vendors.

[0377] "Estimate evaluation" refers to the process of evaluating the content of submitted estimates and determining their validity.

[0378] The system for implementing this invention has the following configuration: The server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for collecting data from various sensors in the factory; means for analyzing the collected data in real time and identifying areas that require repair; means for automatically updating the repair plan based on the analysis results; means for automatically generating an RFP based on the details of the repair work; means for providing an automatic response function to questions from contractors; and means for evaluating submitted estimates and determining their validity.

[0379] Program Processing Description

[0380] The server uses Python to collect data from various sensors. Specifically, it uses the requests library to retrieve data from sensor APIs. The collected data is analyzed in real time to identify areas requiring repair, such as abnormal vibrations or deterioration. Data analysis libraries (e.g., Pandas, NumPy) are used for this analysis. Based on the identified repair areas, the repair plan is automatically updated. This updated repair plan is saved in JSON format.

[0381] Furthermore, an RFP is automatically generated based on the details of the repair work. A template engine (e.g., Jinja2) is used to generate the RFP. Questions from contractors are automatically answered using a generative AI model. This generative AI model uses pre-trained question-answer data. The validity of submitted estimates is judged by an evaluation algorithm. A machine learning model (e.g., Scikit-learn) is used for this evaluation.

[0382] Specific example

[0383] For example, if a vibration sensor in a factory detects abnormal vibrations, the data is collected in real time and automatically added to the repair plan as an area requiring repair. In this process, the server retrieves data from the sensor API and uses a data analysis library to detect anomalies. If an anomaly is detected, the repair plan is automatically updated and an RFP (Request for Proposal) is generated. The AI ​​model automatically responds to questions from contractors, and the submitted quotes are evaluated by a machine learning model.

[0384] Example of a prompt

[0385] "Create a Python program that collects vibration data from IoT sensors within the factory and automatically updates the repair plan when abnormal vibrations are detected."

[0386] In this way, it becomes possible to respond to real-time changes in the factory environment, efficiently update repair plans, and coordinate with contractors.

[0387] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[0388] Step 1:

[0389] The server collects data from various sensors within the factory. Specifically, it sends requests to the sensor API to obtain data such as temperature, humidity, vibration, and deterioration. The input is the sensor ID, and the output is the data obtained from the sensor.

[0390] Step 2:

[0391] The server analyzes the collected data in real time. Specifically, it uses data analysis libraries (e.g., Pandas, NumPy) to detect abnormal vibrations and signs of deterioration. The input is data acquired from sensors, and the output is the result of whether or not an anomaly was detected.

[0392] Step 3:

[0393] The server automatically updates the repair plan based on the analysis results. Specifically, it adds the detected anomalies to the repair plan and saves it in JSON format. The input is the anomaly detection result, and the output is the updated repair plan.

[0394] Step 4:

[0395] The server automatically generates an RFP based on the details of the repair work. Specifically, it uses a template engine (e.g., Jinja2) to embed the details of the repair work into a template and generate the RFP. The input is the updated repair plan, and the output is the generated RFP.

[0396] Step 5:

[0397] The server automatically responds to questions from vendors. Specifically, it uses a generative AI model to generate automatic responses based on pre-trained question-answer data. The input is the question from the vendor, and the output is the automatically generated response.

[0398] Step 6:

[0399] The server evaluates the submitted estimates and determines their validity. Specifically, it uses a machine learning model (e.g., Scikit-learn) to evaluate the content of the estimates and determine their validity. The input is the submitted estimate, and the output is the evaluation result.

[0400] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0401] "Example of form 1"

[0402] One embodiment of the present invention provides a means for inputting basic building information such as total floor area, year of construction, and intelligence. This information can be directly entered by, for example, the building manager, or obtained from an existing building management system. It also incorporates an emotion engine that recognizes the user's emotions. This emotion engine recognizes emotions, for example, from the user's tone of voice and facial expressions.

[0403] "Example of form 2"

[0404] In another embodiment of the present invention, a means for automatically formulating a repair plan based on the basic information is provided. Specifically, a repair plan is automatically generated based on the basic information of the building and a general schedule for repair work. An emotion engine that recognizes the user's emotions is also incorporated. This emotion engine recognizes emotions, for example, from the tone of the user's voice and facial expressions.

[0405] "Example of form 3"

[0406] In a further embodiment of the present invention, a means is provided to support everything from planning and process management to ordering. Specifically, it manages the progress of repair work and supports ordering as needed. It also incorporates an emotion engine that recognizes the user's emotions. This emotion engine recognizes emotions, for example, from the user's tone of voice and facial expressions.

[0407] The following describes the processing flow for each example of the form.

[0408] "Example of form 1"

[0409] Step 1: Enter basic building information such as total floor area, year of construction, and building code. This information is entered directly by, for example, the building manager.

[0410] Step 2: Obtain basic information from the existing building management system.

[0411] Step 3: Activate the emotion engine to recognize the user's emotions. This emotion engine recognizes emotions from, for example, the user's tone of voice and facial expressions.

[0412] "Example of form 2"

[0413] Step 1: Based on the building's basic information and a general repair schedule, the system automatically generates a repair plan.

[0414] Step 2: Save the generated repair plan and update it as needed.

[0415] Step 3: Activate the emotion engine to recognize the user's emotions. This emotion engine recognizes emotions from, for example, the user's tone of voice and facial expressions.

[0416] "Example of form 3"

[0417] Step 1: Manage the progress of the repair work. Specifically, check the progress of each stage and update the repair work plan as needed.

[0418] Step 2: Provide support for ordering as needed. Specifically, place orders with appropriate contractors based on the repair work plan.

[0419] Step 3: Activate the emotion engine to recognize the user's emotions. This emotion engine recognizes emotions from, for example, the user's tone of voice and facial expressions.

[0420] (Example 1)

[0421] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0422] Traditional building management systems can formulate repair plans based on basic building information and support process management and ordering, but they cannot provide feedback that takes user emotions into account. Therefore, it was difficult to properly understand user satisfaction and dissatisfaction and reflect them in management operations. Furthermore, the lack of a means to integrate and analyze emotional data meant that appropriate responses based on user emotions were not possible.

[0423] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0424] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for generating emotional data from the user's voice tone and facial expressions using an emotion engine that recognizes the user's emotions; means for integrating and analyzing the generated emotional data and basic information; and means for providing feedback of the analysis results to the user. This makes it possible to provide feedback that takes the user's emotions into consideration, appropriately grasp the user's satisfaction and dissatisfaction, and reflect them in management operations.

[0425] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0426] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0427] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[0428] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0429] A "repair plan" refers to a schedule and procedures for systematically carrying out repairs and maintenance on a building.

[0430] "Process management" refers to the process of managing the progress of work based on the repair plan.

[0431] "Ordering operations" refers to the process of ordering necessary materials and services from external contractors for repairs and maintenance.

[0432] An "emotion engine" refers to software or algorithms that recognize emotions from a user's voice tone and facial expressions.

[0433] "Emotional data" refers to data about a user's emotions generated by the emotion engine.

[0434] "Analysis" refers to the process of integrating collected data and extracting meaningful information.

[0435] "Feedback" refers to the process of providing users with analysis results and communicating areas for improvement and evaluations.

[0436] This invention is a system that takes basic building information as input and recognizes and analyzes the user's emotions. A specific embodiment of this system is described below.

[0437] System Configuration

[0438] The system consists of three main elements: servers, terminals, and users. The server plays a central role in storing, analyzing, and providing feedback on data. Terminals provide an interface for users to input information and record audio. Users, such as building managers and building occupants, provide information to the system.

[0439] Hardware and software to be used

[0440] Servers: Database management systems (MySQL, PostgreSQL), data analysis software (Python, R), emotion engines (IBM Watson®, Google Cloud Speech-to-Text)

[0441] Device: Computer or smart device with a microphone

[0442] Users: Building administrators, building users

[0443] Data entry and saving

[0444] Users log in to the system using a terminal and enter basic building information. Specifically, they enter information such as total floor area, year of construction, and building code into an input form. The server receives this information and stores it in a database.

[0445] Recognition and analysis of emotions

[0446] When a user speaks about the building's management status, the terminal records the user's voice. The recorded audio data is sent to a server, which uses an emotion engine to analyze the audio data. The emotion engine generates emotion data from the user's tone of voice and facial expressions.

[0447] Data integration and feedback

[0448] The server integrates and analyzes basic building information and sentiment data. Data analysis software such as Python and R is used for the analysis. The analysis results are provided to building managers as feedback. For example, feedback such as "There are many complaints about the cleanliness" is provided via email or a dashboard.

[0449] Specific example

[0450] For example, when a building manager enters information about a new building into the system, they would follow these steps:

[0451] 1. The building manager accesses the system's input screen and enters basic information such as total floor area, year of construction, and other relevant details.

[0452] 2. The server saves the entered information to the database.

[0453] 3. When a user talks about the building's management status, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[0454] 4. The server integrates and analyzes basic building information and sentiment data, and provides feedback to building managers.

[0455] Example of a prompt

[0456] Examples of prompt statements to input into a generative AI model include the following:

[0457] "The building has a total floor area of ​​5,000 square meters and is 10 years old. Analyze the tone of voice and facial expressions of the building manager when they talk about the building's management status to recognize the user's emotions."

[0458] By using this prompt, the generative AI model can integrate and analyze basic building information and user sentiment data to provide appropriate feedback.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 17.

[0460] Step 1:

[0461] The user logs into the system.

[0462] Input: User ID and password

[0463] Operation: Users access the system using a dedicated terminal or web browser and enter their user ID and password on the login screen.

[0464] Output: If authentication is successful, the user can access the system's main screen.

[0465] Step 2:

[0466] The user enters the building's basic information.

[0467] Input: Basic information such as total floor area, year of construction, and intelligence.

[0468] Operation: The user enters information such as total floor area, year of construction, and age into the system's input form. For example, the user might enter "5000 square meters" for the total floor area and "10 years" for the year of construction.

[0469] Output: The entered basic information is sent to the server.

[0470] Step 3:

[0471] The server saves the entered information to the database.

[0472] Input: Basic information entered by the user

[0473] Operation: The server receives basic building information entered by the user and stores it in a database. Database management systems such as MySQL or PostgreSQL are used for storage.

[0474] Output: Basic information is saved to the database.

[0475] Step 4:

[0476] The user talks about the building's management status.

[0477] Input: User's voice

[0478] Operation: The user uses a device with a microphone to talk about the building's maintenance status. For example, they might say, "The building's cleaning is not good."

[0479] Output: Audio data is recorded on the device.

[0480] Step 5:

[0481] The device records the user's voice.

[0482] Input: User's voice

[0483] Operation: The device records the user's voice and sends the audio data to the server.

[0484] Output: Audio data is sent to the server.

[0485] Step 6:

[0486] The server uses an emotion engine to analyze the audio data.

[0487] Input: Audio data

[0488] Operation: The server uses an emotion engine (e.g., IBM Watson or Google Cloud Speech-to-Text) to analyze the user's voice tone and facial expressions and generate emotion data. For example, it can recognize "dissatisfaction" from the user's voice tone.

[0489] Output: Emotional data is generated.

[0490] Step 7:

[0491] The server integrates basic building information and emotional data.

[0492] Input: Basic information, sentiment data

[0493] Operation: The server retrieves basic building information from the database and integrates it with emotion data obtained from the emotion engine.

[0494] Output: Integrated data is generated.

[0495] Step 8:

[0496] The server analyzes the integrated data.

[0497] Input: Integrated data

[0498] Operation: The server uses data analysis software such as Python or R to analyze the integrated data. For example, it might identify user dissatisfaction with the cleanliness of a building and analyze the causes of that dissatisfaction.

[0499] Output: Analysis results are generated.

[0500] Step 9:

[0501] The server provides feedback on the analysis results to the building manager.

[0502] Input: Analysis results

[0503] Operation: The server notifies the building manager of the analysis results. For example, it might provide feedback via email or a dashboard stating that "there are many complaints about the cleaning situation."

[0504] Output: Feedback is provided to the building manager.

[0505] (Application Example 1)

[0506] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0507] Conventional building management systems could input basic information such as total floor area, building age, and intelligence, and formulate repair plans, but they did not optimize the work environment by considering the emotional state of the workers. As a result, worker stress management and improvements in work efficiency were not adequately addressed. Furthermore, real-time responses based on emotional states were difficult, which sometimes delayed worker health management and improvements to the work environment.

[0508] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0509] In this invention, the server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process management of the plan and ordering operations; means for monitoring the emotional state of workers using an emotion engine that recognizes user emotions; and means for optimizing the work environment based on the emotional state. This makes it possible to grasp the emotional state of workers in real time and take appropriate action, thereby optimizing the work environment and managing the health of workers.

[0510] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0511] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0512] "Intelligence" refers to information related to the intelligent management and operation of a building.

[0513] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0514] A "repair plan" is a schedule and procedure for systematically carrying out repairs and maintenance on a building.

[0515] "Process management" is the process of managing the progress of work based on the repair plan.

[0516] "Ordering operations" refer to the process of ordering materials and services necessary for repairs and maintenance.

[0517] An "emotion engine" is a technology that recognizes emotions from the user's voice tone and facial expressions.

[0518] "Workers" are people who carry out repairs and maintenance on buildings.

[0519] "Monitoring" refers to the continuous monitoring of a specific state or situation.

[0520] "Work environment" refers to the place and conditions in which workers perform their tasks.

[0521] "Optimization" means bringing something into a state that is most effective for a specific purpose.

[0522] As an example of how to implement this invention, a smart factory management application will be described. The server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for monitoring the emotional state of workers using an emotion engine that recognizes the emotions of users; and means for optimizing the work environment based on the emotional state.

[0523] Program Processing Description

[0524] The server uses the following hardware and software:

[0525] Hardware: Camera (webcam, etc.)

[0526] Software: OpenCV (image processing library), EmotionEngine (emotion recognition engine), BuildingInfo (building information management class)

[0527] The server first inputs basic building information. This is done either by the building manager entering data such as total floor area, age of the building, and other information, or by retrieving it from an existing building management system. Next, it automatically generates a repair plan based on this basic information. This plan includes the repair schedule and procedures.

[0528] Furthermore, the server supports everything from planning and process management to ordering. This includes managing the progress of repair plans and ordering necessary materials and services.

[0529] To recognize user emotions, the server uses an emotion engine. The emotion engine analyzes image data acquired from the camera and recognizes emotions from the user's tone of voice and facial expressions. This allows for real-time monitoring of the worker's emotional state.

[0530] Based on emotional states, the server optimizes the work environment. For example, if a worker is stressed, the server takes appropriate action to improve the work environment.

[0531] Specific example

[0532] As a concrete example, consider stress management for workers in a factory. If a worker is experiencing stress, the emotion engine detects this and notifies the manager. This allows the manager to respond quickly and improve the work environment.

[0533] Examples of prompts to input into a generative AI model

[0534] Design a system that monitors the emotions of factory workers in real time and notifies managers if they are experiencing stress. Basic building information (total floor area, age of construction, etc.) will also be entered.

[0535] In this way, by understanding the emotional state of workers in real time and taking appropriate action, it becomes possible to optimize the work environment and manage the health of workers.

[0536] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[0537] Step 1:

[0538] The server receives basic building information. Building managers input data such as total floor area, year of construction, and building status, or this data is retrieved from an existing building management system. The input data is stored in a database on the server.

[0539] Step 2:

[0540] The server automatically generates a repair plan based on the entered basic information. Specifically, it analyzes data such as total floor area and building age to identify areas and timings for repairs. The repair plan, including schedules and procedures, is generated and stored on the server.

[0541] Step 3:

[0542] The server manages the repair plan process. It monitors the progress of the work in real time based on the repair plan and supports the ordering of necessary materials and services. Progress and ordering information are recorded in a database on the server.

[0543] Step 4:

[0544] The server acquires image data of workers using cameras. The cameras are installed in the work areas of the factory and capture the faces and expressions of workers in real time. The acquired image data is sent to the server.

[0545] Step 5:

[0546] The server uses an emotion engine to recognize the worker's emotional state. Specifically, it analyzes the acquired image data to identify emotions from the worker's tone of voice and facial expressions. The emotional state is recorded in a database on the server.

[0547] Step 6:

[0548] The server optimizes the work environment based on the user's emotional state. For example, if a worker is experiencing stress, the server sends a notification to the administrator prompting appropriate action. The notification content and response history are stored in a database on the server.

[0549] Step 7:

[0550] The server integrates all data and uses it to improve the work environment and manage worker health. The integrated data is displayed on a dashboard accessible to administrators, enabling real-time monitoring and analysis.

[0551] (Example 2)

[0552] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0553] Conventional repair planning systems can generate repair plans based on basic building information, but they have challenges in presenting plans that take user sentiment into consideration and automatically generating optimal repair schedules. Furthermore, support for process management and ordering is insufficient, placing a heavy burden on users.

[0554] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0555] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for analyzing the user's emotions using an emotion engine that recognizes the user's emotions; and means for calculating the optimal repair schedule using a generated AI model. This enables the automatic generation of an optimal repair plan that takes the user's emotions into consideration, as well as the efficient management of the plan's process and ordering operations.

[0556] "Total floor area" refers to the sum of the floor areas of each floor of a building, and is an indicator of the overall size of the building.

[0557] "Building age" refers to the number of years that have passed since a building was constructed, and it is important information for evaluating the building's deterioration and the need for repairs.

[0558] "Intelligence" refers to knowledge and information about the design and structure of a building, and serves as the basic data when formulating a repair plan.

[0559] "Basic information" refers to a general term for fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0560] A "repair plan" is a document that specifically outlines the content and schedule of repair work on a building, and serves as a guideline for efficiently carrying out repairs.

[0561] "Process management" refers to management activities to ensure that each stage of repair work proceeds according to plan, and is necessary to ensure the progress and quality of the work.

[0562] "Ordering operations" refer to the process of ordering necessary materials and services from external contractors to carry out repair work, and are essential for the smooth execution of the work.

[0563] An "emotion engine" refers to software or hardware that analyzes a user's voice tone and facial expressions to recognize their emotions.

[0564] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to analyze data and automatically generate the optimal repair schedule.

[0565] An "optimal repair schedule" refers to a schedule that allows for the most effective and efficient repair work, based on the building's condition and usage.

[0566] This invention is a system that automatically formulates a repair plan based on basic building information and provides an optimal repair schedule that takes into account the user's feelings. A specific embodiment of this system is described below.

[0567] System Configuration

[0568] This system consists of three main elements: a server, terminals, and users. The server is responsible for data collection, analysis, storage, and the generation of repair plans, while the terminals function as the interface with the users. Users are responsible for inputting basic building information and reviewing the repair plans.

[0569] Hardware and software to be used

[0570] Server: A high-performance computer equipped with a database, generative AI models, and an emotion engine.

[0571] Device: A computer or smart device equipped with a camera and microphone.

[0572] Database: Data storage for storing basic building information and general schedules for repair work.

[0573] Generative AI model: An artificial intelligence algorithm for calculating the optimal repair schedule.

[0574] Emotion engine: Software that analyzes the user's voice tone and facial expressions to recognize their emotions.

[0575] Program processing

[0576] The server stores basic building information (e.g., total floor area, age of building, etc.) in a database and generates a repair plan based on a typical repair schedule. Specifically, the server processes the information in the following steps:

[0577] 1. Data Collection: The server receives basic building information transmitted from the terminal and stores it in the database.

[0578] 2. Data Analysis: The server analyzes stored basic information and general repair schedules to identify areas and timings where repairs are needed.

[0579] 3. Plan Generation: The server uses a generation AI model to calculate the optimal repair schedule and generate a repair plan.

[0580] The device captures the user's voice tone and facial expressions through its camera and microphone and sends them to the emotion engine. The emotion engine analyzes this data to recognize the user's emotions.

[0581] Specific example

[0582] For example, if the basic information for a building is entered as "Year built: 2000", "Structure: Reinforced concrete", and "Materials used: Concrete, steel", the server will generate a repair plan like the following:

[0583] Exterior wall inspection and repair in 2020

[0584] Inspection of internal equipment in 2025

[0585] Inspect and repair of the exterior walls again in 2030.

[0586] Example of a prompt:

[0587] "If the building was constructed in 2000, is made of reinforced concrete, and uses concrete and steel as materials, please generate a repair plan based on a typical repair schedule."

[0588] This system eliminates the need for users to plan repairs themselves and provides support for efficient repair work. Furthermore, by considering user emotions, it enables the provision of a more satisfying service.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 19.

[0590] Step 1:

[0591] Entering basic information

[0592] The user enters basic building information into the terminal's input form, such as "Year of Construction: 2000," "Structure: Reinforced Concrete," and "Materials Used: Concrete, Steel."

[0593] Input: Basic information such as the year of construction, structure, and materials used.

[0594] Output: Sending basic information from the terminal to the server.

[0595] Specific operation: When the user enters the required information into the input form on the device and presses the "Submit" button, the device sends the entered information to the server.

[0596] Step 2:

[0597] Data collection and storage

[0598] The server receives basic information sent from the terminal.

[0599] Input: Basic information sent from the device.

[0600] Output: Basic information stored in the database.

[0601] Specific operation: The server saves the received basic information to the "Building Information" table in the database. It also retrieves a general repair schedule from the "Repair Schedule" table.

[0602] Step 3:

[0603] Generating a repair plan

[0604] The server generates a repair plan based on basic information stored in the database and a general schedule for repair work.

[0605] Input: Basic information and repair schedule stored in the database.

[0606] Output: Generated repair plan.

[0607] Specific operation: The server inputs the prompt "Generate a repair plan based on a general repair schedule, given that the building was constructed in 2000, is made of reinforced concrete, and uses concrete and steel materials" to the generated AI model, and calculates the optimal repair schedule. The calculation results are compiled into a repair plan and saved to the "Repair Plan" table in the database.

[0608] Step 4:

[0609] Execution of emotion recognition

[0610] The user interacts with the system through the device's camera and microphone.

[0611] Input: User's voice tone and facial expression.

[0612] Output: Analyzed user sentiment.

[0613] Specific operation: The device captures the user's voice tone and facial expressions and sends them to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotions.

[0614] Step 5:

[0615] Presentation of repair plan

[0616] The server sends the generated repair plan to the terminal.

[0617] Input: Generated repair plan.

[0618] Output: Repair plan displayed on the terminal.

[0619] Specific operation: The server sends the generated repair plan to the terminal, and the terminal displays the repair plan to the user. The user reviews the displayed repair plan and confirms the plan by pressing the "Approve" button.

[0620] In this way, the system efficiently plans repairs and provides optimal information while taking user sentiment into consideration.

[0621] (Application Example 2)

[0622] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0623] Conventional repair planning systems can automatically generate repair plans for building and factory equipment, but they lack the ability to propose plans at the appropriate time, taking user sentiment into consideration, and to monitor the progress of the repair plans in real time. This can lead to decreased user satisfaction during the execution of repair plans. Furthermore, because plan updates are performed manually, it is difficult to maintain repair plans that reflect the latest situation. To address these issues, a system is needed that recognizes user sentiment, proposes repair plans at the appropriate time, and has real-time progress monitoring capabilities.

[0624] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0625] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for proposing a repair plan at an appropriate time using an emotion engine that recognizes the user's emotions; and means for monitoring the progress of the repair plan in real time. This makes it possible to propose a repair plan at an appropriate time that takes the user's emotions into consideration, and to grasp the progress of the repair plan in real time, thereby improving user satisfaction.

[0626] "Basic information" refers to fundamental data about a building and its facilities, such as total floor area, year of construction, and building code.

[0627] A "repair plan" is the process of planning the schedule and details of repair work for a building or its facilities.

[0628] "Process management" refers to the means of managing the progress of repair work and ensuring that it proceeds according to plan.

[0629] "Ordering operations" refer to the process of ordering materials and services necessary for repair work from external contractors.

[0630] An "emotion engine" is a technology that recognizes emotions from the user's voice tone and facial expressions.

[0631] "The right timing" refers to the most effective period based on the user's emotions and circumstances.

[0632] "Progress status" refers to the current state of progress in the implementation of the repair plan.

[0633] "Real-time" means being able to instantly grasp the current situation.

[0634] In order to implement this invention, the following system configuration is necessary. The server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for proposing a repair plan at an appropriate time using an emotion engine that recognizes the user's emotions; and means for monitoring the progress of the repair plan in real time.

[0635] Hardware and software configuration

[0636] Hardware:

[0637] Factory robots: Execute repair plans and monitor their progress.

[0638] Camera: Used to recognize the user's facial expressions.

[0639] Microphone: Used to recognize the tone of the user's voice.

[0640] software:

[0641] Python: Used for implementing programs.

[0642] Emotion recognition library: Recognizes user emotions using libraries such as OpenCV and librosa.

[0643] Database: Used to manage basic information and repair plan data.

[0644] Data processing and data calculation

[0645] The server first inputs basic information into a database and automatically generates a repair plan based on this information. Next, it uses an emotion engine to recognize the user's emotions from their tone of voice and facial expressions, and proposes a repair plan at the appropriate time. Furthermore, it uses factory robots to monitor the progress of the repair plan in real time and record it in the database.

[0646] Specific example

[0647] For example, the equipment "Machine A" underwent its last maintenance on January 1, 2023, with a maintenance interval of 180 days. The next maintenance date is June 30, 2023. The server uses this information to formulate a maintenance plan, recognizes the user's emotions, and proposes this plan at the appropriate time. Furthermore, factory robots execute the maintenance work, and their progress is monitored in real time.

[0648] Example of a prompt

[0649] "Recognize the user's emotions from their tone of voice and facial expressions, and propose a repair plan at the appropriate time. For example, if a user says, 'Please tell me the repair plan for Machine A,' calculate and propose the next repair date."

[0650] In this way, it becomes possible to propose repair plans at the appropriate time, taking into account the user's feelings, and to monitor the progress of the repair plan in real time, thereby improving user satisfaction.

[0651] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[0652] Step 1:

[0653] The server inputs basic information such as total floor area, year of construction, and intelligence into the database.

[0654] Input: Basic information such as total floor area, year of construction, and intelligence.

[0655] Data processing: Save basic information to a database.

[0656] Output: Basic information stored in the database

[0657] Step 2:

[0658] The server automatically creates a repair plan based on this basic information.

[0659] Input: Basic information stored in the database

[0660] Data calculation: Calculate the repair work schedule based on basic information.

[0661] Output: Automatically generated repair plan

[0662] Step 3:

[0663] The server supports everything from planning and process management to ordering.

[0664] Input: Automated repair plan

[0665] Data processing: Manage the repair plan process and order necessary materials and services.

[0666] Output: Progress of order processing

[0667] Step 4:

[0668] The server uses an emotion engine to recognize user emotions and propose a repair plan at the appropriate time.

[0669] Input: User's voice tone and facial expression

[0670] Data processing: Analyze user emotions using an emotion engine.

[0671] Output: Proposal of a repair plan based on user sentiment.

[0672] Step 5:

[0673] The server monitors the progress of the repair plan in real time.

[0674] Input: Progress data of the repair plan

[0675] Data processing: Update progress in real time and record it in the database.

[0676] Output: Real-time updated progress data

[0677] Step 6:

[0678] The user reviews the repair plan proposed by the server and makes any necessary modifications.

[0679] Input: Repair plan proposed by the server

[0680] Data processing: Modify the repair plan based on user feedback.

[0681] Output: Revised repair plan

[0682] Step 7:

[0683] Factory robots perform repair work based on the repair plan.

[0684] Input: Revised repair plan

[0685] Data calculation: Calculate the steps for executing the repair work and start the work.

[0686] Output: Results of the repair work performed

[0687] Step 8:

[0688] The server records the results of the repair work in a database and incorporates them into the next repair plan.

[0689] Input: Results of repair work performed

[0690] Data processing: Save the results of repair work to a database and use them to inform the next repair plan.

[0691] Output: Updated repair plan data

[0692] In this way, a system is realized in which servers, terminals, users, and factory robots work together to plan, execute, and monitor repairs.

[0693] (Example 3)

[0694] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0695] Traditional repair management systems often rely on manual processes for developing repair plans based on basic information, managing schedules, and supporting ordering, resulting in inefficiency. Furthermore, they struggle to respond quickly to changes in building conditions, leading to delays in updating repair plans. Additionally, creating requests for proposals, answering questions, and evaluating the validity of quoted prices are all done manually, requiring significant time and effort. The lack of user-centric feedback prevents improvements in the user experience.

[0696] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[0697] This invention includes a server comprising means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for collecting data from sensors installed in the building; means for analyzing the collected data; means for automatically updating the repair plan based on the analysis results; means for automatically generating a request for proposal based on detailed information of the repair work; means for automatically responding to questions from contractors; means for evaluating estimates submitted by contractors and determining their validity; means for collecting the tone of the user's voice and facial expressions; means for recognizing the user's emotions by analyzing the collected data; and means for providing appropriate feedback based on the analysis results. This enables efficient planning, process management, and ordering of repair work, and allows for quick responses to changes in the building's condition. Furthermore, the creation of requests for proposals for contractors, answering questions, and evaluating the validity of estimated costs are automated, reducing time and effort. In addition, feedback that takes the user's emotions into consideration is provided, improving the user experience.

[0698] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0699] A "repair plan" is a plan for efficiently carrying out repair work on a building, and it includes the areas to be repaired and the schedule for the work.

[0700] "Process management" is the process of monitoring the progress of repair work and making adjustments to ensure it proceeds according to plan.

[0701] "Ordering operations" refer to the process of ordering materials and services necessary for repair work from contractors.

[0702] A "sensor" is a device used to collect environmental data (temperature, humidity, vibration, etc.) within a building.

[0703] "Data analysis" is the process of analyzing collected data to detect outliers and patterns.

[0704] A Request for Proposal (RFP) is a document that details the repair work and requests proposals from contractors.

[0705] "Question and answer" refers to the process of providing appropriate answers to questions from vendors.

[0706] "Evaluating the appropriateness of the estimated price" is the process of determining whether the estimate submitted by the contractor is reasonable.

[0707] "Emotion recognition" is a technology that analyzes a user's voice tone and facial expressions to identify their emotions.

[0708] "Feedback" refers to advice and information provided to the user based on the analysis results.

[0709] Modes for carrying out the invention

[0710] This invention is a system that efficiently handles everything from planning and process management to ordering for repair work. A specific embodiment of this system is described below.

[0711] Inputting basic information and developing a repair plan.

[0712] The server provides a means for inputting basic information such as total floor area, building age, and intelligence. Based on this basic information, the server automatically develops a repair plan. The software used is data analysis tools such as Python or R.

[0713] Sensor-based data collection and analysis

[0714] The server collects data from sensors installed within the building. The hardware used includes IoT devices such as Raspberry Pi and Arduino. The collected data is analyzed using the Python Pandas library. Based on the analysis results, the server automatically updates the repair plan.

[0715] As a concrete example, a sensor collects vibration data from a part of a building, and if abnormal vibrations are detected, repairs to that part are added to the plan.

[0716] Automated generation of Request for Proposal and Q&A

[0717] The server automatically generates a Request for Proposal (RFP) based on detailed information about the repair work. The software used includes, for example, Microsoft Word or the Google Docs API. Furthermore, the server uses a generative AI model (e.g., GPT-3) to automatically respond to questions from contractors.

[0718] As a concrete example, an RFP is generated based on detailed information about the repair work and sent to contractors. Questions from contractors are received, appropriate answers are generated using a generative AI model, and these answers are sent back to the contractors.

[0719] Evaluation of the reasonableness of the estimated price

[0720] The server uses a price comparison algorithm to evaluate quotes submitted by vendors and determine their reasonableness. It compares the submitted quotes to historical data to determine if they are unusually high or low.

[0721] Emotion recognition and feedback provision

[0722] The device collects the user's voice tone and facial expressions through its camera and microphone. The hardware used is, for example, a webcam and microphone. The server uses an emotion recognition engine (for example, OpenCV or DeepFace) to analyze the collected data. Based on the analysis results, the server recognizes the user's emotions and provides appropriate feedback.

[0723] As a concrete example, a camera captures the user's facial expressions, and a microphone records their voice tone. The collected facial data is analyzed using DeepFace, and the voice tone is analyzed using a voice analysis library. If the user is experiencing stress, advice on how to relax is provided.

[0724] Example of a prompt

[0725] "Please generate a program that analyzes building vibration data and updates the repair plan if any anomalies are detected."

[0726] "Please generate a program that creates an RFP based on detailed information about the repair work and automatically responds to questions from contractors."

[0727] "Please create a program that analyzes the user's voice tone and facial expressions to recognize their emotions."

[0728] The above describes specific embodiments for carrying out this invention. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[0729] Step 1:

[0730] The server provides a means for inputting basic information such as total floor area, year of construction, and other details. Users input this basic information. The entered basic information is stored in a database.

[0731] Specific operation: The user enters basic information through a web interface, and the server saves that information to a database.

[0732] Input: Basic information such as total floor area, year of construction, and intelligence.

[0733] Output: Basic information stored in the database

[0734] Step 2:

[0735] The server automatically generates a repair plan based on the entered basic information. The server uses data analysis tools (such as Python or R) to calculate the repair locations and construction schedules.

[0736] Specific operation: Execute a Python script to analyze basic information and generate a repair plan.

[0737] Input: Basic information stored in the database

[0738] Output: Automatically generated repair plan

[0739] Step 3:

[0740] The server collects data from sensors installed within the building. The hardware used includes IoT devices such as Raspberry Pi and Arduino.

[0741] Specific operation: IoT sensors measure data such as temperature, humidity, and vibration in real time and send this data to a server.

[0742] Input: Real-time data from sensors

[0743] Output: Sensor data sent to the server

[0744] Step 4:

[0745] The server uses the Python Pandas library to analyze the collected data. The server then executes an algorithm to detect anomalies.

[0746] Specific operation: Use the Pandas library to load data and run an algorithm to detect outliers.

[0747] Input: Sensor data sent to the server

[0748] Output: Analysis results (presence or absence of outliers)

[0749] Step 5:

[0750] The server automatically updates the repair plan based on the analysis results. If an anomaly is detected, it adds the repair of that part to the plan and saves the updated plan to the database.

[0751] Specific action: Add the detected anomaly to the repair plan and update the database.

[0752] Input: Analysis results

[0753] Output: Updated repair plan

[0754] Step 6:

[0755] The server automatically generates a Request for Proposal (RFP) based on detailed information about the repair work. The software used includes, for example, Microsoft Word or the Google Docs API.

[0756] Specific actions: Embed detailed information about the repair work into a template and generate an RFP document.

[0757] Input: Details of repair work

[0758] Output: Automated RFP document

[0759] Step 7:

[0760] The server uses a generative AI model (e.g., GPT-3) to automatically respond to questions from vendors.

[0761] Specific operation: Receives questions from vendors, generates appropriate answers using a generative AI model, and sends them back to the vendors.

[0762] Input: Question from a vendor

[0763] Output: Auto-generated answer

[0764] Step 8:

[0765] The server uses a price comparison algorithm to evaluate quotes submitted by vendors and determine their validity.

[0766] Specific operation: Compare the submitted estimate with past data to determine if it is unusually high or low.

[0767] Input: Estimate submitted by the vendor

[0768] Output: Estimate Validity Evaluation Results

[0769] Step 9:

[0770] The device collects the user's voice tone and facial expressions through its camera and microphone. The hardware used includes, for example, a webcam and a microphone.

[0771] Specific operation: The camera captures the user's facial expressions, and the microphone records the tone of their voice.

[0772] Input: User's voice tone, facial expression data

[0773] Output: Collected audio and facial expression data

[0774] Step 10:

[0775] The server uses an emotion recognition engine (such as OpenCV or DeepFace) to analyze the collected data.

[0776] Specific operations: Collected facial expression data is analyzed using DeepFace, and voice tone is analyzed using a voice analysis library.

[0777] Input: Collected audio and facial expression data

[0778] Output: Emotion recognition result

[0779] Step 11:

[0780] The server recognizes the user's emotions based on the analysis results and provides appropriate feedback.

[0781] Specific action: If the user is feeling stressed, provide advice on how to relax.

[0782] Input: Emotion recognition result

[0783] Output: Provided feedback

[0784] (Application Example 3)

[0785] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0786] Traditional maintenance management systems often rely on manual processes for planning, updating, and ordering maintenance plans, resulting in inefficiency. Furthermore, they struggle to detect equipment deterioration in real time and respond quickly. Creating requests for proposals (RFPs) for contractors and evaluating the validity of their bids are also time-consuming processes. There is a need to solve these problems and achieve efficient and effective maintenance management.

[0787] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[0788] In this invention, the server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for acquiring sensor data within the factory and automatically updating the repair plan; means for creating a request for proposal for contractors, and for evaluating the appropriateness of questions, answers, and estimated costs; and means for recognizing the user's emotions and providing appropriate feedback. This enables automatic updating of the repair plan, increased efficiency in ordering, smoother communication with contractors, and support tailored to the user's emotions.

[0789] "Total floor area" refers to the sum of the floor areas of each floor of a building, and is an indicator of the overall size of the building.

[0790] "Years since construction" refers to the number of years that have passed since a building was constructed, and is important information for judging the degree of deterioration of the building.

[0791] "Intelligence" refers to knowledge and information related to the management and operation of a building, and is part of the basic information necessary for formulating and updating repair plans.

[0792] "Basic information" refers to information about the building necessary for formulating a repair plan, and includes the total floor area, age of the building, and other relevant details.

[0793] A "repair plan" is a document that outlines the schedule and details for systematically carrying out repairs to buildings and facilities.

[0794] "Planning and process management" refers to the means of managing the progress of repair work based on the repair plan and ensuring that it proceeds appropriately.

[0795] "Ordering operations" refer to the process of ordering necessary materials and services from contractors to carry out repair work.

[0796] "Sensor data" refers to data acquired from sensors within a factory, and is information used to understand the status of equipment and changes in the environment in real time.

[0797] A "Request for Proposal" is a document used to present the scope and conditions of work to contractors performing repair work and to request their proposals.

[0798] "Question and answer session" is the process of answering questions from vendors based on the Request for Proposal and providing necessary information.

[0799] "Evaluating the appropriateness of the estimated price" is the process of evaluating whether the price quoted by the contractor is appropriate.

[0800] An "emotion engine" is a system that recognizes emotions from the user's tone of voice and facial expressions and provides appropriate feedback.

[0801] "Feedback" refers to advice and information provided in response to the user's emotions and circumstances.

[0802] The system for implementing this invention has the following configuration: The server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for acquiring sensor data within the factory and automatically updating the repair plan; means for creating a request for proposal for contractors, and for evaluating the appropriateness of questions, answers, and estimated costs; and means for recognizing the user's emotions and providing appropriate feedback.

[0803] Hardware and software to use

[0804] Hardware: Smartphones, head-mounted displays, IoT sensors

[0805] Software: Python, EmotionRecognition library, IoTSensorData library, RFPGenerator library, EstimateEvaluator library

[0806] Data processing and data calculation

[0807] The server first acquires sensor data from IoT sensors within the factory. This data is used to understand the status of equipment and environmental changes in real time. Next, based on the acquired sensor data, it identifies areas that require repair and automatically updates the repair plan. This makes it possible to quickly detect equipment deterioration and formulate an appropriate repair plan.

[0808] Furthermore, the server creates requests for proposals (RFPs) for vendors and evaluates the reasonableness of their quoted prices and answers to questions. This allows for efficient selection of vendors to carry out repair work. In addition, an emotion engine is used to recognize the user's emotions from their tone of voice and facial expressions, and to provide appropriate feedback. This reduces user stress and enables smooth communication.

[0809] Specific example

[0810] Specific Example 1

[0811] IoT sensors detect when factory robots are aging and automatically add them to the repair plan. This helps prevent robot breakdowns and maintain factory production efficiency.

[0812] Specific Example 2

[0813] Administrators use their smartphones to send requests for proposals to contractors and evaluate the reasonableness of their estimates. This optimizes the cost of repair work and ensures that repairs are completed within budget.

[0814] Specific example 3

[0815] Technicians use a head-mounted display, and an emotion engine recognizes their emotions during repair work and provides appropriate feedback. This can reduce technician stress and improve work efficiency.

[0816] Example of a prompt

[0817] Develop an application that automatically updates robot repair plans based on data acquired from IoT sensors within the factory and supports ordering as needed. Also, incorporate a function that recognizes user emotions and provides appropriate feedback.

[0818] In this way, a system can be realized that streamlines the maintenance management of factory robots and provides support tailored to the user's needs.

[0819] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[0820] Step 1:

[0821] The server acquires sensor data from IoT sensors within the factory. This sensor data includes information about equipment status and environmental changes. By acquiring this data, equipment deterioration and malfunctions can be detected in real time. The input is sensor data, and the output is the acquired sensor data.

[0822] Step 2:

[0823] The server analyzes the acquired sensor data to identify areas requiring repair. Specifically, it analyzes the sensor data to detect abnormal values ​​and signs of deterioration. The input is the acquired sensor data, and the output is a list of areas requiring repair.

[0824] Step 3:

[0825] The server automatically updates the repair plan based on the identified repair areas. The repair plan includes the areas that need repair, the repair priorities, and the materials and work required. The input is a list of areas that need repair, and the output is the updated repair plan.

[0826] Step 4:

[0827] The server generates a Request for Proposal (RFP) for contractors based on the updated repair plan. The RFP includes details of the repair, required materials, and working conditions. The input is the updated repair plan, and the output is the RFP.

[0828] Step 5:

[0829] The server sends a Request for Proposal (RFP) to vendors and conducts a question-and-answer session. It provides appropriate answers to vendors' questions and shares necessary information. The input is the RFP and vendors' questions, and the output is the answers and shared information.

[0830] Step 6:

[0831] The server evaluates the quotes submitted by vendors and determines their validity. It analyzes the content of the quotes and evaluates whether the costs and scope of work are appropriate. The input is the quote from the vendor, and the output is the evaluation result.

[0832] Step 7:

[0833] The device uses an emotion engine to recognize the user's emotions. It recognizes emotions from the user's tone of voice and facial expressions and provides appropriate feedback. The input is the user's voice and facial expression data, and the output is the recognized emotion and the feedback provided.

[0834] Step 8:

[0835] The user proceeds with the repair work based on the feedback provided. They adjust the work according to the feedback to perform repairs efficiently. The input is the provided feedback, and the output is the progress of the repair work.

[0836] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0837] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0838] Other examples of generative AI include Gemini® (registered trademark) (Internet search). <url: https: gemini.google.com ?hl="ja">) are some examples.

[0839] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0840] [Second Embodiment]

[0841] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0842] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0843] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0844] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0845] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0846] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0847] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0848] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0849] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0850] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0851] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0852] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0853] "Example of form 1"

[0854] Embodiments of the present invention include means for inputting basic building information, such as total floor area, year of construction, and other relevant information. This information can be input directly by, for example, the building manager, or obtained from an existing building management system.

[0855] "Example of form 2"

[0856] Furthermore, an embodiment of the present invention includes means for automatically formulating a repair plan based on the above basic information. Specifically, it automatically generates a repair plan based on the basic information of the building and a general schedule for repair work.

[0857] "Example of form 3"

[0858] Furthermore, the embodiment of the present invention includes means to support everything from planning and process management to ordering. Specifically, it manages the progress of repair work and supports ordering as needed.

[0859] "Example of form 4"

[0860] Furthermore, the embodiment of the present invention includes means for automatically updating the plan based on IoT sensor data, using basic information as a basis. Specifically, it analyzes data acquired from IoT sensors within a building and automatically updates the repair plan. For example, if an IoT sensor detects that a part of the building is deteriorating, the repair of that part is added to the plan.

[0861] "Example of form 5"

[0862] Furthermore, the embodiment of the present invention includes means for creating an RFP for contractors, answering questions, and evaluating the appropriateness of the estimated costs when carrying out construction work. Specifically, it creates an RFP based on the details of the repair work and responds to questions from contractors. It also evaluates the estimates submitted by contractors and determines their appropriateness.

[0863] The following describes the processing flow for each example of the form.

[0864] "Example of form 1"

[0865] Step 1: Enter basic building information, such as total floor area, year of construction, and building code. This information can be entered directly by the building manager or retrieved from an existing building management system.

[0866] "Example of form 2"

[0867] Step 1: Enter the basic information of the building.

[0868] Step 2: Automatically create a repair plan based on the basic information above. Specifically, the system automatically generates a repair plan based on the building's basic information and a general schedule for repair work.

[0869] "Example of form 3"

[0870] Step 1: Develop a repair plan.

[0871] Step 2: Implement project schedule management.

[0872] Step 3: Provide support for ordering as needed.

[0873] "Example of form 4"

[0874] Step 1: Enter the building's basic information and create a repair plan.

[0875] Step 2: Obtain data from IoT sensors within the building.

[0876] Step 3: Analyze the acquired data and automatically update the repair plan. For example, if an IoT sensor detects that a part of the building is deteriorating, add the repair of that part to the plan.

[0877] "Example of form 5"

[0878] Step 1: Create an RFP based on the details of the repair work.

[0879] Step 2: Respond to questions from the vendor.

[0880] Step 3: Evaluate the quotes submitted by the contractors and determine their validity.

[0881] (Example 1)

[0882] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0883] Traditional building management systems had problems such as requiring manual input of basic building information and consuming a lot of time and effort when formulating repair plans. Furthermore, retrieving information from existing management systems and formatting the data was cumbersome, making efficient planning difficult. In addition, the generation of prompt messages using AI models was not automated, placing a significant burden on users.

[0884] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0885] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for receiving said basic information and storing it in a database; means for obtaining additional information from an existing management system; means for formatting the obtained information and converting it into the required format; means for inputting prompt sentences into a generating AI model; means for automatically formulating a repair plan; and means for supporting everything from planning process management to ordering. This makes it possible to automate the entire process from inputting basic building information to formulating a repair plan and generating prompt sentences using a generating AI model.

[0886] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0887] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0888] "Intelligence" refers to information and data related to the intelligent management and operation of a building.

[0889] "Basic information" refers to fundamental data such as the total floor area of ​​a building, its age, and its intellectual property.

[0890] A "database" is a system for systematically organizing and storing information.

[0891] A "management system" is a general term for the software and hardware used for the operation and maintenance of a building.

[0892] "Additional information" refers to building data other than basic information, including energy consumption data and maintenance history.

[0893] "Formatting" refers to the process of converting acquired data into the required format, making it easier to use.

[0894] A "generative AI model" is a model that uses artificial intelligence to perform a specific task.

[0895] A "prompt" refers to an instruction or question that is input into a generative AI model.

[0896] A "repair plan" refers to planning the schedule and details of repairs and maintenance for a building.

[0897] "Process management" refers to managing each stage in the execution of a repair plan.

[0898] "Ordering operations" refers to the process of ordering materials and services necessary for repairs and maintenance.

[0899] A "Request for Proposal" is a document used to request specific proposals from vendors.

[0900] "Question and answer session" refers to the process of answering questions from vendors based on the Request for Proposal (RFP).

[0901] "Evaluating the appropriateness of the quoted price" refers to assessing whether the quoted price submitted by the contractor is reasonable.

[0902] This invention is a system that takes basic building information as input, formulates a repair plan, and generates prompt messages using a generation AI model. A specific embodiment of this system is described below.

[0903] First, the user accesses the web application provided by the server using a device such as a PC or tablet. They open a web browser and enter basic information such as the building's total floor area, age, and intelligence level. For example, the user might enter "Total floor area: 5000 square meters," "Age: 10 years," and "Intelligence level: High."

[0904] Next, the server receives the basic building information entered by the user. The received information is stored in a database server (e.g., MySQL, PostgreSQL). The server executes SQL queries against the database and inserts the information into the appropriate tables.

[0905] Furthermore, the server retrieves additional building information through the APIs of existing management systems. This includes, for example, building energy consumption data and maintenance history. The server sends API requests and receives the data returned as responses.

[0906] The server then formats the received additional information and converts it to the required format. For example, it might parse JSON data, extract the necessary fields, and format them. The server uses data processing scripts (e.g., Python, JavaScript) to perform this process.

[0907] Finally, the server generates prompts to input into the AI ​​model based on the formatted information. For example, it might generate a prompt such as, "Please enter the basic information of a building with a total floor area of ​​5,000 square meters, an age of 10 years, and high intelligence." The server inputs this prompt into the AI ​​model and receives a response from the model.

[0908] As a concrete example, the following prompt sentence can be input into the generation AI model.

[0909] Example of a prompt:

[0910] "Please enter the basic information for a building with a total floor area of ​​5,000 square meters, built 10 years ago, and with high intelligence."

[0911] This system enables the complete automation of the entire process, from inputting basic building information and formulating repair plans to generating prompt messages using a generation AI model. This reduces the burden on users and enables efficient building management.

[0912] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0913] Step 1:

[0914] The user enters the building's basic information.

[0915] Users access a web application provided by the server using a device such as a PC or tablet. They open a web browser and enter basic information such as the building's total floor area, age, and intelligence level. The entered information is sent to the server. Specifically, the user enters "Total floor area: 5000 square meters," "Age: 10 years," and "Intelligence level: High" into the form and clicks the submit button.

[0916] Input: Basic information such as the total floor area of ​​the building, the year it was built, and its location.

[0917] Output: Basic information sent to the server

[0918] Step 2:

[0919] The server receives the entered information and saves it to the database.

[0920] The server receives basic building information entered by the user. The received information is stored in a database server (e.g., MySQL, PostgreSQL). The server executes an SQL query against the database and inserts the information into the appropriate table. Specifically, the server executes the SQL query "INSERT INTO buildings (area, age, intelligence) VALUES (5000, 10, 'high');".

[0921] Input: Basic information submitted by the user

[0922] Output: Basic information stored in the database

[0923] Step 3:

[0924] The server retrieves additional information from the existing management system.

[0925] The server retrieves additional building information through the existing management system's API. This includes, for example, building energy consumption data and maintenance history. The server sends an API request and receives the data returned as a response. Specifically, the server sends an API request called "GET / buildings / 12345 / additional_info".

[0926] Input: API Request

[0927] Output: Additional information obtained

[0928] Step 4:

[0929] The server formats the acquired information and converts it into the required format.

[0930] The server formats the received additional information and converts it to the required format. For example, it parses JSON data, extracts the necessary fields, and formats them. The server uses a data processing script (e.g., Python, JavaScript) to perform this process. Specifically, the server executes "json.loads(response_data)" to extract the necessary fields.

[0931] Input: Additional information obtained

[0932] Output: Formatted data

[0933] Step 5:

[0934] The server inputs prompt messages into the generated AI model.

[0935] The server generates prompt statements to input into the AI ​​model based on the formatted information. For example, it might generate a prompt statement like, "Please enter the basic information of a building with a total floor area of ​​5000 square meters, an age of 10 years, and high intelligence." The server inputs this prompt statement into the AI ​​model and receives a response from the model. Specifically, the server performs the process of "inputting a prompt statement into the AI ​​model and receiving a response."

[0936] Input: Formatted data

[0937] Output: Response from the generative AI model

[0938] (Application Example 1)

[0939] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0940] Traditional building management systems supported the planning, scheduling, and ordering of repairs based on basic building information, but lacked security risk assessment, real-time security alert generation, and specific security countermeasures proposals. Therefore, comprehensively managing building safety was difficult, and a rapid response to security risks was required.

[0941] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0942] In this invention, the server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for evaluating security risks based on the basic information; means for generating real-time security alerts based on the security risks; and means for proposing security measures based on the security risks. This enables not only the formulation of repair plans, process management, and ordering support based on the building's basic information, but also the evaluation of security risks, the generation of real-time security alerts, and the proposal of specific security measures.

[0943] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[0944] "Years since construction" refers to the number of years that have passed since a building was constructed.

[0945] "Intelligence" refers to information about the building's location and surrounding environment.

[0946] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[0947] A "repair plan" is a plan that outlines the schedule and details for systematically carrying out repairs and maintenance on a building.

[0948] "Process management" refers to managing the progress of work based on the repair plan.

[0949] "Ordering operations" refer to the process of ordering materials and services necessary for repairs and maintenance.

[0950] "Security risk" refers to potential safety threats or dangers to a building.

[0951] A "real-time security alert" is a warning that is immediately sent when a security risk occurs.

[0952] "Security measures" refer to specific means and methods for mitigating or eliminating security risks.

[0953] As an embodiment of this invention, a building management system is constructed that inputs basic information such as total floor area, building age, and intelligence, and based on this information, formulates a repair plan and supports process management and ordering operations. In addition, a function is added to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[0954] The server provides a means for inputting basic information such as total floor area, building age, and other relevant data. This basic information can be entered directly by the building manager or obtained from an existing building management system. Based on the entered basic information, the server automatically creates a repair plan and supports everything from project management to ordering.

[0955] Furthermore, the server is equipped with a means to assess security risks based on basic information. The security risk assessment is performed based on information such as the building's age, total floor area, and location. Based on the assessment results, the server generates real-time security alerts and notifies the building manager.

[0956] The server also provides a means to suggest specific security measures based on security risks. This allows building managers to quickly implement appropriate security measures.

[0957] The hardware used includes devices such as smartphones and tablets. The software used will be Python, JSON, and the datetime module. This will enable efficient management of basic building information, assessment of security risks, and implementation of appropriate countermeasures.

[0958] As a concrete example, a building manager uses a smartphone to input information about a building with a total floor area of ​​12,000 square meters, built in 1980, and located in a high-crime area. Based on this information, the server assesses the security risks and determines that the risk is high. A real-time security alert is generated and notified to the building manager. Furthermore, the server proposes specific countermeasures such as installing an advanced monitoring system and increasing security personnel.

[0959] Examples of prompt statements include the following:

[0960] "Please enter the basic information about the building. Enter the total floor area, year of construction, and location."

[0961] In this way, in addition to supporting the development of repair plans, process management, and ordering operations based on the building's basic information, it becomes possible to assess security risks, generate real-time security alerts, and propose specific security measures.

[0962] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0963] Step 1:

[0964] The user uses a terminal to input basic building information (total floor area, year of construction, etc.).

[0965] Input: Basic information such as total floor area, year of construction, and structural integrity.

[0966] Output: The entered basic information is sent to the server.

[0967] Specific operation: The user opens the application on their smartphone or tablet and enters information such as total floor area, year of construction, and other details into the input form. Once the input is complete, they press the submit button to send the information to the server.

[0968] Step 2:

[0969] The server saves the basic information it receives and automatically creates a repair plan.

[0970] Input: Basic information submitted by the user

[0971] Output: Results of the repair plan

[0972] Specific operation: The server saves the received basic information to the database. Then, it automatically generates a repair plan based on the saved information and saves the plan's contents to the database.

[0973] Step 3:

[0974] The server supports the process management and ordering of repair plans.

[0975] Input: Results of the repair plan

[0976] Output: Process management schedule and order list

[0977] Specific operation: Based on the repair plan, the server creates a schedule for each stage and generates an order list for necessary materials and services. This information is then communicated to the building manager.

[0978] Step 4:

[0979] The server assesses security risks based on basic information.

[0980] Input: Basic information submitted by the user

[0981] Output: Security risk assessment results

[0982] Specific operation: The server analyzes basic information and assesses security risks by considering factors such as the building's age, total floor area, and location. The assessment results are calculated as a risk score.

[0983] Step 5:

[0984] The server generates real-time security alerts based on the security risk assessment results.

[0985] Input: Security risk assessment results

[0986] Output: Security alert notification

[0987] Specific operation: When the risk score exceeds a certain threshold, the server generates a real-time security alert and notifies the building administrator's terminal.

[0988] Step 6:

[0989] The server proposes specific security measures based on the security risk assessment results.

[0990] Input: Security risk assessment results

[0991] Output: Security countermeasures proposal

[0992] Specific operation: The server proposes appropriate security measures based on the risk score. For example, specific measures such as installing an advanced monitoring system or increasing security personnel may be proposed. The proposed measures are notified to the building manager.

[0993] In this way, in addition to supporting the development of repair plans, process management, and ordering operations based on the building's basic information, it becomes possible to assess security risks, generate real-time security alerts, and propose specific security measures.

[0994] (Example 2)

[0995] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0996] Conventional repair planning systems required manual creation of repair plans based on basic building information, which was time-consuming and labor-intensive. Furthermore, updating or modifying the plans was also done manually, resulting in inefficiency and inaccuracies. Additionally, there was a lack of easy ways for users to review the generated repair plans.

[0997] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0998] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for sending prompt messages to a generating AI model to generate a repair plan; means for saving the generated repair plan to a database; and means for users to request a repair plan and display it on a terminal. This enables the automatic generation and efficient management of repair plans.

[0999] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[1000] "Years since construction" refers to the number of years that have passed since a building was constructed.

[1001] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[1002] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1003] A "repair plan" refers to a plan outlining the schedule and details of repair work for a building.

[1004] A "generative AI model" refers to a model that uses artificial intelligence to generate data.

[1005] A "prompt statement" refers to an instruction given to a generative AI model.

[1006] A "database" refers to a system for systematically storing and managing data.

[1007] A "server" refers to a computer that provides data and services over a network.

[1008] "Terminal" refers to a device used by a user to operate something (such as a personal computer, tablet, or smartphone).

[1009] A "user" refers to a person who uses the system.

[1010] "Process management" refers to managing the progress of repair work.

[1011] "Ordering operations" refers to the process of ordering materials and services necessary for repair work.

[1012] "RFP" is an abbreviation for Request for Proposal, which refers to a document requesting proposals from vendors.

[1013] "Question and answer session" refers to questions and answers regarding proposals or plans.

[1014] "Evaluating the appropriateness of the quoted price" refers to assessing whether the submitted estimate is reasonable or not.

[1015] This invention is a system for automatically planning and managing building repairs. The system consists of three main elements: a server, terminals, and users.

[1016] Server Role

[1017] The server receives basic building information and stores it in a database. This basic information includes total floor area, age of the building, and intelligence. Based on this information, the server sends prompt messages to a generating AI model to generate a repair plan. The generated repair plan is then stored in the database again.

[1018] The server uses the following hardware and software:

[1019] High-performance database servers (e.g., MySQL, PostgreSQL)

[1020] Generative AI models (e.g., GPT-4)

[1021] Terminal role

[1022] The terminal provides an interface for users to input basic building information and view repair plans. When a user requests a repair plan by operating the terminal, the terminal sends a request to the server. Upon receiving the repair plan from the server, the terminal displays its contents to the user.

[1023] The device uses the following hardware and software:

[1024] PCs, tablets, smartphones

[1025] Web browser (e.g., Google Chrome, Mozilla Firefox)

[1026] User roles

[1027] The user enters basic building information using a terminal. Once the input is complete, the user requests the generation of a repair plan. The user then reviews the generated repair plan and requests modifications or additions as needed.

[1028] Specific example

[1029] The user enters the following information into the terminal's form: "Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago". The terminal sends this information to the server, which then sends the following prompt to the generated AI model:

[1030] "The basic information about the building is as follows: Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior walls repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[1031] The AI ​​model generates a repair plan based on this prompt and sends it back to the server. The server saves the generated repair plan to a database and sends it to the terminal when requested by the user. The terminal displays the received repair plan to the user.

[1032] In this way, users can easily plan and manage building maintenance.

[1033] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1034] Step 1:

[1035] The user enters the building's basic information.

[1036] The user inputs basic building information through the terminal interface. Specifically, they input information such as total floor area, year of construction, and structural integrity. The input information is temporarily stored in the terminal's memory. An example of input data is: "Structure: Reinforced concrete, Year of construction: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago."

[1037] Step 2:

[1038] The terminal sends the entered information to the server.

[1039] The terminal sends basic information entered by the user to the server using an HTTP request. The data sent is in JSON format, which the server receives and parses. The input data is temporarily stored in the server's memory.

[1040] Step 3:

[1041] The server stores basic information in a database.

[1042] The server stores the received basic information in a database. Specifically, it uses a database management system such as MySQL or PostgreSQL. Storing this information in a database makes it accessible for later processing.

[1043] Step 4:

[1044] The server sends a prompt message to the generated AI model.

[1045] The server generates prompts based on the stored basic information. The generated prompts are then sent to the AI ​​model. An example of a prompt is: "The basic information of the building is as follows: Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior walls repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[1046] Step 5:

[1047] The generative AI model generates the repair plan.

[1048] The generation AI model generates a repair plan based on the received prompt text. The generated repair plan is sent back to the server. An example of the output data is a plan that states, "The next repair will be to repaint the exterior walls in two years, and then waterproof the roof five years later."

[1049] Step 6:

[1050] The server saves the generated repair plan to the database.

[1051] The server stores the repair plans received from the generated AI models in a database. This allows users to review and modify the repair plans later.

[1052] Step 7:

[1053] The user requests a repair plan.

[1054] The user requests to view the repair plan using their device. Specifically, they click the "View Repair Plan" button on the device's interface. The request is made using an HTTP GET request.

[1055] Step 8:

[1056] The terminal requests a repair plan from the server.

[1057] The terminal, upon receiving a user request, requests the server to retrieve the repair plan. The server retrieves the repair plan from the database and sends it to the terminal. The data sent is in JSON format.

[1058] Step 9:

[1059] The server sends the repair plan to the terminal.

[1060] The server retrieves the repair plan from the database and sends it to the terminal. The data sent is in JSON format, and the terminal receives and parses it.

[1061] Step 10:

[1062] The device displays the repair plan to the user.

[1063] The terminal displays the received repair plan to the user. Specifically, it displays the details of the repair plan in a web browser. The user can review the displayed repair plan and make requests for modifications or additions as needed.

[1064] (Application Example 2)

[1065] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1066] Conventional repair planning systems struggled to automatically generate repair plans based on basic information about building and factory equipment, and they could not monitor the progress of the plans in real time or update them as needed. As a result, the accuracy and efficiency of repair plans decreased, leading to significant effort and cost in maintaining the equipment.

[1067] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1068] In this invention, the server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for automatically generating a repair plan for each piece of equipment based on the basic information of the equipment and the general schedule of repair work; means for monitoring the progress of the repair plan in real time and updating the plan as necessary; and means for generating a repair plan using a generated AI model. This makes it possible to improve the accuracy and efficiency of repair plans and reduce the effort and cost involved in the maintenance and management of equipment.

[1069] "Total floor area" is a term that refers to the sum of the floor areas of each floor of a building.

[1070] "Years since construction" is a term that refers to the number of years that have passed since a building was constructed.

[1071] "Intelligence" is a term that refers to the knowledge and information necessary for managing buildings and facilities.

[1072] "Basic information" is a term that refers to fundamental data about a building and its facilities, such as total floor area, year of construction, and structural integrity.

[1073] A "repair plan" is a term that refers to planning the schedule and details of repair work for buildings and facilities.

[1074] "Process management" is a term that refers to the means of managing the progress of repair work and ensuring that it proceeds according to plan.

[1075] "Ordering operations" refers to the process of ordering materials and services necessary for repair work from external contractors.

[1076] "Equipment" is a term that refers to machinery and devices installed inside buildings such as factories and office buildings.

[1077] "Repair work" is a term that refers to construction work to repair or improve malfunctions or deterioration of buildings and equipment.

[1078] "General schedule" refers to a standard schedule that is generally applied in repair work.

[1079] "Automatic generation" is a term that refers to a system automatically creating plans and data without human intervention.

[1080] "Progress status" is a term that refers to information indicating how far along the repair work plan is.

[1081] "Real-time" is a term that refers to reflecting the current situation immediately.

[1082] A "generative AI model" is a term that refers to a model that uses artificial intelligence to analyze data and generate plans and predictions.

[1083] The system for implementing this invention operates in cooperation with three parties: a server, a terminal, and a user. The server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for automatically generating a repair plan for each piece of equipment based on the basic information of the equipment and the general schedule of the repair work; means for monitoring the progress of the repair plan in real time and updating the plan as needed; and means for generating a repair plan using a generated AI model.

[1084] The server implements programs using programming languages ​​such as Python to perform data input, analysis, plan generation, and progress monitoring. Specifically, the server acquires basic equipment information and calculates the next repair date based on a typical repair schedule. The calculation results are output in JSON format for user review. The server also monitors progress in real time and updates the plan as needed.

[1085] The terminal provides an interface for users to access the server, enter basic information, and check repair plans. The terminal communicates with the server via a web browser or dedicated application, allowing users to input and retrieve necessary information.

[1086] Users access the server via their terminal and input basic equipment information. This information is sent to the server, which automatically generates a repair plan based on it. The generated repair plan can be viewed by the user via their terminal. Furthermore, users can monitor the progress in real time and update the plan as needed.

[1087] As a specific example, Equipment 1 was installed on January 1, 2020, is frequently used, and has been repaired in the past on January 1, 2021 and January 1, 2022. In this case, the next repair date would be January 1, 2023. The user enters this information into the terminal, and the server calculates the next repair date based on that information and generates a plan.

[1088] Examples of prompts to input into a generative AI model are as follows:

[1089] Based on the basic information of the equipment and the general repair schedule, please calculate the next repair date. The following is an example of input data.

[1090] Basic information about the equipment:

[1091] {

[1092] "Equipment 1": {"Installation Date": "2020-01-01", "Frequency of Use": "High", "Past Repair History": ["2021-01-01", "2022-01-01"]},

[1093] "Equipment 2": {"Installation Date": "2019-01-01", "Usage Frequency": "Medium", "Past Repair History": ["2020-01-01"]}

[1094] }

[1095] Typical schedule for repair work:

[1096] {

[1097] "High": {"Period": 1},

[1098] "medium": {"period": 2},

[1099] "Low": {"Period": 3}

[1100] }

[1101] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1102] Step 1:

[1103] Users access the server via their terminal and input basic information about the equipment (such as total floor area, year of construction, maintenance status, installation date, frequency of use, and past repair history). The entered information is then sent to the server.

[1104] Input: Basic information about the equipment

[1105] Output: Basic information data sent to the server

[1106] Step 2:

[1107] The server uses the received basic information to refer to a general repair schedule and calculates the next repair date. Specifically, it applies a repair cycle based on usage frequency and calculates the next repair date from past repair history.

[1108] Input: Basic information data, general schedule of repair work

[1109] Output: Next repair date

[1110] Step 3:

[1111] The server automatically generates a repair plan based on the calculated next repair date. The generated repair plan is saved in JSON format for user review.

[1112] Input: Next repair date

[1113] Output: Repair plan (JSON format)

[1114] Step 4:

[1115] Users can access the server through their terminal and review the generated repair plan. They can modify or update the repair plan as needed.

[1116] Input: Repair plan (JSON format)

[1117] Output: Revised and updated repair plan

[1118] Step 5:

[1119] The server monitors the progress of the repair plan in real time. Using IoT sensor data, it monitors the condition of the equipment and verifies that the plan is progressing as scheduled. It automatically updates the plan as needed.

[1120] Input: IoT sensor data, repair plan

[1121] Output: Updated repair plan

[1122] Step 6:

[1123] The server uses a generative AI model to optimize the repair plan. Specifically, it analyzes past data and the current situation to propose the optimal repair schedule.

[1124] Input: Basic information data, repair plan, IoT sensor data

[1125] Output: Optimized repair plan

[1126] Step 7:

[1127] The user reviews the optimized repair plan through their terminal and confirms the final repair schedule. The confirmed repair schedule is saved on the server and then executed.

[1128] Input: Optimized repair plan

[1129] Output: Confirmed repair schedule

[1130] (Example 3)

[1131] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1132] Conventional repair management systems often involve manual processes for planning, managing, and assisting with ordering repairs, resulting in inefficiency. Furthermore, the lack of integrated processes such as automated plan updates using sensor data, creation of requests for proposals for contractors, Q&A, and evaluation of the reasonableness of quoted prices makes overall management cumbersome. This can lead to delays in repair work and increased costs.

[1133] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1134] In this invention, the server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from planning process management to ordering; means for using a database for managing the progress of repair work; means for acquiring and analyzing data from sensors within the building; means for automatically updating the repair plan; means for creating requests for proposals for contractors and responding to questions; and means for evaluating estimates submitted by contractors and determining their validity. This makes it possible to efficiently carry out a series of processes from planning and managing the progress of repair work to ordering and evaluating estimates.

[1135] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[1136] "Years since construction" refers to the number of years that have passed since a building was constructed.

[1137] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[1138] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1139] A "repair plan" refers to planning the details and schedule of repair work for a building.

[1140] "Process management" refers to managing the progress of each stage of repair work.

[1141] "Ordering operations" refers to the process of ordering materials and services necessary for repair work from contractors.

[1142] A "database" refers to a system used to store information about the progress of repair work and other related information.

[1143] A "sensor" refers to a device used to acquire environmental data (e.g., temperature, humidity) within a building.

[1144] A "Request for Proposal (RFP)" is a document that details the repair work and requests proposals from contractors.

[1145] "Question and answer session" refers to the process of answering questions from vendors.

[1146] "Evaluating the appropriateness of the estimated price" refers to assessing whether the price quoted by the contractor is reasonable.

[1147] Modes for carrying out the invention

[1148] This invention is a system for efficiently carrying out repair work, from planning and progress management to ordering and cost estimation. A specific embodiment of this system is described below.

[1149] System Configuration

[1150] The server implements the system using the following hardware and software.

[1151] Hardware: High-performance server machine (e.g., Intel Xeon processor, 64GB RAM, 1TB SSD)

[1152] Software: Database management systems (e.g., MySQL), sensor management APIs, generative AI models (e.g., GPT-3)

[1153] A terminal is a device used by a user to access the system and uses the following hardware and software:

[1154] Hardware: Personal computers, tablets, smartphones

[1155] Software: Web browser

[1156] System operation

[1157] The server first provides a means for inputting basic information such as total floor area, year of construction, and other relevant data. This allows the user to input fundamental data about the building. Next, the server has a means for automatically formulating a repair plan based on this basic information. This allows the user to efficiently create a repair plan.

[1158] The server provides the means to support everything from planning and process management to ordering. Specifically, it uses a database to manage the progress of repair work and updates the progress of each stage of the work in real time. The server also has the means to acquire and analyze data from sensors within the building. This allows the repair plan to be automatically updated based on the information obtained when sensors detect deterioration in a part of the building.

[1159] Furthermore, the server creates Requests for Proposals (RFPs) for vendors and provides a means to respond to their questions. Using a generative AI model, it generates appropriate answers to vendor questions and sends them to the vendors. The server also has a means to evaluate the quotes submitted by vendors and determine their validity. This allows the user to review the evaluation results of the quotes and make a final decision.

[1160] Specific example

[1161] For example, when a user is creating a building repair plan, an IoT sensor detects that a part of the building is deteriorating. Based on this information, the server automatically updates the repair plan and supports the necessary ordering process.

[1162] Example of a prompt:

[1163] "Please describe a system that automatically updates building maintenance plans based on data from IoT sensors and supports necessary ordering tasks."

[1164] This system enables efficient construction management by allowing users to check the progress of repair work in real time and automatically performing necessary ordering tasks. The flow of specific processing in Example 3 will be explained using Figure 15.

[1165] Step 1:

[1166] Entering basic information

[1167] Users input basic information such as total floor area, building age, and other details using a terminal. The server receives this input data and stores it in a database. This input data is used as foundational data necessary for developing repair plans. Specifically, the user enters information into a web form and clicks the submit button, which sends the data to the server.

[1168] Step 2:

[1169] Planning of repairs

[1170] The server automatically generates a repair plan based on stored basic information. The server considers past repair data and building characteristics to generate an optimal repair schedule. Input data is basic information, and output data is the repair plan. Specifically, the server uses an algorithm to analyze the data and generate the repair plan.

[1171] Step 3:

[1172] Progress Management

[1173] The server uses a database to manage the progress of repair work. Users can check the progress using a terminal. Input data is information about the progress of the work, and output data is a progress report. Specifically, the server periodically updates the database to provide users with the latest progress information.

[1174] Step 4:

[1175] Acquisition and analysis of sensor data

[1176] The server acquires data from sensors within the building and analyzes it in real time. The input data is environmental data acquired from the sensors, and the output data is the analysis results. Specifically, the server uses a sensor management API to acquire data and executes algorithms to detect anomalies.

[1177] Step 5:

[1178] Renewal of repair plan

[1179] The server automatically updates the repair plan based on the analysis results of sensor data. The input data is the analysis results of the sensor data, and the output data is the updated repair plan. Specifically, if an anomaly is detected, the server recalculates the repair plan based on that information and updates the database.

[1180] Step 6:

[1181] Preparation of Request for Proposal and Q&A

[1182] The server automatically generates a Request for Proposal (RFP) based on the details of the repair work. Users can review the RFP using a terminal and modify it as needed. The input data is the details of the repair work, and the output data is the RFP. Specifically, the server generates the RFP using a generation AI model and provides it to the user.

[1183] Step 7:

[1184] Estimate Evaluation

[1185] The server evaluates quotes submitted by vendors and determines their validity. The input data is the vendor's quote, and the output data is the evaluation result. Specifically, the server evaluates whether the quoted amount is appropriate based on past data and notifies the user.

[1186] (Application Example 3)

[1187] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1188] Conventional repair management systems supported the creation of repair plans, process management, and ordering operations based on basic information, but struggled to respond to real-time changes in the factory environment. Furthermore, manual processes were required for creating RFPs for contractors, handling inquiries, and evaluating the validity of quoted prices, necessitating efficient operation. This resulted in problems such as delays in updating repair plans and insufficient evaluation of estimates.

[1189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1190] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for collecting data from various sensors in the factory; means for analyzing the collected data in real time and identifying areas that require repair; means for automatically updating the repair plan based on the analysis results; means for automatically generating an RFP based on the details of the repair work; means for providing an automatic response function to questions from contractors; and means for evaluating submitted estimates and determining their validity. This enables efficient updating of the repair plan and communication with contractors in response to real-time changes in the factory environment.

[1191] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[1192] "Years since construction" refers to the number of years that have passed since a building was constructed.

[1193] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[1194] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1195] A "repair plan" is a document that outlines the details and schedule of repair work to a building.

[1196] "Process management" refers to managing the progress of repair work.

[1197] "Ordering operations" refer to the tasks of ordering materials and services necessary for repair work.

[1198] A "sensor" is a device that detects physical phenomena and outputs them as data.

[1199] "Data collection" refers to gathering information from sensors and other sources.

[1200] "Real-time analysis" means analyzing collected data immediately.

[1201] An "RFP" is a request for proposals that details the repair work.

[1202] The "automatic response function" is a function that automatically answers questions from vendors.

[1203] "Estimate evaluation" refers to the process of evaluating the content of submitted estimates and determining their validity.

[1204] The system for implementing this invention has the following configuration: The server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for collecting data from various sensors in the factory; means for analyzing the collected data in real time and identifying areas that require repair; means for automatically updating the repair plan based on the analysis results; means for automatically generating an RFP based on the details of the repair work; means for providing an automatic response function to questions from contractors; and means for evaluating submitted estimates and determining their validity.

[1205] Program Processing Description

[1206] The server uses Python to collect data from various sensors. Specifically, it uses the requests library to retrieve data from sensor APIs. The collected data is analyzed in real time to identify areas requiring repair, such as abnormal vibrations or deterioration. Data analysis libraries (e.g., Pandas, NumPy) are used for this analysis. Based on the identified repair areas, the repair plan is automatically updated. This updated repair plan is saved in JSON format.

[1207] Furthermore, an RFP is automatically generated based on the details of the repair work. A template engine (e.g., Jinja2) is used to generate the RFP. Questions from contractors are automatically answered using a generative AI model. This generative AI model uses pre-trained question-answer data. The validity of submitted estimates is judged by an evaluation algorithm. A machine learning model (e.g., Scikit-learn) is used for this evaluation.

[1208] Specific example

[1209] For example, if a vibration sensor in a factory detects abnormal vibrations, the data is collected in real time and automatically added to the repair plan as an area requiring repair. In this process, the server retrieves data from the sensor API and uses a data analysis library to detect anomalies. If an anomaly is detected, the repair plan is automatically updated and an RFP (Request for Proposal) is generated. The AI ​​model automatically responds to questions from contractors, and the submitted quotes are evaluated by a machine learning model.

[1210] Example of a prompt

[1211] "Create a Python program that collects vibration data from IoT sensors within the factory and automatically updates the repair plan when abnormal vibrations are detected."

[1212] In this way, it becomes possible to respond to real-time changes in the factory environment, efficiently update repair plans, and coordinate with contractors.

[1213] The flow of the specific processing in Application Example 3 will be explained using Figure 16.

[1214] Step 1:

[1215] The server collects data from various sensors within the factory. Specifically, it sends requests to the sensor API to obtain data such as temperature, humidity, vibration, and deterioration. The input is the sensor ID, and the output is the data obtained from the sensor.

[1216] Step 2:

[1217] The server analyzes the collected data in real time. Specifically, it uses data analysis libraries (e.g., Pandas, NumPy) to detect abnormal vibrations and signs of deterioration. The input is data acquired from sensors, and the output is the result of whether or not an anomaly was detected.

[1218] Step 3:

[1219] The server automatically updates the repair plan based on the analysis results. Specifically, it adds the detected anomalies to the repair plan and saves it in JSON format. The input is the anomaly detection result, and the output is the updated repair plan.

[1220] Step 4:

[1221] The server automatically generates an RFP based on the details of the repair work. Specifically, it uses a template engine (e.g., Jinja2) to embed the details of the repair work into a template and generate the RFP. The input is the updated repair plan, and the output is the generated RFP.

[1222] Step 5:

[1223] The server automatically responds to questions from vendors. Specifically, it uses a generative AI model to generate automatic responses based on pre-trained question-answer data. The input is the question from the vendor, and the output is the automatically generated response.

[1224] Step 6:

[1225] The server evaluates the submitted estimates and determines their validity. Specifically, it uses a machine learning model (e.g., Scikit-learn) to evaluate the content of the estimates and determine their validity. The input is the submitted estimate, and the output is the evaluation result.

[1226] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1227] "Example of form 1"

[1228] One embodiment of the present invention provides a means for inputting basic building information such as total floor area, year of construction, and intelligence. This information can be directly entered by, for example, the building manager, or obtained from an existing building management system. It also incorporates an emotion engine that recognizes the user's emotions. This emotion engine recognizes emotions, for example, from the user's tone of voice and facial expressions.

[1229] "Example of form 2"

[1230] In another embodiment of the present invention, a means for automatically formulating a repair plan based on the basic information is provided. Specifically, a repair plan is automatically generated based on the basic information of the building and a general schedule for repair work. An emotion engine that recognizes the user's emotions is also incorporated. This emotion engine recognizes emotions, for example, from the tone of the user's voice and facial expressions.

[1231] "Example of form 3"

[1232] In a further embodiment of the present invention, a means is provided to support everything from planning and process management to ordering. Specifically, it manages the progress of repair work and supports ordering as needed. It also incorporates an emotion engine that recognizes the user's emotions. This emotion engine recognizes emotions, for example, from the user's tone of voice and facial expressions.

[1233] The following describes the processing flow for each example of the form.

[1234] "Example of form 1"

[1235] Step 1: Enter basic building information such as total floor area, year of construction, and building code. This information is entered directly by, for example, the building manager.

[1236] Step 2: Obtain basic information from the existing building management system.

[1237] Step 3: Activate the emotion engine to recognize the user's emotions. This emotion engine recognizes emotions from, for example, the user's tone of voice and facial expressions.

[1238] "Example of form 2"

[1239] Step 1: Based on the building's basic information and a general repair schedule, the system automatically generates a repair plan.

[1240] Step 2: Save the generated repair plan and update it as needed.

[1241] Step 3: Activate the emotion engine to recognize the user's emotions. This emotion engine recognizes emotions from, for example, the user's tone of voice and facial expressions.

[1242] "Example of form 3"

[1243] Step 1: Manage the progress of the repair work. Specifically, check the progress of each stage and update the repair work plan as needed.

[1244] Step 2: Provide support for ordering as needed. Specifically, place orders with appropriate contractors based on the repair work plan.

[1245] Step 3: Activate the emotion engine to recognize the user's emotions. This emotion engine recognizes emotions from, for example, the user's tone of voice and facial expressions.

[1246] (Example 1)

[1247] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1248] Traditional building management systems can formulate repair plans based on basic building information and support process management and ordering, but they cannot provide feedback that takes user emotions into account. Therefore, it was difficult to properly understand user satisfaction and dissatisfaction and reflect them in management operations. Furthermore, the lack of a means to integrate and analyze emotional data meant that appropriate responses based on user emotions were not possible.

[1249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1250] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for generating emotional data from the user's voice tone and facial expressions using an emotion engine that recognizes the user's emotions; means for integrating and analyzing the generated emotional data and basic information; and means for providing feedback of the analysis results to the user. This makes it possible to provide feedback that takes the user's emotions into consideration, appropriately grasp the user's satisfaction and dissatisfaction, and reflect them in management operations.

[1251] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[1252] "Years since construction" refers to the number of years that have passed since a building was constructed.

[1253] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[1254] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1255] A "repair plan" refers to a schedule and procedures for systematically carrying out repairs and maintenance on a building.

[1256] "Process management" refers to the process of managing the progress of work based on the repair plan.

[1257] "Ordering operations" refers to the process of ordering necessary materials and services from external contractors for repairs and maintenance.

[1258] An "emotion engine" refers to software or algorithms that recognize emotions from a user's voice tone and facial expressions.

[1259] "Emotional data" refers to data about a user's emotions generated by the emotion engine.

[1260] "Analysis" refers to the process of integrating collected data and extracting meaningful information.

[1261] "Feedback" refers to the process of providing users with analysis results and communicating areas for improvement and evaluations.

[1262] This invention is a system that takes basic building information as input and recognizes and analyzes the user's emotions. A specific embodiment of this system is described below.

[1263] System Configuration

[1264] The system consists of three main elements: servers, terminals, and users. The server plays a central role in storing, analyzing, and providing feedback on data. Terminals provide an interface for users to input information and record audio. Users, such as building managers and building occupants, provide information to the system.

[1265] Hardware and software to be used

[1266] Servers: Database management systems (MySQL, PostgreSQL), data analysis software (Python, R), emotion engines (IBM Watson, Google Cloud Speech-to-Text)

[1267] Device: Computer or smart device with a microphone

[1268] Users: Building administrators, building users

[1269] Data entry and saving

[1270] Users log in to the system using a terminal and enter basic building information. Specifically, they enter information such as total floor area, year of construction, and building code into an input form. The server receives this information and stores it in a database.

[1271] Recognition and analysis of emotions

[1272] When a user speaks about the building's management status, the terminal records the user's voice. The recorded audio data is sent to a server, which uses an emotion engine to analyze the audio data. The emotion engine generates emotion data from the user's tone of voice and facial expressions.

[1273] Data integration and feedback

[1274] The server integrates and analyzes basic building information and sentiment data. Data analysis software such as Python and R is used for the analysis. The analysis results are provided to building managers as feedback. For example, feedback such as "There are many complaints about the cleanliness" is provided via email or a dashboard.

[1275] Specific example

[1276] For example, when a building manager enters information about a new building into the system, they would follow these steps:

[1277] 1. The building manager accesses the system's input screen and enters basic information such as total floor area, year of construction, and other relevant details.

[1278] 2. The server saves the entered information to the database.

[1279] 3. When a user talks about the building's management status, the emotion engine analyzes the user's tone of voice and facial expressions to generate emotion data.

[1280] 4. The server integrates and analyzes basic building information and sentiment data, and provides feedback to building managers.

[1281] Example of a prompt

[1282] Examples of prompt statements to input into a generative AI model include the following:

[1283] "The building has a total floor area of ​​5,000 square meters and is 10 years old. Analyze the tone of voice and facial expressions of the building manager when they talk about the building's management status to recognize the user's emotions."

[1284] By using this prompt, the generative AI model can integrate and analyze basic building information and user sentiment data to provide appropriate feedback.

[1285] The flow of the specific processing in Example 1 will be explained using Figure 17.

[1286] Step 1:

[1287] The user logs into the system.

[1288] Input: User ID and password

[1289] Operation: Users access the system using a dedicated terminal or web browser and enter their user ID and password on the login screen.

[1290] Output: If authentication is successful, the user can access the system's main screen.

[1291] Step 2:

[1292] The user enters the building's basic information.

[1293] Input: Basic information such as total floor area, year of construction, and intelligence.

[1294] Operation: The user enters information such as total floor area, year of construction, and age into the system's input form. For example, the user might enter "5000 square meters" for the total floor area and "10 years" for the year of construction.

[1295] Output: The entered basic information is sent to the server.

[1296] Step 3:

[1297] The server saves the entered information to the database.

[1298] Input: Basic information entered by the user

[1299] Operation: The server receives basic building information entered by the user and stores it in a database. Database management systems such as MySQL or PostgreSQL are used for storage.

[1300] Output: Basic information is saved to the database.

[1301] Step 4:

[1302] The user talks about the building's management status.

[1303] Input: User's voice

[1304] Operation: The user uses a device with a microphone to talk about the building's maintenance status. For example, they might say, "The building's cleaning is not good."

[1305] Output: Audio data is recorded on the device.

[1306] Step 5:

[1307] The device records the user's voice.

[1308] Input: User's voice

[1309] Operation: The device records the user's voice and sends the audio data to the server.

[1310] Output: Audio data is sent to the server.

[1311] Step 6:

[1312] The server uses an emotion engine to analyze the audio data.

[1313] Input: Audio data

[1314] Operation: The server uses an emotion engine (e.g., IBM Watson or Google Cloud Speech-to-Text) to analyze the user's voice tone and facial expressions and generate emotion data. For example, it can recognize "dissatisfaction" from the user's voice tone.

[1315] Output: Emotional data is generated.

[1316] Step 7:

[1317] The server integrates basic building information and emotional data.

[1318] Input: Basic information, sentiment data

[1319] Operation: The server retrieves basic building information from the database and integrates it with emotion data obtained from the emotion engine.

[1320] Output: Integrated data is generated.

[1321] Step 8:

[1322] The server analyzes the integrated data.

[1323] Input: Integrated data

[1324] Operation: The server uses data analysis software such as Python or R to analyze the integrated data. For example, it might identify user dissatisfaction with the cleanliness of a building and analyze the causes of that dissatisfaction.

[1325] Output: Analysis results are generated.

[1326] Step 9:

[1327] The server provides feedback on the analysis results to the building manager.

[1328] Input: Analysis results

[1329] Operation: The server notifies the building manager of the analysis results. For example, it might provide feedback via email or a dashboard stating that "there are many complaints about the cleaning situation."

[1330] Output: Feedback is provided to the building manager.

[1331] (Application Example 1)

[1332] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1333] Conventional building management systems could input basic information such as total floor area, building age, and intelligence, and formulate repair plans, but they did not optimize the work environment by considering the emotional state of the workers. As a result, worker stress management and improvements in work efficiency were not adequately addressed. Furthermore, real-time responses based on emotional states were difficult, which sometimes delayed worker health management and improvements to the work environment.

[1334] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1335] In this invention, the server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process management of the plan and ordering operations; means for monitoring the emotional state of workers using an emotion engine that recognizes user emotions; and means for optimizing the work environment based on the emotional state. This makes it possible to grasp the emotional state of workers in real time and take appropriate action, thereby optimizing the work environment and managing the health of workers.

[1336] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[1337] "Years since construction" refers to the number of years that have passed since a building was constructed.

[1338] "Intelligence" refers to information related to the intelligent management and operation of a building.

[1339] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1340] A "repair plan" is a schedule and procedure for systematically carrying out repairs and maintenance on a building.

[1341] "Process management" is the process of managing the progress of work based on the repair plan.

[1342] "Ordering operations" refer to the process of ordering materials and services necessary for repairs and maintenance.

[1343] An "emotion engine" is a technology that recognizes emotions from the user's voice tone and facial expressions.

[1344] "Workers" are people who carry out repairs and maintenance on buildings.

[1345] "Monitoring" refers to the continuous monitoring of a specific state or situation.

[1346] "Work environment" refers to the place and conditions in which workers perform their tasks.

[1347] "Optimization" means bringing something into a state that is most effective for a specific purpose.

[1348] As an example of how to implement this invention, a smart factory management application will be described. The server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for monitoring the emotional state of workers using an emotion engine that recognizes the emotions of users; and means for optimizing the work environment based on the emotional state.

[1349] Program Processing Description

[1350] The server uses the following hardware and software:

[1351] Hardware: Camera (webcam, etc.)

[1352] Software: OpenCV (image processing library), EmotionEngine (emotion recognition engine), BuildingInfo (building information management class)

[1353] The server first inputs basic building information. This is done either by the building manager entering data such as total floor area, age of the building, and other information, or by retrieving it from an existing building management system. Next, it automatically generates a repair plan based on this basic information. This plan includes the repair schedule and procedures.

[1354] Furthermore, the server supports everything from planning and process management to ordering. This includes managing the progress of repair plans and ordering necessary materials and services.

[1355] To recognize user emotions, the server uses an emotion engine. The emotion engine analyzes image data acquired from the camera and recognizes emotions from the user's tone of voice and facial expressions. This allows for real-time monitoring of the worker's emotional state.

[1356] Based on emotional states, the server optimizes the work environment. For example, if a worker is stressed, the server takes appropriate action to improve the work environment.

[1357] Specific example

[1358] As a concrete example, consider stress management for workers in a factory. If a worker is experiencing stress, the emotion engine detects this and notifies the manager. This allows the manager to respond quickly and improve the work environment.

[1359] Examples of prompts to input into a generative AI model

[1360] Design a system that monitors the emotions of factory workers in real time and notifies managers if they are experiencing stress. Basic building information (total floor area, age of construction, etc.) will also be entered.

[1361] In this way, by understanding the emotional state of workers in real time and taking appropriate action, it becomes possible to optimize the work environment and manage the health of workers.

[1362] The flow of a specific process in Application Example 1 will be explained using Figure 18.

[1363] Step 1:

[1364] The server receives basic building information. Building managers input data such as total floor area, year of construction, and building status, or this data is retrieved from an existing building management system. The input data is stored in a database on the server.

[1365] Step 2:

[1366] The server automatically generates a repair plan based on the entered basic information. Specifically, it analyzes data such as total floor area and building age to identify areas and timings for repairs. The repair plan, including schedules and procedures, is generated and stored on the server.

[1367] Step 3:

[1368] The server manages the repair plan process. It monitors the progress of the work in real time based on the repair plan and supports the ordering of necessary materials and services. Progress and ordering information are recorded in a database on the server.

[1369] Step 4:

[1370] The server acquires image data of workers using cameras. The cameras are installed in the work areas of the factory and capture the faces and expressions of workers in real time. The acquired image data is sent to the server.

[1371] Step 5:

[1372] The server uses an emotion engine to recognize the worker's emotional state. Specifically, it analyzes the acquired image data to identify emotions from the worker's tone of voice and facial expressions. The emotional state is recorded in a database on the server.

[1373] Step 6:

[1374] The server optimizes the work environment based on the user's emotional state. For example, if a worker is experiencing stress, the server sends a notification to the administrator prompting appropriate action. The notification content and response history are stored in a database on the server.

[1375] Step 7:

[1376] The server integrates all data and uses it to improve the work environment and manage worker health. The integrated data is displayed on a dashboard accessible to administrators, enabling real-time monitoring and analysis.

[1377] (Example 2)

[1378] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1379] Conventional repair planning systems can generate repair plans based on basic building information, but they have challenges in presenting plans that take user sentiment into consideration and automatically generating optimal repair schedules. Furthermore, support for process management and ordering is insufficient, placing a heavy burden on users.

[1380] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1381] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for analyzing the user's emotions using an emotion engine that recognizes the user's emotions; and means for calculating the optimal repair schedule using a generated AI model. This enables the automatic generation of an optimal repair plan that takes the user's emotions into consideration, as well as the efficient management of the plan's process and ordering operations.

[1382] "Total floor area" refers to the sum of the floor areas of each floor of a building, and is an indicator of the overall size of the building.

[1383] "Building age" refers to the number of years that have passed since a building was constructed, and it is important information for evaluating the building's deterioration and the need for repairs.

[1384] "Intelligence" refers to knowledge and information about the design and structure of a building, and serves as the basic data when formulating a repair plan.

[1385] "Basic information" refers to a general term for fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1386] A "repair plan" is a document that specifically outlines the content and schedule of repair work on a building, and serves as a guideline for efficiently carrying out repairs.

[1387] "Process management" refers to management activities to ensure that each stage of repair work proceeds according to plan, and is necessary to ensure the progress and quality of the work.

[1388] "Ordering operations" refer to the process of ordering necessary materials and services from external contractors to carry out repair work, and are essential for the smooth execution of the work.

[1389] An "emotion engine" refers to software or hardware that analyzes a user's voice tone and facial expressions to recognize their emotions.

[1390] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to analyze data and automatically generate the optimal repair schedule.

[1391] An "optimal repair schedule" refers to a schedule that allows for the most effective and efficient repair work, based on the building's condition and usage.

[1392] This invention is a system that automatically formulates a repair plan based on basic building information and provides an optimal repair schedule that takes into account the user's feelings. A specific embodiment of this system is described below.

[1393] System Configuration

[1394] This system consists of three main elements: a server, terminals, and users. The server is responsible for data collection, analysis, storage, and the generation of repair plans, while the terminals function as the interface with the users. Users are responsible for inputting basic building information and reviewing the repair plans.

[1395] Hardware and software to be used

[1396] Server: A high-performance computer equipped with a database, generative AI models, and an emotion engine.

[1397] Device: A computer or smart device equipped with a camera and microphone.

[1398] Database: Data storage for storing basic building information and general schedules for repair work.

[1399] Generative AI model: An artificial intelligence algorithm for calculating the optimal repair schedule.

[1400] Emotion engine: Software that analyzes the user's voice tone and facial expressions to recognize their emotions.

[1401] Program processing

[1402] The server stores basic building information (e.g., total floor area, age of building, etc.) in a database and generates a repair plan based on a typical repair schedule. Specifically, the server processes the information in the following steps:

[1403] 1. Data Collection: The server receives basic building information transmitted from the terminal and stores it in the database.

[1404] 2. Data Analysis: The server analyzes stored basic information and general repair schedules to identify areas and timings where repairs are needed.

[1405] 3. Plan Generation: The server uses a generation AI model to calculate the optimal repair schedule and generate a repair plan.

[1406] The device captures the user's voice tone and facial expressions through its camera and microphone and sends them to the emotion engine. The emotion engine analyzes this data to recognize the user's emotions.

[1407] Specific example

[1408] For example, if the basic information for a building is entered as "Year built: 2000", "Structure: Reinforced concrete", and "Materials used: Concrete, steel", the server will generate a repair plan like the following:

[1409] Exterior wall inspection and repair in 2020

[1410] Inspection of internal equipment in 2025

[1411] Inspect and repair of the exterior walls again in 2030.

[1412] Example of a prompt:

[1413] "If the building was constructed in 2000, is made of reinforced concrete, and uses concrete and steel as materials, please generate a repair plan based on a typical repair schedule."

[1414] This system eliminates the need for users to plan repairs themselves and provides support for efficient repair work. Furthermore, by considering user emotions, it enables the provision of a more satisfying service.

[1415] The flow of the specific processing in Example 2 will be explained using Figure 19.

[1416] Step 1:

[1417] Entering basic information

[1418] The user enters basic building information into the terminal's input form, such as "Year of Construction: 2000," "Structure: Reinforced Concrete," and "Materials Used: Concrete, Steel."

[1419] Input: Basic information such as the year of construction, structure, and materials used.

[1420] Output: Sending basic information from the terminal to the server.

[1421] Specific operation: When the user enters the required information into the input form on the device and presses the "Submit" button, the device sends the entered information to the server.

[1422] Step 2:

[1423] Data collection and storage

[1424] The server receives basic information sent from the terminal.

[1425] Input: Basic information sent from the device.

[1426] Output: Basic information stored in the database.

[1427] Specific operation: The server saves the received basic information to the "Building Information" table in the database. It also retrieves a general repair schedule from the "Repair Schedule" table.

[1428] Step 3:

[1429] Generating a repair plan

[1430] The server generates a repair plan based on basic information stored in the database and a general schedule for repair work.

[1431] Input: Basic information and repair schedule stored in the database.

[1432] Output: Generated repair plan.

[1433] Specific operation: The server inputs the prompt "Generate a repair plan based on a general repair schedule, given that the building was constructed in 2000, is made of reinforced concrete, and uses concrete and steel materials" to the generated AI model, and calculates the optimal repair schedule. The calculation results are compiled into a repair plan and saved to the "Repair Plan" table in the database.

[1434] Step 4:

[1435] Execution of emotion recognition

[1436] The user interacts with the system through the device's camera and microphone.

[1437] Input: User's voice tone and facial expression.

[1438] Output: Analyzed user sentiment.

[1439] Specific operation: The device captures the user's voice tone and facial expressions and sends them to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotions.

[1440] Step 5:

[1441] Presentation of repair plan

[1442] The server sends the generated repair plan to the terminal.

[1443] Input: Generated repair plan.

[1444] Output: Repair plan displayed on the terminal.

[1445] Specific operation: The server sends the generated repair plan to the terminal, and the terminal displays the repair plan to the user. The user reviews the displayed repair plan and confirms the plan by pressing the "Approve" button.

[1446] In this way, the system efficiently plans repairs and provides optimal information while taking user sentiment into consideration.

[1447] (Application Example 2)

[1448] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1449] Conventional repair planning systems can automatically generate repair plans for building and factory equipment, but they lack the ability to propose plans at the appropriate time, taking user sentiment into consideration, and to monitor the progress of the repair plans in real time. This can lead to decreased user satisfaction during the execution of repair plans. Furthermore, because plan updates are performed manually, it is difficult to maintain repair plans that reflect the latest situation. To address these issues, a system is needed that recognizes user sentiment, proposes repair plans at the appropriate time, and has real-time progress monitoring capabilities.

[1450] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1451] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for proposing a repair plan at an appropriate time using an emotion engine that recognizes the user's emotions; and means for monitoring the progress of the repair plan in real time. This makes it possible to propose a repair plan at an appropriate time that takes the user's emotions into consideration, and to grasp the progress of the repair plan in real time, thereby improving user satisfaction.

[1452] "Basic information" refers to fundamental data about a building and its facilities, such as total floor area, year of construction, and building code.

[1453] A "repair plan" is the process of planning the schedule and details of repair work for a building or its facilities.

[1454] "Process management" refers to the means of managing the progress of repair work and ensuring that it proceeds according to plan.

[1455] "Ordering operations" refer to the process of ordering materials and services necessary for repair work from external contractors.

[1456] An "emotion engine" is a technology that recognizes emotions from the user's voice tone and facial expressions.

[1457] "The right timing" refers to the most effective period based on the user's emotions and circumstances.

[1458] "Progress status" refers to the current state of progress in the implementation of the repair plan.

[1459] "Real-time" means being able to instantly grasp the current situation.

[1460] In order to implement this invention, the following system configuration is necessary. The server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for proposing a repair plan at an appropriate time using an emotion engine that recognizes the user's emotions; and means for monitoring the progress of the repair plan in real time.

[1461] Hardware and software configuration

[1462] Hardware:

[1463] Factory robots: Execute repair plans and monitor their progress.

[1464] Camera: Used to recognize the user's facial expressions.

[1465] Microphone: Used to recognize the tone of the user's voice.

[1466] software:

[1467] Python: Used for implementing programs.

[1468] Emotion recognition library: Recognizes user emotions using libraries such as OpenCV and librosa.

[1469] Database: Used to manage basic information and repair plan data.

[1470] Data processing and data calculation

[1471] The server first inputs basic information into a database and automatically generates a repair plan based on this information. Next, it uses an emotion engine to recognize the user's emotions from their tone of voice and facial expressions, and proposes a repair plan at the appropriate time. Furthermore, it uses factory robots to monitor the progress of the repair plan in real time and record it in the database.

[1472] Specific example

[1473] For example, the equipment "Machine A" underwent its last maintenance on January 1, 2023, with a maintenance interval of 180 days. The next maintenance date is June 30, 2023. The server uses this information to formulate a maintenance plan, recognizes the user's emotions, and proposes this plan at the appropriate time. Furthermore, factory robots execute the maintenance work, and their progress is monitored in real time.

[1474] Example of a prompt

[1475] "Recognize the user's emotions from their tone of voice and facial expressions, and propose a repair plan at the appropriate time. For example, if a user says, 'Please tell me the repair plan for Machine A,' calculate and propose the next repair date."

[1476] In this way, it becomes possible to propose repair plans at the appropriate time, taking into account the user's feelings, and to monitor the progress of the repair plan in real time, thereby improving user satisfaction.

[1477] The flow of a specific process in Application Example 2 will be explained using Figure 20.

[1478] Step 1:

[1479] The server inputs basic information such as total floor area, year of construction, and intelligence into the database.

[1480] Input: Basic information such as total floor area, year of construction, and intelligence.

[1481] Data processing: Save basic information to a database.

[1482] Output: Basic information stored in the database

[1483] Step 2:

[1484] The server automatically creates a repair plan based on this basic information.

[1485] Input: Basic information stored in the database

[1486] Data calculation: Calculate the repair work schedule based on basic information.

[1487] Output: Automatically generated repair plan

[1488] Step 3:

[1489] The server supports everything from planning and process management to ordering.

[1490] Input: Automated repair plan

[1491] Data processing: Manage the repair plan process and order necessary materials and services.

[1492] Output: Progress of order processing

[1493] Step 4:

[1494] The server uses an emotion engine to recognize user emotions and propose a repair plan at the appropriate time.

[1495] Input: User's voice tone and facial expression

[1496] Data processing: Analyze user emotions using an emotion engine.

[1497] Output: Proposal of a repair plan based on user sentiment.

[1498] Step 5:

[1499] The server monitors the progress of the repair plan in real time.

[1500] Input: Progress data of the repair plan

[1501] Data processing: Update progress in real time and record it in the database.

[1502] Output: Real-time updated progress data

[1503] Step 6:

[1504] The user reviews the repair plan proposed by the server and makes any necessary modifications.

[1505] Input: Repair plan proposed by the server

[1506] Data processing: Modify the repair plan based on user feedback.

[1507] Output: Revised repair plan

[1508] Step 7:

[1509] Factory robots perform repair work based on the repair plan.

[1510] Input: Revised repair plan

[1511] Data calculation: Calculate the steps for executing the repair work and start the work.

[1512] Output: Results of the repair work performed

[1513] Step 8:

[1514] The server records the results of the repair work in a database and incorporates them into the next repair plan.

[1515] Input: Results of repair work performed

[1516] Data processing: Save the results of repair work to a database and use them to inform the next repair plan.

[1517] Output: Updated repair plan data

[1518] In this way, a system is realized in which servers, terminals, users, and factory robots work together to plan, execute, and monitor repairs.

[1519] (Example 3)

[1520] Next, we will describe Embodiment 3 of Embodiment Example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[1521] Traditional repair management systems often rely on manual processes for developing repair plans based on basic information, managing schedules, and supporting ordering, resulting in inefficiency. Furthermore, they struggle to respond quickly to changes in building conditions, leading to delays in updating repair plans. Additionally, creating requests for proposals, answering questions, and evaluating the validity of quoted prices are all done manually, requiring significant time and effort. The lack of user-centric feedback prevents improvements in the user experience.

[1522] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means.

[1523] This invention includes a server comprising means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for collecting data from sensors installed in the building; means for analyzing the collected data; means for automatically updating the repair plan based on the analysis results; means for automatically generating a request for proposal based on detailed information of the repair work; means for automatically responding to questions from contractors; means for evaluating estimates submitted by contractors and determining their validity; means for collecting the tone of the user's voice and facial expressions; means for recognizing the user's emotions by analyzing the collected data; and means for providing appropriate feedback based on the analysis results. This enables efficient planning, process management, and ordering of repair work, and allows for quick responses to changes in the building's condition. Furthermore, the creation of requests for proposals for contractors, answering questions, and evaluating the validity of estimated costs are automated, reducing time and effort. In addition, feedback that takes the user's emotions into consideration is provided, improving the user experience.

[1524] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1525] A "repair plan" is a plan for efficiently carrying out repair work on a building, and it includes the areas to be repaired and the schedule for the work.

[1526] "Process management" is the process of monitoring the progress of repair work and making adjustments to ensure it proceeds according to plan.

[1527] "Ordering operations" refer to the process of ordering materials and services necessary for repair work from contractors.

[1528] A "sensor" is a device used to collect environmental data (temperature, humidity, vibration, etc.) within a building.

[1529] "Data analysis" is the process of analyzing collected data to detect outliers and patterns.

[1530] A Request for Proposal (RFP) is a document that details the repair work and requests proposals from contractors.

[1531] "Question and answer" refers to the process of providing appropriate answers to questions from vendors.

[1532] "Evaluating the appropriateness of the estimated price" is the process of determining whether the estimate submitted by the contractor is reasonable.

[1533] "Emotion recognition" is a technology that analyzes a user's voice tone and facial expressions to identify their emotions.

[1534] "Feedback" refers to advice and information provided to the user based on the analysis results.

[1535] Modes for carrying out the invention

[1536] This invention is a system that efficiently handles everything from planning and process management to ordering for repair work. A specific embodiment of this system is described below.

[1537] Inputting basic information and developing a repair plan.

[1538] The server provides a means for inputting basic information such as total floor area, building age, and intelligence. Based on this basic information, the server automatically develops a repair plan. The software used is data analysis tools such as Python or R.

[1539] Sensor-based data collection and analysis

[1540] The server collects data from sensors installed within the building. The hardware used includes IoT devices such as Raspberry Pi and Arduino. The collected data is analyzed using the Python Pandas library. Based on the analysis results, the server automatically updates the repair plan.

[1541] As a concrete example, a sensor collects vibration data from a part of a building, and if abnormal vibrations are detected, repairs to that part are added to the plan.

[1542] Automated generation of Request for Proposal and Q&A

[1543] The server automatically generates a Request for Proposal (RFP) based on detailed information about the repair work. The software used includes, for example, Microsoft Word or the Google Docs API. Furthermore, the server uses a generative AI model (e.g., GPT-3) to automatically respond to questions from contractors.

[1544] As a concrete example, an RFP is generated based on detailed information about the repair work and sent to contractors. Questions from contractors are received, appropriate answers are generated using a generative AI model, and these answers are sent back to the contractors.

[1545] Evaluation of the reasonableness of the estimated price

[1546] The server uses a price comparison algorithm to evaluate quotes submitted by vendors and determine their reasonableness. It compares the submitted quotes to historical data to determine if they are unusually high or low.

[1547] Emotion recognition and feedback provision

[1548] The device collects the user's voice tone and facial expressions through its camera and microphone. The hardware used is, for example, a webcam and microphone. The server uses an emotion recognition engine (for example, OpenCV or DeepFace) to analyze the collected data. Based on the analysis results, the server recognizes the user's emotions and provides appropriate feedback.

[1549] As a concrete example, a camera captures the user's facial expressions, and a microphone records their voice tone. The collected facial data is analyzed using DeepFace, and the voice tone is analyzed using a voice analysis library. If the user is experiencing stress, advice on how to relax is provided.

[1550] Example of a prompt

[1551] "Please generate a program that analyzes building vibration data and updates the repair plan if any anomalies are detected."

[1552] "Please generate a program that creates an RFP based on detailed information about the repair work and automatically responds to questions from contractors."

[1553] "Please create a program that analyzes the user's voice tone and facial expressions to recognize their emotions."

[1554] The above describes specific embodiments for carrying out this invention. The flow of the specific processing in Example 3 will be explained with reference to Figure 21.

[1555] Step 1:

[1556] The server provides a means for inputting basic information such as total floor area, year of construction, and other details. Users input this basic information. The entered basic information is stored in a database.

[1557] Specific operation: The user enters basic information through a web interface, and the server saves that information to a database.

[1558] Input: Basic information such as total floor area, year of construction, and intelligence.

[1559] Output: Basic information stored in the database

[1560] Step 2:

[1561] The server automatically generates a repair plan based on the entered basic information. The server uses data analysis tools (such as Python or R) to calculate the repair locations and construction schedules.

[1562] Specific operation: Execute a Python script to analyze basic information and generate a repair plan.

[1563] Input: Basic information stored in the database

[1564] Output: Automatically generated repair plan

[1565] Step 3:

[1566] The server collects data from sensors installed within the building. The hardware used includes IoT devices such as Raspberry Pi and Arduino.

[1567] Specific operation: IoT sensors measure data such as temperature, humidity, and vibration in real time and send this data to a server.

[1568] Input: Real-time data from sensors

[1569] Output: Sensor data sent to the server

[1570] Step 4:

[1571] The server uses the Python Pandas library to analyze the collected data. The server then executes an algorithm to detect anomalies.

[1572] Specific operation: Use the Pandas library to load data and run an algorithm to detect outliers.

[1573] Input: Sensor data sent to the server

[1574] Output: Analysis results (presence or absence of outliers)

[1575] Step 5:

[1576] The server automatically updates the repair plan based on the analysis results. If an anomaly is detected, it adds the repair of that part to the plan and saves the updated plan to the database.

[1577] Specific action: Add the detected anomaly to the repair plan and update the database.

[1578] Input: Analysis results

[1579] Output: Updated repair plan

[1580] Step 6:

[1581] The server automatically generates a Request for Proposal (RFP) based on detailed information about the repair work. The software used includes, for example, Microsoft Word or the Google Docs API.

[1582] Specific actions: Embed detailed information about the repair work into a template and generate an RFP document.

[1583] Input: Details of repair work

[1584] Output: Automated RFP document

[1585] Step 7:

[1586] The server uses a generative AI model (e.g., GPT-3) to automatically respond to questions from vendors.

[1587] Specific operation: Receives questions from vendors, generates appropriate answers using a generative AI model, and sends them back to the vendors.

[1588] Input: Question from a vendor

[1589] Output: Auto-generated answer

[1590] Step 8:

[1591] The server uses a price comparison algorithm to evaluate quotes submitted by vendors and determine their validity.

[1592] Specific operation: Compare the submitted estimate with past data to determine if it is unusually high or low.

[1593] Input: Estimate submitted by the vendor

[1594] Output: Estimate Validity Evaluation Results

[1595] Step 9:

[1596] The device collects the user's voice tone and facial expressions through its camera and microphone. The hardware used includes, for example, a webcam and a microphone.

[1597] Specific operation: The camera captures the user's facial expressions, and the microphone records the tone of their voice.

[1598] Input: User's voice tone, facial expression data

[1599] Output: Collected audio and facial expression data

[1600] Step 10:

[1601] The server uses an emotion recognition engine (such as OpenCV or DeepFace) to analyze the collected data.

[1602] Specific operations: Collected facial expression data is analyzed using DeepFace, and voice tone is analyzed using a voice analysis library.

[1603] Input: Collected audio and facial expression data

[1604] Output: Emotion recognition result

[1605] Step 11:

[1606] The server recognizes the user's emotions based on the analysis results and provides appropriate feedback.

[1607] Specific action: If the user is feeling stressed, provide advice on how to relax.

[1608] Input: Emotion recognition result

[1609] Output: Provided feedback

[1610] (Application Example 3)

[1611] Next, we will describe application example 3 of form example 3. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[1612] Traditional maintenance management systems often rely on manual processes for planning, updating, and ordering maintenance plans, resulting in inefficiency. Furthermore, they struggle to detect equipment deterioration in real time and respond quickly. Creating requests for proposals (RFPs) for contractors and evaluating the validity of their bids are also time-consuming processes. There is a need to solve these problems and achieve efficient and effective maintenance management.

[1613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means.

[1614] In this invention, the server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for acquiring sensor data within the factory and automatically updating the repair plan; means for creating a request for proposal for contractors, and for evaluating the appropriateness of questions, answers, and estimated costs; and means for recognizing the user's emotions and providing appropriate feedback. This enables automatic updating of the repair plan, increased efficiency in ordering, smoother communication with contractors, and support tailored to the user's emotions.

[1615] "Total floor area" refers to the sum of the floor areas of each floor of a building, and is an indicator of the overall size of the building.

[1616] "Years since construction" refers to the number of years that have passed since a building was constructed, and is important information for judging the degree of deterioration of the building.

[1617] "Intelligence" refers to knowledge and information related to the management and operation of a building, and is part of the basic information necessary for formulating and updating repair plans.

[1618] "Basic information" refers to information about the building necessary for formulating a repair plan, and includes the total floor area, age of the building, and other relevant details.

[1619] A "repair plan" is a document that outlines the schedule and details for systematically carrying out repairs to buildings and facilities.

[1620] "Planning and process management" refers to the means of managing the progress of repair work based on the repair plan and ensuring that it proceeds appropriately.

[1621] "Ordering operations" refer to the process of ordering necessary materials and services from contractors to carry out repair work.

[1622] "Sensor data" refers to data acquired from sensors within a factory, and is information used to understand the status of equipment and changes in the environment in real time.

[1623] A "Request for Proposal" is a document used to present the scope and conditions of work to contractors performing repair work and to request their proposals.

[1624] "Question and answer session" is the process of answering questions from vendors based on the Request for Proposal and providing necessary information.

[1625] "Evaluating the appropriateness of the estimated price" is the process of evaluating whether the price quoted by the contractor is appropriate.

[1626] An "emotion engine" is a system that recognizes emotions from the user's tone of voice and facial expressions and provides appropriate feedback.

[1627] "Feedback" refers to advice and information provided in response to the user's emotions and circumstances.

[1628] The system for implementing this invention has the following configuration: The server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for acquiring sensor data within the factory and automatically updating the repair plan; means for creating a request for proposal for contractors, and for evaluating the appropriateness of questions, answers, and estimated costs; and means for recognizing the user's emotions and providing appropriate feedback.

[1629] Hardware and software to use

[1630] Hardware: Smartphones, head-mounted displays, IoT sensors

[1631] Software: Python, EmotionRecognition library, IoTSensorData library, RFPGenerator library, EstimateEvaluator library

[1632] Data processing and data calculation

[1633] The server first acquires sensor data from IoT sensors within the factory. This data is used to understand the status of equipment and environmental changes in real time. Next, based on the acquired sensor data, it identifies areas that require repair and automatically updates the repair plan. This makes it possible to quickly detect equipment deterioration and formulate an appropriate repair plan.

[1634] Furthermore, the server creates requests for proposals (RFPs) for vendors and evaluates the reasonableness of their quoted prices and answers to questions. This allows for efficient selection of vendors to carry out repair work. In addition, an emotion engine is used to recognize the user's emotions from their tone of voice and facial expressions, and to provide appropriate feedback. This reduces user stress and enables smooth communication.

[1635] Specific example

[1636] Specific Example 1

[1637] IoT sensors detect when factory robots are aging and automatically add them to the repair plan. This helps prevent robot breakdowns and maintain factory production efficiency.

[1638] Specific Example 2

[1639] Administrators use their smartphones to send requests for proposals to contractors and evaluate the reasonableness of their estimates. This optimizes the cost of repair work and ensures that repairs are completed within budget.

[1640] Specific example 3

[1641] Technicians use a head-mounted display, and an emotion engine recognizes their emotions during repair work and provides appropriate feedback. This can reduce technician stress and improve work efficiency.

[1642] Example of a prompt

[1643] Develop an application that automatically updates robot repair plans based on data acquired from IoT sensors within the factory and supports ordering as needed. Also, incorporate a function that recognizes user emotions and provides appropriate feedback.

[1644] In this way, a system can be realized that streamlines the maintenance management of factory robots and provides support tailored to the user's needs.

[1645] The flow of the specific processing in Application Example 3 will be explained using Figure 22.

[1646] Step 1:

[1647] The server acquires sensor data from IoT sensors within the factory. This sensor data includes information about equipment status and environmental changes. By acquiring this data, equipment deterioration and malfunctions can be detected in real time. The input is sensor data, and the output is the acquired sensor data.

[1648] Step 2:

[1649] The server analyzes the acquired sensor data to identify areas requiring repair. Specifically, it analyzes the sensor data to detect abnormal values ​​and signs of deterioration. The input is the acquired sensor data, and the output is a list of areas requiring repair.

[1650] Step 3:

[1651] The server automatically updates the repair plan based on the identified repair areas. The repair plan includes the areas that need repair, the repair priorities, and the materials and work required. The input is a list of areas that need repair, and the output is the updated repair plan.

[1652] Step 4:

[1653] The server generates a Request for Proposal (RFP) for contractors based on the updated repair plan. The RFP includes details of the repair, required materials, and working conditions. The input is the updated repair plan, and the output is the RFP.

[1654] Step 5:

[1655] The server sends a Request for Proposal (RFP) to vendors and conducts a question-and-answer session. It provides appropriate answers to vendors' questions and shares necessary information. The input is the RFP and vendors' questions, and the output is the answers and shared information.

[1656] Step 6:

[1657] The server evaluates the quotes submitted by vendors and determines their validity. It analyzes the content of the quotes and evaluates whether the costs and scope of work are appropriate. The input is the quote from the vendor, and the output is the evaluation result.

[1658] Step 7:

[1659] The device uses an emotion engine to recognize the user's emotions. It recognizes emotions from the user's tone of voice and facial expressions and provides appropriate feedback. The input is the user's voice and facial expression data, and the output is the recognized emotion and the feedback provided.

[1660] Step 8:

[1661] The user proceeds with the repair work based on the feedback provided. They adjust the work according to the feedback to perform repairs efficiently. The input is the provided feedback, and the output is the progress of the repair work.

[1662] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1663] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1664] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1665] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1666] [Third Embodiment]

[1667] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1668] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1669] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1670] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1671] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1672] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1673] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1674] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1675] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1676] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1677] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1678] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[1679] "Example of form 1"

[1680] Embodiments of the present invention include means for inputting basic building information, such as total floor area, year of construction, and other relevant information. This information can be input directly by, for example, the building manager, or obtained from an existing building management system.

[1681] "Example of form 2"

[1682] Furthermore, an embodiment of the present invention includes means for automatically formulating a repair plan based on the above basic information. Specifically, it automatically generates a repair plan based on the basic information of the building and a general schedule for repair work.

[1683] "Example of form 3"

[1684] Furthermore, the embodiment of the present invention includes means to support everything from planning and process management to ordering. Specifically, it manages the progress of repair work and supports ordering as needed.

[1685] "Example of form 4"

[1686] Furthermore, the embodiment of the present invention includes means for automatically updating the plan based on IoT sensor data, using basic information as a basis. Specifically, it analyzes data acquired from IoT sensors within a building and automatically updates the repair plan. For example, if an IoT sensor detects that a part of the building is deteriorating, the repair of that part is added to the plan.

[1687] "Example of form 5"

[1688] Furthermore, the embodiment of the present invention includes means for creating an RFP for contractors, answering questions, and evaluating the appropriateness of the estimated costs when carrying out construction work. Specifically, it creates an RFP based on the details of the repair work and responds to questions from contractors. It also evaluates the estimates submitted by contractors and determines their appropriateness.

[1689] The following describes the processing flow for each example of the form.

[1690] "Example of form 1"

[1691] Step 1: Enter basic building information, such as total floor area, year of construction, and building code. This information can be entered directly by the building manager or retrieved from an existing building management system.

[1692] "Example of form 2"

[1693] Step 1: Enter the basic information of the building.

[1694] Step 2: Automatically create a repair plan based on the basic information above. Specifically, the system automatically generates a repair plan based on the building's basic information and a general schedule for repair work.

[1695] "Example of form 3"

[1696] Step 1: Develop a repair plan.

[1697] Step 2: Implement project schedule management.

[1698] Step 3: Provide support for ordering as needed.

[1699] "Example of form 4"

[1700] Step 1: Enter the building's basic information and create a repair plan.

[1701] Step 2: Obtain data from IoT sensors within the building.

[1702] Step 3: Analyze the acquired data and automatically update the repair plan. For example, if an IoT sensor detects that a part of the building is deteriorating, add the repair of that part to the plan.

[1703] "Example of form 5"

[1704] Step 1: Create an RFP based on the details of the repair work.

[1705] Step 2: Respond to questions from the vendor.

[1706] Step 3: Evaluate the quotes submitted by the contractors and determine their validity.

[1707] (Example 1)

[1708] Next, we will describe Embodiment 1 of Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1709] Traditional building management systems had problems such as requiring manual input of basic building information and consuming a lot of time and effort when formulating repair plans. Furthermore, retrieving information from existing management systems and formatting the data was cumbersome, making efficient planning difficult. In addition, the generation of prompt messages using AI models was not automated, placing a significant burden on users.

[1710] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1711] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for receiving said basic information and storing it in a database; means for obtaining additional information from an existing management system; means for formatting the obtained information and converting it into the required format; means for inputting prompt sentences into a generating AI model; means for automatically formulating a repair plan; and means for supporting everything from planning process management to ordering. This makes it possible to automate the entire process from inputting basic building information to formulating a repair plan and generating prompt sentences using a generating AI model.

[1712] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[1713] "Years since construction" refers to the number of years that have passed since a building was constructed.

[1714] "Intelligence" refers to information and data related to the intelligent management and operation of a building.

[1715] "Basic information" refers to fundamental data such as the total floor area of ​​a building, its age, and its intellectual property.

[1716] A "database" is a system for systematically organizing and storing information.

[1717] A "management system" is a general term for the software and hardware used for the operation and maintenance of a building.

[1718] "Additional information" refers to building data other than basic information, including energy consumption data and maintenance history.

[1719] "Formatting" refers to the process of converting acquired data into the required format, making it easier to use.

[1720] A "generative AI model" is a model that uses artificial intelligence to perform a specific task.

[1721] A "prompt" refers to an instruction or question that is input into a generative AI model.

[1722] A "repair plan" refers to planning the schedule and details of repairs and maintenance for a building.

[1723] "Process management" refers to managing each stage in the execution of a repair plan.

[1724] "Ordering operations" refers to the process of ordering materials and services necessary for repairs and maintenance.

[1725] A "Request for Proposal" is a document used to request specific proposals from vendors.

[1726] "Question and answer session" refers to the process of answering questions from vendors based on the Request for Proposal (RFP).

[1727] "Evaluating the appropriateness of the quoted price" refers to assessing whether the quoted price submitted by the contractor is reasonable.

[1728] This invention is a system that takes basic building information as input, formulates a repair plan, and generates prompt messages using a generation AI model. A specific embodiment of this system is described below.

[1729] First, the user accesses the web application provided by the server using a device such as a PC or tablet. They open a web browser and enter basic information such as the building's total floor area, age, and intelligence level. For example, the user might enter "Total floor area: 5000 square meters," "Age: 10 years," and "Intelligence level: High."

[1730] Next, the server receives the basic building information entered by the user. The received information is stored in a database server (e.g., MySQL, PostgreSQL). The server executes SQL queries against the database and inserts the information into the appropriate tables.

[1731] Furthermore, the server retrieves additional building information through the APIs of existing management systems. This includes, for example, building energy consumption data and maintenance history. The server sends API requests and receives the data returned as responses.

[1732] The server then formats the received additional information and converts it to the required format. For example, it might parse JSON data, extract the necessary fields, and format them. The server uses data processing scripts (e.g., Python, JavaScript) to perform this process.

[1733] Finally, the server generates prompts to input into the AI ​​model based on the formatted information. For example, it might generate a prompt such as, "Please enter the basic information of a building with a total floor area of ​​5,000 square meters, an age of 10 years, and high intelligence." The server inputs this prompt into the AI ​​model and receives a response from the model.

[1734] As a concrete example, the following prompt sentence can be input into the generation AI model.

[1735] Example of a prompt:

[1736] "Please enter the basic information for a building with a total floor area of ​​5,000 square meters, built 10 years ago, and with high intelligence."

[1737] This system enables the complete automation of the entire process, from inputting basic building information and formulating repair plans to generating prompt messages using a generation AI model. This reduces the burden on users and enables efficient building management.

[1738] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1739] Step 1:

[1740] The user enters the building's basic information.

[1741] Users access a web application provided by the server using a device such as a PC or tablet. They open a web browser and enter basic information such as the building's total floor area, age, and intelligence level. The entered information is sent to the server. Specifically, the user enters "Total floor area: 5000 square meters," "Age: 10 years," and "Intelligence level: High" into the form and clicks the submit button.

[1742] Input: Basic information such as the total floor area of ​​the building, the year it was built, and its location.

[1743] Output: Basic information sent to the server

[1744] Step 2:

[1745] The server receives the entered information and saves it to the database.

[1746] The server receives basic building information entered by the user. The received information is stored in a database server (e.g., MySQL, PostgreSQL). The server executes an SQL query against the database and inserts the information into the appropriate table. Specifically, the server executes the SQL query "INSERT INTO buildings (area, age, intelligence) VALUES (5000, 10, 'high');".

[1747] Input: Basic information submitted by the user

[1748] Output: Basic information stored in the database

[1749] Step 3:

[1750] The server retrieves additional information from the existing management system.

[1751] The server retrieves additional building information through the existing management system's API. This includes, for example, building energy consumption data and maintenance history. The server sends an API request and receives the data returned as a response. Specifically, the server sends an API request called "GET / buildings / 12345 / additional_info".

[1752] Input: API Request

[1753] Output: Additional information obtained

[1754] Step 4:

[1755] The server formats the acquired information and converts it into the required format.

[1756] The server formats the received additional information and converts it to the required format. For example, it parses JSON data, extracts the necessary fields, and formats them. The server uses a data processing script (e.g., Python, JavaScript) to perform this process. Specifically, the server executes "json.loads(response_data)" to extract the necessary fields.

[1757] Input: Additional information obtained

[1758] Output: Formatted data

[1759] Step 5:

[1760] The server inputs prompt messages into the generated AI model.

[1761] The server generates prompt statements to input into the AI ​​model based on the formatted information. For example, it might generate a prompt statement like, "Please enter the basic information of a building with a total floor area of ​​5000 square meters, an age of 10 years, and high intelligence." The server inputs this prompt statement into the AI ​​model and receives a response from the model. Specifically, the server performs the process of "inputting a prompt statement into the AI ​​model and receiving a response."

[1762] Input: Formatted data

[1763] Output: Response from the generative AI model

[1764] (Application Example 1)

[1765] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1766] Traditional building management systems supported the planning, scheduling, and ordering of repairs based on basic building information, but lacked security risk assessment, real-time security alert generation, and specific security countermeasures proposals. Therefore, comprehensively managing building safety was difficult, and a rapid response to security risks was required.

[1767] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1768] In this invention, the server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for evaluating security risks based on the basic information; means for generating real-time security alerts based on the security risks; and means for proposing security measures based on the security risks. This enables not only the formulation of repair plans, process management, and ordering support based on the building's basic information, but also the evaluation of security risks, the generation of real-time security alerts, and the proposal of specific security measures.

[1769] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[1770] "Years since construction" refers to the number of years that have passed since a building was constructed.

[1771] "Intelligence" refers to information about the building's location and surrounding environment.

[1772] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1773] A "repair plan" is a plan that outlines the schedule and details for systematically carrying out repairs and maintenance on a building.

[1774] "Process management" refers to managing the progress of work based on the repair plan.

[1775] "Ordering operations" refer to the process of ordering materials and services necessary for repairs and maintenance.

[1776] "Security risk" refers to potential safety threats or dangers to a building.

[1777] A "real-time security alert" is a warning that is immediately sent when a security risk occurs.

[1778] "Security measures" refer to specific means and methods for mitigating or eliminating security risks.

[1779] As an embodiment of this invention, a building management system is constructed that inputs basic information such as total floor area, building age, and intelligence, and based on this information, formulates a repair plan and supports process management and ordering operations. In addition, a function is added to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[1780] The server provides a means for inputting basic information such as total floor area, building age, and other relevant data. This basic information can be entered directly by the building manager or obtained from an existing building management system. Based on the entered basic information, the server automatically creates a repair plan and supports everything from project management to ordering.

[1781] Furthermore, the server is equipped with a means to assess security risks based on basic information. The security risk assessment is performed based on information such as the building's age, total floor area, and location. Based on the assessment results, the server generates real-time security alerts and notifies the building manager.

[1782] The server also provides a means to suggest specific security measures based on security risks. This allows building managers to quickly implement appropriate security measures.

[1783] The hardware used includes devices such as smartphones and tablets. The software used will be Python, JSON, and the datetime module. This will enable efficient management of basic building information, assessment of security risks, and implementation of appropriate countermeasures.

[1784] As a concrete example, a building manager uses a smartphone to input information about a building with a total floor area of ​​12,000 square meters, built in 1980, and located in a high-crime area. Based on this information, the server assesses the security risks and determines that the risk is high. A real-time security alert is generated and notified to the building manager. Furthermore, the server proposes specific countermeasures such as installing an advanced monitoring system and increasing security personnel.

[1785] Examples of prompt statements include the following:

[1786] "Please enter the basic information about the building. Enter the total floor area, year of construction, and location."

[1787] In this way, in addition to supporting the development of repair plans, process management, and ordering operations based on the building's basic information, it becomes possible to assess security risks, generate real-time security alerts, and propose specific security measures.

[1788] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1789] Step 1:

[1790] The user uses a terminal to input basic building information (total floor area, year of construction, etc.).

[1791] Input: Basic information such as total floor area, year of construction, and structural integrity.

[1792] Output: The entered basic information is sent to the server.

[1793] Specific operation: The user opens the application on their smartphone or tablet and enters information such as total floor area, year of construction, and other details into the input form. Once the input is complete, they press the submit button to send the information to the server.

[1794] Step 2:

[1795] The server saves the basic information it receives and automatically creates a repair plan.

[1796] Input: Basic information submitted by the user

[1797] Output: Results of the repair plan

[1798] Specific operation: The server saves the received basic information to the database. Then, it automatically generates a repair plan based on the saved information and saves the plan's contents to the database.

[1799] Step 3:

[1800] The server supports the process management and ordering of repair plans.

[1801] Input: Results of the repair plan

[1802] Output: Process management schedule and order list

[1803] Specific operation: Based on the repair plan, the server creates a schedule for each stage and generates an order list for necessary materials and services. This information is then communicated to the building manager.

[1804] Step 4:

[1805] The server assesses security risks based on basic information.

[1806] Input: Basic information submitted by the user

[1807] Output: Security risk assessment results

[1808] Specific operation: The server analyzes basic information and assesses security risks by considering factors such as the building's age, total floor area, and location. The assessment results are calculated as a risk score.

[1809] Step 5:

[1810] The server generates real-time security alerts based on the security risk assessment results.

[1811] Input: Security risk assessment results

[1812] Output: Security alert notification

[1813] Specific operation: When the risk score exceeds a certain threshold, the server generates a real-time security alert and notifies the building administrator's terminal.

[1814] Step 6:

[1815] The server proposes specific security measures based on the security risk assessment results.

[1816] Input: Security risk assessment results

[1817] Output: Security countermeasures proposal

[1818] Specific operation: The server proposes appropriate security measures based on the risk score. For example, specific measures such as installing an advanced monitoring system or increasing security personnel may be proposed. The proposed measures are notified to the building manager.

[1819] In this way, in addition to supporting the development of repair plans, process management, and ordering operations based on the building's basic information, it becomes possible to assess security risks, generate real-time security alerts, and propose specific security measures.

[1820] (Example 2)

[1821] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1822] Conventional repair planning systems required manual creation of repair plans based on basic building information, which was time-consuming and labor-intensive. Furthermore, updating or modifying the plans was also done manually, resulting in inefficiency and inaccuracies. Additionally, there was a lack of easy ways for users to review the generated repair plans.

[1823] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1824] In this invention, the server includes means for inputting basic information such as total floor area, age of building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting the process from planning to ordering; means for sending prompt messages to a generating AI model to generate a repair plan; means for saving the generated repair plan to a database; and means for users to request a repair plan and display it on a terminal. This enables the automatic generation and efficient management of repair plans.

[1825] "Total floor area" refers to the sum of the floor areas of each floor of a building.

[1826] "Years since construction" refers to the number of years that have passed since a building was constructed.

[1827] "Intelligence" refers to knowledge and information related to the management and operation of a building.

[1828] "Basic information" refers to fundamental data about a building, such as total floor area, year of construction, and structural integrity.

[1829] A "repair plan" refers to a plan outlining the schedule and details of repair work for a building.

[1830] A "generative AI model" refers to a model that uses artificial intelligence to generate data.

[1831] A "prompt statement" refers to an instruction given to a generative AI model.

[1832] A "database" refers to a system for systematically storing and managing data.

[1833] A "server" refers to a computer that provides data and services over a network.

[1834] "Terminal" refers to a device used by a user to operate something (such as a personal computer, tablet, or smartphone).

[1835] A "user" refers to a person who uses the system.

[1836] "Process management" refers to managing the progress of repair work.

[1837] "Ordering operations" refers to the process of ordering materials and services necessary for repair work.

[1838] "RFP" is an abbreviation for Request for Proposal, which refers to a document requesting proposals from vendors.

[1839] "Question and answer session" refers to questions and answers regarding proposals or plans.

[1840] "Evaluating the appropriateness of the quoted price" refers to assessing whether the submitted estimate is reasonable or not.

[1841] This invention is a system for automatically planning and managing building repairs. The system consists of three main elements: a server, terminals, and users.

[1842] Server Role

[1843] The server receives basic building information and stores it in a database. This basic information includes total floor area, age of the building, and intelligence. Based on this information, the server sends prompt messages to a generating AI model to generate a repair plan. The generated repair plan is then stored in the database again.

[1844] The server uses the following hardware and software:

[1845] High-performance database servers (e.g., MySQL, PostgreSQL)

[1846] Generative AI models (e.g., GPT-4)

[1847] Terminal role

[1848] The terminal provides an interface for users to input basic building information and view repair plans. When a user requests a repair plan by operating the terminal, the terminal sends a request to the server. Upon receiving the repair plan from the server, the terminal displays its contents to the user.

[1849] The device uses the following hardware and software:

[1850] PCs, tablets, smartphones

[1851] Web browser (e.g., Google Chrome, Mozilla Firefox)

[1852] User roles

[1853] The user enters basic building information using a terminal. Once the input is complete, the user requests the generation of a repair plan. The user then reviews the generated repair plan and requests modifications or additions as needed.

[1854] Specific example

[1855] The user enters the following information into the terminal's form: "Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago". The terminal sends this information to the server, which then sends the following prompt to the generated AI model:

[1856] "The basic information about the building is as follows: Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior walls repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[1857] The AI ​​model generates a repair plan based on this prompt and sends it back to the server. The server saves the generated repair plan to a database and sends it to the terminal when requested by the user. The terminal displays the received repair plan to the user.

[1858] In this way, users can easily plan and manage building maintenance.

[1859] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1860] Step 1:

[1861] The user enters the building's basic information.

[1862] The user inputs basic building information through the terminal interface. Specifically, they input information such as total floor area, year of construction, and structural integrity. The input information is temporarily stored in the terminal's memory. An example of input data is: "Structure: Reinforced concrete, Year of construction: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago."

[1863] Step 2:

[1864] The terminal sends the entered information to the server.

[1865] The terminal sends basic information entered by the user to the server using an HTTP request. The data sent is in JSON format, which the server receives and parses. The input data is temporarily stored in the server's memory.

[1866] Step 3:

[1867] The server stores basic information in a database.

[1868] The server stores the received basic information in a database. Specifically, it uses a database management system such as MySQL or PostgreSQL. Storing this information in a database makes it accessible for later processing.

[1869] Step 4:

[1870] The server sends a prompt message to the generated AI model.

[1871] The server generates prompts based on the stored basic information. The generated prompts are then sent to the AI ​​model. An example of a prompt is: "The basic information of the building is as follows: Structure: Reinforced concrete, Year built: 20 years, Materials used: Concrete, Past repair history: Exterior walls repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[1872] Step 5:

[1873] The generative AI model generates the repair plan.

[1874] The generation AI model generates a repair plan based on the received prompt text. The generated repair plan is sent back to the server. An example of the output data is a plan that states, "The next repair will be to repaint the exterior walls in two years, and then waterproof the roof five years later."

[1875] Step 6:

[1876] The server saves the generated repair plan to the database.

[1877] The server stores the repair plans received from the generated AI models in a database. This allows users to review and modify the repair plans later.

[1878] Step 7:

[1879] The user requests a repair plan.

[1880] The user requests to view the repair plan using their device. Specifically, they click the "View Repair Plan" button on the device's interface. The request is made using an HTTP GET request.

[1881] Step 8:

[1882] The terminal requests a repair plan from the server.

[1883] The terminal, upon receiving a user request, requests the server to retrieve the repair plan. The server retrieves the repair plan from the database and sends it to the terminal. The data sent is in JSON format.

[1884] Step 9:

[1885] The server sends the repair plan to the terminal.

[1886] The server retrieves the repair plan from the database and sends it to the terminal. The data sent is in JSON format, and the terminal receives and parses it.

[1887] Step 10:

[1888] The device displays the repair plan to the user.

[1889] The terminal displays the received repair plan to the user. Specifically, it displays the details of the repair plan in a web browser. The user can review the displayed repair plan and make requests for modifications or additions as needed.

[1890] (Application Example 2)

[1891] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1892] Conventional repair planning systems struggled to automatically generate repair plans based on basic information about building and factory equipment, and they could not monitor the progress of the plans in real time or update them as needed. As a result, the accuracy and efficiency of repair plans decreased, leading to significant effort and cost in maintaining the equipment.

[1893] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1894] In this invention, the server includes means for inputting basic information such as total floor area, building age, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for automatically generating a repair plan for each piece of equipment based on the basic information of the equipment and the general schedule of repair work; means for monitoring the progress of the repair plan in real time and updating the plan as necessary; and means for generating a repair plan using a generated AI model. This makes it possible to improve the accuracy and efficiency of repair plans and reduce the effort and cost involved in the maintenance and management of equipment.

[1895] "Total floor area" is a term that refers to the sum of the floor areas of each floor of a building.

[1896] "Years since construction" is a term that refers to the number of years that have passed since a building was constructed.

[1897] "Intelligence" is a term that refers to the knowledge and information necessary for managing buildings and facilities.

[1898] "Basic information" is a term that refers to fundamental data about a building and its facilities, such as total floor area, year of construction, and structural integrity.

[1899] A "repair plan" is a term that refers to planning the schedule and details of repair work for buildings and facilities.

[1900] "Process management" is a term that refers to the means of managing the progress of repair work and ensuring that it proceeds according to plan.

[1901] "Ordering operations" refers to the process of ordering materials and services necessary for repair work from external contractors.

[1902] "Equipment" is a term that refers to machinery and devices installed inside buildings such as factories and office buildings.

[1903] "Repair work" is a term that refers to construction work to repair or improve malfunctions or deterioration of buildings and equipment.

[1904] "General schedule" refers to a standard schedule that is generally applied in repair work.

[1905] "Automatic generation" is a term that refers to a system automatically creating plans and data without human intervention.

[1906] "Progress status" is a term that refers to information indicating how far along the repair work plan is.

[1907] "Real-time" is a term that refers to reflecting the current situation immediately.

[1908] A "generative AI model" is a term that refers to a model that uses artificial intelligence to analyze data and generate plans and predictions.

[1909] The system for implementing this invention operates in cooperation with three parties: a server, a terminal, and a user. The server includes means for inputting basic information such as total floor area, age of the building, and intelligence; means for automatically formulating a repair plan based on the basic information; means for supporting everything from process management of the plan to ordering operations; means for automatically generating a repair plan for each piece of equipment based on the basic information of the equipment and the general schedule of the repair work; means for monitoring the progress of the repair plan in real time and updating the plan as needed; and means for generating a repair plan using a generated AI model.

[1910] The server implements programs using programming languages ​​such as Python to perform data input, analysis, plan generation, and progress monitoring. Specifically, the server acquires basic equipment information and calculates the next repair date based on a typical repair schedule. The calculation results are output in JSON format for user review. The server also monitors progress in real time and updates the plan as needed.

[1911] The terminal provides an interface for users to access the server, enter basic information, and check repair plans. The terminal communicates with the server via a web browser or dedicated application, allowing users to input and retrieve necessary information.

[1912] Users access the server via their terminal and input basic equipment information. This information is sent to the server, which automatically generates a repair plan based on it. The generated repair plan can be viewed by the user via their terminal. Furthermore, users can monitor the progress in real time and update the plan as needed.

[1913] As a specific example, Equipment 1 was installed on January 1, 2020, is frequently used, and has been repaired in the past on January 1, 2021 and January 1, 2022. In this case, the next repair date would be January 1, 2023. The user enters this information into the terminal, and the server calculates the next repair date based on that information and generates a plan.

[1914] Examples of prompts to input into a generative AI model are as follows:

[1915] Based on the basic information of the equipment and the general repair schedule, please calculate the next repair date. The following is an example of input data.

[1916] Basic information about the equipment:

[1917] {

[1918] "Equipment 1": {"Installation Date": "2020-01-01", "Frequency of Use": "High", "Past Repair History": ["2021-01-01", "2022-01-01"]},

[1919] "Equipment 2": {"Installation Date": "2019-01-01", "Usage Frequency": "Medium", "Past Repair History": ["2020-01-01"]}

[1920] }

[1921] Typical schedule for repair work:

[1922] {

[1923] "High": {"Period": 1},

[1924] "medium": {"period": 2},

[1925] "Low": {"Period": 3}

[1926] }

[1927] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1928] Step 1:

[1929] Users access the server via their terminal and input basic information about the equipment (such as total floor area, year of construction, maintenance status, installation date, frequency of use, and past repair history). The entered information is then sent to the server.

[1930] Input: Basic information about the equipment

[1931] Output: Basic information data sent to the server

[1932] Step 2:

[1933] The server uses the received basic information to refer to a general repair schedule and calculates the next repair date. Specifically, it applies a repair cycle based on usage frequency and calculates the next repair date from past repair history.

[1934] Input: Basic information data, general schedule of repair work

[1935] Output: Next repair date

[1936] Step 3:

[1937] The server automatically generates a repair plan based on the calculated next repair date. The generated repair plan is saved in JSON format for user review.

[1938] Input: Next repair date

[1939] Output: Repair plan (JSON f...

Claims

[Claim 1] A system for managing repair work on a building or facility, comprising a server connected to a network, a terminal for inputting and displaying information to the server, and a plurality of IoT sensors installed at the site of the repair work on the building or facility, The aforementioned server, Basic information acquisition means that receives basic information including the total floor area of ​​a building or facility, the year of construction, and knowledge and information regarding the management and operation of the building from the terminal and stores it in a database, A repair schedule storage means stores general schedule data for repair work, which represents a standard schedule generally applied in repair work, in the database. Repair plan generation means generates first text data including the basic information and general schedule data for the repair work, inputs the first text data as a prompt to a generating AI model, obtains first repair plan data including multiple repair work for the building or equipment and the timing of their implementation from the generating AI model, and stores the obtained first repair plan data as a repair plan in the database. A progress analysis means collects sensor data acquired from the multiple IoT sensors via a network as time-series data in real time, identifies the progress of each repair work included in the repair plan and the locations requiring new repairs based on the time-series data and the repair plan, and generates updated repair plan data by automatically updating the first repair plan data. A plan update means inputs the basic information, the update and repair plan data, and the sensor data into the generating AI model, obtains optimized repair plan data from the generating AI model, outputs the optimized repair plan data to the terminal, and stores the repair schedule confirmed and finalized by the user through the terminal in the database. A system characterized by comprising the following features.

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