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]

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

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

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

[0004] A shortage of building managers and the increasing complexity of repair work due to aging buildings are making it difficult to improve the efficiency of management operations overall. [Means for solving the problem]

[0005] By simply entering basic information, the system automatically creates repair plans and supports everything from process management to ordering. Furthermore, the system automatically updates plans based on IoT sensor data and the basic information, and when construction work is carried out, it creates RFPs for contractors, handles Q&A sessions, and evaluates the appropriateness of estimated costs. This will improve the efficiency of building management operations. [Brief explanation of the drawings]

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

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

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

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

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

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

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

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

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

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

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0027] "Example 1"

[0028] This embodiment of the present invention includes a means for inputting basic information about a building, such as total floor area, age, and location. This information is input directly by the building manager or obtained from an existing building management system.

[0029] "Example 2"

[0030] Furthermore, this embodiment of the present invention includes a means for automatically creating a repair plan based on the basic information. Specifically, the repair plan is automatically created based on the basic information of the building and a general schedule of repair work.

[0031] "Example 3"

[0032] Furthermore, the present invention is provided with a means for supporting everything from planning process management to ordering operations. Specifically, the progress of repair work is managed and ordering operations are supported as necessary.

[0033] "Example 4"

[0034] Furthermore, this embodiment of the present invention includes a means for automatically updating the plan based on IoT sensor data, using basic information as a base. Specifically, the system analyzes data acquired from IoT sensors in the building and automatically updates the repair plan. For example, if an IoT sensor detects that a part of the building is aging, the system adds repairs to that part of the building.

[0035] "Example 5"

[0036] Furthermore, this embodiment of the present invention is equipped with a means for creating an RFP for contractors, answering questions, and evaluating the appropriateness of the estimated cost when carrying out construction work. Specifically, the system 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 processing flow of each embodiment will be described below.

[0038] "Example 1"

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

[0040] "Example 2"

[0041] Step 1: Enter basic building information.

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

[0043] "Example 3"

[0044] Step 1: Create a repair plan.

[0045] Step 2: Manage the planning process.

[0046] Step 3: Assist with ordering as needed.

[0047] "Example 4"

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

[0049] Step 2: Acquire data from IoT sensors in the building.

[0050] Step 3: Analyze the acquired data and automatically update the repair plan. For example, if IoT sensors detect that part of the building is aging, add repairs to that part to the plan.

[0051] "Example 5"

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

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

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

[0055] Example 1

[0056] Next, a description will be given of 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."

[0057] Conventional building management systems have the problem that it takes a lot of time and effort to manually input basic building information and create repair plans. Also, retrieving information from existing management systems and formatting data is cumbersome, making it difficult to create efficient plans. Furthermore, the generation of prompts using generative AI models is not automated, placing a heavy burden on users.

[0058] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0059] In this invention, the server includes means for inputting basic information such as total floor area, age, and building location, means for receiving the basic information and saving it in a database, means for acquiring additional information from an existing management system, means for formatting the acquired information and converting it into a required format, means for inputting prompt text to a generative AI model, means for automatically formulating a repair plan, and means for supporting everything from plan process management to ordering operations. This makes it possible to consistently automate the process from inputting basic building information to formulating a repair plan and generating prompt text using a generative AI model.

[0060] "Total floor area" means the total area of ​​the floor area of ​​each floor of a building.

[0061] "Age" refers to the number of years that have passed since the 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 basic data such as the building's total floor area, age, and location.

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

[0065] A "management system" is a general term for software and hardware used to operate and maintain a building.

[0066] "Additional information" is data about the building 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 to make it easier to use.

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

[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 content of building repairs and maintenance.

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

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

[0073] A "request for proposal" is a document that requests specific proposals from contractors.

[0074] "Question and answer session" refers to the process of answering questions from vendors based on a request for proposal.

[0075] "Evaluating the validity of the estimated amount" refers to evaluating whether the estimated amount submitted by the contractor is appropriate.

[0076] This invention is a system that inputs basic building information, formulates a repair plan, and generates prompt sentences using a generative AI model. Specific embodiments of this system are described below.

[0077] First, a 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 about the building, such as its total floor area, age, and cost. For example, a user might enter "Total floor area: 5,000 square meters," "Age: 10 years," and "Cost: high."

[0078] The server then receives the basic building information entered by the user, stores the information in a database server (e.g., MySQL or PostgreSQL), and runs SQL queries against the database to insert the information into the appropriate tables.

[0079] Additionally, the server obtains additional building information through the API of the existing management system, such as the building's energy consumption data and maintenance history. The server sends API requests and receives the data returned as a response.

[0080] The server then formats the additional information it receives and converts it into the required format, for example, parsing the JSON format data and extracting and formatting the required fields. The server performs this processing using a data processing script (e.g., Python, JavaScript (registered trademark)).

[0081] Finally, the server generates a prompt sentence to be input into the generative AI model based on the formatted information. For example, it generates a prompt sentence such as, "Please enter basic information about a building that has a total floor area of ​​5,000 square meters, is 10 years old, and is highly intelligent." The server inputs this prompt sentence into the generative AI model and receives a response from the model.

[0082] As a concrete example, the following prompt sentence can be input to a generative AI model:

[0083] Example prompt sentence:

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

[0085] This system makes it possible to automate everything from inputting basic building information to formulating repair plans and even generating prompts using a generative AI model, thereby reducing the burden on users and enabling efficient building management.

[0086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0087] Step 1:

[0088] The user enters basic building information.

[0089] A user accesses a web application provided by a server using a device such as a PC or tablet. They open a web browser and enter basic information about the building, such as its total floor area, age, and cost. The entered information is then sent to the server. Specifically, the user enters "Total floor area: 5,000 square meters," "Age: 10 years," and "Cost: high" into the form and clicks the submit button.

[0090] Input: Basic information such as total floor area, age, and location of the building

[0091] Output: Basic information sent to the server

[0092] Step 2:

[0093] The server receives the entered information and stores it in a database.

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

[0095] Input: Basic information submitted by the user

[0096] Output: Basic information stored in the database

[0097] Step 3:

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

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

[0100] Input: API request

[0101] Output: Additional information obtained

[0102] Step 4:

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

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

[0105] Input: Additional information obtained

[0106] Output: Formatted data

[0107] Step 5:

[0108] The server inputs a prompt sentence into the generative AI model.

[0109] The server generates a prompt sentence to be input into the generative AI model based on the formatted information. For example, it generates a prompt sentence such as, "Please enter the basic information of a building that has a total floor area of ​​5,000 square meters, is 10 years old, and is highly intelligent." The server inputs this prompt sentence into the generative AI model and receives a response from the model. Specifically, the server executes the process of "inputting a prompt sentence into the generative AI model and receiving a response."

[0110] Input: Formatted data

[0111] Output: The response from the generative AI model

[0112] (Application example 1)

[0113] Next, a description will be given of Application Example 1 of Embodiment 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] Conventional building management systems supported the creation of repair plans, process management, and ordering operations based on basic building information, but lacked the ability to assess security risks, generate real-time security alerts, or propose specific security measures. This made it difficult to comprehensively manage building safety, and a rapid response to security risks was required.

[0115] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, and building location, means for automatically formulating a repair plan based on the basic information, means for supporting processes from plan schedule management 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 makes it possible to support repair plan planning, schedule management, and ordering based on the basic building information, as well as evaluate security risks, generate real-time security alerts, and propose specific security measures.

[0117] "Total floor area" means the total floor area of ​​each floor of a building.

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

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

[0120] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[0121] A "repair plan" is a plan that shows the schedule and content for systematically repairing and maintaining a building.

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

[0123] "Ordering operations" refers to the operations of ordering materials and services required 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 notifies you immediately when a security risk occurs.

[0126] "Security measures" are specific means or methods for reducing 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, age of building, and building location, and then creates a repair plan based on this information, supporting process management and ordering operations. It also adds functions to evaluate security risks, generate real-time security alerts, and propose specific security measures.

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

[0129] Furthermore, the server is equipped with a means to assess security risks based on basic 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, allowing building managers to quickly implement appropriate security measures.

[0131] The hardware used includes devices such as smartphones and tablets, and the software uses Python, JSON, and datetime modules. This makes it possible to efficiently manage basic building information, evaluate security risks, and take appropriate measures.

[0132] For 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 evaluates the security risk and determines that it is high risk. A real-time security alert is generated and notified to the building manager. The server then proposes specific countermeasures, such as installing an advanced monitoring system or increasing security personnel.

[0133] Examples of prompts include:

[0134] "Enter basic building information: total floor area, age, and location."

[0135] In this way, in addition to supporting repair planning, process management, and ordering operations based on basic building information, it will also be possible to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[0136] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0137] Step 1:

[0138] The user uses a terminal to input basic information about the building (total floor area, age, location, etc.).

[0139] Input: Basic information such as total floor area, age, and building size

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

[0141] Specific operation: The user opens the application on their smartphone or tablet and enters information such as the total floor area, age of the building, and location into the input form. Once the input is complete, the user presses the send button to send the information to the server.

[0142] Step 2:

[0143] The server stores the basic information received and automatically creates a repair plan.

[0144] Input: Basic information submitted by the user

[0145] Output: Repair plan results

[0146] Specific operation: The server stores the received basic information in a database, then automatically generates a repair plan based on the stored information and stores the plan contents in the database.

[0147] Step 3:

[0148] The server supports repair plan process management and ordering operations.

[0149] Input: Repair plan results

[0150] Output: Process control schedule and order list

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

[0152] Step 4:

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

[0154] Input: Basic information submitted by the user

[0155] Output: Security risk assessment results

[0156] How it works: The server analyzes basic information and evaluates security risks, taking into account factors such as the building's age, total floor area, and location. The evaluation 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: If the risk score exceeds a certain threshold, the server generates a real-time security alert and notifies the building manager's terminal.

[0162] Step 6:

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

[0164] Input: Security risk assessment results

[0165] Output: Security measures proposal

[0166] Specific actions: Based on the risk score, the server proposes appropriate security measures, such as installing an advanced monitoring system or increasing security personnel. The proposal is then notified to the building manager.

[0167] In this way, in addition to supporting repair planning, process management, and ordering operations based on basic building information, it will also be possible to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[0168] Example 2

[0169] Next, a description will be given of 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 will be referred to as a "terminal."

[0170] Conventional repair planning systems require users to manually create repair plans based on basic building information, which is time-consuming and labor-intensive. Furthermore, when plans need to be updated or revised, this process is inefficient and often lacks accuracy. Furthermore, there was a lack of a way for users to easily check the generated repair plans.

[0171] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0172] In this invention, the server includes a means for inputting basic information such as total floor area, age, and building location, a means for automatically creating a repair plan based on the basic information, a means for supporting everything from plan process management to ordering, a means for generating a repair plan by sending a prompt to a generative AI model, a means for saving the generated repair plan in a database, and a means for a user to request a repair plan and display it on a terminal. This enables automatic generation and efficient management of repair plans.

[0173] "Total floor area" refers to the total floor area of ​​each floor of a building.

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

[0175] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[0176] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[0177] A "repair plan" refers to a plan that outlines the schedule and content of repair work on a building.

[0178] A "generative AI model" refers to a model for generating data using artificial intelligence.

[0179] A "prompt sentence" refers to an instruction sentence input 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 (such as a personal computer, tablet, or smartphone) that a user operates.

[0183] "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 operations of ordering materials and services necessary for repair work.

[0186] "RFP" stands for Request for Proposal, and refers to a document that requests proposals from contractors.

[0187] "Questions and Answers" refers to questions and answers regarding proposals and plans.

[0188] "Evaluating the reasonableness of the estimated amount" refers to evaluating whether the submitted estimate is appropriate.

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

[0190] Server Roles

[0191] The server receives basic information about the building and stores it in a database. This information includes the total floor area, age, and building size. Based on this information, the server sends prompts to the generative AI model to generate a repair plan. The generated repair plan is then stored back in the database.

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

[0193] A high-performance database server (e.g., MySQL, PostgreSQL)

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

[0195] Device Role

[0196] The terminal provides an interface for users to input basic building information and check the repair plan. When a user operates the terminal to request a repair plan, the terminal sends the request to the server. After receiving the repair plan from the server, the terminal displays its contents to the user.

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

[0198] PC, tablet, smartphone

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

[0200] User Roles

[0201] The user uses a terminal to input basic building information. Once input is complete, the user requests the generation of a repair plan. The user can then review the generated repair plan and request corrections or additions as necessary.

[0202] Specific examples

[0203] A user enters the following information into a form on their device: "Structure: Reinforced concrete, Age: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago." The device sends this information to the server, which then sends the following prompt to the generative AI model:

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

[0205] The generative AI model generates a repair plan based on this prompt and sends it back to the server. The server stores the generated repair plan in a database and sends it to the device when the user requests it. The device then displays the received repair plan to the user.

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

[0207] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0208] Step 1:

[0209] The user enters basic building information.

[0210] The user inputs basic building information through the terminal interface. Specifically, the information entered includes total floor area, age, and building size. The input information is temporarily stored in the terminal's memory. An example of input data is "Structure: reinforced concrete, Age: 20 years, Materials used: concrete, Past repair history: Exterior wall repaired 5 years ago."

[0211] Step 2:

[0212] The terminal transmits the input information to the server.

[0213] The terminal sends the 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 analyzes. The input data is temporarily stored in the server's memory.

[0214] Step 3:

[0215] The server stores the 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. By storing the information in the database, it becomes possible to reference the information in later processing.

[0217] Step 4:

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

[0219] The server generates a prompt sentence based on the saved basic information. The generated prompt sentence is sent to the generation AI model. An example of a prompt sentence is, "The basic information about the building is as follows: Structure: reinforced concrete, Age: 20 years, Materials used: concrete, Past repair history: Exterior wall repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[0220] Step 5:

[0221] A generative AI model generates a repair plan.

[0222] The generative AI model generates a repair plan based on the received prompt. The generated repair plan is sent back to the server. An example of output data is a plan such as, "The next repairs will be to repaint the exterior walls in two years, followed by roof waterproofing work in five years."

[0223] Step 6:

[0224] The server stores the generated repair plan in a database.

[0225] The server stores the repair plan received from the generative AI model in a database, allowing users to review and modify the repair plan at a later time.

[0226] Step 7:

[0227] A user requests a repair plan.

[0228] The user uses the terminal to request the display of the repair plan by clicking the "Show repair plan" button in the terminal interface. The request is made using an HTTP GET request.

[0229] Step 8:

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

[0231] Upon receiving a user request, the terminal requests the server to retrieve a 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 is in JSON format, and the terminal receives and analyzes it.

[0235] Step 10:

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

[0237] The terminal displays the received repair plan to the user. Specifically, the details of the repair plan are displayed on a web browser. The user can check the displayed repair plan and make corrections or additions as necessary.

[0238] (Application example 2)

[0239] Next, a description will be given of 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 will be referred to as a "terminal."

[0240] With conventional repair planning systems, it was difficult to automatically generate repair plans based on basic information about building or factory equipment, and it was not possible to monitor the progress of the plan in real time or update the plan as needed. This resulted in issues such as reduced accuracy and efficiency of repair plans and significant effort and cost required for equipment maintenance and management.

[0241] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, age, and building location, means for automatically formulating repair plans based on the basic information, means for supporting everything from plan process management to ordering, means for automatically generating repair plans for each facility based on the basic information about the facility and a general schedule for repair work, means for monitoring the progress of the repair plans in real time and updating the plans as necessary, and means for generating repair plans using a generative AI model. This makes it possible to improve the accuracy and efficiency of repair plans and reduce the effort and cost required for facility maintenance and management.

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

[0244] "Age" refers to the number of years that have passed since the building was constructed.

[0245] "Intellect" 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 basic data about buildings and facilities, such as total floor area, age, and location.

[0247] "Repair plan" is a term that refers to planning the schedule and content of repair work on 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 work of ordering materials and services required for repair work from external contractors.

[0250] "Facility" is a term that refers to machines and devices installed in buildings such as factories and buildings.

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

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

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

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

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

[0256] "Generative AI model" is a term used to describe a model that uses artificial intelligence to analyze data and generate plans and predictions.

[0257] The system for carrying out this invention operates in cooperation with three parties: a server, a terminal, and a user. The server includes a means for inputting basic information such as total floor area, age, and location, a means for automatically formulating a repair plan based on the basic information, a means for supporting everything from plan process management to ordering, a means for automatically generating a repair plan for each facility based on basic information about the facility and a general schedule for repair work, a means for monitoring the progress of the repair plan in real time and updating the plan as necessary, and a means for generating a repair plan using a generative AI model.

[0258] The server implements programs using programming languages ​​such as Python to input data, analyze it, generate plans, and monitor progress. Specifically, the server acquires basic information about the equipment and calculates the next repair date based on the general schedule of repair work. The calculation results are output in JSON format so that the user can check them. The server also monitors progress in real time and updates the plan as necessary.

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

[0260] Users access the server through their devices and input basic information about the equipment. The input information is sent to the server, which then automatically generates a repair plan based on that information. The user can then check the generated repair plan through their device. They can also monitor progress in real time and update the plan as needed.

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

[0262] An example of a prompt to input to a generative AI model is as follows:

[0263] Calculate the next maintenance date based on basic facility information and the general schedule of maintenance work. Below is an example of input data.

[0264] Facility basic information:

[0265] {

[0266] "Facility 1": {"Installation date": "2020-01-01", "Frequency of use": "High", "Past repair history": ["2021-01-01", "2022-01-01"]},

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

[0268] }

[0269] Typical timeline for repair work:

[0270] {

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

[0272] "medium": {"period": 2},

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

[0274] }

[0275] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0276] Step 1:

[0277] The user accesses the server through a terminal and inputs basic information about the facility (total floor area, age, location, installation date, frequency of use, 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] The server then refers to the general schedule of repair work based on the received basic information and calculates the next repair date. Specifically, it applies a repair cycle based on the frequency of use and calculates the next repair date based on 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 maintenance plan based on the calculated next maintenance date. The generated maintenance plan is saved in JSON format and can be viewed by the user.

[0286] Input: Next repair date

[0287] Output: Repair plan (JSON format)

[0288] Step 4:

[0289] The user can access the server through their terminal to check the generated repair plan and modify or update the contents of the repair plan as needed.

[0290] Input: Repair plan (JSON format)

[0291] Output: Modified and updated repair plan

[0292] Step 5:

[0293] The server monitors the progress of the repair plan in real time, using IoT sensor data to monitor the condition of the equipment and ensure that repairs are progressing as planned, and 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 the generative AI model to optimize the repair plan, specifically by analyzing past data and current conditions and proposing 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 can check the optimized repair plan through the terminal and finalize the repair schedule, which is then saved on the server and put into action.

[0302] Input: Optimized repair plan

[0303] Output: Confirmed repair schedule

[0304] Example 3

[0305] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0306] In conventional repair work management systems, the creation of repair plans, progress management, and ordering support are often done manually, resulting in inefficiencies. Furthermore, because processes such as automatic plan updates using sensor data, the creation of requests for proposals for contractors, Q&A sessions, and the evaluation of the validity of estimates are not integrated, overall management is cumbersome. This can lead to delays in the progress of repair work and increased costs.

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

[0308] In this invention, the server includes means for inputting basic information such as total floor area, age, and building location, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan schedule management to ordering, means for using a database for managing the progress of repair work, means for acquiring and analyzing data from sensors in the building, means for automatically updating the repair plan, means for creating a request for proposal 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 for repair work, from planning to progress management, ordering, and estimate evaluation.

[0309] "Total floor area" means the total floor area of ​​each floor of a building.

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

[0311] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[0312] "Basic information" refers to basic data about the building, such as total floor area, age, and building specifications.

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

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

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

[0316] "Database" refers to the system for storing the progress of repair works and other related information.

[0317] "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 validity of the estimated amount" refers to evaluating whether the amount of the estimate submitted by the contractor is appropriate.

[0321] MODE FOR CARRYING OUT THE INVENTION

[0322] This invention is a system for efficiently carrying out repair work planning, progress management, ordering, and estimate evaluation. Specific embodiments of this system will be described below.

[0323] System configuration

[0324] The server uses the following hardware and software to implement the system.

[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 through which a user accesses 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, age, and building location. This allows the user to input basic data about the building. Next, the server has a means for automatically creating a repair plan based on this basic information. This allows the user to efficiently create a repair plan.

[0332] The server provides support for everything from planning process management to ordering. Specifically, it uses a database to manage the progress of repair work, updating the progress of each stage of the work in real time. The server also has the means to collect and analyze data from sensors in the building. This means that if a sensor detects deterioration in part of the building, the repair plan can be automatically updated based on that information.

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

[0334] Specific examples

[0335] For example, if a user is planning repairs to a building and an IoT sensor detects that part of the building is aging, the server can automatically update the repair plan and assist with the necessary ordering.

[0336] Example prompt sentence:

[0337] "Describe a system that automatically updates building repair plans based on data from IoT sensors and assists with placing the necessary orders."

[0338] This system enables users to check the progress of repair work in real time and automatically places the necessary orders, thereby achieving efficient construction management. The flow of the specific processing in the third embodiment will be described with reference to FIG. 15.

[0339] Step 1:

[0340] Enter basic information

[0341] The user uses a terminal to input basic information such as total floor area, age, and building location. The server receives this input data and stores it in a database. The input data is used as the basic data required to develop a repair plan. 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] Repair plan planning

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

[0345] Step 3:

[0346] Progress Management

[0347] The server uses a database to manage the progress of the repair work. Users can check the progress using their terminals. The input data is information about the progress of the work, and the output data is a progress report. Specifically, the server periodically updates the database and provides the user with the latest progress information.

[0348] Step 4:

[0349] Sensor data acquisition and analysis

[0350] The server acquires data from sensors in the building and analyzes it in real time. The input data is the environmental data acquired from the sensors, and the output data is the analysis results. Specifically, the server acquires data using the sensor management API and runs an algorithm to detect anomalies.

[0351] Step 5:

[0352] Update repair plan

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

[0354] Step 6:

[0355] Request for proposal preparation and Q&A

[0356] The server automatically creates a request for proposal (RFP) based on the details of the repair work. The user can use a terminal to review the RFP and modify it as necessary. The input data is the detailed information of the repair work, and the output data is the RFP. Specifically, the server uses the generative AI model to generate the RFP 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 quote from the vendor, and the output data is the evaluation result. Specifically, the server evaluates whether the quote amount is reasonable based on past data and notifies the user.

[0360] (Application example 3)

[0361] Next, a description will be given of 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 will be referred to as a "terminal."

[0362] Conventional repair work management systems supported repair plan creation, process management, and ordering operations based on basic information, but they had difficulty responding to real-time changes in the factory's situation. Furthermore, creating RFPs for contractors, answering questions, and evaluating the validity of estimates required manual processes, requiring efficient operation. This resulted in problems such as delays in updating repair plans and insufficient evaluation of estimates.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, and building location, means for automatically formulating repair plans based on the basic information, means for supporting everything from plan process management to ordering, means for collecting data from various sensors within the factory, means for analyzing the collected data in real time and identifying areas requiring repair, means for automatically updating repair plans based on the analysis results, means for automatically generating an RFP based on details of the repair work, means for automatically responding to questions from contractors, and means for evaluating submitted estimates and determining their validity. This allows for efficient updating of repair plans and contractor support in response to real-time changes in the factory situation.

[0365] "Total floor area" means the total floor area of ​​each floor of a building.

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

[0367] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[0368] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[0369] A "repair plan" is a plan that lays out the content and schedule of repair work on a building.

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

[0371] "Ordering operations" refers to the operations of ordering materials and services required for repair work.

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

[0373] "Data collection" means gathering information from sensors, etc.

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

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

[0376] The "automatic response function" is a function that automatically responds to questions from businesses.

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

[0378] A system for carrying out this invention has the following configuration: The server includes means for inputting basic information such as total floor area, age of building, and location, means for automatically formulating repair plans based on the basic information, means for supporting processes from plan process management to ordering, means for collecting data from various sensors within the factory, means for analyzing the collected data in real time and identifying areas requiring repair, means for automatically updating the repair plan based on the analysis results, means for automatically generating an RFP based on details of the repair work, means for automatically responding to questions from contractors, and means for evaluating submitted estimates and determining their validity.

[0379] Program processing explanation

[0380] The server uses Python to collect data from various sensors. Specifically, it uses the requests library to obtain data from the sensor API. The collected data is analyzed in real time to identify areas that require repair due to abnormal vibrations or aging. This analysis uses data analysis libraries (e.g., Pandas, NumPy). The repair plan is automatically updated based on the identified repair areas. 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 question-and-answer data that has been trained in advance. The validity of submitted estimates is judged using an evaluation algorithm. A machine learning model (e.g., Scikit-learn) is used for this evaluation.

[0382] Specific examples

[0383] For example, if a vibration sensor in a factory detects abnormal vibrations, the data is collected in real time and the area requiring repair is automatically added to the repair plan. At this time, the server obtains the data from the sensor API and detects the abnormality using a data analysis library. If an abnormality is detected, the repair plan is automatically updated and an RFP is generated. The generative AI model automatically responds to questions from contractors, and submitted estimates are evaluated using a machine learning model.

[0384] Prompt Sentence Examples

[0385] "Write a Python program that collects vibration data from IoT sensors in a factory and automatically updates a repair plan if it detects abnormal vibrations."

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

[0387] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0388] Step 1:

[0389] The server collects data from various sensors in the factory. Specifically, it sends a request 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 aging. The input is the data obtained from the sensors, and the output is the result of whether an abnormality 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 results, 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 an automatic response based on pre-trained question-and-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 emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0401] "Example 1"

[0402] In one embodiment of the present invention, a means for inputting basic building information such as total floor area, age, and building size is provided. This information may be input directly by the building manager or obtained from an existing building management system. An emotion engine that recognizes the user's emotions is also incorporated. This emotion engine recognizes emotions from the user's tone of voice and facial expressions, for example.

[0403] "Example 2"

[0404] Another embodiment of the present invention provides a means for automatically creating a repair plan based on the basic information. Specifically, a repair plan is automatically created based on the building's basic information and a general repair schedule. An emotion engine that recognizes the user's emotions is also incorporated. This emotion engine recognizes emotions from, for example, the user's tone of voice or facial expression.

[0405] "Example 3"

[0406] In a further embodiment of the present invention, a means for supporting everything from planning process management to ordering is provided. Specifically, the progress of repair work is managed and ordering is supported as needed. An emotion engine that recognizes the user's emotions is also incorporated. This emotion engine recognizes emotions from the user's tone of voice and facial expressions, for example.

[0407] The processing flow of each embodiment will be described below.

[0408] "Example 1"

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

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

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

[0412] "Example 2"

[0413] Step 1: Automatically generate a repair plan based on basic building information and a general repair schedule.

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

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

[0416] "Example 3"

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

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

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

[0420] Example 1

[0421] Next, a description will be given of 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."

[0422] Conventional building management systems can create repair plans based on basic building information and support schedule management and ordering operations, but they are unable to provide feedback that takes user emotions into account. This makes it difficult to properly understand user satisfaction and dissatisfaction and reflect this in management operations. Furthermore, there is no way to integrate and analyze emotional data, making it impossible to respond appropriately based on user emotions.

[0423] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0424] In this invention, the server includes means for inputting basic information such as total floor area, age, and condition, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan process management to ordering, means for generating emotion data from the user's tone of voice and facial expression using an emotion engine that recognizes the user's emotions, means for integrating and analyzing the generated emotion data with the basic information, and means for feeding back 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 or dissatisfaction, and reflect this in management operations.

[0425] "Total floor area" refers to the total floor area of ​​each floor of a building.

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

[0427] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[0428] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[0429] A "repair plan" refers to the schedule and procedures for systematically repairing and maintaining a building.

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

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

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

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

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

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

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

[0437] System configuration

[0438] The system consists of three main components: a server, terminals, and users. The server plays a central role in storing data, analyzing it, and providing feedback. Terminals provide an interface for users to input information and record voice. Users are building managers and building users who provide information to the system.

[0439] Hardware and software used

[0440] Server: Database management system (MySQL, PostgreSQL), data analysis software (Python, R), emotion engine (IBM Watson (registered trademark), Google Cloud Speech-to-Text)

[0441] Device: A computer or smart device with a microphone

[0442] Users: Building managers, building users

[0443] Entering and saving data

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

[0445] Emotion Recognition and Analysis

[0446] When a user talks about the building's maintenance status, the device records the user's voice. The recorded voice data is sent to the server, which then analyzes the voice data using an emotion engine. 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 emotion data. Data analysis software such as Python and R is used for the analysis. The analysis results are provided as feedback to building managers. For example, feedback such as "There are many complaints about the cleaning situation" may be provided via email or dashboard.

[0449] Specific examples

[0450] For example, when a building manager wants to enter information about a new building into the system, he or she follows these steps:

[0451] 1. The building manager accesses the system's input screen and enters basic information such as total floor area, age of the building, and building location.

[0452] 2. The server stores the entered information in a 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 emotion data, and provides feedback to the building manager.

[0455] Prompt Sentence Examples

[0456] Examples of prompts to be input to a generative AI model include:

[0457] "The building has a total floor area of ​​5,000 square meters and is 10 years old. Recognize the user's emotions by analyzing the tone of voice and facial expressions of the building manager when he talks about the building's maintenance status."

[0458] Using this prompt, the generative AI model can integrate and analyze basic building information and the user's emotional data to provide appropriate feedback.

[0459] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0460] Step 1:

[0461] A user logs in to the system.

[0462] Input: User ID and password

[0463] How it works: A user accesses the system using a dedicated terminal or a web browser and enters their user ID and password on the login screen.

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

[0465] Step 2:

[0466] The user enters basic building information.

[0467] Input: Basic information such as total floor area, age, and building size

[0468] Operation: The user enters information such as the total floor area, age, and building age into the system's input form. For example, the user enters the total floor area as "5,000 square meters" and the age as "10 years."

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

[0470] Step 3:

[0471] The server stores the entered information in a database.

[0472] Input: Basic information entered by the user

[0473] How it works: The server takes the basic building information entered by the user and stores it in a database using a database management system such as MySQL or PostgreSQL.

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

[0475] Step 4:

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

[0477] Input: User's voice

[0478] How it works: A user uses a device with a microphone to talk about the building's maintenance status. For example, they might say, "The building's cleaning status is poor."

[0479] Output: The audio data is recorded to the device.

[0480] Step 5:

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

[0482] Input: User's voice

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

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

[0485] Step 6:

[0486] The server analyzes the voice data using an emotion engine.

[0487] Input: Audio data

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

[0489] Output: Emotion data is generated.

[0490] Step 7:

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

[0492] Input: Basic information, emotion data

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

[0494] Output: Consolidated data is generated.

[0495] Step 8:

[0496] The server analyzes the aggregated data.

[0497] Input: Integrated data

[0498] How it works: The server uses data analysis software such as Python or R to analyze the integrated data. For example, it can identify user dissatisfaction with the building's cleaning status and analyze the causes of this.

[0499] Output: Analysis results are generated.

[0500] Step 9:

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

[0502] Input: Analysis results

[0503] Operation: The server notifies the building manager of the analysis results, for example by email or via the dashboard, providing feedback such as "There are many complaints about the cleaning situation."

[0504] Output: Feedback is provided to building management.

[0505] (Application example 1)

[0506] Next, a description will be given of Application Example 1 of Embodiment 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 allow users to input basic information such as total floor area, age, and building condition to create repair plans, but do not optimize the work environment by taking into account the emotional state of workers. As a result, they are unable to adequately manage worker stress or improve work efficiency. Furthermore, because it is difficult to respond in real time based on emotional state, improvements to worker health and the work environment can be delayed.

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

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

[0510] "Total floor area" means the total floor area of ​​each floor of a building.

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

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

[0513] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[0514] A "repair plan" is a schedule and procedures for systematically repairing and maintaining a building.

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

[0516] "Ordering operations" refers to the operations of ordering materials and services required for repairs and maintenance.

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

[0518] "Workers" are people who repair and maintain buildings.

[0519] "Monitoring" means to continuously watch for a particular condition or situation.

[0520] "Work environment" refers to the location and conditions under which workers work.

[0521] "Optimization" means making something the most effective for a particular purpose.

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

[0523] Program processing explanation

[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 can be done by the building manager entering data such as total floor area, age, and location, or by obtaining it from an existing building management system. Next, it automatically creates a repair plan based on this basic information. This plan includes the repair schedule and procedures.

[0528] Additionally, the server supports planning and ordering, including tracking the progress of repair projects and ordering the necessary materials and services.

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

[0530] Based on the emotional state, the server optimizes the working environment. For example, if the worker is feeling stressed, the server will take appropriate action to improve the working environment.

[0531] Specific examples

[0532] As a concrete example, consider the stress management of workers in a factory. If a worker is feeling stressed, the emotion engine will detect this and notify the manager, allowing the manager to respond quickly and improve the working environment.

[0533] Example prompts to input to the generative AI model

[0534] Design a system to monitor the emotions of factory workers in real time and notify managers if they are feeling stressed. Basic building information (total floor area, age, etc.) will also be entered.

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

[0536] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0537] Step 1:

[0538] The server inputs basic building information, such as total floor area, age, and building location, either through the building manager or by obtaining this data from an existing building management system. The input data is stored in a database on the server.

[0539] Step 2:

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

[0541] Step 3:

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

[0543] Step 4:

[0544] The server acquires image data of workers using cameras. The cameras are installed in the work area 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 captured image data and identifies the worker's emotion from their tone of voice and facial expression. The emotional state is recorded in a database within the server.

[0547] Step 6:

[0548] The server optimizes the work environment based on the worker's emotional state. For example, if a worker is feeling stressed, the server will send a notification to the manager and prompt them to take appropriate action. The content of the notification and the response history are stored in a database on the server.

[0549] Step 7:

[0550] The server consolidates all data and uses it to improve the working environment and manage worker health. The consolidated data is displayed on a dashboard accessible to managers, enabling real-time monitoring and analysis.

[0551] Example 2

[0552] Next, a description will be given of 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 will be referred to as a "terminal."

[0553] Conventional repair planning systems can create repair plans based on basic building information, but they have issues with presenting plans that take user emotions into account and automatically generating optimal repair schedules. Furthermore, they lack support for planning process management and ordering, placing a heavy burden on users.

[0554] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0555] In this invention, the server includes a means for inputting basic information such as total floor area, age of building, and building condition, a means for automatically formulating a repair plan based on the basic information, a means for supporting everything from plan process management to ordering, a means for analyzing user emotions using an emotion engine that recognizes user emotions, and a means for calculating an optimal repair schedule using a generative AI model. This makes it possible to automatically generate an optimal repair plan that takes user emotions into consideration, and to streamline plan process management and ordering.

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

[0557] "Age" refers to the number of years that have passed since the building was constructed, and is important information for assessing the deterioration of a building and the need for repairs.

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

[0559] "Basic information" is a general term for basic data about a building, such as total floor area, age, and building specifications.

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

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

[0562] "Ordering operations" refers to the work of ordering materials and services necessary for carrying out repair work from external contractors, and is essential for the smooth implementation of the work.

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

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

[0565] An "optimal repair schedule" refers to a schedule that allows repair work to be carried out most effectively and efficiently based on the condition and usage of the building.

[0566] This invention is a system that automatically creates a repair plan based on basic information about a building and provides an optimal repair schedule that takes into account the user's emotions. Specific embodiments of this system will be described below.

[0567] System configuration

[0568] This system consists of three main components: a server, a terminal, and a user. The server is responsible for collecting, analyzing, and storing data, and generating repair plans, while the terminal functions as an interface with the user. The user is responsible for entering basic building information and checking repair plans.

[0569] Hardware and software 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 with a camera and microphone.

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

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

[0574] Emotion engine: Software that recognizes emotions by analyzing the user's tone of voice and facial expressions.

[0575] Program processing

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

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

[0578] 2. Data analysis: The server analyzes the stored basic information and the general schedule of repair work to identify where and when repairs are needed.

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

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

[0581] Specific examples

[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 the following repair plan:

[0583] Exterior wall inspection and repair in 2020

[0584] Inspection of internal facilities in 2025

[0585] Exterior wall inspection and repairs to be carried out again in 2030

[0586] Example prompt sentence:

[0587] "Generate a repair plan based on a typical repair work schedule for a building built in 2000, made of reinforced concrete, and made of concrete and steel."

[0588] This system saves users the trouble of creating repair plans and provides support for carrying out repair work efficiently.In addition, by taking into account the user's emotions, it is possible to provide a service that provides greater satisfaction.

[0589] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0590] Step 1:

[0591] Enter basic information

[0592] The user enters basic building information such as "Year built: 2000," "Structure: reinforced concrete," and "Materials used: concrete, steel" into the input form on the terminal.

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

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

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

[0596] Step 2:

[0597] Data collection and storage

[0598] The server receives the 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 stores the received basic information in the "Building Information" table of the database, and also retrieves the general schedule of repair work from the "Repair Schedule" table.

[0602] Step 3:

[0603] Generate repair plans

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

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

[0606] Output: The generated repair plan.

[0607] Specific operation: The server inputs the prompt statement "Generate a repair plan based on a typical repair work schedule for a building constructed in 2000, with reinforced concrete and concrete and steel materials" into the generative AI model, and calculates the optimal repair schedule. The calculation results are compiled as a repair plan and saved in the "repair plan" table in the database.

[0608] Step 4:

[0609] Performing emotion recognition

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

[0611] Input: The user's tone of voice and facial expression.

[0612] Output: Parsed user sentiment.

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

[0614] Step 5:

[0615] Presentation of repair plan

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

[0617] Input: The 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 checks the displayed repair plan and presses the "Approve" button to confirm the plan.

[0620] In this way, the system efficiently plans repairs and provides optimal information while taking into account the user's feelings.

[0621] (Application example 2)

[0622] Next, a description will be given of 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 will be referred to as a "terminal."

[0623] Conventional repair planning systems can automatically create repair plans for building and factory equipment, but they lack the ability to make timely proposals that take user emotions into account and to monitor the progress of repair plans in real time. This can lead to a decrease in user satisfaction with the execution of repair plans. Furthermore, because plans are updated manually, it is difficult to maintain repair plans that are up to date with the latest conditions. To solve these issues, a system is needed that recognizes user emotions, proposes repair plans at the appropriate time, and monitors progress in real time.

[0624] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, and building size, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan process management to ordering, 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 taking 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 basic data about the building and its facilities, such as total floor area, age, and location.

[0627] A "repair plan" is a plan for the schedule and content of repair work on buildings and facilities.

[0628] "Process management" is a means of managing the progress of repair work and ensuring that it proceeds as planned.

[0629] "Ordering work" refers to the work of ordering materials and services required for repair work from external contractors.

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

[0631] The "right time" refers to the most effective time depending on the user's emotions and situation.

[0632] "Progress" 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] To implement this invention, the following system configuration is required: The server includes means for inputting basic information such as total floor area, age of building, and building location, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan process management to ordering, 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: Implement repair plans and monitor 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 to implement the program.

[0642] Emotion recognition library: Recognizes user emotions using OpenCV, librosa, etc.

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

[0644] Data processing and calculation

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

[0646] Specific examples

[0647] For example, the equipment "Machine A" was last repaired on January 1, 2023, and the repair interval is 180 days. The next repair date is June 30, 2023. The server uses this information to create a repair plan, recognizes the user's emotions, and proposes this plan at the appropriate time. Factory robots also perform the repair work, monitoring its progress in real time.

[0648] Prompt Sentence Examples

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

[0650] In this way, it becomes possible to propose repair plans at appropriate times that take into account the user's emotions, and the progress of the repair plans can be grasped in real time, thereby improving user satisfaction.

[0651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0652] Step 1:

[0653] The server inputs basic information such as total floor area, age, and location into a database.

[0654] Input: Basic information such as total floor area, age, and building size

[0655] Data processing: Save basic information to the database

[0656] Output: Basic information stored in the database

[0657] Step 2:

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

[0659] Input: Basic information stored in the database

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

[0661] Output: Automatically generated repair plan

[0662] Step 3:

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

[0664] Input: Auto-generated repair plan

[0665] Data processing: Managing the repair planning process and ordering the necessary materials and services

[0666] Output: Order processing status

[0667] Step 4:

[0668] The server uses an emotion engine that recognizes the user's emotions to propose repair plans at appropriate times.

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

[0670] Data Computation: Analyzing user emotions using an emotion engine

[0671] Output: Proposed repair plan based on user's sentiment

[0672] Step 5:

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

[0674] Input: Repair plan progress data

[0675] Data processing: Real-time updates of progress and recording in a database

[0676] Output: Real-time updated progress data

[0677] Step 6:

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

[0679] Input: Repair plan proposed by the server

[0680] Data manipulation: Modifying repair plans based on user feedback

[0681] Output: Revised repair plan

[0682] Step 7:

[0683] The factory robot carries out the repair work based on the repair plan.

[0684] Input: Revised repair plan

[0685] Data calculation: Calculate the repair work procedure and start the work

[0686] Output: Results of repair work performed

[0687] Step 8:

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

[0689] Input: Results of repair work performed

[0690] Data processing: Save the results of repair work in a database and reflect them in 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 repair plans.

[0693] Example 3

[0694] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0695] In conventional repair work management systems, the creation of repair plans based on basic information, process management, and ordering support are often performed manually, resulting in inefficiency. It is also difficult to respond quickly to changes in the building's condition, which can lead to delays in updating repair plans. Furthermore, creating requests for proposals for contractors, answering questions, and evaluating the validity of estimated costs are all performed manually, which takes time and effort. Feedback that takes user emotions into account is not provided, resulting in poor user experience.

[0696] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0697] In this invention, the server includes: a means for inputting basic information such as total floor area, age, and building location; a means for automatically creating a repair plan based on the basic information; a means for supporting everything from schedule management of the plan to ordering; a means for collecting data from sensors installed in the building; a means for analyzing the collected data; a means for automatically updating the repair plan based on the analysis results; a means for automatically generating a request for proposal based on detailed repair work information; a means for automatically responding to questions from contractors; a means for evaluating estimates submitted by contractors and determining their validity; a means for collecting the user's tone of voice and facial expression; a means for analyzing the collected data to recognize the user's emotions; and a means for providing appropriate feedback based on the analysis results. This enables efficient repair work planning, schedule management, and ordering, and allows for rapid response to changes in the building's condition. Furthermore, the creation of a request for proposal for contractors, question response, and evaluation of the validity of estimates are automated, reducing time and effort. Furthermore, feedback that takes the user's emotions into consideration is provided, improving the user experience.

[0698] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

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

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

[0701] "Ordering work" refers to the work of ordering materials and services required 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 analytics" is the process of analyzing collected data to detect outliers and patterns.

[0704] A "Request for Proposal (RFP)" is a document that lists detailed information about repair work and requests proposals from contractors.

[0705] "Question answering" is the process of providing appropriate answers to questions from businesses.

[0706] "Evaluating the validity of an estimate" is the process of determining whether an estimate submitted by a contractor is appropriate.

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

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

[0709] MODE FOR CARRYING OUT THE INVENTION

[0710] This invention is a system for efficiently carrying out repair work planning, process management, and ordering operations. Specific embodiments of this system will be described below.

[0711] Enter basic information and create a repair plan

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

[0713] Sensor-based data collection and analysis

[0714] The server collects data from sensors installed in the building. The hardware used is 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, sensors collect vibration data from parts of a building, and if abnormal vibrations are detected, repairs to that part are added to the plan.

[0716] Automatic generation of requests for proposals and question answering

[0717] The server automatically generates a request for proposal (RFP) based on the details of the repair work, using software such as Microsoft Word or Google Docs API. The server also uses a generative AI model (e.g., GPT-3) to automatically respond to questions from contractors.

[0718] For example, an RFP is generated based on the details of the repair work and sent to the contractor. After receiving questions from the contractor, the generative AI model is used to generate appropriate answers and send them back to the contractor.

[0719] Evaluating the validity of the estimated amount

[0720] The server uses a price comparison algorithm to evaluate quotes submitted by vendors and determine their reasonableness, comparing submitted quotes with historical data to determine whether they are unusually high or low.

[0721] Emotion recognition and feedback provision

[0722] The device collects the user's tone of voice and facial expressions through a camera or microphone. The hardware used is, for example, a webcam or microphone. The server uses an emotion recognition engine (e.g., 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] For example, a camera captures the user's facial expressions and a microphone records the tone of voice. The collected facial expression data is analyzed by DeepFace, and the tone of voice is analyzed by a voice analysis library. If the user is feeling stressed, advice on how to relax is provided.

[0724] Prompt Sentence Examples

[0725] "Generate a program that analyzes the building's vibration data and updates the repair plan if anomalies are detected."

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

[0727] "Generate a program that recognizes emotions by analyzing the user's tone of voice and facial expressions."

[0728] The above is a specific embodiment for carrying out the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0729] Step 1:

[0730] The server provides a means for users to input basic information such as total floor area, age, and location. The input basic information is stored in a database.

[0731] What it does: The user enters basic information through a web interface, and the server stores that information in a database.

[0732] Input: Basic information such as total floor area, age, and building size

[0733] Output: Basic information stored in the database

[0734] Step 2:

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

[0736] What it does: Runs a Python script to parse 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 in the building, using IoT hardware such as Raspberry Pi and Arduino.

[0741] How it works: 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 and runs an algorithm to detect outliers.

[0746] What it does: It uses 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 abnormality is detected, the server adds the repair of that part to the plan and saves the updated plan in the database.

[0751] Specific operation: The location where the abnormality was detected is added to the repair plan and the database is updated.

[0752] Input: Analysis results

[0753] Output: Updated repair plan

[0754] Step 6:

[0755] The server automatically generates a request for proposal (RFP) based on the details of the repair work, using software such as Microsoft Word or Google Docs API.

[0756] Specific operation: Fill in the detailed information of the repair work into the template and generate the RFP document.

[0757] Input: Detailed information about repair work

[0758] Output: Auto-generated RFP document

[0759] Step 7:

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

[0761] What it does: Receives a question from a merchant, uses a generative AI model to generate an appropriate answer, and sends it back to the merchant.

[0762] Input: Question from 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] What it does: Compare submitted estimates to historical data to determine whether they are unusually high or low.

[0767] Input: Quote submitted by vendor

[0768] Output: Estimate validity evaluation result

[0769] Step 9:

[0770] The device collects the user's tone of voice and facial expressions through a camera or microphone, such as a webcam or microphone.

[0771] What it does: The camera captures your facial expressions and the microphone records your tone of voice.

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

[0773] Output: Collected speech and facial expression data

[0774] Step 10:

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

[0776] Specific operation: The collected facial expression data is analyzed using DeepFace, and the tone of voice is analyzed using a voice analysis library.

[0777] Input: Collected voice 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] What it does: If the user is feeling stressed, it offers advice on how to relax.

[0782] Input: Emotion recognition results

[0783] Output: Feedback provided

[0784] (Application example 3)

[0785] Next, a description will be given of 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 will be referred to as a "terminal."

[0786] In conventional repair management systems, planning repair plans, updating, and ordering support are often done manually, resulting in inefficiency. It is also difficult to detect the deterioration of factory equipment in real time and respond quickly. Furthermore, creating requests for proposals for contractors and assessing the validity of estimated costs are also time-consuming. There is a need to solve these problems and achieve efficient and effective repair management.

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

[0788] In this invention, the server includes a means for inputting basic information such as total floor area, age, and building location, a means for automatically formulating a repair plan based on the basic information, a means for supporting processes from plan management to ordering, a means for acquiring sensor data within the factory and automatically updating the repair plan, a means for creating a request for proposal for contractors, a means for answering questions, and a means for evaluating the validity of estimated costs, and a means for recognizing the user's emotions and providing appropriate feedback. This enables automatic updating of repair plans, streamlining of ordering, smoother communication with contractors, and support according to the user's emotions.

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

[0790] "Age" indicates the number of years that have passed since the building was constructed, and is important information for determining the degree of deterioration of the building.

[0791] "Intellect" refers to knowledge and information related to the management and operation of buildings, and is part of the basic information required for planning and updating repair plans.

[0792] "Basic information" refers to information about the building that is necessary for formulating a repair plan, and includes the total floor area, age, and building specifications.

[0793] A "repair plan" indicates the schedule and content for carrying out planned repairs to buildings and facilities.

[0794] "Plan process management" is a means of managing the progress of repair work based on the repair plan and ensuring that it proceeds appropriately.

[0795] "Ordering work" refers to the work of ordering materials and services required to carry out repair work from contractors.

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

[0797] A "request for proposal" is a document that presents the work content and conditions to contractors who will carry out the repair work and requests their proposals.

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

[0799] "Evaluating the validity of an estimate" is the process of evaluating whether the estimate submitted by a contractor is appropriate.

[0800] The "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 or information provided based on the user's emotions and situation.

[0802] A system for carrying out this invention has the following configuration: The server includes means for inputting basic information such as total floor area, age of building, and location, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan process management 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 means for evaluating the validity of questions and estimates, and means for recognizing the user's emotions and providing appropriate feedback.

[0803] Hardware and software used

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

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

[0806] Data processing and 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 changes in the environment in real time. Next, based on the acquired sensor data, it identifies areas that need repair and automatically updates repair plans. This makes it possible to quickly detect equipment deterioration and develop appropriate repair plans.

[0808] The server then creates a request for proposal for contractors, answers questions, and evaluates the appropriateness of the estimated cost. This allows for efficient selection of a contractor to carry out the repair work. The server also uses an emotion engine that recognizes the user's emotions, recognizing their tone of voice and facial expressions and providing appropriate feedback. This reduces user stress and enables smooth communication.

[0809] Specific examples

[0810] Example 1

[0811] IoT sensors can detect when a robot in a factory is aging and automatically add it to a repair plan, preventing robot breakdowns and maintaining factory production efficiency.

[0812] Example 2

[0813] Managers can use their smartphones to send requests for proposals to contractors and evaluate the validity of quotes, optimizing repair costs and ensuring repairs are completed within budget.

[0814] Example 3

[0815] The technician uses a head-mounted display, and the emotion engine recognizes the technician's emotions while performing repair work and provides appropriate feedback, thereby reducing the technician's stress and improving work efficiency.

[0816] Prompt Sentence Examples

[0817] Develop an application that automatically updates robot repair plans and assists with ordering as needed based on data obtained from IoT sensors in the factory. Also, incorporate functionality to recognize user emotions and provide appropriate feedback.

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

[0819] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0820] Step 1:

[0821] The server acquires sensor data from IoT sensors in the factory. The sensor data contains information about the state of the equipment and changes in the environment. By acquiring this data, it is possible to grasp equipment deterioration and abnormalities 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 and identifies areas that need repair. Specifically, it analyzes the sensor data and detects abnormal values ​​and signs of aging. The input is the acquired sensor data, and the output is a list of areas that need 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 priority of the repairs, and the materials and work required for the repairs. The input is a list of areas that need repairs, and the output is an updated repair plan.

[0826] Step 4:

[0827] The server creates a request for proposal for contractors based on the updated repair plan. The request for proposal includes details of the repair, the required materials, and the work conditions. The input is the updated repair plan, and the output is the request for proposal.

[0828] Step 5:

[0829] The server sends a request for proposal to the vendor and conducts a question-and-answer session. It provides appropriate answers to the vendor's questions and shares the necessary information. The input is the request for proposal and the vendor's questions, and the output is the answers and the shared information.

[0830] Step 6:

[0831] The server evaluates quotes submitted by contractors and determines their validity. It analyzes the quote contents and evaluates whether the cost and work content are appropriate. The input is the quote from the contractor, 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 provided feedback.

[0834] Step 8:

[0835] The user progresses the repair work based on the provided feedback. The work is adjusted according to the feedback to perform the repair 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0837] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0838] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

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

[0840] [Second embodiment]

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

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

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

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

[0845] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0847] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0848] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0849] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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 a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0851] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0853] "Example 1"

[0854] This embodiment of the present invention includes a means for inputting basic information about a building, such as total floor area, age, and location. This information is input directly by the building manager or obtained from an existing building management system.

[0855] "Example 2"

[0856] Furthermore, this embodiment of the present invention includes a means for automatically creating a repair plan based on the basic information. Specifically, the repair plan is automatically created based on the basic information of the building and a general schedule of repair work.

[0857] "Example 3"

[0858] Furthermore, the present invention is provided with a means for supporting everything from planning process management to ordering operations. Specifically, the progress of repair work is managed and ordering operations are supported as necessary.

[0859] "Example 4"

[0860] Furthermore, this embodiment of the present invention includes a means for automatically updating the plan based on IoT sensor data, using basic information as a base. Specifically, the system analyzes data acquired from IoT sensors in the building and automatically updates the repair plan. For example, if an IoT sensor detects that a part of the building is aging, the system adds repairs to that part of the building.

[0861] "Example 5"

[0862] Furthermore, this embodiment of the present invention is equipped with a means for creating an RFP for contractors, answering questions, and evaluating the appropriateness of the estimated cost when carrying out construction work. Specifically, the system 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 processing flow of each embodiment will be described below.

[0864] "Example 1"

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

[0866] "Example 2"

[0867] Step 1: Enter basic building information.

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

[0869] "Example 3"

[0870] Step 1: Create a repair plan.

[0871] Step 2: Manage the planning process.

[0872] Step 3: Assist with ordering as needed.

[0873] "Example 4"

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

[0875] Step 2: Acquire data from IoT sensors in the building.

[0876] Step 3: Analyze the acquired data and automatically update the repair plan. For example, if IoT sensors detect that part of the building is aging, add repairs to that part to the plan.

[0877] "Example 5"

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

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

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

[0881] Example 1

[0882] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0883] Conventional building management systems have the problem that it takes a lot of time and effort to manually input basic building information and create repair plans. Also, retrieving information from existing management systems and formatting data is cumbersome, making it difficult to create efficient plans. Furthermore, the generation of prompts using generative AI models is not automated, placing a heavy burden on users.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0885] In this invention, the server includes means for inputting basic information such as total floor area, age, and building location, means for receiving the basic information and saving it in a database, means for acquiring additional information from an existing management system, means for formatting the acquired information and converting it into a required format, means for inputting prompt text to a generative AI model, means for automatically formulating a repair plan, and means for supporting everything from plan process management to ordering operations. This makes it possible to consistently automate the process from inputting basic building information to formulating a repair plan and generating prompt text using a generative AI model.

[0886] "Total floor area" means the total area of ​​the floor area of ​​each floor of a building.

[0887] "Age" refers to the number of years that have passed since the 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 basic data such as the building's total floor area, age, and location.

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

[0891] A "management system" is a general term for software and hardware used to operate and maintain a building.

[0892] "Additional information" is data about the building 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 to make it easier to use.

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

[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 content of building repairs and maintenance.

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

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

[0899] A "request for proposal" is a document that requests specific proposals from contractors.

[0900] "Question and answer session" refers to the process of answering questions from vendors based on a request for proposal.

[0901] "Evaluating the validity of the estimated amount" refers to evaluating whether the estimated amount submitted by the contractor is appropriate.

[0902] This invention is a system that inputs basic building information, formulates a repair plan, and generates prompt sentences using a generative AI model. Specific embodiments of this system are described below.

[0903] First, a 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 about the building, such as its total floor area, age, and cost. For example, a user might enter "Total floor area: 5,000 square meters," "Age: 10 years," and "Cost: high."

[0904] The server then receives the basic building information entered by the user, stores it in a database server (e.g., MySQL, PostgreSQL), and runs SQL queries against the database to insert the information into the appropriate tables.

[0905] Additionally, the server obtains additional building information through the API of the existing management system, such as the building's energy consumption data and maintenance history. The server sends API requests and receives the data returned as a response.

[0906] The server then formats the additional information it receives and converts it into the required format, for example by parsing the JSON data and extracting and formatting the required fields. The server performs this processing using a data processing script (e.g. Python, JavaScript).

[0907] Finally, the server generates a prompt sentence to be input into the generative AI model based on the formatted information. For example, it generates a prompt sentence such as, "Please enter basic information about a building that has a total floor area of ​​5,000 square meters, is 10 years old, and is highly intelligent." The server inputs this prompt sentence into the generative AI model and receives a response from the model.

[0908] As a concrete example, the following prompt sentence can be input to a generative AI model:

[0909] Example prompt sentence:

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

[0911] This system makes it possible to automate everything from inputting basic building information to formulating repair plans and even generating prompts using a generative AI model, thereby reducing the burden on users and enabling efficient building management.

[0912] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0913] Step 1:

[0914] The user enters basic building information.

[0915] A user accesses a web application provided by a server using a device such as a PC or tablet. They open a web browser and enter basic information about the building, such as its total floor area, age, and cost. The entered information is then sent to the server. Specifically, the user enters "Total floor area: 5,000 square meters," "Age: 10 years," and "Cost: high" into the form and clicks the submit button.

[0916] Input: Basic information such as total floor area, age, and location of the building

[0917] Output: Basic information sent to the server

[0918] Step 2:

[0919] The server receives the entered information and stores it in a database.

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

[0921] Input: Basic information submitted by the user

[0922] Output: Basic information stored in the database

[0923] Step 3:

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

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

[0926] Input: API request

[0927] Output: Additional information obtained

[0928] Step 4:

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

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

[0931] Input: Additional information obtained

[0932] Output: Formatted data

[0933] Step 5:

[0934] The server inputs a prompt sentence into the generative AI model.

[0935] The server generates a prompt sentence to be input into the generative AI model based on the formatted information. For example, it generates a prompt sentence such as, "Please enter the basic information of a building that has a total floor area of ​​5,000 square meters, is 10 years old, and is highly intelligent." The server inputs this prompt sentence into the generative AI model and receives a response from the model. Specifically, the server executes the process of "inputting a prompt sentence into the generative AI model and receiving a response."

[0936] Input: Formatted data

[0937] Output: The response from the generative AI model

[0938] (Application example 1)

[0939] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0940] Conventional building management systems supported the creation of repair plans, process management, and ordering operations based on basic building information, but lacked the ability to assess security risks, generate real-time security alerts, or propose specific security measures. This made it difficult to comprehensively manage building safety, and a rapid response to security risks was required.

[0941] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, and building location, means for automatically formulating a repair plan based on the basic information, means for supporting processes from plan schedule management 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 makes it possible to support repair plan planning, schedule management, and ordering based on the basic building information, as well as evaluate security risks, generate real-time security alerts, and propose specific security measures.

[0943] "Total floor area" means the total floor area of ​​each floor of a building.

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

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

[0946] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[0947] A "repair plan" is a plan that shows the schedule and content for systematically repairing and maintaining a building.

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

[0949] "Ordering operations" refers to the operations of ordering materials and services required 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 notifies you immediately when a security risk occurs.

[0952] "Security measures" are specific means or methods for reducing 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, age of building, and building location, and then creates a repair plan based on this information, supporting process management and ordering operations. It also adds functions to evaluate security risks, generate real-time security alerts, and propose specific security measures.

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

[0955] Furthermore, the server is equipped with a means to assess security risks based on basic 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, allowing building managers to quickly implement appropriate security measures.

[0957] The hardware used includes devices such as smartphones and tablets, and the software uses Python, JSON, and datetime modules. This makes it possible to efficiently manage basic building information, evaluate security risks, and take appropriate measures.

[0958] For 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 evaluates the security risk and determines that it is high risk. A real-time security alert is generated and notified to the building manager. The server then proposes specific countermeasures, such as installing an advanced monitoring system or increasing security personnel.

[0959] Examples of prompts include:

[0960] "Enter basic building information: total floor area, age, and location."

[0961] In this way, in addition to supporting repair planning, process management, and ordering operations based on basic building information, it will also be possible to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[0962] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0963] Step 1:

[0964] The user uses a terminal to input basic information about the building (total floor area, age, location, etc.).

[0965] Input: Basic information such as total floor area, age, and building size

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

[0967] Specific operation: The user opens the application on their smartphone or tablet and enters information such as the total floor area, age of the building, and location into the input form. Once the input is complete, the user presses the send button to send the information to the server.

[0968] Step 2:

[0969] The server stores the basic information received and automatically creates a repair plan.

[0970] Input: Basic information submitted by the user

[0971] Output: Repair plan results

[0972] Specific operation: The server stores the received basic information in a database, then automatically generates a repair plan based on the stored information and stores the plan contents in the database.

[0973] Step 3:

[0974] The server supports repair plan process management and ordering operations.

[0975] Input: Repair plan results

[0976] Output: Process control schedule and order list

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

[0978] Step 4:

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

[0980] Input: Basic information submitted by the user

[0981] Output: Security risk assessment results

[0982] How it works: The server analyzes basic information and evaluates security risks, taking into account factors such as the building's age, total floor area, and location. The evaluation 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: If the risk score exceeds a certain threshold, the server generates a real-time security alert and notifies the building manager's terminal.

[0988] Step 6:

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

[0990] Input: Security risk assessment results

[0991] Output: Security measures proposal

[0992] Specific actions: Based on the risk score, the server proposes appropriate security measures, such as installing an advanced monitoring system or increasing security personnel. The proposal is then notified to the building manager.

[0993] In this way, in addition to supporting repair planning, process management, and ordering operations based on basic building information, it will also be possible to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[0994] Example 2

[0995] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[0996] Conventional repair planning systems require users to manually create repair plans based on basic building information, which is time-consuming and labor-intensive. Furthermore, when plans need to be updated or revised, this process is inefficient and often lacks accuracy. Furthermore, there was a lack of a way for users to easily check the generated repair plans.

[0997] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0998] In this invention, the server includes a means for inputting basic information such as total floor area, age, and building location, a means for automatically creating a repair plan based on the basic information, a means for supporting everything from plan process management to ordering, a means for generating a repair plan by sending a prompt to a generative AI model, a means for saving the generated repair plan in a database, and a means for a user to request a repair plan and display it on a terminal. This enables automatic generation and efficient management of repair plans.

[0999] "Total floor area" refers to the total floor area of ​​each floor of a building.

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

[1001] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[1002] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[1003] A "repair plan" refers to a plan that outlines the schedule and content of repair work on a building.

[1004] A "generative AI model" refers to a model for generating data using artificial intelligence.

[1005] A "prompt sentence" refers to an instruction sentence input 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 (such as a personal computer, tablet, or smartphone) that a user operates.

[1009] "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 operations of ordering materials and services necessary for repair work.

[1012] "RFP" stands for Request for Proposal, and refers to a document that requests proposals from contractors.

[1013] "Questions and Answers" refers to questions and answers regarding proposals and plans.

[1014] "Evaluating the reasonableness of the estimated amount" refers to evaluating whether the submitted estimate is appropriate.

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

[1016] Server Roles

[1017] The server receives basic information about the building and stores it in a database. This information includes the total floor area, age, and building size. Based on this information, the server sends prompts to the generative AI model to generate a repair plan. The generated repair plan is then stored back in the database.

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

[1019] A high-performance database server (e.g., MySQL, PostgreSQL)

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

[1021] Device Role

[1022] The terminal provides an interface for users to input basic building information and check the repair plan. When a user operates the terminal to request a repair plan, the terminal sends the request to the server. After receiving the repair plan from the server, the terminal displays its contents to the user.

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

[1024] PC, tablet, smartphone

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

[1026] User Roles

[1027] The user uses a terminal to input basic building information. Once input is complete, the user requests the generation of a repair plan. The user can then review the generated repair plan and request corrections or additions as necessary.

[1028] Specific examples

[1029] A user enters the following information into a form on their device: "Structure: Reinforced concrete, Age: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago." The device sends this information to the server, which then sends the following prompt to the generative AI model:

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

[1031] The generative AI model generates a repair plan based on this prompt and sends it back to the server. The server stores the generated repair plan in a database and sends it to the device when the user requests it. The device then displays the received repair plan to the user.

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

[1033] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1034] Step 1:

[1035] The user enters basic building information.

[1036] The user inputs basic building information through the terminal interface. Specifically, the information entered includes total floor area, age, and building size. The input information is temporarily stored in the terminal's memory. An example of input data is "Structure: reinforced concrete, Age: 20 years, Materials used: concrete, Past repair history: Exterior wall repaired 5 years ago."

[1037] Step 2:

[1038] The terminal transmits the input information to the server.

[1039] The terminal sends the 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 analyzes. The input data is temporarily stored in the server's memory.

[1040] Step 3:

[1041] The server stores the 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. By storing the information in the database, it becomes possible to reference the information in later processing.

[1043] Step 4:

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

[1045] The server generates a prompt sentence based on the saved basic information. The generated prompt sentence is sent to the generation AI model. An example of a prompt sentence is, "The basic information about the building is as follows: Structure: reinforced concrete, Age: 20 years, Materials used: concrete, Past repair history: Exterior wall repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[1046] Step 5:

[1047] A generative AI model generates a repair plan.

[1048] The generative AI model generates a repair plan based on the received prompt. The generated repair plan is sent back to the server. An example of output data is a plan such as, "The next repairs will be to repaint the exterior walls in two years, followed by roof waterproofing work in five years."

[1049] Step 6:

[1050] The server stores the generated repair plan in a database.

[1051] The server stores the repair plan received from the generative AI model in a database, allowing users to review and modify the repair plan at a later time.

[1052] Step 7:

[1053] A user requests a repair plan.

[1054] The user uses the terminal to request the display of the repair plan by clicking the "Show repair plan" button in the terminal interface. The request is made using an HTTP GET request.

[1055] Step 8:

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

[1057] Upon receiving a user request, the terminal requests the server to retrieve a 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 is in JSON format, and the terminal receives and analyzes it.

[1061] Step 10:

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

[1063] The terminal displays the received repair plan to the user. Specifically, the details of the repair plan are displayed on a web browser. The user can check the displayed repair plan and make corrections or additions as necessary.

[1064] (Application example 2)

[1065] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1066] With conventional repair planning systems, it was difficult to automatically generate repair plans based on basic information about building or factory equipment, and it was not possible to monitor the progress of the plan in real time or update the plan as needed. This resulted in issues such as reduced accuracy and efficiency of repair plans and significant effort and cost required for equipment maintenance and management.

[1067] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, age, and building location, means for automatically formulating repair plans based on the basic information, means for supporting everything from plan process management to ordering, means for automatically generating repair plans for each facility based on the basic information about the facility and a general schedule for repair work, means for monitoring the progress of the repair plans in real time and updating the plans as necessary, and means for generating repair plans using a generative AI model. This makes it possible to improve the accuracy and efficiency of repair plans and reduce the effort and cost required for facility maintenance and management.

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

[1070] "Age" refers to the number of years that have passed since the building was constructed.

[1071] "Intellect" 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 basic data about buildings and facilities, such as total floor area, age, and location.

[1073] "Repair plan" is a term that refers to planning the schedule and content of repair work on 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 work of ordering materials and services required for repair work from external contractors.

[1076] "Facility" is a term that refers to machines and devices installed in buildings such as factories and buildings.

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

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

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

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

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

[1082] "Generative AI model" is a term used to describe a model that uses artificial intelligence to analyze data and generate plans and predictions.

[1083] The system for carrying out this invention operates in cooperation with three parties: a server, a terminal, and a user. The server includes a means for inputting basic information such as total floor area, age, and location, a means for automatically formulating a repair plan based on the basic information, a means for supporting everything from plan process management to ordering, a means for automatically generating a repair plan for each facility based on basic information about the facility and a general schedule for repair work, a means for monitoring the progress of the repair plan in real time and updating the plan as necessary, and a means for generating a repair plan using a generative AI model.

[1084] The server implements programs using programming languages ​​such as Python to input data, analyze it, generate plans, and monitor progress. Specifically, the server acquires basic information about the equipment and calculates the next repair date based on the general schedule of repair work. The calculation results are output in JSON format so that the user can check them. The server also monitors progress in real time and updates the plan as necessary.

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

[1086] Users access the server through their devices and input basic information about the equipment. The input information is sent to the server, which then automatically generates a repair plan based on that information. The user can then check the generated repair plan through their device. They can also monitor progress in real time and update the plan as needed.

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

[1088] An example of a prompt to input to a generative AI model is as follows:

[1089] Calculate the next maintenance date based on basic facility information and the general schedule of maintenance work. Below is an example of input data.

[1090] Facility basic information:

[1091] {

[1092] "Facility 1": {"Installation date": "2020-01-01", "Frequency of use": "High", "Past repair history": ["2021-01-01", "2022-01-01"]},

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

[1094] }

[1095] Typical timeline for repair work:

[1096] {

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

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

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

[1100] }

[1101] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1102] Step 1:

[1103] The user accesses the server through a terminal and inputs basic information about the facility (total floor area, age, location, installation date, frequency of use, past repair history, etc.). The input information is sent to the server.

[1104] Input: Basic information of the facility

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

[1106] Step 2:

[1107] The server then refers to the general schedule of repair work based on the received basic information and calculates the next repair date. Specifically, it applies a repair cycle based on the frequency of use and calculates the next repair date based on 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 maintenance plan based on the calculated next maintenance date. The generated maintenance plan is saved in JSON format and can be viewed by the user.

[1112] Input: Next repair date

[1113] Output: Repair plan (JSON format)

[1114] Step 4:

[1115] The user can access the server through their terminal to check the generated repair plan and modify or update the contents of the repair plan as needed.

[1116] Input: Repair plan (JSON format)

[1117] Output: Modified and updated repair plan

[1118] Step 5:

[1119] The server monitors the progress of the repair plan in real time, using IoT sensor data to monitor the condition of the equipment and ensure that repairs are progressing as planned, and 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 the generative AI model to optimize the repair plan, specifically by analyzing past data and current conditions and proposing 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 can check the optimized repair plan through the terminal and finalize the repair schedule, which is then saved on the server and put into action.

[1128] Input: Optimized repair plan

[1129] Output: Confirmed repair schedule

[1130] Example 3

[1131] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1132] In conventional repair work management systems, the creation of repair plans, progress management, and ordering support are often done manually, resulting in inefficiencies. Furthermore, because processes such as automatic plan updates using sensor data, the creation of requests for proposals for contractors, Q&A sessions, and the evaluation of the validity of estimates are not integrated, overall management is cumbersome. This can lead to delays in the progress of repair work and increased costs.

[1133] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1134] In this invention, the server includes means for inputting basic information such as total floor area, age, and building location, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan schedule management to ordering, means for using a database for managing the progress of repair work, means for acquiring and analyzing data from sensors in the building, means for automatically updating the repair plan, means for creating a request for proposal 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 for repair work, from planning to progress management, ordering, and estimate evaluation.

[1135] "Total floor area" means the total floor area of ​​each floor of a building.

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

[1137] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[1138] "Basic information" refers to basic data about the building, such as total floor area, age, and building specifications.

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

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

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

[1142] "Database" refers to the system for storing the progress of repair works and other related information.

[1143] "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 validity of the estimated amount" refers to evaluating whether the amount of the estimate submitted by the contractor is appropriate.

[1147] MODE FOR CARRYING OUT THE INVENTION

[1148] This invention is a system for efficiently carrying out repair work planning, progress management, ordering, and estimate evaluation. Specific embodiments of this system will be described below.

[1149] System configuration

[1150] The server uses the following hardware and software to implement the system.

[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 through which a user accesses 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, age, and building location. This allows the user to input basic data about the building. Next, the server has a means for automatically creating a repair plan based on this basic information. This allows the user to efficiently create a repair plan.

[1158] The server provides support for everything from planning process management to ordering. Specifically, it uses a database to manage the progress of repair work, updating the progress of each stage of the work in real time. The server also has the means to collect and analyze data from sensors in the building. This means that if a sensor detects deterioration in part of the building, the repair plan can be automatically updated based on that information.

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

[1160] Specific examples

[1161] For example, if a user is planning repairs to a building and an IoT sensor detects that part of the building is aging, the server can automatically update the repair plan and assist with the necessary ordering.

[1162] Example prompt sentence:

[1163] "Describe a system that automatically updates building repair plans based on data from IoT sensors and assists with placing the necessary orders."

[1164] This system enables users to check the progress of repair work in real time and automatically places the necessary orders, thereby achieving efficient construction management. The flow of the specific processing in the third embodiment will be described with reference to FIG. 15.

[1165] Step 1:

[1166] Enter basic information

[1167] The user uses a terminal to input basic information such as total floor area, age, and building location. The server receives this input data and stores it in a database. The input data is used as the basic data required to develop a repair plan. 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] Repair plan planning

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

[1171] Step 3:

[1172] Progress Management

[1173] The server uses a database to manage the progress of the repair work. Users can check the progress using their terminals. The input data is information about the progress of the work, and the output data is a progress report. Specifically, the server periodically updates the database and provides the user with the latest progress information.

[1174] Step 4:

[1175] Sensor data acquisition and analysis

[1176] The server acquires data from sensors in the building and analyzes it in real time. The input data is the environmental data acquired from the sensors, and the output data is the analysis results. Specifically, the server acquires data using the sensor management API and runs an algorithm to detect anomalies.

[1177] Step 5:

[1178] Update repair plan

[1179] The server automatically updates the repair plan based on the results of the sensor data analysis. The input data is the analysis result of the sensor data, and the output data is the updated repair plan. Specifically, if the server detects an abnormality, it recalculates the repair plan based on that information and updates the database.

[1180] Step 6:

[1181] Request for proposal preparation and Q&A

[1182] The server automatically creates a request for proposal (RFP) based on the details of the repair work. The user can use a terminal to review the RFP and modify it as necessary. The input data is the detailed information of the repair work, and the output data is the RFP. Specifically, the server uses the generative AI model to generate the RFP 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 quote from the vendor, and the output data is the evaluation result. Specifically, the server evaluates whether the quote amount is reasonable based on past data and notifies the user.

[1186] (Application example 3)

[1187] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1188] Conventional repair work management systems supported repair plan creation, process management, and ordering operations based on basic information, but they had difficulty responding to real-time changes in the factory's situation. Furthermore, creating RFPs for contractors, answering questions, and evaluating the validity of estimates required manual processes, requiring efficient operation. This resulted in problems such as delays in updating repair plans and insufficient evaluation of estimates.

[1189] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, and building location, means for automatically formulating repair plans based on the basic information, means for supporting everything from plan process management to ordering, means for collecting data from various sensors within the factory, means for analyzing the collected data in real time and identifying areas requiring repair, means for automatically updating repair plans based on the analysis results, means for automatically generating an RFP based on details of the repair work, means for automatically responding to questions from contractors, and means for evaluating submitted estimates and determining their validity. This allows for efficient updating of repair plans and contractor support in response to real-time changes in the factory situation.

[1191] "Total floor area" means the total floor area of ​​each floor of a building.

[1192] "Age" refers to the number of years that have passed since the building was constructed.

[1193] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[1194] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[1195] A "repair plan" is a plan that lays out the content and schedule of repair work on a building.

[1196] "Process management" means managing the progress of repair work.

[1197] "Ordering operations" refers to the operations of ordering materials and services required for repair work.

[1198] A "sensor" is a device that detects physical phenomena and outputs them as data.

[1199] "Data collection" means gathering information from sensors, etc.

[1200] "Real-time analysis" means analyzing collected data immediately.

[1201] An "RFP" is a request for proposal that details the repair work.

[1202] The "automatic response function" is a function that automatically responds to questions from businesses.

[1203] "Quotation evaluation" refers to the process of evaluating the content of submitted quotations and determining their validity.

[1204] A system for carrying out this invention has the following configuration: The server includes means for inputting basic information such as total floor area, age of building, and location, means for automatically formulating repair plans based on the basic information, means for supporting processes from plan process management to ordering, means for collecting data from various sensors within the factory, means for analyzing the collected data in real time and identifying areas requiring repair, means for automatically updating the repair plan based on the analysis results, means for automatically generating an RFP based on details of the repair work, means for automatically responding to questions from contractors, and means for evaluating submitted estimates and determining their validity.

[1205] Program processing explanation

[1206] The server uses Python to collect data from various sensors. Specifically, it uses the requests library to obtain data from the sensor API. The collected data is analyzed in real time to identify areas that require repair due to abnormal vibrations or aging. This analysis uses data analysis libraries (e.g., Pandas, NumPy). The repair plan is automatically updated based on the identified repair areas. 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 question-and-answer data that has been trained in advance. The validity of submitted estimates is judged using an evaluation algorithm. A machine learning model (e.g., Scikit-learn) is used for this evaluation.

[1208] Specific examples

[1209] For example, if a vibration sensor in a factory detects abnormal vibrations, the data is collected in real time and the area requiring repair is automatically added to the repair plan. At this time, the server obtains the data from the sensor API and detects the abnormality using a data analysis library. If an abnormality is detected, the repair plan is automatically updated and an RFP is generated. The generative AI model automatically responds to questions from contractors, and submitted estimates are evaluated using a machine learning model.

[1210] Prompt Sentence Examples

[1211] "Write a Python program that collects vibration data from IoT sensors in a factory and automatically updates a repair plan if it detects abnormal vibrations."

[1212] In this way, it becomes possible to respond to real-time changes in the situation within the factory, and to efficiently update repair plans and respond to contractors.

[1213] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1214] Step 1:

[1215] The server collects data from various sensors in the factory. Specifically, it sends a request 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 aging. The input is the data obtained from the sensors, and the output is the result of whether an abnormality 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 results, 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 an automatic response based on pre-trained question-and-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 emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1227] "Example 1"

[1228] In one embodiment of the present invention, a means for inputting basic building information such as total floor area, age, and building size is provided. This information may be input directly by the building manager or obtained from an existing building management system. An emotion engine that recognizes the user's emotions is also incorporated. This emotion engine recognizes emotions from the user's tone of voice and facial expressions, for example.

[1229] "Example 2"

[1230] Another embodiment of the present invention provides a means for automatically creating a repair plan based on the basic information. Specifically, a repair plan is automatically created based on the building's basic information and a general repair schedule. An emotion engine that recognizes the user's emotions is also incorporated. This emotion engine recognizes emotions from, for example, the user's tone of voice or facial expression.

[1231] "Example 3"

[1232] In a further embodiment of the present invention, a means for supporting everything from planning process management to ordering is provided. Specifically, the progress of repair work is managed and ordering is supported as needed. An emotion engine that recognizes the user's emotions is also incorporated. This emotion engine recognizes emotions from the user's tone of voice and facial expressions, for example.

[1233] The processing flow of each embodiment will be described below.

[1234] "Example 1"

[1235] Step 1: Enter basic building information such as total floor area, age, and building location. This information is entered directly by the building manager, for example.

[1236] Step 2: Obtain basic information from your existing building management system.

[1237] Step 3: Activate the emotion engine that recognizes the user's emotions. This emotion engine recognizes emotions from the user's tone of voice and facial expressions, for example.

[1238] "Example 2"

[1239] Step 1: Automatically generate a repair plan based on basic building information and a general repair schedule.

[1240] Step 2: Save the generated repair plan and update it as needed.

[1241] Step 3: Activate the emotion engine that recognizes the user's emotions. This emotion engine recognizes emotions from the user's tone of voice and facial expressions, for example.

[1242] "Example 3"

[1243] Step 1: Manage the progress of the repair work. Specifically, check the progress of each step and update the repair work plan as necessary.

[1244] Step 2: Assist with ordering as needed. Specifically, place orders with appropriate contractors based on the repair work plan.

[1245] Step 3: Activate the emotion engine that recognizes the user's emotions. This emotion engine recognizes emotions from the user's tone of voice and facial expressions, for example.

[1246] Example 1

[1247] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1248] Conventional building management systems can create repair plans based on basic building information and support schedule management and ordering operations, but they are unable to provide feedback that takes user emotions into account. This makes it difficult to properly understand user satisfaction and dissatisfaction and reflect this in management operations. Furthermore, there is no way to integrate and analyze emotional data, making it impossible to respond appropriately based on user emotions.

[1249] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1250] In this invention, the server includes means for inputting basic information such as total floor area, age, and condition, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan process management to ordering, means for generating emotion data from the user's tone of voice and facial expression using an emotion engine that recognizes the user's emotions, means for integrating and analyzing the generated emotion data with the basic information, and means for feeding back 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 or dissatisfaction, and reflect this in management operations.

[1251] "Total floor area" refers to the total floor area of ​​each floor of a building.

[1252] "Age" refers to the number of years that have passed since the building was constructed.

[1253] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[1254] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[1255] A "repair plan" refers to the schedule and procedures for systematically repairing and maintaining a building.

[1256] "Process control" refers to the process of managing the progress of work based on the repair plan.

[1257] "Ordering operations" refers to the operations of ordering materials and services required for repairs and maintenance from external vendors.

[1258] An "emotion engine" refers to software or algorithms that recognize emotions from a user's tone of voice and facial expressions.

[1259] "Emotion data" refers to data relating to a user's emotions generated by the emotion engine.

[1260] "Analysis" refers to the process of synthesizing collected data and extracting meaningful information.

[1261] "Feedback" refers to the process of providing analysis results to users and communicating areas for improvement and evaluation.

[1262] This invention is a system that inputs basic information about a building and recognizes and analyzes the user's emotions. A specific embodiment of this system will be described below.

[1263] System configuration

[1264] The system consists of three main components: a server, terminals, and users. The server plays a central role in storing data, analyzing it, and providing feedback. Terminals provide an interface for users to input information and record voice. Users are building managers and building users who provide information to the system.

[1265] Hardware and software used

[1266] Server: Database management system (MySQL, PostgreSQL), data analysis software (Python, R), emotion engine (IBM Watson, Google Cloud Speech-to-Text)

[1267] Device: A computer or smart device with a microphone

[1268] Users: Building managers, building users

[1269] Entering and saving data

[1270] Users log in to the system using a terminal and enter basic building information, such as total floor area, age, and building size, into an input form. The server receives this information and stores it in a database.

[1271] Emotion Recognition and Analysis

[1272] When a user talks about the building's maintenance status, the device records the user's voice. The recorded voice data is sent to the server, which then analyzes the voice data using an emotion engine. 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 emotion data. Data analysis software such as Python and R is used for the analysis. The analysis results are provided as feedback to building managers. For example, feedback such as "There are many complaints about the cleaning situation" may be provided via email or dashboard.

[1275] Specific examples

[1276] For example, when a building manager wants to enter information about a new building into the system, he or she follows these steps:

[1277] 1. The building manager accesses the system's input screen and enters basic information such as total floor area, age of the building, and building location.

[1278] 2. The server stores the entered information in a 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 emotion data, and provides feedback to the building manager.

[1281] Prompt Sentence Examples

[1282] Examples of prompts to be input to a generative AI model include:

[1283] "The building has a total floor area of ​​5,000 square meters and is 10 years old. Recognize the user's emotions by analyzing the tone of voice and facial expressions of the building manager when he talks about the building's maintenance status."

[1284] Using this prompt, the generative AI model can integrate and analyze basic building information and the user's emotional data to provide appropriate feedback.

[1285] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1286] Step 1:

[1287] A user logs in to the system.

[1288] Input: User ID and password

[1289] How it works: A user accesses the system using a dedicated terminal or a web browser and enters their user ID and password on the login screen.

[1290] Output: If authentication is successful, the user can access the main screen of the system.

[1291] Step 2:

[1292] The user enters basic building information.

[1293] Input: Basic information such as total floor area, age, and building size

[1294] Operation: The user enters information such as the total floor area, age, and building age into the system's input form. For example, the user enters the total floor area as "5,000 square meters" and the age as "10 years."

[1295] Output: The basic information entered is sent to the server.

[1296] Step 3:

[1297] The server stores the entered information in a database.

[1298] Input: Basic information entered by the user

[1299] How it works: The server takes the basic building information entered by the user and stores it in a database using a database management system such as MySQL or PostgreSQL.

[1300] Output: Basic information is saved in the database.

[1301] Step 4:

[1302] The user talks about the building's management situation.

[1303] Input: User's voice

[1304] How it works: A user uses a device with a microphone to talk about the building's maintenance status. For example, they might say, "The building's cleaning status is poor."

[1305] Output: The audio data is recorded to the device.

[1306] Step 5:

[1307] The device records the user's voice.

[1308] Input: User's voice

[1309] How it works: The device records the user's voice and sends the audio data to the server.

[1310] Output: The audio data is sent to the server.

[1311] Step 6:

[1312] The server analyzes the voice data using an emotion engine.

[1313] Input: Audio data

[1314] How it works: The server uses an emotion engine (e.g., IBM Watson or Google Cloud Speech-to-Text) to analyze the user's tone of voice and facial expressions to generate emotion data. For example, it can recognize "dissatisfaction" from the user's tone of voice.

[1315] Output: Emotion data is generated.

[1316] Step 7:

[1317] The server integrates basic building information with emotional data.

[1318] Input: Basic information, emotion data

[1319] How it works: The server retrieves basic building information from the database and integrates it with emotion data obtained from the emotion engine.

[1320] Output: Consolidated data is generated.

[1321] Step 8:

[1322] The server analyzes the aggregated data.

[1323] Input: Integrated data

[1324] How it works: The server uses data analysis software such as Python or R to analyze the integrated data. For example, it can identify user dissatisfaction with the building's cleaning status and analyze the causes of this.

[1325] Output: Analysis results are generated.

[1326] Step 9:

[1327] The server provides the analysis results as feedback to the building manager.

[1328] Input: Analysis results

[1329] Operation: The server notifies the building manager of the analysis results, for example by email or via the dashboard, providing feedback such as "There are many complaints about the cleaning situation."

[1330] Output: Feedback is provided to building management.

[1331] (Application example 1)

[1332] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1333] Conventional building management systems allow users to input basic information such as total floor area, age, and building condition to create repair plans, but do not optimize the work environment by taking into account the emotional state of workers. As a result, they are unable to adequately manage worker stress or improve work efficiency. Furthermore, because it is difficult to respond in real time based on emotional state, improvements to worker health and the work environment can be delayed.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1335] In this invention, the server includes a means for inputting basic information such as total floor area, age of building, and building condition, a means for automatically formulating a repair plan based on the basic information, a means for supporting processes from plan process management to ordering, a means for monitoring the emotional state of a worker using an emotion engine that recognizes the user's emotions, and a means for optimizing the work environment based on the emotional state. This makes it possible to grasp the emotional state of a worker in real time and take appropriate measures, thereby optimizing the work environment and managing the health of the worker.

[1336] "Total floor area" means the total floor area of ​​each floor of a building.

[1337] "Age" refers to the number of years that have passed since the building was constructed.

[1338] "Intelligence" refers to information regarding the intelligent management and operation of a building.

[1339] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[1340] A "repair plan" is a schedule and procedures for systematically repairing and maintaining a building.

[1341] "Process control" is the process of managing the progress of work based on a repair plan.

[1342] "Ordering operations" refers to the operations of ordering materials and services required for repairs and maintenance.

[1343] The "emotion engine" is a technology that recognizes emotions from the user's tone of voice and facial expressions.

[1344] "Workers" are people who repair and maintain buildings.

[1345] "Monitoring" means to continuously watch for a particular condition or situation.

[1346] "Work environment" refers to the location and conditions under which workers work.

[1347] "Optimization" means making something the most effective for a particular purpose.

[1348] As an embodiment of the present invention, a smart factory management application will be described as an example. The server includes a means for inputting basic information such as total floor area, age of building, and intelligence, a means for automatically formulating a repair plan based on the basic information, a means for supporting everything from process management of the plan to ordering, a means for monitoring the emotional state of workers using an emotion engine that recognizes user emotions, and a means for optimizing the work environment based on the emotional state.

[1349] Program processing explanation

[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 can be done by the building manager entering data such as total floor area, age, and location, or by obtaining it from an existing building management system. Next, it automatically creates a repair plan based on this basic information. This plan includes the repair schedule and procedures.

[1354] Additionally, the server supports planning and ordering, including tracking the progress of repair projects and ordering the necessary materials and services.

[1355] To recognize the user's emotions, the server uses an emotion engine that analyzes image data acquired from the camera and recognizes emotions from the user's tone of voice and facial expressions. This allows the emotional state of the worker to be monitored in real time.

[1356] Based on the emotional state, the server optimizes the working environment. For example, if the worker is feeling stressed, the server will take appropriate action to improve the working environment.

[1357] Specific examples

[1358] As a concrete example, consider the stress management of workers in a factory. If a worker is feeling stressed, the emotion engine will detect this and notify the manager, allowing the manager to respond quickly and improve the working environment.

[1359] Example prompts to input to the generative AI model

[1360] Design a system to monitor the emotions of factory workers in real time and notify managers if they are feeling stressed. Basic building information (total floor area, age, etc.) will also be entered.

[1361] In this way, by understanding the emotional state of the worker in real time and taking appropriate measures, it becomes possible to optimize the work environment and manage the worker's health.

[1362] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1363] Step 1:

[1364] The server inputs basic building information, such as total floor area, age, and building location, either through the building manager or by obtaining this data from an existing building management system. The input data is stored in a database on the server.

[1365] Step 2:

[1366] The server automatically creates a repair plan based on the basic information entered. Specifically, it analyzes data such as total floor area and age of the building to identify areas and timing of repairs. The repair plan is generated, including schedules and procedures, and is saved on the server.

[1367] Step 3:

[1368] The server manages the repair plan process. It monitors the progress of work based on the repair plan in real time and supports the ordering of necessary materials and services. Progress and ordering information are recorded in a database within the server.

[1369] Step 4:

[1370] The server acquires image data of workers using cameras. The cameras are installed in the work area 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 captured image data and identifies the worker's emotion from their tone of voice and facial expression. The emotional state is recorded in a database within the server.

[1373] Step 6:

[1374] The server optimizes the work environment based on the worker's emotional state. For example, if a worker is feeling stressed, the server will send a notification to the manager and prompt them to take appropriate action. The content of the notification and the response history are stored in a database on the server.

[1375] Step 7:

[1376] The server consolidates all data and uses it to improve the working environment and manage worker health. The consolidated data is displayed on a dashboard accessible to managers, enabling real-time monitoring and analysis.

[1377] Example 2

[1378] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1379] Conventional repair planning systems can create repair plans based on basic building information, but they have issues with presenting plans that take user emotions into account and automatically generating optimal repair schedules. Furthermore, they lack support for planning process management and ordering, placing a heavy burden on users.

[1380] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1381] In this invention, the server includes a means for inputting basic information such as total floor area, age of building, and building condition, a means for automatically formulating a repair plan based on the basic information, a means for supporting everything from plan process management to ordering, a means for analyzing user emotions using an emotion engine that recognizes user emotions, and a means for calculating an optimal repair schedule using a generative AI model. This makes it possible to automatically generate an optimal repair plan that takes user emotions into consideration, and to streamline plan process management and ordering.

[1382] "Total floor area" is the sum of the floor areas of each floor of a building, and is an indicator of the size of the entire building.

[1383] "Age" refers to the number of years that have passed since the building was constructed, and is important information for assessing the deterioration of a building and the need for repairs.

[1384] "Intellect" refers to knowledge and information about the design and structure of a building, and serves as basic data when formulating repair plans.

[1385] "Basic information" is a general term for basic data about a building, such as total floor area, age, and building specifications.

[1386] A "repair plan" is a document that specifies the content and schedule of building repair work, and serves as a guideline for carrying out repairs efficiently.

[1387] "Process management" refers to management activities to ensure that each stage of repair work proceeds as planned, and is necessary to ensure the progress and quality of the work.

[1388] "Ordering operations" refers to the work of ordering materials and services necessary for carrying out repair work from external contractors, and is essential for the smooth implementation of the work.

[1389] An "emotion engine" refers to software or hardware that analyzes a user's tone of voice and facial expressions to recognize emotions.

[1390] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to analyze data and automatically generate an optimal repair schedule.

[1391] An "optimal repair schedule" refers to a schedule that allows repair work to be carried out most effectively and efficiently based on the condition and usage of the building.

[1392] This invention is a system that automatically creates a repair plan based on basic information about a building and provides an optimal repair schedule that takes into account the user's emotions. Specific embodiments of this system will be described below.

[1393] System configuration

[1394] This system consists of three main components: a server, a terminal, and a user. The server is responsible for collecting, analyzing, and storing data, and generating repair plans, while the terminal functions as an interface with the user. The user is responsible for entering basic building information and checking repair plans.

[1395] Hardware and software 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 with a camera and microphone.

[1398] Database: Data storage for storing basic building information and general schedules of repair works.

[1399] Generative AI model: An artificial intelligence algorithm for calculating optimal repair schedules.

[1400] Emotion engine: Software that recognizes emotions by analyzing the user's tone of voice and facial expressions.

[1401] Program processing

[1402] The server stores basic building information (e.g., total floor area, age, etc.) in a database and generates a repair plan based on a general repair schedule. Specifically, the server performs the following steps:

[1403] 1. Data collection: The server receives the basic building information sent from the terminal and stores it in a database.

[1404] 2. Data analysis: The server analyzes the stored basic information and the general schedule of repair work to identify where and when repairs are needed.

[1405] 3. Plan generation: The server uses the generative AI model to calculate the optimal repair schedule and generate a repair plan.

[1406] The device captures the user's tone of voice and facial expressions through a camera and microphone and sends them to the emotion engine, which analyzes this data and recognizes the user's emotions.

[1407] Specific examples

[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 the following repair plan:

[1409] Exterior wall inspection and repair in 2020

[1410] Inspection of internal facilities in 2025

[1411] Exterior wall inspection and repairs to be carried out again in 2030

[1412] Example prompt sentence:

[1413] "Generate a repair plan based on a typical repair work schedule for a building built in 2000, made of reinforced concrete, and made of concrete and steel."

[1414] This system saves users the trouble of creating repair plans and provides support for carrying out repair work efficiently.In addition, by taking into account the user's emotions, it is possible to provide a service that provides greater satisfaction.

[1415] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1416] Step 1:

[1417] Enter basic information

[1418] The user enters basic building information such as "Year built: 2000," "Structure: reinforced concrete," and "Materials used: concrete, steel" into the input form on the terminal.

[1419] Input: Basic information such as year of construction, structure, materials used, etc.

[1420] Output: Sending basic information from the device to the server.

[1421] Specific operation: When the user enters the necessary information into the input form on the terminal and presses the "Send" button, the terminal sends the entered information to the server.

[1422] Step 2:

[1423] Data collection and storage

[1424] The server receives the 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 stores the received basic information in the "Building Information" table of the database, and also retrieves the general schedule of repair work from the "Repair Schedule" table.

[1428] Step 3:

[1429] Generate repair plans

[1430] The server generates a repair plan based on basic information stored in the database and a general schedule of repair work.

[1431] Input: Basic information and repair schedule stored in the database.

[1432] Output: The generated repair plan.

[1433] Specific operation: The server inputs the prompt statement "Generate a repair plan based on a typical repair work schedule for a building constructed in 2000, with reinforced concrete and concrete and steel materials" into the generative AI model, and calculates the optimal repair schedule. The calculation results are compiled as a repair plan and saved in the "repair plan" table in the database.

[1434] Step 4:

[1435] Performing emotion recognition

[1436] The user interacts with the system through the device's camera and microphone.

[1437] Input: The user's tone of voice and facial expression.

[1438] Output: Parsed user sentiment.

[1439] Specific operation: The device captures the user's tone of voice and facial expressions and sends them to the emotion engine, which analyzes this data and recognizes the user's emotions.

[1440] Step 5:

[1441] Presentation of repair plan

[1442] The server transmits the generated repair plan to the terminal.

[1443] Input: The 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 checks the displayed repair plan and presses the "Approve" button to confirm the plan.

[1446] In this way, the system efficiently plans repairs and provides optimal information while taking into account the user's feelings.

[1447] (Application example 2)

[1448] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1449] Conventional repair planning systems can automatically create repair plans for building and factory equipment, but they lack the ability to make timely proposals that take user emotions into account and to monitor the progress of repair plans in real time. This can lead to a decrease in user satisfaction with the execution of repair plans. Furthermore, because plans are updated manually, it is difficult to maintain repair plans that are up to date with the latest conditions. To solve these issues, a system is needed that recognizes user emotions, proposes repair plans at the appropriate time, and monitors progress in real time.

[1450] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, and building condition, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan process management to ordering, 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 taking 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 basic data about the building and its facilities, such as total floor area, age, and location.

[1453] A "repair plan" is a plan for the schedule and content of repair work on buildings and facilities.

[1454] "Process management" is a means of managing the progress of repair work and ensuring that it proceeds as planned.

[1455] "Ordering work" refers to the work of ordering materials and services required for repair work from external contractors.

[1456] The "emotion engine" is a technology that recognizes emotions from the user's tone of voice and facial expressions.

[1457] The "right time" refers to the most effective time depending on the user's emotions and situation.

[1458] "Progress" 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] To implement this invention, the following system configuration is required: The server includes means for inputting basic information such as total floor area, age of building, and building location, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan process management to ordering, 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: Implement repair plans and monitor 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 to implement the program.

[1468] Emotion recognition library: Recognizes user emotions using OpenCV, librosa, etc.

[1469] Database: Used to manage basic information and repair plan data.

[1470] Data processing and calculation

[1471] The server first inputs basic information into a database and then automatically creates a repair plan based on this information. It then uses an emotion engine to recognize emotions from the user's tone of voice and facial expressions, and suggests repair plans at the appropriate time. Furthermore, it uses factory robots to monitor the progress of the repair plan in real time and record the results in the database.

[1472] Specific examples

[1473] For example, the equipment "Machine A" was last repaired on January 1, 2023, and the repair interval is 180 days. The next repair date is June 30, 2023. The server uses this information to create a repair plan, recognizes the user's emotions, and proposes this plan at the appropriate time. Factory robots also perform the repair work, monitoring its progress in real time.

[1474] Prompt Sentence Examples

[1475] "Recognize the user's emotions from their tone of voice and facial expressions, and suggest a maintenance plan at the appropriate time. For example, if the user says, 'Please tell me the maintenance plan for Machine A,' calculate the next maintenance date and suggest it."

[1476] In this way, it becomes possible to propose repair plans at appropriate times that take into account the user's emotions, and the progress of the repair plans can be grasped in real time, thereby improving user satisfaction.

[1477] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1478] Step 1:

[1479] The server inputs basic information such as total floor area, age, and location into a database.

[1480] Input: Basic information such as total floor area, age, and building size

[1481] Data processing: Save basic information to the database

[1482] Output: Basic information stored in the database

[1483] Step 2:

[1484] The server automatically creates a repair plan based on the basic information.

[1485] Input: Basic information stored in the database

[1486] Data calculation: Calculate the repair schedule based on basic information

[1487] Output: Automatically generated repair plan

[1488] Step 3:

[1489] The server supports everything from planning process management to ordering operations.

[1490] Input: Auto-generated repair plan

[1491] Data processing: Managing the repair planning process and ordering the necessary materials and services

[1492] Output: Order processing status

[1493] Step 4:

[1494] The server uses an emotion engine that recognizes the user's emotions to propose repair plans at appropriate times.

[1495] Input: User's tone of voice and facial expressions

[1496] Data Computation: Analyzing user emotions using an emotion engine

[1497] Output: Proposed repair plan based on user's sentiment

[1498] Step 5:

[1499] The server monitors the progress of the repair plan in real time.

[1500] Input: Repair plan progress data

[1501] Data processing: Real-time updates of progress and recording in a database

[1502] Output: Real-time updated progress data

[1503] Step 6:

[1504] The user checks the repair plan proposed by the server and makes any necessary corrections.

[1505] Input: Repair plan proposed by the server

[1506] Data manipulation: Modifying repair plans based on user feedback

[1507] Output: Revised repair plan

[1508] Step 7:

[1509] The factory robot carries out the repair work based on the repair plan.

[1510] Input: Revised repair plan

[1511] Data calculation: Calculate the repair work procedure and start the work

[1512] Output: Results of repair work performed

[1513] Step 8:

[1514] The server records the results of the repair work in a database and reflects them in the next repair plan.

[1515] Input: Results of repair work performed

[1516] Data processing: Save the results of repair work in a database and reflect them in 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 repair plans.

[1519] Example 3

[1520] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1521] In conventional repair work management systems, the creation of repair plans based on basic information, process management, and ordering support are often performed manually, resulting in inefficiency. It is also difficult to respond quickly to changes in the building's condition, which can lead to delays in updating repair plans. Furthermore, creating requests for proposals for contractors, answering questions, and evaluating the validity of estimated costs are all performed manually, which takes time and effort. Feedback that takes user emotions into account is not provided, resulting in poor user experience.

[1522] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1523] In this invention, the server includes: a means for inputting basic information such as total floor area, age, and building location; a means for automatically creating a repair plan based on the basic information; a means for supporting everything from schedule management of the plan to ordering; a means for collecting data from sensors installed in the building; a means for analyzing the collected data; a means for automatically updating the repair plan based on the analysis results; a means for automatically generating a request for proposal based on detailed repair work information; a means for automatically responding to questions from contractors; a means for evaluating estimates submitted by contractors and determining their validity; a means for collecting the user's tone of voice and facial expression; a means for analyzing the collected data to recognize the user's emotions; and a means for providing appropriate feedback based on the analysis results. This enables efficient repair work planning, schedule management, and ordering, and allows for rapid response to changes in the building's condition. Furthermore, the creation of a request for proposal for contractors, question response, and evaluation of the validity of estimates are automated, reducing time and effort. Furthermore, feedback that takes the user's emotions into consideration is provided, improving the user experience.

[1524] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[1525] A "repair plan" is a plan for efficiently carrying out repair work on a building, and includes the areas to be repaired and the construction schedule.

[1526] "Process control" is the process of monitoring the progress of repair work and making adjustments to ensure that it proceeds according to plan.

[1527] "Ordering work" refers to the work of ordering materials and services required 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 analytics" is the process of analyzing collected data to detect outliers and patterns.

[1530] A "Request for Proposal (RFP)" is a document that lists detailed information about repair work and requests proposals from contractors.

[1531] "Question answering" is the process of providing appropriate answers to questions from businesses.

[1532] "Evaluating the validity of an estimate" is the process of determining whether an estimate submitted by a contractor is appropriate.

[1533] "Emotion recognition" is a technology that analyzes the user's tone of voice and facial expressions to identify the user's emotions.

[1534] "Feedback" refers to advice or information provided to the user based on the analysis results.

[1535] MODE FOR CARRYING OUT THE INVENTION

[1536] This invention is a system for efficiently carrying out repair work planning, process management, and ordering operations. Specific embodiments of this system will be described below.

[1537] Enter basic information and create a repair plan

[1538] The server provides a means for inputting basic information such as total floor area, age, and building location. Based on this information, the server automatically creates a repair plan. The software used is a data analysis tool such as Python or R.

[1539] Sensor-based data collection and analysis

[1540] The server collects data from sensors installed in the building. The hardware used is 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, sensors collect vibration data from parts of a building, and if abnormal vibrations are detected, repairs to that part are added to the plan.

[1542] Automatic generation of requests for proposals and question answering

[1543] The server automatically generates a request for proposal (RFP) based on the details of the repair work, using software such as Microsoft Word or Google Docs API. Additionally, the server uses a generative AI model (e.g., GPT-3) to automatically respond to questions from contractors.

[1544] For example, an RFP is generated based on the details of the repair work and sent to the contractor. After receiving questions from the contractor, the generative AI model is used to generate appropriate answers and send them back to the contractor.

[1545] Evaluating the validity of the estimated amount

[1546] The server uses a price comparison algorithm to evaluate quotes submitted by vendors and determine their reasonableness, comparing submitted quotes with historical data to determine whether they are unusually high or low.

[1547] Emotion recognition and feedback provision

[1548] The device collects the user's tone of voice and facial expressions through a camera or microphone. The hardware used is, for example, a webcam or microphone. The server uses an emotion recognition engine (e.g., 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] For example, a camera captures the user's facial expressions and a microphone records the tone of voice. The collected facial expression data is analyzed by DeepFace, and the tone of voice is analyzed by a voice analysis library. If the user is feeling stressed, advice on how to relax is provided.

[1550] Prompt Sentence Examples

[1551] "Generate a program that analyzes the building's vibration data and updates the repair plan if anomalies are detected."

[1552] "Generate an RFP based on detailed information about the repair work, and create a program that automatically responds to questions from contractors."

[1553] "Generate a program that recognizes emotions by analyzing the user's tone of voice and facial expressions."

[1554] The above is a specific embodiment for carrying out the present invention. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1555] Step 1:

[1556] The server provides a means for users to input basic information such as total floor area, age, and location. The input basic information is stored in a database.

[1557] What it does: The user enters basic information through a web interface, and the server stores that information in a database.

[1558] Input: Basic information such as total floor area, age, and building size

[1559] Output: Basic information stored in the database

[1560] Step 2:

[1561] The server automatically creates a repair plan based on the basic information entered, and uses data analysis tools (such as Python or R) to calculate repair locations and construction schedules.

[1562] What it does: Runs a Python script to parse 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 in the building, using IoT hardware such as Raspberry Pi and Arduino.

[1567] How it works: 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 and runs an algorithm to detect outliers.

[1572] What it does: It uses 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 abnormality is detected, the server adds the repair of that part to the plan and saves the updated plan in the database.

[1577] Specific operation: The location where the abnormality was detected is added to the repair plan and the database is updated.

[1578] Input: Analysis results

[1579] Output: Updated repair plan

[1580] Step 6:

[1581] The server automatically generates a request for proposal (RFP) based on the details of the repair work, using software such as Microsoft Word or Google Docs API.

[1582] Specific operation: Fill in the detailed information of the repair work into the template and generate the RFP document.

[1583] Input: Detailed information about repair work

[1584] Output: Auto-generated RFP document

[1585] Step 7:

[1586] The server uses a generative AI model (e.g., GPT-3) to automatically respond to questions from merchants.

[1587] What it does: Receives a question from a merchant, uses a generative AI model to generate an appropriate answer, and sends it back to the merchant.

[1588] Input: Question from 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] What it does: Compare submitted estimates to historical data to determine whether they are unusually high or low.

[1593] Input: Quote submitted by vendor

[1594] Output: Estimate validity evaluation result

[1595] Step 9:

[1596] The device collects the user's tone of voice and facial expressions through a camera or microphone, such as a webcam or microphone.

[1597] What it does: The camera captures your facial expressions and the microphone records your tone of voice.

[1598] Input: User's tone of voice, facial expression data

[1599] Output: Collected speech and facial expression data

[1600] Step 10:

[1601] The server uses an emotion recognition engine (e.g., OpenCV or DeepFace) to analyze the collected data.

[1602] Specific operation: The collected facial expression data is analyzed using DeepFace, and the tone of voice is analyzed using a voice analysis library.

[1603] Input: Collected voice 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] What it does: If the user is feeling stressed, it offers advice on how to relax.

[1608] Input: Emotion recognition results

[1609] Output: Feedback provided

[1610] (Application example 3)

[1611] Next, a description will be given of 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 glasses 214 will be referred to as a "terminal."

[1612] In conventional repair management systems, planning repair plans, updating, and ordering support are often done manually, resulting in inefficiency. It is also difficult to detect the deterioration of factory equipment in real time and respond quickly. Furthermore, creating requests for proposals for contractors and assessing the validity of estimated costs are also time-consuming. There is a need to solve these problems and achieve efficient and effective repair management.

[1613] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1614] In this invention, the server includes a means for inputting basic information such as total floor area, age, and building location, a means for automatically formulating a repair plan based on the basic information, a means for supporting processes from plan management to ordering, a means for acquiring sensor data within the factory and automatically updating the repair plan, a means for creating a request for proposal for contractors, a means for answering questions, and a means for evaluating the validity of estimated costs, and a means for recognizing the user's emotions and providing appropriate feedback. This enables automatic updating of repair plans, streamlining of ordering, smoother communication with contractors, and support according to the user's emotions.

[1615] "Total floor area" is the sum of the floor areas of each floor of a building, and is an indicator of the size of the entire building.

[1616] "Age" indicates the number of years that have passed since the building was constructed, and is important information for determining the degree of deterioration of the building.

[1617] "Intellect" refers to knowledge and information related to the management and operation of buildings, and is part of the basic information required for planning and updating repair plans.

[1618] "Basic information" refers to information about the building that is necessary for formulating a repair plan, and includes the total floor area, age, and building specifications.

[1619] A "repair plan" indicates the schedule and content for carrying out planned repairs to buildings and facilities.

[1620] "Plan process management" is a means of managing the progress of repair work based on the repair plan and ensuring that it proceeds appropriately.

[1621] "Ordering work" refers to the work of ordering materials and services required to carry out repair work from contractors.

[1622] "Sensor data" is data obtained from sensors within a factory, and is information used to understand the state of equipment and changes in the environment in real time.

[1623] A "request for proposal" is a document that presents the work content and conditions to contractors who will carry out the repair work and requests their proposals.

[1624] "Question and answer" is the process of answering questions from vendors based on the request for proposal and providing necessary information.

[1625] "Evaluating the validity of an estimate" is the process of evaluating whether the estimate submitted by a contractor is appropriate.

[1626] The "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 or information provided based on the user's emotions and situation.

[1628] A system for carrying out this invention has the following configuration: The server includes means for inputting basic information such as total floor area, age of building, and location, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan process management 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 means for evaluating the validity of questions and estimates, and means for recognizing the user's emotions and providing appropriate feedback.

[1629] Hardware and software used

[1630] Hardware: Smartphones, head-mounted displays, IoT sensors

[1631] Software: Python, EmotionRecognition library, IoTSensorData library, RFPGenerator library, EstimateEvaluator library

[1632] Data processing and 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 changes in the environment in real time. Next, based on the acquired sensor data, it identifies areas that need repair and automatically updates repair plans. This makes it possible to quickly detect equipment deterioration and develop appropriate repair plans.

[1634] The server then creates a request for proposal for contractors, answers questions, and evaluates the appropriateness of the estimated cost. This allows for efficient selection of a contractor to carry out the repair work. The server also uses an emotion engine that recognizes the user's emotions, recognizing their tone of voice and facial expressions and providing appropriate feedback. This reduces user stress and enables smooth communication.

[1635] Specific examples

[1636] Example 1

[1637] IoT sensors can detect when a robot in a factory is aging and automatically add it to a repair plan, preventing robot breakdowns and maintaining factory production efficiency.

[1638] Example 2

[1639] Managers can use their smartphones to send requests for proposals to contractors and evaluate the validity of quotes, optimizing repair costs and ensuring repairs are completed within budget.

[1640] Example 3

[1641] The technician uses a head-mounted display, and the emotion engine recognizes the technician's emotions while performing repair work and provides appropriate feedback, thereby reducing the technician's stress and improving work efficiency.

[1642] Prompt Sentence Examples

[1643] Develop an application that automatically updates robot repair plans and assists with ordering as needed based on data obtained from IoT sensors in the factory. Also, incorporate functionality to recognize user emotions and provide appropriate feedback.

[1644] In this way, a system can be realized that streamlines repair management of factory robots and provides support that responds to the user's emotions.

[1645] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1646] Step 1:

[1647] The server acquires sensor data from IoT sensors in the factory. The sensor data contains information about the state of the equipment and changes in the environment. By acquiring this data, it is possible to grasp equipment deterioration and abnormalities 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 and identifies areas that need repair. Specifically, it analyzes the sensor data and detects abnormal values ​​and signs of aging. The input is the acquired sensor data, and the output is a list of areas that need 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 priority of the repairs, and the materials and work required for the repairs. The input is a list of areas that need repairs, and the output is an updated repair plan.

[1652] Step 4:

[1653] The server creates a request for proposal for contractors based on the updated repair plan. The request for proposal includes details of the repair, the required materials, and the work conditions. The input is the updated repair plan, and the output is the request for proposal.

[1654] Step 5:

[1655] The server sends a request for proposal to the vendor and conducts a question-and-answer session. It provides appropriate answers to the vendor's questions and shares the necessary information. The input is the request for proposal and the vendor's questions, and the output is the answers and the shared information.

[1656] Step 6:

[1657] The server evaluates quotes submitted by contractors and determines their validity. It analyzes the quote contents and evaluates whether the cost and work content are appropriate. The input is the quote from the contractor, 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 provided feedback.

[1660] Step 8:

[1661] The user progresses the repair work based on the provided feedback. The work is adjusted according to the feedback to perform the repair 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1663] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1664] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1665] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1666] [Third embodiment]

[1667] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1668] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1669] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1670] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1671] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1673] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1674] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1675] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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 a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1677] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1678] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1679] "Example 1"

[1680] This embodiment of the present invention includes a means for inputting basic information about a building, such as total floor area, age, and location. This information is input directly by the building manager or obtained from an existing building management system.

[1681] "Example 2"

[1682] Furthermore, this embodiment of the present invention includes a means for automatically creating a repair plan based on the basic information. Specifically, the repair plan is automatically created based on the basic information of the building and a general schedule of repair work.

[1683] "Example 3"

[1684] Furthermore, the present invention is provided with a means for supporting everything from planning process management to ordering operations. Specifically, the progress of repair work is managed and ordering operations are supported as necessary.

[1685] "Example 4"

[1686] Furthermore, this embodiment of the present invention includes a means for automatically updating the plan based on IoT sensor data, using basic information as a base. Specifically, the system analyzes data acquired from IoT sensors in the building and automatically updates the repair plan. For example, if an IoT sensor detects that a part of the building is aging, the system adds repairs to that part of the building.

[1687] "Example 5"

[1688] Furthermore, this embodiment of the present invention is equipped with a means for creating an RFP for contractors, answering questions, and evaluating the appropriateness of the estimated cost when carrying out construction work. Specifically, the system 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 processing flow of each embodiment will be described below.

[1690] "Example 1"

[1691] Step 1: Enter basic building information, such as total floor area, age, and location. This information can be entered directly by the building manager or obtained from an existing building management system.

[1692] "Example 2"

[1693] Step 1: Enter basic building information.

[1694] Step 2: Automatically create a repair plan based on the basic information above. Specifically, a repair plan is automatically generated based on the building's basic information and the general repair schedule.

[1695] "Example 3"

[1696] Step 1: Create a repair plan.

[1697] Step 2: Manage the planning process.

[1698] Step 3: Assist with ordering as needed.

[1699] "Example 4"

[1700] Step 1: Enter basic building information and create a repair plan.

[1701] Step 2: Acquire data from IoT sensors in the building.

[1702] Step 3: Analyze the acquired data and automatically update the repair plan. For example, if IoT sensors detect that part of the building is aging, add repairs to that part to the plan.

[1703] "Example 5"

[1704] Step 1: Create an RFP based on the details of the repair work.

[1705] Step 2: Respond to the vendor's questions.

[1706] Step 3: Evaluate the quotes submitted by the vendors and determine their validity.

[1707] Example 1

[1708] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1709] Conventional building management systems have the problem that it takes a lot of time and effort to manually input basic building information and create repair plans. Also, retrieving information from existing management systems and formatting data is cumbersome, making it difficult to create efficient plans. Furthermore, the generation of prompts using generative AI models is not automated, placing a heavy burden on users.

[1710] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1711] In this invention, the server includes means for inputting basic information such as total floor area, age, and building location, means for receiving the basic information and saving it in a database, means for acquiring additional information from an existing management system, means for formatting the acquired information and converting it into a required format, means for inputting prompt text to a generative AI model, means for automatically formulating a repair plan, and means for supporting everything from plan process management to ordering operations. This makes it possible to consistently automate the process from inputting basic building information to formulating a repair plan and generating prompt text using a generative AI model.

[1712] "Total floor area" means the total area of ​​the floor area of ​​each floor of a building.

[1713] "Age" refers to the number of years that have passed since the 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 basic data such as the building's total floor area, age, and location.

[1716] A "database" is a system for systematically organizing and storing information.

[1717] A "management system" is a general term for software and hardware used to operate and maintain a building.

[1718] "Additional information" is data about the building 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 to make it easier to use.

[1720] A "generative AI model" is a model that uses artificial intelligence to perform specific tasks.

[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 content of building repairs and maintenance.

[1723] "Process management" refers to managing each process in the execution of a repair plan.

[1724] "Ordering operations" refers to the operations of ordering materials and services required for repairs and maintenance.

[1725] A "request for proposal" is a document that requests specific proposals from contractors.

[1726] "Question and answer session" refers to the process of answering questions from vendors based on a request for proposal.

[1727] "Evaluating the validity of the estimated amount" refers to evaluating whether the estimated amount submitted by the contractor is appropriate.

[1728] This invention is a system that inputs basic building information, formulates a repair plan, and generates prompt sentences using a generative AI model. Specific embodiments of this system are described below.

[1729] First, a 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 about the building, such as its total floor area, age, and cost. For example, a user might enter "Total floor area: 5,000 square meters," "Age: 10 years," and "Cost: high."

[1730] The server then receives the basic building information entered by the user, stores it in a database server (e.g., MySQL, PostgreSQL), and runs SQL queries against the database to insert the information into the appropriate tables.

[1731] Additionally, the server obtains additional building information through the API of the existing management system, such as the building's energy consumption data and maintenance history. The server sends API requests and receives the data returned as a response.

[1732] The server then formats the additional information it receives and converts it into the required format, for example by parsing the JSON data and extracting and formatting the required fields. The server performs this processing using a data processing script (e.g. Python, JavaScript).

[1733] Finally, the server generates a prompt sentence to be input into the generative AI model based on the formatted information. For example, it generates a prompt sentence such as, "Please enter basic information about a building that has a total floor area of ​​5,000 square meters, is 10 years old, and is highly intelligent." The server inputs this prompt sentence into the generative AI model and receives a response from the model.

[1734] As a concrete example, the following prompt sentence can be input to a generative AI model:

[1735] Example prompt sentence:

[1736] "Please enter the basic information of a building with a total floor area of ​​5,000 square meters, age of 10 years, and high intelligence."

[1737] This system makes it possible to automate everything from inputting basic building information to formulating repair plans and even generating prompts using a generative AI model, thereby reducing the burden on users and enabling efficient building management.

[1738] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1739] Step 1:

[1740] The user enters basic building information.

[1741] A user accesses a web application provided by a server using a device such as a PC or tablet. They open a web browser and enter basic information about the building, such as its total floor area, age, and cost. The entered information is then sent to the server. Specifically, the user enters "Total floor area: 5,000 square meters," "Age: 10 years," and "Cost: high" into the form and clicks the submit button.

[1742] Input: Basic information such as total floor area, age, and location of the building

[1743] Output: Basic information sent to the server

[1744] Step 2:

[1745] The server receives the entered information and stores it in a database.

[1746] The server receives the basic building information entered by the user. This information is stored in a database server (e.g., MySQL, PostgreSQL). The server runs an SQL query against the database to insert the information into the appropriate table. Specifically, the server executes the SQL query "INSERT INTO buildings (area, age, intelligence) VALUES (5000, 10, 'tall');".

[1747] Input: Basic information submitted by the user

[1748] Output: Basic information stored in the database

[1749] Step 3:

[1750] The server obtains additional information from the existing management system.

[1751] The server obtains additional building information through the API of the existing management system. For example, this includes the building's energy consumption data and maintenance history. The server sends an API request and receives the data returned as a response. Specifically, the server sends the API request "GET / buildings / 12345 / additional_info".

[1752] Input: API request

[1753] Output: Additional information obtained

[1754] Step 4:

[1755] The server formats the information it receives and converts it into the required format.

[1756] The server formats the additional information it receives and converts it into the required format. For example, it parses the JSON format data, extracts the required fields, and formats them. The server performs this processing using a data processing script (e.g., Python or JavaScript). Specifically, the server executes "json.loads(response_data)" to extract the required fields.

[1757] Input: Additional information obtained

[1758] Output: Formatted data

[1759] Step 5:

[1760] The server inputs a prompt sentence into the generative AI model.

[1761] The server generates a prompt sentence to be input into the generative AI model based on the formatted information. For example, it generates a prompt sentence such as, "Please enter the basic information of a building that has a total floor area of ​​5,000 square meters, is 10 years old, and is highly intelligent." The server inputs this prompt sentence into the generative AI model and receives a response from the model. Specifically, the server executes the process of "inputting a prompt sentence into the generative AI model and receiving a response."

[1762] Input: Formatted data

[1763] Output: The response from the generative AI model

[1764] (Application example 1)

[1765] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal."

[1766] Conventional building management systems supported the creation of repair plans, process management, and ordering operations based on basic building information, but lacked the ability to assess security risks, generate real-time security alerts, or propose specific security measures. This made it difficult to comprehensively manage building safety, and a rapid response to security risks was required.

[1767] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, and building location, means for automatically formulating a repair plan based on the basic information, means for supporting processes from plan schedule management 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 makes it possible to support repair plan planning, schedule management, and ordering based on the basic building information, as well as evaluate security risks, generate real-time security alerts, and propose specific security measures.

[1769] "Total floor area" means the total floor area of ​​each floor of a building.

[1770] "Age" refers to the number of years that have passed since the building was constructed.

[1771] "Intelligence" refers to information about the building's location and surrounding environment.

[1772] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[1773] A "repair plan" is a plan that shows the schedule and content for systematically repairing and maintaining a building.

[1774] "Process management" refers to managing the progress of work based on the repair plan.

[1775] "Ordering operations" refers to the operations of ordering materials and services required 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 notifies you immediately when a security risk occurs.

[1778] "Security measures" are specific means or methods for reducing 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, age of building, and building location, and then creates a repair plan based on this information, supporting process management and ordering operations. It also adds functions to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[1780] The server provides a means to input basic information such as total floor area, age, and building location. This basic information can be entered directly by the building manager or obtained from an existing building management system. Based on the input basic information, the server automatically creates a repair plan and supports everything from planning process management to ordering operations.

[1781] Furthermore, the server is equipped with a means to assess security risks based on basic 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, allowing building managers to quickly implement appropriate security measures.

[1783] The hardware used includes devices such as smartphones and tablets, and the software uses Python, JSON, and datetime modules. This makes it possible to efficiently manage basic building information, evaluate security risks, and take appropriate measures.

[1784] For 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 evaluates the security risk and determines that it is high risk. A real-time security alert is generated and notified to the building manager. The server then proposes specific countermeasures, such as installing an advanced monitoring system or increasing security personnel.

[1785] Examples of prompts include:

[1786] "Enter basic building information: total floor area, age, and location."

[1787] In this way, in addition to supporting repair planning, process management, and ordering operations based on basic building information, it will also be possible to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[1788] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1789] Step 1:

[1790] The user uses a terminal to input basic information about the building (total floor area, age, location, etc.).

[1791] Input: Basic information such as total floor area, age, and building size

[1792] Output: The basic information entered is sent to the server.

[1793] Specific operation: The user opens the application on their smartphone or tablet and enters information such as the total floor area, age of the building, and location into the input form. Once the input is complete, the user presses the send button to send the information to the server.

[1794] Step 2:

[1795] The server stores the basic information received and automatically creates a repair plan.

[1796] Input: Basic information submitted by the user

[1797] Output: Repair plan results

[1798] Specific operation: The server stores the received basic information in a database, then automatically generates a repair plan based on the stored information and stores the plan contents in the database.

[1799] Step 3:

[1800] The server supports repair plan process management and ordering operations.

[1801] Input: Repair plan results

[1802] Output: Process control schedule and order list

[1803] Specific operation: Based on the repair plan, the server creates a schedule for each process and generates an order list for the necessary materials and services. This information is then notified to the building manager.

[1804] Step 4:

[1805] The server assesses the security risk based on basic information.

[1806] Input: Basic information submitted by the user

[1807] Output: Security risk assessment results

[1808] How it works: The server analyzes basic information and evaluates security risks, taking into account factors such as the building's age, total floor area, and location. The evaluation 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: If the risk score exceeds a certain threshold, the server generates a real-time security alert and notifies the building manager's terminal.

[1814] Step 6:

[1815] The server proposes specific security measures based on the results of the security risk assessment.

[1816] Input: Security risk assessment results

[1817] Output: Security measures proposal

[1818] Specific actions: Based on the risk score, the server proposes appropriate security measures, such as installing an advanced monitoring system or increasing security personnel. The proposal is then notified to the building manager.

[1819] In this way, in addition to supporting repair planning, process management, and ordering operations based on basic building information, it will also be possible to evaluate security risks, generate real-time security alerts, and propose specific security measures.

[1820] Example 2

[1821] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1822] Conventional repair planning systems require users to manually create repair plans based on basic building information, which is time-consuming and labor-intensive. Furthermore, when plans need to be updated or revised, this process is inefficient and often lacks accuracy. Furthermore, there was a lack of a way for users to easily check the generated repair plans.

[1823] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1824] In this invention, the server includes a means for inputting basic information such as total floor area, age, and building location, a means for automatically creating a repair plan based on the basic information, a means for supporting everything from plan process management to ordering, a means for generating a repair plan by sending a prompt to a generative AI model, a means for saving the generated repair plan in a database, and a means for a user to request a repair plan and display it on a terminal. This enables automatic generation and efficient management of repair plans.

[1825] "Total floor area" refers to the total floor area of ​​each floor of a building.

[1826] "Age" refers to the number of years that have passed since the building was constructed.

[1827] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[1828] "Basic information" refers to basic data about the building, such as total floor area, age, and location.

[1829] A "repair plan" refers to a plan that outlines the schedule and content of repair work on a building.

[1830] A "generative AI model" refers to a model for generating data using artificial intelligence.

[1831] A "prompt sentence" refers to an instruction sentence input 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 (such as a personal computer, tablet, or smartphone) that a user operates.

[1835] "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 operations of ordering materials and services necessary for repair work.

[1838] "RFP" stands for Request for Proposal, and refers to a document that requests proposals from contractors.

[1839] "Questions and Answers" refers to questions and answers regarding proposals and plans.

[1840] "Evaluating the reasonableness of the estimated amount" refers to evaluating whether the submitted estimate is appropriate.

[1841] This invention is a system for automatically planning and managing building repair plans. The system consists of three main elements: a server, terminals, and users.

[1842] Server Roles

[1843] The server receives basic information about the building and stores it in a database. This information includes the total floor area, age, and building size. Based on this information, the server sends prompts to the generative AI model to generate a repair plan. The generated repair plan is then stored back in the database.

[1844] The server uses the following hardware and software:

[1845] A high-performance database server (e.g., MySQL, PostgreSQL)

[1846] Generative AI models (e.g., GPT-4)

[1847] Device Role

[1848] The terminal provides an interface for users to input basic building information and check the repair plan. When a user operates the terminal to request a repair plan, the terminal sends the request to the server. After receiving the repair plan from the server, the terminal displays its contents to the user.

[1849] The terminal uses the following hardware and software:

[1850] PC, tablet, smartphone

[1851] Web browser (e.g. Google Chrome, Mozilla Firefox)

[1852] User Roles

[1853] The user uses a terminal to input basic building information. Once input is complete, the user requests the generation of a repair plan. The user can then review the generated repair plan and request corrections or additions as necessary.

[1854] Specific examples

[1855] A user enters the following information into a form on their device: "Structure: Reinforced concrete, Age: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago." The device sends this information to the server, which then sends the following prompt to the generative AI model:

[1856] "The basic information for the building is as follows: Structure: Reinforced concrete, Age: 20 years, Materials used: Concrete, Past repair history: Exterior wall repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[1857] The generative AI model generates a repair plan based on this prompt and sends it back to the server. The server stores the generated repair plan in a database and sends it to the device when the user requests it. The device then displays the received repair plan to the user.

[1858] In this way, users can easily plan and manage building repair plans.

[1859] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1860] Step 1:

[1861] The user enters basic building information.

[1862] The user inputs basic building information through the terminal interface. Specifically, the information entered includes total floor area, age, and building size. The input information is temporarily stored in the terminal's memory. An example of input data is "Structure: reinforced concrete, Age: 20 years, Materials used: concrete, Past repair history: Exterior wall repaired 5 years ago."

[1863] Step 2:

[1864] The terminal transmits the input information to the server.

[1865] The terminal sends the 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 analyzes. The input data is temporarily stored in the server's memory.

[1866] Step 3:

[1867] The server stores the 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. By storing the information in the database, it becomes possible to reference the information in later processing.

[1869] Step 4:

[1870] The server sends a prompt to the generative AI model.

[1871] The server generates a prompt sentence based on the saved basic information. The generated prompt sentence is sent to the generation AI model. An example of a prompt sentence is, "The basic information about the building is as follows: Structure: reinforced concrete, Age: 20 years, Materials used: concrete, Past repair history: Exterior wall repaired 5 years ago. Based on this, please create a repair plan for the next 10 years."

[1872] Step 5:

[1873] A generative AI model generates a repair plan.

[1874] The generative AI model generates a repair plan based on the received prompt. The generated repair plan is sent back to the server. An example of output data is a plan such as, "The next repairs will be to repaint the exterior walls in two years, followed by roof waterproofing work in five years."

[1875] Step 6:

[1876] The server stores the generated repair plan in a database.

[1877] The server stores the repair plan received from the generative AI model in a database, allowing users to review and modify the repair plan at a later time.

[1878] Step 7:

[1879] A user requests a repair plan.

[1880] The user uses the terminal to request the display of the repair plan by clicking the "Show repair plan" button in the terminal interface. The request is made using an HTTP GET request.

[1881] Step 8:

[1882] The device requests a repair plan from the server.

[1883] Upon receiving a user request, the terminal requests the server to retrieve a 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 is in JSON format, and the terminal receives and analyzes it.

[1887] Step 10:

[1888] The terminal displays the repair plan to the user.

[1889] The terminal displays the received repair plan to the user. Specifically, the details of the repair plan are displayed on a web browser. The user can check the displayed repair plan and make corrections or additions as necessary.

[1890] (Application example 2)

[1891] Next, a description will be given of 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 headset type terminal 314 will be referred to as a "terminal."

[1892] With conventional repair planning systems, it was difficult to automatically generate repair plans based on basic information about building or factory equipment, and it was not possible to monitor the progress of the plan in real time or update the plan as needed. This resulted in issues such as reduced accuracy and efficiency of repair plans and significant effort and cost required for equipment maintenance and management.

[1893] The specific processing by the specific processing unit 290 of the data processing device 12 in the 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, age, and building location, means for automatically formulating repair plans based on the basic information, means for supporting everything from plan process management to ordering, means for automatically generating repair plans for each facility based on the basic information about the facility and a general schedule for repair work, means for monitoring the progress of the repair plans in real time and updating the plans as necessary, and means for generating repair plans using a generative AI model. This makes it possible to improve the accuracy and efficiency of repair plans and reduce the effort and cost required for facility maintenance and management.

[1895] "Total floor area" is a term that refers to the total floor area of ​​each floor of a building.

[1896] "Age" refers to the number of years that have passed since the building was constructed.

[1897] "Intellect" 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 basic data about buildings and facilities, such as total floor area, age, and location.

[1899] "Repair plan" is a term that refers to planning the schedule and content of repair work on 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 work of ordering materials and services required for repair work from external contractors.

[1902] "Facility" is a term that refers to machines and devices installed in buildings such as factories and buildings.

[1903] "Repair work" is a term that refers to work to repair or improve breakdowns or deterioration of buildings or equipment.

[1904] "General schedule" is a term that refers to a standard schedule that is generally applied in repair work.

[1905] "Automatic generation" is a term that refers to the system automatically creating plans and data without human intervention.

[1906] "Progress" is a term that refers to information that indicates how far along the repair work plan is.

[1907] "Real-time" is a term that refers to reflecting the current situation immediately.

[1908] "Generative AI model" is a term used to describe a model that uses artificial intelligence to analyze data and generate plans and predictions.

[1909] The system for carrying out this invention operates in cooperation with three parties: a server, a terminal, and a user. The server includes a means for inputting basic information such as total floor area, age, and location, a means for automatically formulating a repair plan based on the basic information, a means for supporting everything from plan process management to ordering, a means for automatically generating a repair plan for each facility based on basic information about the facility and a general schedule for repair work, a means for monitoring the progress of the repair plan in real time and updating the plan as necessary, and a means for generating a repair plan using a generative AI model.

[1910] The server implements programs using programming languages ​​such as Python to input data, analyze it, generate plans, and monitor progress. Specifically, the server acquires basic information about the equipment and calculates the next repair date based on the general schedule of repair work. The calculation results are output in JSON format so that the user can check them. The server also monitors progress in real time and updates the plan as necessary.

[1911] The terminal provides an interface for users to access the server, input basic information, check repair plans, etc. The terminal communicates with the server through a web browser or dedicated application, allowing users to input and retrieve the necessary information.

[1912] Users access the server through their devices and enter basic information about the equipment. The entered information is sent to the server, which then automatically generates a repair plan based on that information. The user can then check the generated repair plan through their device. They can also monitor progress in real time and update the plan as needed.

[1913] As a specific example, assume that equipment 1 was installed on January 1, 2020, is frequently used, and has previously been repaired on January 1, 2021 and January 1, 2022. In this case, the next repair date is January 1, 2023. The user enters this information into the terminal, and the server calculates the next repair date based on that and generates a plan.

[1914] An example of a prompt to input to a generative AI model is as follows:

[1915] Calculate the next maintenance date based on basic facility information and the general schedule of maintenance work. Below is an example of input data.

[1916] Facility basic information:

[1917] {

[1918] "Facility 1": {"Installation date": "2020-01-01", "Frequency of use": "High", "Past repair history": ["2021-01-01", "2022-01-01"]},

[1919] "Facility 2": {"Installation Date": "2019-01-01", "Frequency of Use": "Medium", "Past Repair History": ["2020-01-01"]}

[1920] }

[1921] Typical timeline for repair work:

[1922] {

[1923] "High": {"Period": 1},

[1924] "medium": {"period": 2},

[1925] "Low": {"Period": 3}

[1926] }

[1927] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1928] Step 1:

[1929] The user accesses the server through a terminal and inputs basic information about the facility (total floor area, age, location, installation date, frequency of use, past repair history, etc.). The input information is sent to the server.

[1930] Input: Basic information of the facility

[1931] Output: Basic information data sent to the server

[1932] Step 2:

[1933] The server then refers to the general schedule of repair work based on the received basic information and calculates the next repair date. Specifically, it applies a repair cycle based on the frequency of use and calculates the next repair date based on 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 maintenance plan based on the calculated next maintenance date. The generated maintenance plan is saved in JSON format and can be viewed by the user.

[1938] Input: Next repair date

[1939] Output: Repair plan (JSON format)

[1940] Step 4:

[1941] The user can access the server through their terminal to check the generated repair plan and modify or update the contents of the repair plan as needed.

[1942] Input: Repair plan (JSON format)

[1943] Output: Modified and updated repair plan

[1944] Step 5:

[1945] The server monitors the progress of the repair plan in real time, using IoT sensor data to monitor the condition of the equipment and ensure that repairs are progressing as planned, and automatically updates the plan as needed.

[1946] Input: IoT sensor data, repair plan

[1947] Output: Updated repair plan

[1948] Step 6:

[1949] The server uses the generative AI model to optimize the repair plan, specifically by analyzing past data and current conditions and proposing the optimal repair schedule.

[1950] Input: Basic information data, repair plan, IoT sensor data

[1951] Output: Optimized repair plan

[1952] Step 7:

[1953] The user can check the optimized repair plan through their device and finalize the repair schedule, which is then saved on the server and put into action.

[1954] Input: Optimized repair plan

[1955] Output: Confirmed repair schedule

[1956] Example 3

[1957] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1958] In conventional repair work management systems, the creation of repair plans, progress management, and ordering support are often done manually, resulting in inefficiencies. Furthermore, because processes such as automatic plan updates using sensor data, the creation of requests for proposals for contractors, Q&A sessions, and the evaluation of the validity of estimates are not integrated, overall management is cumbersome. This can lead to delays in the progress of repair work and increased costs.

[1959] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1960] In this invention, the server includes means for inputting basic information such as total floor area, age, and building location, means for automatically formulating a repair plan based on the basic information, means for supporting everything from plan schedule management to ordering, means for using a database for managing the progress of repair work, means for acquiring and analyzing data from sensors in the building, means for automatically updating the repair plan, means for creating a request for proposal 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 for repair work, from planning to progress management, ordering, and estimate evaluation.

[1961] "Total floor area" means the total floor area of ​​each floor of a building.

[1962] "Age" refers to the number of years that have passed since the building was constructed.

[1963] "Intellect" refers to knowledge and information related to the management and operation of buildings.

[1964] "Basic information" refers to basic data about the building, such as total floor area, age, and building specifications.

[1965] A "repair plan" refers to planning the content and schedule of building repair work.

[1966] "Process management" refers to managing the progress of each stage of repair work.

[1967] "Ordering work" refers to the work of ordering materials and services necessary for repair work from contractors.

[1968] "Database" refers to the system for storing the progress of repair works and other related information.

[1969] "Sensor" refers to a device used to acquire environmental data (e.g., temperature, humidity) within a building.

[1970] A "Request for Proposal (RFP)" is a document that details the repair work and requests proposals from contractors.

[1971] "Question and answer session" refers to the process of answering questions from vendors.

[1972] "Evaluating the validity of the estimated amount" refers to evaluating whether the amount of the estimate submitted by the contractor is appropriate.

[1973] MODE FOR CARRYING OUT THE INVENTION

[1974] This invention is a system for efficiently carrying out repair work planning, progress management, ordering, and estimate evaluation. Specific embodiments ...

Claims

1. A means for inputting the total floor area, the age, and the rationale as basic information of the facility; A means for generating a prompt sentence to be input to a generative AI model based on the basic information and a general schedule of repair work, and inputting the prompt sentence to the generative AI model to generate a repair plan; means for monitoring the progress of said repair plan in real time and updating said repair plan as necessary; A system including:

2. The system according to claim 1 , wherein the means for updating the repair plan automatically updates the plan based on IoT sensor data while using the basic information as a base.

3. 2. The system according to claim 1, further comprising a means for preparing a request for proposal for a contractor, answering questions, and evaluating the validity of an estimated amount when carrying out construction work.

Images