system

Abstract

To provide a system.SOLUTION: A system for managing a fixed asset includes means for reading a QR code attached to the fixed asset, means for acquiring information of the fixed asset on the basis of the read QR code, means for transmitting information of the acquired fixed asset by an e-mail, means for aggregating information of the fixed asset transmitted by the e-mail, means for tracking the fixed asset on the basis of the aggregated information of the fixed asset, means for predicting and preventing loss from signs related to non-updating and non-operation of data of the fixed asset, and means for recognizing feelings of a user using the fixed asset and adjusting a method for managing the fixed asset on the basis of the feelings.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] In managing fixed assets, fixed assets may become inactive due to transfers, retirements, changes in duties, or the end of a project, and their whereabouts may become unknown due to subsequent circumstances. This can make fixed asset management difficult and may result in the loss of assets. [Means for solving the problem]

[0005] This invention reads QR codes (registered trademark) attached to fixed assets, acquires information about the fixed assets based on the read QR codes, and consolidates the acquired information about the fixed assets. Furthermore, it tracks the fixed assets based on the consolidated information about the fixed assets, and predicts and prevents loss from signs such as non-updating of fixed asset data or non-operation. This makes it easier to manage fixed assets and prevents asset loss. [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. 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] In one embodiment of the present invention, a QR code attached to a fixed asset is read using a smartphone camera. Information about the fixed asset is obtained based on the read QR code, and the obtained information about the fixed asset is sent via email via the smartphone. The transmitted information about the fixed asset is centrally aggregated. The fixed asset is tracked based on the aggregated information about the fixed asset. Loss is predicted based on signs of fixed asset data not being updated or not being used, and loss is prevented before it occurs.

[0029] "Example 2"

[0030] As a specific example, a company affixes a QR code to a computer, which is a fixed asset. This QR code contains information such as the computer's asset number, purchase date, and the person in charge. When the person in charge is transferred or leaves the company, the new person can read the QR code using their smartphone camera to obtain information about the fixed asset. The obtained information is sent via email via the smartphone and centrally aggregated. Based on the aggregated information, the fixed asset can be tracked to prevent loss.

[0031] The processing flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: Use your smartphone camera to read the QR code attached to the fixed asset.

[0034] Step 2: Obtain fixed asset information based on the scanned QR code.

[0035] Step 3: Information about the acquired fixed assets is sent via email via smartphone. Step 4: The sent information about the fixed assets is centrally consolidated.

[0036] Step 5: Track fixed assets based on the aggregated fixed asset information.

[0037] Step 6: Predict losses based on signs such as non-updating of fixed asset data or non-operation, and prevent them from occurring.

[0038] "Example 2"

[0039] Step 1: A QR code is attached to a computer, which is a fixed asset of a company. This QR code contains information such as the computer's asset number, purchase date, and person in charge.

[0040] Step 2: If the person in charge is transferred or resigns, the new person in charge will read the QR code using the smartphone camera.

[0041] Step 3: Obtain fixed asset information based on the scanned QR code.

[0042] Step 4: The information obtained is sent via email via your smartphone.

[0043] Step 5: The submitted information on fixed assets is centrally aggregated.

[0044] Step 6: Use the aggregated information to track fixed assets and prevent loss.

[0045] Example 1

[0046] 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."

[0047] In the area of ​​fixed asset management, it is difficult to efficiently acquire, consolidate, and track information on fixed assets. In addition, there is a lack of methods to predict and prevent loss based on signs such as inactivity or non-operation of fixed asset data.

[0048] 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.

[0049] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring fixed asset information based on the read QR codes, means for sending the acquired fixed asset information by email, means for aggregating the transmitted fixed asset information, means for tracking fixed assets based on the aggregated fixed asset information, and means for predicting and preventing loss based on signs such as non-updating of fixed asset data or non-operation. This allows for efficient management of fixed asset information and makes it possible to predict and prevent loss.

[0050] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[0051] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0052] A "mobile information terminal" is a portable information processing device such as a smartphone or tablet.

[0053] A "camera" is a device for taking images or videos.

[0054] "Email" is a means of communication for sending and receiving text and files over the Internet.

[0055] A "server" is a computer system that provides services to other computers on a network.

[0056] A "database" is a system for efficiently storing, searching, and managing data.

[0057] "Tracking" is the act of continuously monitoring the location and status of an object or information.

[0058] "Not updated" refers to a state in which data has not been updated for a certain period of time.

[0059] "Inoperation" refers to a state in which equipment or systems are not in use for a certain period of time.

[0060] "Loss prediction" is the act of predicting the possibility of loss of an item based on data analysis.

[0061] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and make predictions and classifications.

[0062] This invention is a system for efficiently managing fixed assets, and includes means for reading QR codes affixed to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for sending the acquired information about the fixed assets by email, means for aggregating the transmitted information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, and means for predicting loss based on signs such as non-updating of fixed asset data or non-operation, and for preventing loss before it occurs.

[0063] The user uses the smartphone camera to read the QR code attached to the fixed asset. Using a QR code reader app (e.g., QR Code Reader, Google (registered trademark) Lens), the user places the QR code within the camera's frame, and the app automatically scans the QR code and acquires the data.

[0064] The terminal sends the data from the QR code it reads to the server. The server accesses a database (e.g., MySQL (registered trademark), PostgreSQL) and retrieves information about the fixed asset corresponding to the QR code. The retrieved information includes the name, installation location, purchase date, current condition, etc. of the fixed asset.

[0065] The information about the fixed assets acquired by the terminal is sent by email. The email is sent using the SMTP protocol and an email service such as Gmail or Outlook. The body of the email contains detailed information about the fixed assets.

[0066] The server analyzes the received emails and aggregates the information about fixed assets into a centralized database. The aggregated data is then stored in a fixed asset management system (e.g., SAP, Oracle Asset Management).

[0067] The server tracks fixed assets based on the aggregated information on fixed assets. Tracking includes the movement history and current location information of fixed assets. The server periodically updates the database to keep the information up to date.

[0068] The server analyzes fixed asset data and detects signs of non-renewal, non-operation, etc. A generative AI model (e.g., TENSORFLOW (registered trademark), PyTorch) is used to predict loss and send alerts to administrators to prevent it from happening.

[0069] For example, a user uses their smartphone to scan a QR code attached to an office printer. The QR code contains the printer's ID, which the device sends to a server. The server retrieves the printer's details from a database, and the device sends the information to an administrator via email. The server aggregates the received information into a database and tracks printer usage. If the printer has not been used for a long period of time, a generative AI model is used to predict the possibility of loss and send an alert to the administrator.

[0070] Example prompt sentence:

[0071] "Scan the QR code attached to the office printer with your smartphone. The printer information will be acquired based on the scanned QR code and sent to you by email. The information will be aggregated on the server and used to track the printer and predict if it has been lost."

[0072] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0073] Step 1:

[0074] The user activates the smartphone camera and reads the QR code attached to the fixed asset using a QR code reader app (e.g., QR Code Reader, Google Lens). When the user holds the QR code within the camera's frame, the app automatically scans the QR code and acquires the data.

[0075] Input: QR code

[0076] Output: QR code data

[0077] Specific operation: The user opens the camera app on their smartphone and points the camera at a QR code. The app recognizes the QR code and reads the data.

[0078] Step 2:

[0079] The terminal sends the data from the QR code it reads to the server, which then accesses a database (e.g., MySQL, PostgreSQL) to retrieve information about the fixed asset corresponding to the QR code.

[0080] Input: QR code data

[0081] Output: Fixed asset information

[0082] What happens: The device sends the QR code data to the server, which queries the database to retrieve the fixed asset information.

[0083] Step 3:

[0084] The information about the fixed assets acquired by the terminal is sent by email. The email is sent using the SMTP protocol and an email service such as Gmail or Outlook. The body of the email contains detailed information about the fixed assets.

[0085] Input: Fixed asset information

[0086] Output: Email

[0087] Specific operation: The terminal inserts the fixed asset information into the email body, and then sends the email through the SMTP server.

[0088] Step 4:

[0089] The server analyzes the received emails and aggregates the information about fixed assets into a centralized database. The aggregated data is then stored in a fixed asset management system (e.g., SAP, Oracle Asset Management).

[0090] Input: Email

[0091] Output: Aggregated fixed asset information

[0092] Specific operation: The server analyzes the received email and saves the analysis results in a database.

[0093] Step 5:

[0094] The server tracks fixed assets based on the aggregated information on fixed assets. Tracking includes the movement history and current location information of fixed assets. The server periodically updates the database to keep the information up to date.

[0095] Input: Aggregated fixed asset information

[0096] Output: Fixed asset tracking information

[0097] Specific operation: The server records the fixed asset movement history in a database. The server updates the database periodically.

[0098] Step 6:

[0099] The server analyzes fixed asset data and detects signs of non-renewal, non-operation, etc. A generative AI model (e.g., TensorFlow, PyTorch) is used to predict loss and send alerts to administrators to prevent it from happening.

[0100] Input: Fixed asset tracking information

[0101] Output: Loss prediction alert

[0102] How it works: The server analyzes the data using the generated AI model. The server predicts the possibility of loss and generates an alert. The server sends the alert to an administrator.

[0103] (Application example 1)

[0104] 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."

[0105] With conventional fixed asset management systems, it was difficult to monitor the location and status of fixed assets in real time, which increased the risk of loss or non-operation, especially in large-scale factory asset management. Furthermore, the time-consuming task of manually aggregating fixed asset information made efficient management difficult.

[0106] 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.

[0107] In this invention, the server includes: means for reading QR codes affixed to fixed assets; means for acquiring information about the fixed assets based on the read QR codes; means for aggregating the acquired information about the fixed assets; means for tracking the fixed assets based on the aggregated information about the fixed assets; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; means installed on robots in the factory for transmitting information about the fixed assets in real time to the central management system; and means for monitoring the location and status of the fixed assets based on the information transmitted to the central management system. This makes it possible to monitor the location and status of fixed assets in the factory in real time and reduce the risk of loss or non-operation.

[0108] "Fixed assets" are physical assets that a business or organization holds for long-term use.

[0109] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0110] "Means of reading" refers to the equipment or technology used to decode the QR code and obtain its information.

[0111] "Means of obtaining information" refers to devices and technologies for collecting detailed information about fixed assets based on data read from QR codes.

[0112] "Means of aggregating information" refers to devices or technologies for centrally compiling information on acquired fixed assets.

[0113] "Tracking means" refers to devices and technologies that monitor the location and status of assets based on aggregated fixed asset information.

[0114] "Means for predicting and preventing loss" refers to devices and technologies that analyze signs of fixed asset data not being updated or not being operational, and predict and prevent the risk of loss.

[0115] A "factory robot" is an automated mechanical device used to perform tasks in a factory.

[0116] A "central management system" is a system for centrally managing and monitoring fixed asset information within a factory.

[0117] "Real-time transmission means" refers to devices or technologies for instantly transmitting fixed asset information to the central management system.

[0118] "Means for monitoring location and status" refers to devices and technologies for continuously monitoring the location and status of assets based on information about fixed assets sent to the central management system.

[0119] The present invention relates to a system for improving the efficiency of fixed asset management within a factory. A specific embodiment of this system will be described below.

[0120] First, QR codes are attached to fixed assets within the factory. The QR codes contain the identification information of the fixed assets. Robots within the factory are equipped with cameras and periodically patrol the factory, reading the QR codes attached to the fixed assets.

[0121] The robot uses the OpenCV library to decode the QR code and retrieve information, such as the asset's identification and location. The robot then transmits the information to a central management system in real time using wireless communication technologies such as Wi-Fi and Bluetooth.

[0122] The central management system centrally aggregates the received information on fixed assets. The aggregated information is stored in a database and used to monitor the location and status of fixed assets in real time. A relational database management system (RDBMS) such as MySQL or PostgreSQL is used for database management.

[0123] Furthermore, the central management system analyzes signs of fixed asset data inactivity and non-operation to predict loss, using machine learning algorithms such as the Python scikit-learn library to analyze fixed asset data and predict the risk of loss.

[0124] This system makes it possible to monitor the location and status of fixed assets within a factory in real time, reducing the risk of loss or non-operation. It also eliminates the need to manually collect information on fixed assets, enabling more efficient management.

[0125] For example, a robot in a factory periodically patrols the area and reads the QR codes attached to each machine and tool. The robot sends the read information to a central management system, which monitors the status of the assets in real time. If it detects signs of asset inactivity or loss, it sends an alert to the manager.

[0126] Example prompt for a generative AI model:

[0127] To streamline fixed asset management within the factory, develop an application that allows a robot to read QR codes and send asset information to a central management system. This application will have the function of monitoring the location and status of assets in real time, reducing the risk of loss or non-operation.

[0128] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0129] Step 1:

[0130] The robot patrols the factory and uses a camera to take pictures of QR codes attached to fixed assets.

[0131] Input: QR code attached to fixed assets in the factory

[0132] Output: QR code image data

[0133] Specific operation: The robot periodically moves around the factory and uses a camera to photograph QR codes affixed to fixed assets.

[0134] Step 2:

[0135] The robot uses the OpenCV library to decode the QR code and obtain the fixed asset's identifying information.

[0136] Input: QR code image data

[0137] Output: Fixed asset identification information (text data)

[0138] Specific operation: The robot inputs the image data of the captured QR code into the OpenCV library, decodes the QR code, and obtains the fixed asset identification information.

[0139] Step 3:

[0140] The robot transmits the identification information of fixed assets acquired in real time to a central management system.

[0141] Input: Fixed asset identification information (text data)

[0142] Output: Fixed asset identification information sent to the central management system

[0143] Specific operation: The robot uses wireless communication technologies such as Wi-Fi and Bluetooth to transmit the identification information of the acquired fixed assets to the central management system.

[0144] Step 4:

[0145] The central management system centrally collects the fixed asset information it receives and stores it in a database.

[0146] Input: Fixed asset identification information sent by the robot

[0147] Output: Fixed asset information stored in the database

[0148] Specific operation: The central management system stores the received fixed asset information in a relational database management system (RDBMS).

[0149] Step 5:

[0150] The central management system monitors the location and status of fixed assets in real time based on aggregated fixed asset information.

[0151] Input: Fixed asset information stored in the database

[0152] Output: Fixed asset location and status monitoring results

[0153] How it works: The central management system periodically checks the information stored in the database and monitors the location and status of fixed assets.

[0154] Step 6:

[0155] The central management system analyzes signs such as non-updating or non-operating fixed asset data and predicts losses.

[0156] Input: Fixed asset information stored in the database

[0157] Output: Loss prediction result

[0158] How it works: The central management system uses machine learning algorithms to analyze fixed asset data and predict the risk of loss.

[0159] Step 7:

[0160] The central management system sends an alert to the administrator based on the loss prediction results.

[0161] Input: Loss prediction result

[0162] Output: Alert notification to administrator

[0163] Specific operation: Based on the loss prediction results, the central management system will send an alert to the administrator via email, SMS, etc.

[0164] Example 2

[0165] 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."

[0166] In conventional fixed asset management systems, asset information was often updated and tracked manually, resulting in delays in updating information when personnel were transferred or retired. Furthermore, there were insufficient mechanisms for preventing the loss of fixed assets, making asset management cumbersome. This increased the risk of fixed assets being lost or misused.

[0167] 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.

[0168] In this invention, the server includes means for generating and printing QR codes, means for reading QR codes, means for sending acquired fixed asset information by email, means for analyzing received emails and saving the information in a database, and means for periodically checking the information in the database and issuing an alert if an abnormality is detected. This makes it possible to centrally manage fixed asset information, quickly update information when personnel are transferred or resign, and prevent the loss of fixed assets.

[0169] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[0170] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0171] "Reading means" refers to a device or software that scans the QR code and obtains its contents as digital data.

[0172] The "means of acquisition" refers to a device or software for extracting the necessary information from the contents of the scanned QR code.

[0173] "Aggregating means" refers to a device or software that stores and organizes acquired information in a database for centralized management.

[0174] A "tracking instrument" is a device or software that monitors and manages the location and status of fixed assets based on aggregated information.

[0175] "Means for predicting and preventing loss" refers to devices or software that detect signs of fixed asset data not being updated or not being operational, predict the risk of loss, and take measures to prevent it.

[0176] A "generating and printing means" is a device or software that converts fixed asset information into a QR code and physically prints the QR code.

[0177] The "means for sending by e-mail" is a device or software for sending the acquired information to the server via e-mail.

[0178] The "means for analyzing and storing in a database" refers to a device or software for analyzing the content of received e-mail and storing the information in a database.

[0179] An "alert issuing means" is a device or software that periodically checks the information in the database and notifies the administrator if an abnormality is detected.

[0180] This invention is a system for efficiently managing fixed assets owned by companies and organizations and preventing their loss. This system manages asset information using QR codes affixed to fixed assets.

[0181] The server hosts a fixed asset management system, and uses a Python script to convert each computer's asset information (asset number, purchase date, person in charge, etc.) into a QR code, which is then printed using a dedicated label printer and affixed to each computer.

[0182] The user (new person in charge) uses the smartphone camera to scan the QR code attached to the computer. The device (smartphone) reads the contents of the QR code using a dedicated app. The asset information obtained from the QR code is displayed on the smartphone app screen. The user checks the displayed information and enters the new person in charge information.

[0183] The acquired asset information is sent to the server via email via a smartphone app. The device checks the integrity of the information before sending it and converts it into the required format (e.g., JSON). The server parses the received email and uses a Python script to save the asset information to a database. The server compares it with existing data and updates the contact information as necessary.

[0184] The server tracks fixed assets based on information collected in a database. The server periodically runs a Python script to check asset information. If an abnormality is detected, the server sends an alert email to the administrator.

[0185] As a concrete example, the following scenario can be considered.

[0186] Person A in the IT department of a certain company is retiring. Person B is to take over his computer. Person B uses the camera on his smartphone to scan the QR code attached to the computer. The QR code contains the computer's asset number "PC-12345," the purchase date "2022-01-15," and information about the previous person in charge, "A." Person B then sends the scanned information to the server via email using an app on his smartphone. The server saves the received information in a database and updates the person in charge information to "B."

[0187] An example of a prompt sentence might be:

[0188] "How can I use my smartphone camera to scan the QR code attached to a company's fixed asset computer and send the acquired asset information to the server via email?"

[0189] In this way, information on fixed assets can be managed centrally, and information can be updated quickly when personnel are transferred or leave the company, making it possible to prevent the loss of fixed assets.

[0190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0191] Step 1: Generate and paste a QR code

[0192] The server uses the fixed asset management system to convert the asset information of each computer (asset number, purchase date, person in charge, etc.) into a QR code. Specifically, a Python script is used to encode the asset information into QR code format. The input is the asset information of each computer, and the output is a QR code image. The generated QR code is printed using a dedicated label printer and affixed to each computer.

[0193] Step 2: Scan the QR code

[0194] The user uses the camera on their smartphone to scan the QR code attached to their computer. The device (smartphone) then reads the contents of the QR code using a dedicated app. The input is the QR code image, and the output is the asset information extracted from the QR code. Specifically, the user launches the camera app on their smartphone and scans the QR code.

[0195] Step 3: Obtain asset information

[0196] The terminal (smartphone) displays the asset information obtained from the QR code. The input is the asset information extracted from the QR code, and the output is the asset information displayed on the smartphone screen. The user checks the displayed information and enters new contact information as necessary. Specifically, the user checks the asset information on the dedicated app screen and enters new contact information in the form.

[0197] Step 4: Submit asset information

[0198] The user sends asset information obtained through a smartphone app to the server by email. The device checks the integrity of the information before sending and converts it into the required format (e.g., JSON format). The input is the asset information, including the new contact information entered by the user, and the output is data in email format. Specifically, the user taps the "Send" button on the dedicated app.

[0199] Step 5: Aggregate and update asset information

[0200] The server parses the received email and saves the asset information in a database. The server compares it with existing data and updates the contact information as needed. The input is the asset information in email format, and the output is the updated asset information saved in the database. Specifically, the server uses a Python script to parse the email and save the information in the database.

[0201] Step 6: Track fixed assets

[0202] The server tracks fixed assets based on information aggregated in a database. The server periodically runs a Python script to check asset information. The input is the asset information stored in the database, and the output is an alert notification if an abnormality is detected. Specifically, the server periodically scans the database, and if an abnormality is detected, it sends an alert email to the administrator.

[0203] In this way, information on fixed assets can be managed centrally, and information can be updated quickly when personnel are transferred or leave the company, making it possible to prevent the loss of fixed assets.

[0204] (Application example 2)

[0205] 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."

[0206] The traditional method of managing machines and robots used in factories is time-consuming and difficult to do efficiently. Furthermore, due to insufficient tracking of machine maintenance history and personnel information, machines can be lost or improperly managed. This leads to issues such as reduced factory operational efficiency and increased costs.

[0207] 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.

[0208] In this invention, the server includes means for reading QR codes affixed to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, means for reading QR codes affixed to machines in the factory, means for sending the read machine information by email, and means for notifying the manager of the sent information. This allows for efficient management of machines and robots in the factory, makes it easy to track maintenance history and information about the person in charge, and makes it possible to prevent loss or improper management of machines.

[0209] "Fixed assets" are assets that a company or organization holds for long-term use, and include buildings, machinery, equipment, etc.

[0210] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0211] "Reading means" refers to a device or method for scanning a QR code and obtaining its contents as digital data.

[0212] "Means for acquiring information" refers to a device or method for extracting the necessary information from the data in the scanned QR code.

[0213] "Means for aggregating information" refers to a device or method for centrally organizing and managing acquired information.

[0214] A "tracking means" is a device or method for monitoring and managing the location and status of fixed assets based on aggregated information.

[0215] "Means for predicting and preventing loss" refers to devices and methods for analyzing signs such as non-updating of fixed asset data or non-operation, predicting the risk of loss, and taking measures.

[0216] "Machinery in a factory" refers to machinery and equipment such as production equipment and robots used in a factory.

[0217] "Means for sending by e-mail" refers to a device or method for sending the acquired information to a designated recipient via e-mail.

[0218] The "means for notifying the administrator" refers to a device or method for notifying the administrator of the transmitted information.

[0219] A system for carrying out this invention is designed to improve the efficiency of managing machines and robots in a factory. The system includes: means for reading QR codes affixed to fixed assets; means for acquiring information about the fixed assets based on the read QR codes; means for aggregating the acquired information about the fixed assets; means for tracking the fixed assets based on the aggregated information about the fixed assets; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; means for reading QR codes affixed to machines in a factory; means for sending the read information about the machines by email; and means for notifying an administrator of the sent information.

[0220] Hardware and software used

[0221] Hardware: Smartphone or tablet with camera

[0222] Software: Python, qrcode library (QR code generation), OpenCV (QR code reading), smtplib (email sending)

[0223] Data processing and calculation

[0224] 1. QR code generation: The server uses the qrcode library to generate a QR code containing the machine's asset information (asset number, purchase date, contact person, etc.).

[0225] 2. QR Code Reading: Using a smartphone camera, we read the QR code attached to a machine in the factory. We use OpenCV to extract data from the QR code.

[0226] 3. Information Acquisition and Aggregation: The smartphone processes the scanned data and extracts the necessary information. The extracted information is sent via email to a server where it is centrally aggregated.

[0227] 4. Information tracking and loss prediction: The server uses the collected information to monitor and manage the location and status of machines. It also analyzes signs of data non-update or non-operation, predicts the risk of loss, and takes appropriate measures.

[0228] 5. Notification: The transmitted information is sent to the administrator via email, allowing the administrator to understand the machine status in real time and take appropriate action.

[0229] Specific examples

[0230] For example, by attaching a QR code to a robot in a factory and having a maintenance person read the QR code with their smartphone, they can instantly obtain the robot's maintenance history and information about the person in charge and report it to the manager. This system allows for efficient management of machines and robots in a factory, makes it easy to track maintenance history and information about the person in charge, and makes it possible to prevent loss of machines or improper management.

[0231] Prompt Sentence Examples

[0232] Develop an application that reads the QR code attached to a robot in a factory with a smartphone, obtains the robot's asset information (asset number, purchase date, person in charge, maintenance history, etc.), and sends it to the manager by email.

[0233] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0234] Step 1:

[0235] The server uses the qrcode library to generate a QR code containing the machine's asset information (asset number, purchase date, contact person, etc.). The machine's asset information is required as input, and a QR code image is generated as output. This QR code is then affixed to the machine.

[0236] Step 2:

[0237] A user uses a smartphone camera to read a QR code attached to a machine in a factory. The QR code image is required as input, and the QR code data is obtained as output. This includes the specific actions of scanning a QR code using a smartphone camera app.

[0238] Step 3:

[0239] The device uses OpenCV to extract data from the QR code. The input is a QR code image, and the output is the asset information contained in the QR code. Specifically, the device analyzes the QR code image and decodes the data.

[0240] Step 4:

[0241] The device sends the extracted asset information to the server via email. The input requires the asset information extracted from the QR code, and the output is an email. Specific operations include creating an email using smtplib and sending it to the specified address.

[0242] Step 5:

[0243] The server aggregates asset information from received emails. The email content is required as input, and the aggregated asset information is obtained as output. Specifically, the server analyzes the email content and stores it in a database.

[0244] Step 6:

[0245] The server monitors and manages the location and status of the machines based on the aggregated information. The aggregated asset information is required as input, and the monitoring results of the machine's location and status are obtained as output. Specific operations include retrieving information from the database and inputting it into the monitoring system.

[0246] Step 7:

[0247] The server analyzes signs of data inactivity or non-operation, predicts the risk of loss, and takes countermeasures. Aggregated asset information is required as input, and the predicted risk of loss is obtained as output. Specific operations include using data analysis algorithms to assess risk and propose countermeasures.

[0248] Step 8:

[0249] The server notifies the administrator of the sent information by email. The input is the aggregated asset information, and the output is a notification to the administrator. Specific operations include creating a notification email using smtplib and sending it to the administrator.

[0250] 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.

[0251] "Example 1"

[0252] One embodiment of the present invention is a system that combines a fixed asset management system with an emotion engine. This system recognizes a user's emotions and adjusts the method of managing fixed assets based on those emotions. Specifically, the emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. For example, if the system recognizes that the user is stressed about losing a fixed asset, it takes measures such as strengthening the tracking of fixed assets. On the other hand, if the system recognizes that the user is indifferent to managing fixed assets, it takes measures such as automating the management of fixed assets. This enables optimal fixed asset management according to the user's emotions.

[0253] "Example 2"

[0254] Another embodiment of the present invention provides specific means for the emotion engine to recognize the user's emotion. Specifically, the user's facial expression is photographed with a camera, and the emotion is recognized from the image data. The user's voice is also recorded with a microphone, and the emotion is recognized from the voice data. Furthermore, if the user inputs text, the emotion is recognized from the text data. By combining these means, it is possible to recognize the user's emotion more accurately.

[0255] The processing flow of each embodiment will be described below.

[0256] "Example 1"

[0257] Step 1: The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. Step 2: Based on the emotions recognized by the emotion engine, the method of managing fixed assets is adjusted. For example, if the system recognizes that the user is feeling stressed about losing a fixed asset, it will take measures such as strengthening tracking of the fixed asset.

[0258] Step 3: If the system recognizes that the user is uninterested in managing fixed assets, it will take measures such as automating the management of fixed assets. This will enable optimal fixed asset management according to the user's feelings.

[0259] "Example 2"

[0260] Step 1: The user's facial expression is captured by a camera, and the emotion engine recognizes the emotion from the image data.

[0261] Step 2: The user's voice is recorded with a microphone, and the emotion engine recognizes emotions from the voice data.

[0262] Step 3: When the user enters text, the emotion engine recognizes the emotion from the text data. By combining these methods, it is possible to recognize the user's emotion more accurately.

[0263] Example 1

[0264] 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."

[0265] Conventional fixed asset management systems require manual input of fixed asset information, making management cumbersome. It is also difficult to detect early signs of loss or non-operation of fixed assets, making it difficult to take appropriate measures. Furthermore, management methods are not adjusted to take into account user emotions, resulting in insufficient support for user stress and apathy. To solve these issues, a system that automatically acquires, aggregates, and tracks fixed asset information is needed.

[0266] 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.

[0267] In this invention, the server includes: means for reading QR codes attached to fixed assets; means for acquiring fixed asset information based on the read QR codes; means for sending the acquired fixed asset information by email; means for aggregating the transmitted fixed asset information; means for tracking fixed assets based on the aggregated fixed asset information; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; and means for recognizing user emotions and adjusting the fixed asset management method based on the emotions. This enables automatic acquisition, aggregation, and tracking of fixed asset information, enabling early detection of signs of loss or non-operation and appropriate countermeasures. It also enables optimal fixed asset management based on user emotions.

[0268] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[0269] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0270] A "means of reading" is a device or software that analyzes the QR code and obtains the information contained within it.

[0271] The "means of obtaining information" refers to devices or software for collecting detailed information about fixed assets based on data read from QR codes.

[0272] The "means for sending by e-mail" refers to a device or software for sending information about the acquired fixed assets to a specified address via e-mail.

[0273] The "means for aggregating information" refers to a device or software for centrally collecting and managing the transmitted information on fixed assets.

[0274] "Tracking means" refers to devices or software that monitor the location and usage of fixed assets based on aggregated fixed asset information.

[0275] "Means to predict and prevent loss" refers to devices and software that detect signs of fixed asset data not being updated or not being used, predict the possibility of loss, and take measures to prevent it.

[0276] "Means for recognizing emotions" refers to devices or software that analyze emotions from a user's facial expressions, voice, text input, etc.

[0277] The "means for adjusting the management method" is a device or software for optimizing the management method of fixed assets based on the recognized user's emotions.

[0278] The present invention provides a system for improving the efficiency of fixed asset management and reducing the risk of loss or non-operation. Specific embodiments of this system will be described below.

[0279] System configuration

[0280] 1. Scan the QR code

[0281] The user uses the camera of a mobile information terminal (e.g., a smartphone) to read the QR code attached to the fixed asset.

[0282] The terminal uses a QR code reader app (e.g., QR Code Reader) to analyze the QR code and obtain information about the fixed asset.

[0283] 2. Acquisition of information

[0284] The terminal temporarily stores the fixed asset information (e.g., serial number, purchase date, installation location) obtained from the QR code in its memory.

[0285] 3. Transmission of Information

[0286] The terminal automatically inputs the acquired information about the fixed assets into an email and sends it to the email address of the specified server.

[0287] The device uses an app that provides email sending functionality (e.g., Gmail).

[0288] 4. Receiving and collecting information

[0289] The server analyzes the received email and extracts information about the fixed assets.

[0290] The server stores the extracted information in a database and manages it centrally using a database management system (e.g., MySQL).

[0291] 5. Fixed asset tracking

[0292] The server periodically checks the information on fixed assets stored in the database and monitors their location and usage.

[0293] The server records the movement history and frequency of use of fixed assets and checks for any abnormalities.

[0294] 6. Loss prediction and prevention

[0295] The server uses machine learning models (e.g., Scikit-learn) to detect signs of fixed asset data not being updated or not being used.

[0296] If the server determines that there is a high possibility of loss, it will send an alert email to the administrator.

[0297] 7. Emotional Recognition

[0298] The user inputs emotions using the camera, microphone, and keyboard of the mobile information terminal.

[0299] The device uses an emotion recognition engine (e.g., Microsoft® Azure® Cognitive Services) to analyze emotions from the user's facial expressions, voice, and text.

[0300] 8. Sending Emotional Data

[0301] The device sends the analyzed emotion data to the server using the HTTPS protocol.

[0302] 9. Adjusting Emotional Management

[0303] The server analyzes the received emotion data and determines the user's emotional state.

[0304] If the server determines that the user is stressed about losing an asset, it will strengthen the tracking of the asset, such as by increasing the frequency of real-time location updates.

[0305] If the server determines that the user is not interested in managing fixed assets, it automates the management of fixed assets by periodically performing automatic checks and sending reminders.

[0306] Specific examples

[0307] When a user wants to read a QR code attached to an office computer with their smartphone, they launch the smartphone's camera app and select the QR code reader app.

[0308] The terminal analyzes the QR code and obtains the serial number "ABC123", purchase date "2022-01-01", and installation location "Office A".

[0309] The device automatically enters the acquired information into the email app and sends it to the server's email address "asset_management@example.com."

[0310] The server analyzes the received email and stores it in the database as "PC, serial number: ABC123, purchase date: 2022-01-01, installation location: Office A."

[0311] The server periodically checks the database, and if a computer has not been used for a long period of time, it sends an alert email to the administrator stating, "The computer (serial number: ABC123) has not been used for a long period of time. Please check."

[0312] Prompt Sentence Examples

[0313] "Please scan the QR code attached to the office computer with your smartphone and send the obtained information by email."

[0314] "If a user is stressed about losing a fixed asset, what action will the system take?"

[0315] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0316] Step 1:

[0317] The user activates the camera on their mobile information terminal and reads the QR code attached to the fixed asset.

[0318] Input: QR code attached to fixed asset

[0319] Output: QR code data

[0320] Specific operation: The user launches the camera app on their smartphone, selects the QR code reader app, and scans the QR code.

[0321] Step 2:

[0322] The terminal uses a QR code reader app to analyze the QR code and obtain information about the fixed asset.

[0323] Input: QR code data

[0324] Output: Fixed asset information (e.g., serial number, purchase date, installation location)

[0325] Specific operation: The device analyzes the QR code through a QR code reader app and extracts information such as the serial number "ABC123", purchase date "2022-01-01", and installation location "Office A".

[0326] Step 3:

[0327] The terminal automatically enters the acquired fixed asset information into an email and sends it to the specified server email address.

[0328] Input: Fixed asset information

[0329] Output: Email

[0330] Specific operation: The device launches the email application, automatically enters the acquired fixed asset information into the email body, and sends it to the server's email address "asset_management@example.com."

[0331] Step 4:

[0332] The server analyzes the received email and extracts information about the fixed assets.

[0333] Input: Email

[0334] Output: Fixed asset information

[0335] Specific operation: The server analyzes the email received through the email server and extracts fixed asset information from the email body.

[0336] Step 5:

[0337] The server stores the extracted information in a database and manages it centrally.

[0338] Input: Fixed asset information

[0339] Output: Information stored in the database

[0340] Specific operation: The server uses a database management system (e.g., MySQL) to store information about fixed assets in a database.

[0341] Step 6:

[0342] The server periodically checks the information on fixed assets stored in the database and monitors their location and usage.

[0343] Input: Information stored in a database

[0344] Output: Monitoring results

[0345] Specific operation: The server sets up a scheduled task to periodically query the database to check the location and usage status of fixed assets.

[0346] Step 7:

[0347] The server uses machine learning models to detect signs of fixed asset data not being updated or not being used.

[0348] Input: Monitoring results

[0349] Output: Anomaly detection results

[0350] Specific operation: The server uses a machine learning model (e.g., Scikit-learn) to analyze fixed asset data for signs of non-update or non-operation and detect anomalies.

[0351] Step 8:

[0352] If the server determines that there is a high possibility of loss, it will send an alert email to the administrator.

[0353] Input: Anomaly detection result

[0354] Output: Alert email

[0355] Specific operation: Based on the anomaly detection results, the server sends an alert email to the administrator's email address.

[0356] Step 9:

[0357] The user inputs emotions using the camera, microphone, and keyboard of the mobile information terminal.

[0358] Input: User facial expressions, voice, and text input

[0359] Output: Emotion data

[0360] Specific operations: The user captures their facial expressions with the smartphone camera, records audio with the microphone, and enters text with the keyboard.

[0361] Step 10:

[0362] The device uses an emotion recognition engine to analyze emotions from the user's facial expressions, voice, and text.

[0363] Input: User facial expressions, voice, and text input

[0364] Output: Parsed emotion data

[0365] Specific operation: The device uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotions.

[0366] Step 11:

[0367] The device transmits the analyzed emotion data to the server.

[0368] Input: Parsed emotion data

[0369] Output: Emotion data sent to the server

[0370] Specific operation: The device uses the HTTPS protocol to send the analyzed emotion data to the server.

[0371] Step 12:

[0372] The server analyzes the received emotion data and determines the user's emotional state.

[0373] Input: Emotion data sent to the server

[0374] Output: User's emotional state

[0375] Specific operation: The server analyzes the received emotional data and determines whether the user is stressed or indifferent.

[0376] Step 13:

[0377] The server adjusts how the fixed assets are managed based on the user's emotional state.

[0378] Input: User's emotional state

[0379] Output: Coordinated management

[0380] Specific behavior: The server tightens the tracking of fixed assets when the user is stressed, and automates management when the user is disinterested.

[0381] (Application example 1)

[0382] 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."

[0383] In conventional fixed asset management systems, fixed asset information is managed manually, which can lead to delays in updating information and an increased risk of loss. In addition, the management method is not adjusted according to the user's emotions, which can lead to user stress and apathy, reducing the efficiency of fixed asset management.

[0384] 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.

[0385] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, and means for recognizing user emotions and adjusting the management method for the fixed assets based on the emotions. This enables efficient management of fixed assets and flexible adjustment of the management method according to the user's emotions.

[0386] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[0387] A "QR code" is a two-dimensional barcode for storing information, and the information can be obtained using a reading device such as a camera.

[0388] "Reading means" refers to a device or method for obtaining information such as a QR code.

[0389] "Means for acquiring information" refers to a device or method for extracting data related to fixed assets from the scanned QR code.

[0390] "Means for aggregating information" refers to devices and methods for centrally managing information on acquired fixed assets.

[0391] A "tracking means" is a device or method for monitoring the location and status of an asset based on aggregated information about the asset.

[0392] "Means for predicting and preventing loss" refers to devices and methods for predicting the risk of loss based on signs such as non-updating of fixed asset data or non-operation, and preventing it.

[0393] "Means for recognizing emotions and adjusting management methods based on those emotions" refers to a device or method for recognizing emotions from a user's facial expressions, voice, text input, etc., and changing the management method for fixed assets in accordance with those emotions.

[0394] A system for carrying out the present invention includes an application installed on a robot that manages fixed assets in a factory. A specific embodiment of this system will be described below.

[0395] Generating a Program

[0396] The program of this system includes the following main functions:

[0397] 1. Ability to read QR codes attached to fixed assets

[0398] 2. Ability to obtain fixed asset information based on scanned QR codes

[0399] 3. A function to consolidate information on acquired fixed assets

[0400] 4. Ability to track fixed assets based on aggregated fixed asset information

[0401] 5. A function that predicts and prevents loss based on signs of non-updating or non-operating fixed asset data

[0402] 6. The ability to recognize user emotions and adjust how fixed assets are managed based on those emotions

[0403] Program processing explanation

[0404] Scanning QR codes

[0405] The robot uses the OpenCV library to read QR codes that are affixed to each fixed asset in the factory, and the robot periodically makes rounds to read them.

[0406] Obtaining and sending fixed asset information

[0407] The fixed asset information is obtained from the scanned QR code and sent to the administrator via email using smtplib and email.mime.text. This allows the fixed asset information to be centrally collected.

[0408] Fixed asset tracking and loss prediction

[0409] Based on the aggregated information, the location and status of fixed assets are tracked, signs of non-updating or non-operating fixed asset data are detected, and the risk of loss is predicted and prevented.

[0410] Adjusting emotion recognition and management strategies

[0411] Use the emotion_recognition library to recognize user emotions. Analyze emotions from facial expressions, voice, and text inputs to enhance asset tracking if the user is stressed, or automate management if the user is apathetic.

[0412] Specific examples

[0413] For example, a robot in a factory periodically patrols the factory and reads the QR code attached to each machine. The information read is sent to the manager via email, and if the manager's emotions are recognized as stress, the robot will strengthen its tracking.

[0414] Prompt Sentence Examples

[0415] Develop a robot application that manages fixed assets in a factory. This application reads QR codes, obtains information about fixed assets, and sends it via email. It also includes an emotion engine that adjusts management methods according to the manager's emotions. The libraries used are OpenCV, smtplib, and emotion_recognition.

[0416] In this way, the present invention realizes efficient management of fixed assets and flexible adjustment of management methods according to the user's feelings.

[0417] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0418] Step 1:

[0419] The robot patrols the factory and uses a camera to read QR codes attached to fixed assets. The input is an image of the QR code, and the output is the information about the fixed asset extracted from the QR code. The QR code data is decoded using the OpenCV library.

[0420] Step 2:

[0421] The robot temporarily saves the information about the fixed asset obtained from the scanned QR code. The input is the information about the fixed asset extracted from the QR code, and the output is the temporarily saved information about the fixed asset. The information is stored in a database or memory.

[0422] Step 3:

[0423] The robot sends the acquired fixed asset information to the administrator via email. The input is the temporarily saved fixed asset information, and the output is the email sent to the administrator. The robot uses smtplib and email.mime.text to compose and send the email.

[0424] Step 4:

[0425] The server aggregates the fixed asset information received from the administrator. The input is the email received from the administrator, and the output is the aggregated fixed asset information. The information is integrated into a database and managed centrally.

[0426] Step 5:

[0427] The server tracks fixed assets based on the aggregated fixed asset information. The input is the aggregated fixed asset information, and the output is the tracking results of the fixed asset's location and status. The location information and operating status are monitored and recorded in a database.

[0428] Step 6:

[0429] The server detects signs of fixed asset data not being updated or not being used, and performs loss prediction. The input is the fixed asset tracking results, and the output is the loss prediction results. Data analysis algorithms are used to detect abnormal patterns.

[0430] Step 7:

[0431] The robot uses the emotion_recognition library to recognize the user's emotions. The input is the user's facial expressions, voice, and text input, and the output is the recognized emotion. The data is collected using a camera and microphone and analyzed with an emotion recognition model.

[0432] Step 8:

[0433] The server adjusts the method of managing fixed assets based on the recognized emotion. The input is the recognized emotion, and the output is the adjusted management method. Depending on the emotion, the tracking frequency can be changed or settings can be made to automate management.

[0434] In this way, the system realizes efficient management of fixed assets and flexible adjustment of management methods according to the user's feelings.

[0435] Example 2

[0436] 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."

[0437] In conventional fixed asset management systems, fixed asset information is often managed manually, making updating and tracking the information cumbersome. Furthermore, there is a lack of effective means to prevent the loss of fixed assets. Furthermore, there is no integrated system for recognizing user emotions, making it difficult to accurately grasp the user's status. To solve these issues, a system that integrates fixed asset management and user emotion recognition is needed.

[0438] 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.

[0439] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, means for photographing a user's facial expression and recognizing emotions from the image data, means for recording the user's voice and recognizing emotions from the voice data, means for recognizing emotions from text data entered by the user, and means for integrating these emotion data to more accurately recognize the user's emotions. This integrates fixed asset management and user emotion recognition, making it possible to prevent loss of fixed assets and accurately grasp the user's condition.

[0440] "Fixed assets" are assets owned by a company or organization that are used over a long period of time, and include buildings, machinery, equipment, computers, etc.

[0441] A "QR code" is a type of two-dimensional barcode that visually encodes information and can be read by a camera or scanner.

[0442] "Means of reading" refers to devices or technologies used to capture QR codes or other information, including cameras and scanners.

[0443] "Means of obtaining information" refers to the technology or process used to extract the necessary information from the scanned QR code or other data.

[0444] "Means of aggregating information" refers to technologies and systems for centrally compiling and managing acquired information.

[0445] "Tracking measures" refers to the technologies and processes used to monitor and manage the location and condition of fixed assets based on aggregated information.

[0446] "Means to predict and prevent loss" refers to technologies and systems that analyze signs of fixed asset data not being updated or not being operational, and predict and prevent the possibility of loss.

[0447] "Means for capturing facial expressions" refers to technology or devices for capturing images of a user's facial expressions with a camera and acquiring the image data.

[0448] "Means for recognizing emotions" refers to technologies and systems for analyzing and recognizing a user's emotions from acquired image data, audio data, and text data.

[0449] "Means for recording audio" refers to the technology or device for recording the user's audio with a microphone and acquiring the audio data.

[0450] "Means for recognizing emotions from text data" refers to technologies and systems that analyze text data entered by a user and recognize emotions from its content.

[0451] "Means for integrating emotion data" refers to technologies and systems that integrate data obtained from multiple emotion recognition means to more accurately recognize a user's emotions.

[0452] MODE FOR CARRYING OUT THE INVENTION

[0453] The present invention is a system that integrates fixed asset management and user emotion recognition. A specific embodiment of this system will be described below.

[0454] Fixed Asset Management System

[0455] 1. Generate and attach a QR code

[0456] The server generates a QR code based on the information (asset number, purchase date, person in charge, etc.) from the computer registered in the fixed asset management system. To generate the QR code, for example, use the Python qrcode library.

[0457] The user prints out the generated QR code and attaches it to their computer.

[0458] 2. Scan the QR code

[0459] The new employee scans the QR code using the camera on their smartphone, which has either a camera app or a dedicated QR code reader app installed.

[0460] The terminal (smartphone) analyzes the contents of the QR code and obtains the computer's asset information.

[0461] 3. Transmission of Information

[0462] The device will then send the acquired asset information via email, automatically entering the information into the body of the email using the smartphone's email app.

[0463] The server receives the information at the specified email address.

[0464] 4. Information collection and management

[0465] The server analyzes the content of the received email and registers the information in the database of the fixed asset management system. For example, the Python imaplib library is used for email analysis.

[0466] The server periodically updates the database (e.g., MySQL) to keep the asset information up to date.

[0467] 5. Fixed asset tracking

[0468] The server uses a database to track changes in the location and ownership of fixed assets, and issues alerts in the event of unusual activity or possible loss.

[0469] Emotion Engine System

[0470] 1. Photographing facial expressions

[0471] The user stands in front of the camera and shows their facial expression, which is then captured by the camera and image data is acquired.

[0472] 2. Recording Audio

[0473] A user speaks into a microphone, which records the voice and captures the audio data.

[0474] 3. Enter text

[0475] The user inputs text using a keyboard or a touch screen, and the terminal acquires the input text data.

[0476] 4. Emotion Analysis

[0477] The server analyzes the image data and recognizes the user's emotions from their facial expressions. For image analysis, the OpenCV library is used, for example.

[0478] The server analyzes the voice data and recognizes emotions from the user's voice. For voice analysis, Google Cloud Speech-to-Text is used, for example.

[0479] The server analyzes the text data and recognizes emotions from the user's text, using natural language processing, such as the NLTK library.

[0480] 5. Emotional Data Integration

[0481] The server integrates these emotion data and determines the final emotion. For example, if the facial expression, voice, and text are "joy," the final emotion will be "joy."

[0482] The server notifies the user of the result.

[0483] Examples and prompts

[0484] Example: A company's IT department purchases a new computer and affixes a QR code to it. When the person in charge is transferred, the new person scans the QR code with their smartphone to retrieve the information. The information is then sent via email to the IT department's management system.

[0485] Example prompt: "Scan the QR code on your new computer and email us the information you get."

[0486] Example: A user smiles in front of a camera. The system analyzes the facial expression and recognizes that the user is "happy." Also, if the user enters text such as "Today was a fun day," the system will recognize the emotion of "happy" from that text as well.

[0487] Example prompt: "Smile for the camera and text us how you're feeling today."

[0488] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0489] Fixed Asset Management System Processing Steps

[0490] Step 1: Generate and paste a QR code

[0491] The server generates a QR code based on the information on the computer registered in the fixed asset management system (asset number, purchase date, person in charge, etc.). The computer information is required as input, and a QR code is generated as output.

[0492] Specific operation: The server generates a QR code using a QR code generation library (e.g., qrcode for Python), and outputs the generated QR code as an image file.

[0493] The user prints the generated QR code and pastes it on their computer. An image file of the generated QR code is required as input, and the QR code is pasted on the computer as output.

[0494] Step 2: Scan the QR code

[0495] The new employee reads the QR code using the camera on their smartphone. The QR code is required as input, and the content of the QR code is obtained as output.

[0496] How it works: Scan the QR code using your smartphone's camera app or a dedicated QR code reader app. The app will analyze the contents of the QR code and display the asset information.

[0497] Step 3: Submit your information

[0498] The terminal sends the acquired asset information by email. The acquired asset information is required as input, and an email is sent as output.

[0499] Specific operation: The smartphone launches the email app and automatically enters the acquired asset information into the email body. The user presses the send button to send the email. The server receives the email and saves the contents in a database.

[0500] Step 4: Aggregating and managing information

[0501] The server analyzes the content of the received email and registers the information in the database of the fixed asset management system. The received email is required as input, and the information is registered in the database as output.

[0502] Specific operation: The server analyzes received emails using an email analysis library (e.g., imaplib for Python). It stores the analyzed information in a database (e.g., MySQL). It periodically cleans the database to keep it up to date.

[0503] Step 5: Track fixed assets

[0504] The server tracks changes in the location and ownership of fixed assets based on a database. It requires database information as input and provides tracking results as output.

[0505] Specific behavior: The server executes database queries to retrieve asset information. If it detects any abnormal activity (e.g., a change in the person in charge within a short period of time), it sends an email or notification to the administrator.

[0506] Emotion engine system processing steps

[0507] Step 1: Capture facial expressions

[0508] The user stands in front of the camera and shows their facial expression. The user's facial expression is required as input, and image data is obtained as output.

[0509] Specific operation: The user shows a smile or an angry expression in front of the camera. The camera captures that moment and saves the image data.

[0510] Step 2: Record your audio

[0511] The user speaks into a microphone: the user's voice is required as input and audio data is obtained as output.

[0512] What happens: The user says, "Today was a fun day." The microphone records the speech and saves it as an audio file.

[0513] Step 3: Entering text

[0514] The user inputs text using a keyboard or a touch screen. The user's text is required as input, and text data is obtained as output.

[0515] Specific operation: The user types the text "Today was a fun day" into their smartphone or computer. The device saves the text data.

[0516] Step 4: Sentiment Analysis

[0517] The server analyzes the image data and recognizes the user's emotions from their facial expressions. Image data is required as input, and emotion recognition results are obtained as output.

[0518] Specific operation: The server analyzes facial expressions using an image analysis library (e.g., OpenCV).

[0519] The server analyzes the voice data and recognizes the emotion from the user's voice. The voice data is required as input, and the emotion recognition result is obtained as output.

[0520] What happens: The server analyzes the audio using a speech analysis library (e.g., Google Cloud Speech-to-Text).

[0521] The server analyzes the text data and recognizes the emotion from the user's text. The text data is required as input, and the emotion recognition result is obtained as output.

[0522] What happens: The server parses the text using a natural language processing library (e.g., NLTK).

[0523] Step 5: Integrating Emotional Data

[0524] The server integrates these emotion data and determines the final emotion. Each emotion recognition result is required as input, and the integrated emotion recognition result is obtained as output.

[0525] Specific operation: The server integrates the results of each analysis and determines the user's overall emotion. For example, if the facial expression, voice, and text are all "happy," the final emotion will be "happy." The server then notifies the user of the result.

[0526] (Application example 2)

[0527] 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."

[0528] Conventional fixed asset management systems require manual management of fixed asset information, resulting in delays in updating information and an increased risk of loss. Furthermore, they do not take into account the emotions of workers, which can lead to reduced work efficiency. To solve these problems, a system is needed that automatically acquires and manages fixed asset information, and also recognizes the emotions of workers to provide appropriate support.

[0529] 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.

[0530] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs such as non-updating of fixed asset data or non-operation, means for recognizing the emotions of workers, and means for providing appropriate support to the workers based on the recognized emotions. This not only makes it possible to automatically manage information about fixed assets and prevent loss, but also to provide appropriate support that takes into account the emotions of the workers.

[0531] "Fixed assets" are assets owned by a company or organization that are used for a long period of time, and include buildings, machinery, equipment, vehicles, etc.

[0532] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0533] "Means for reading" refers to the device or software used to scan the QR code and obtain its information.

[0534] "Means of obtaining information" refers to the equipment or software used to analyze the data read from the QR code and extract the necessary information.

[0535] "Means of aggregating information" refers to databases or software for centrally managing acquired information.

[0536] "Tracking instruments" refers to devices and software used to monitor the location and condition of fixed assets based on aggregated information.

[0537] "Means to predict and prevent loss" refers to devices and software that detect signs of fixed asset data not being updated or not being used, predict the risk of loss, and take measures to prevent it.

[0538] "Means for recognizing emotions" refers to devices and software for analyzing emotions from workers' facial expressions, voice, and text data.

[0539] "Means for providing appropriate support" refers to devices and software for providing necessary assistance and advice to workers based on the recognized emotions.

[0540] "Smart devices" refers to mobile information terminals with advanced functions, such as smartphones and tablets.

[0541] "Email" is a means of communication for sending and receiving text and files over the Internet.

[0542] As an embodiment of the present invention, a method for installing an application that combines a fixed asset management system using QR codes and an emotion engine on a robot in a factory will be described.

[0543] System Program

[0544] This system operates using the following hardware and software:

[0545] Hardware: Camera, microphone

[0546] Software: OpenCV (QR code reading), smtplib (email sending), EmotionRecognizer (emotion recognition)

[0547] Program processing

[0548] The server first reads the QR code from the image captured by the camera. It uses OpenCV to scan the QR code and obtain the information. It then analyzes the obtained fixed asset information and extracts the necessary data. This data is then sent to the administrator via email using smtplib. The sent information is then aggregated in a database and managed centrally.

[0549] Furthermore, the server recognizes the worker's emotions from images captured by the camera and audio recorded by the microphone. Using EmotionRecognizer, it analyzes the image and audio data to identify the worker's emotions. Based on the recognized emotions, it provides appropriate support to the worker. For example, if a worker is feeling stressed, it can respond by displaying a message encouraging them to take a break.

[0550] Specific examples

[0551] For example, a robot in a factory can periodically read the QR code on each machine and send fixed asset information to the manager. Also, if a worker is feeling stressed, the robot can recognize that emotion and provide appropriate support. In this way, fixed asset management and worker support can be performed simultaneously.

[0552] Prompt Sentence Examples

[0553] "Develop an application that allows a robot in a factory to read QR codes to manage information on fixed assets, and recognizes the emotions of workers to provide appropriate support. Use OpenCV to read QR codes and EmotionRecognizer to recognize emotions."

[0554] The above is a specific embodiment for carrying out the present invention. This system not only automatically manages information on fixed assets and prevents loss, but also provides appropriate support that takes into account the emotions of workers.

[0555] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0556] Step 1:

[0557] The server reads the QR code from the image captured by the camera. As input, it receives the image data captured by the camera and scans the QR code using OpenCV. As output, it obtains the fixed asset information contained in the QR code.

[0558] Step 2:

[0559] The server analyzes the fixed asset information based on the scanned QR code. As input, it receives and analyzes the data obtained from the QR code. As output, it extracts detailed information such as the fixed asset number, purchase date, and person in charge.

[0560] Step 3:

[0561] The server sends the acquired fixed asset information by email. As input, it receives the parsed fixed asset information and sends it to the administrator's email address using smtplib. As output, the fixed asset information is notified to the administrator.

[0562] Step 4:

[0563] The server aggregates the transmitted fixed asset information in a database. As input, it receives the fixed asset information transmitted by email and stores it in the database. As output, the fixed asset information is managed in a unified manner.

[0564] Step 5:

[0565] The server recognizes the worker's emotions from images captured by a camera and audio recorded by a microphone. It receives image and audio data as input and analyzes emotions using EmotionRecognizer. The output identifies the worker's emotional state.

[0566] Step 6:

[0567] The server provides appropriate support to the worker based on the recognized emotion. As input, it receives the identified emotional state and determines the appropriate support content. As output, it displays a message or advice encouraging the worker to take a break.

[0568] These are the specific processing steps of this system. By clarifying the operations and inputs / outputs performed at each step, it is easier to understand the flow of the entire system.

[0569] 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.

[0570] 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.

[0571] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0572] 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.

[0573] [Second embodiment]

[0574] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0586] "Example 1"

[0587] In one embodiment of the present invention, a QR code attached to a fixed asset is read using a smartphone camera. Information about the fixed asset is obtained based on the read QR code, and the obtained information about the fixed asset is sent via email via the smartphone. The transmitted information about the fixed asset is centrally aggregated. The fixed asset is tracked based on the aggregated information about the fixed asset. Loss is predicted based on signs of fixed asset data not being updated or not being used, and loss is prevented before it occurs.

[0588] "Example 2"

[0589] As a specific example, a company affixes a QR code to a computer, which is a fixed asset. This QR code contains information such as the computer's asset number, purchase date, and the person in charge. When the person in charge is transferred or leaves the company, the new person can read the QR code using their smartphone camera to obtain information about the fixed asset. The obtained information is sent via email via the smartphone and centrally aggregated. Based on the aggregated information, the fixed asset can be tracked to prevent loss.

[0590] The processing flow of each embodiment will be described below.

[0591] "Example 1"

[0592] Step 1: Use your smartphone camera to read the QR code attached to the fixed asset.

[0593] Step 2: Obtain fixed asset information based on the scanned QR code.

[0594] Step 3: Information about the acquired fixed assets is sent via email via smartphone. Step 4: The sent information about the fixed assets is centrally consolidated.

[0595] Step 5: Track fixed assets based on the aggregated fixed asset information.

[0596] Step 6: Predict losses based on signs such as non-updating of fixed asset data or non-operation, and prevent them from occurring.

[0597] "Example 2"

[0598] Step 1: A QR code is attached to a computer, which is a fixed asset of a company. This QR code contains information such as the computer's asset number, purchase date, and person in charge.

[0599] Step 2: If the person in charge is transferred or resigns, the new person in charge will read the QR code using the smartphone camera.

[0600] Step 3: Obtain fixed asset information based on the scanned QR code.

[0601] Step 4: The information obtained is sent via email via your smartphone.

[0602] Step 5: The submitted information on fixed assets is centrally aggregated.

[0603] Step 6: Use the aggregated information to track fixed assets and prevent loss.

[0604] Example 1

[0605] 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."

[0606] In the area of ​​fixed asset management, it is difficult to efficiently acquire, consolidate, and track information on fixed assets. In addition, there is a lack of methods to predict and prevent loss based on signs such as inactivity or non-operation of fixed asset data.

[0607] 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.

[0608] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring fixed asset information based on the read QR codes, means for sending the acquired fixed asset information by email, means for aggregating the transmitted fixed asset information, means for tracking fixed assets based on the aggregated fixed asset information, and means for predicting and preventing loss based on signs such as non-updating of fixed asset data or non-operation. This allows for efficient management of fixed asset information and makes it possible to predict and prevent loss.

[0609] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[0610] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0611] A "mobile information terminal" is a portable information processing device such as a smartphone or tablet.

[0612] A "camera" is a device for taking images or videos.

[0613] "Email" is a means of communication for sending and receiving text and files over the Internet.

[0614] A "server" is a computer system that provides services to other computers on a network.

[0615] A "database" is a system for efficiently storing, searching, and managing data.

[0616] "Tracking" is the act of continuously monitoring the location and status of an object or information.

[0617] "Not updated" refers to a state in which data has not been updated for a certain period of time.

[0618] "Inoperation" refers to a state in which equipment or systems are not in use for a certain period of time.

[0619] "Loss prediction" is the act of predicting the possibility of loss of an item based on data analysis.

[0620] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and make predictions and classifications.

[0621] This invention is a system for efficiently managing fixed assets, and includes means for reading QR codes affixed to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for sending the acquired information about the fixed assets by email, means for aggregating the transmitted information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, and means for predicting loss based on signs such as non-updating of fixed asset data or non-operation, and for preventing loss before it occurs.

[0622] The user uses the smartphone camera to read the QR code attached to the fixed asset. Using a QR code reader app (e.g., QR Code Reader, Google Lens), the user places the QR code within the camera's frame, and the app automatically scans the QR code and acquires the data.

[0623] The data from the QR code read by the device is sent to the server. The server accesses a database (e.g., MySQL, PostgreSQL) and retrieves information about the fixed asset corresponding to the QR code. The retrieved information includes the name, location, purchase date, current condition, etc. of the fixed asset.

[0624] The information about the fixed assets acquired by the terminal is sent by email. The email is sent using the SMTP protocol and an email service such as Gmail or Outlook. The body of the email contains detailed information about the fixed assets.

[0625] The server analyzes the received emails and aggregates the information about fixed assets into a centralized database. The aggregated data is then stored in a fixed asset management system (e.g., SAP, Oracle Asset Management).

[0626] The server tracks fixed assets based on the aggregated information on fixed assets. Tracking includes the movement history and current location information of fixed assets. The server periodically updates the database to keep the information up to date.

[0627] The server analyzes fixed asset data and detects signs of non-renewal, non-operation, etc. A generative AI model (e.g., TensorFlow, PyTorch) is used to predict loss and send alerts to administrators to prevent it from happening.

[0628] For example, a user uses their smartphone to scan a QR code attached to an office printer. The QR code contains the printer's ID, which the device sends to a server. The server retrieves the printer's details from a database, and the device sends the information to an administrator via email. The server aggregates the received information into a database and tracks printer usage. If the printer has not been used for a long period of time, a generative AI model is used to predict the possibility of loss and send an alert to the administrator.

[0629] Example prompt sentence:

[0630] "Scan the QR code attached to the office printer with your smartphone. The printer information will be acquired based on the scanned QR code and sent to you by email. The information will be aggregated on the server and used to track the printer and predict if it has been lost."

[0631] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0632] Step 1:

[0633] The user activates the smartphone camera and reads the QR code attached to the fixed asset using a QR code reader app (e.g., QR Code Reader, Google Lens). When the user holds the QR code within the camera's frame, the app automatically scans the QR code and acquires the data.

[0634] Input: QR code

[0635] Output: QR code data

[0636] Specific operation: The user opens the camera app on their smartphone and points the camera at a QR code. The app recognizes the QR code and reads the data.

[0637] Step 2:

[0638] The terminal sends the data from the QR code it reads to the server, which then accesses a database (e.g., MySQL, PostgreSQL) to retrieve information about the fixed asset corresponding to the QR code.

[0639] Input: QR code data

[0640] Output: Fixed asset information

[0641] What happens: The device sends the QR code data to the server, which queries the database to retrieve the fixed asset information.

[0642] Step 3:

[0643] The information about the fixed assets acquired by the terminal is sent by email. The email is sent using the SMTP protocol and an email service such as Gmail or Outlook. The body of the email contains detailed information about the fixed assets.

[0644] Input: Fixed asset information

[0645] Output: Email

[0646] Specific operation: The terminal inserts the fixed asset information into the email body, and then sends the email through the SMTP server.

[0647] Step 4:

[0648] The server analyzes the received emails and aggregates the information about fixed assets into a centralized database. The aggregated data is then stored in a fixed asset management system (e.g., SAP, Oracle Asset Management).

[0649] Input: Email

[0650] Output: Aggregated fixed asset information

[0651] Specific operation: The server analyzes the received email and saves the analysis results in a database.

[0652] Step 5:

[0653] The server tracks fixed assets based on the aggregated information on fixed assets. Tracking includes the movement history and current location information of fixed assets. The server periodically updates the database to keep the information up to date.

[0654] Input: Aggregated fixed asset information

[0655] Output: Fixed asset tracking information

[0656] Specific operation: The server records the fixed asset movement history in a database. The server updates the database periodically.

[0657] Step 6:

[0658] The server analyzes fixed asset data and detects signs of non-renewal, non-operation, etc. A generative AI model (e.g., TensorFlow, PyTorch) is used to predict loss and send alerts to administrators to prevent it from happening.

[0659] Input: Fixed asset tracking information

[0660] Output: Loss prediction alert

[0661] How it works: The server analyzes the data using the generated AI model. The server predicts the possibility of loss and generates an alert. The server sends the alert to an administrator.

[0662] (Application example 1)

[0663] 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."

[0664] With conventional fixed asset management systems, it was difficult to monitor the location and status of fixed assets in real time, which increased the risk of loss or non-operation, especially in large-scale factory asset management. Furthermore, the time-consuming task of manually aggregating fixed asset information made efficient management difficult.

[0665] 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.

[0666] In this invention, the server includes: means for reading QR codes affixed to fixed assets; means for acquiring information about the fixed assets based on the read QR codes; means for aggregating the acquired information about the fixed assets; means for tracking the fixed assets based on the aggregated information about the fixed assets; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; means installed on robots in the factory for transmitting information about the fixed assets in real time to the central management system; and means for monitoring the location and status of the fixed assets based on the information transmitted to the central management system. This makes it possible to monitor the location and status of fixed assets in the factory in real time and reduce the risk of loss or non-operation.

[0667] "Fixed assets" are physical assets that a business or organization holds for long-term use.

[0668] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0669] "Means of reading" refers to the equipment or technology used to decode the QR code and obtain its information.

[0670] "Means of obtaining information" refers to devices and technologies for collecting detailed information about fixed assets based on data read from QR codes.

[0671] "Means of aggregating information" refers to devices or technologies for centrally compiling information on acquired fixed assets.

[0672] "Tracking means" refers to devices and technologies that monitor the location and status of assets based on aggregated fixed asset information.

[0673] "Means for predicting and preventing loss" refers to devices and technologies that analyze signs of fixed asset data not being updated or not being operational, and predict and prevent the risk of loss.

[0674] A "factory robot" is an automated mechanical device used to perform tasks in a factory.

[0675] A "central management system" is a system for centrally managing and monitoring fixed asset information within a factory.

[0676] "Real-time transmission means" refers to devices or technologies for instantly transmitting fixed asset information to the central management system.

[0677] "Means for monitoring location and status" refers to devices and technologies for continuously monitoring the location and status of assets based on information about fixed assets sent to the central management system.

[0678] The present invention relates to a system for improving the efficiency of fixed asset management within a factory. A specific embodiment of this system will be described below.

[0679] First, QR codes are attached to fixed assets within the factory. The QR codes contain the identification information of the fixed assets. Robots within the factory are equipped with cameras and periodically patrol the factory, reading the QR codes attached to the fixed assets.

[0680] The robot uses the OpenCV library to decode the QR code and retrieve information, such as the asset's identification and location. The robot then transmits the information to a central management system in real time using wireless communication technologies such as Wi-Fi and Bluetooth.

[0681] The central management system centrally aggregates the received information on fixed assets. The aggregated information is stored in a database and used to monitor the location and status of fixed assets in real time. A relational database management system (RDBMS) such as MySQL or PostgreSQL is used for database management.

[0682] Furthermore, the central management system analyzes signs of fixed asset data inactivity and non-operation to predict loss, using machine learning algorithms such as the Python scikit-learn library to analyze fixed asset data and predict the risk of loss.

[0683] This system makes it possible to monitor the location and status of fixed assets within a factory in real time, reducing the risk of loss or non-operation. It also eliminates the need to manually collect information on fixed assets, enabling more efficient management.

[0684] For example, a robot in a factory periodically patrols the area and reads the QR codes attached to each machine and tool. The robot sends the read information to a central management system, which monitors the status of the assets in real time. If it detects signs of asset inactivity or loss, it sends an alert to the manager.

[0685] Example prompt for a generative AI model:

[0686] To streamline fixed asset management within the factory, develop an application that allows a robot to read QR codes and send asset information to a central management system. This application will have the function of monitoring the location and status of assets in real time, reducing the risk of loss or non-operation.

[0687] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0688] Step 1:

[0689] The robot patrols the factory and uses a camera to take pictures of QR codes attached to fixed assets.

[0690] Input: QR code attached to fixed assets in the factory

[0691] Output: QR code image data

[0692] Specific operation: The robot periodically moves around the factory and uses a camera to photograph QR codes affixed to fixed assets.

[0693] Step 2:

[0694] The robot uses the OpenCV library to decode the QR code and obtain the fixed asset's identifying information.

[0695] Input: QR code image data

[0696] Output: Fixed asset identification information (text data)

[0697] Specific operation: The robot inputs the image data of the captured QR code into the OpenCV library, decodes the QR code, and obtains the fixed asset identification information.

[0698] Step 3:

[0699] The robot transmits the identification information of fixed assets acquired in real time to a central management system.

[0700] Input: Fixed asset identification information (text data)

[0701] Output: Fixed asset identification information sent to the central management system

[0702] Specific operation: The robot uses wireless communication technologies such as Wi-Fi and Bluetooth to transmit the identification information of the acquired fixed assets to the central management system.

[0703] Step 4:

[0704] The central management system centrally collects the fixed asset information it receives and stores it in a database.

[0705] Input: Fixed asset identification information sent by the robot

[0706] Output: Fixed asset information stored in the database

[0707] Specific operation: The central management system stores the received fixed asset information in a relational database management system (RDBMS).

[0708] Step 5:

[0709] The central management system monitors the location and status of fixed assets in real time based on aggregated fixed asset information.

[0710] Input: Fixed asset information stored in the database

[0711] Output: Fixed asset location and status monitoring results

[0712] How it works: The central management system periodically checks the information stored in the database and monitors the location and status of fixed assets.

[0713] Step 6:

[0714] The central management system analyzes signs such as non-updating or non-operating fixed asset data and predicts losses.

[0715] Input: Fixed asset information stored in the database

[0716] Output: Loss prediction result

[0717] How it works: The central management system uses machine learning algorithms to analyze fixed asset data and predict the risk of loss.

[0718] Step 7:

[0719] The central management system sends an alert to the administrator based on the loss prediction results.

[0720] Input: Loss prediction result

[0721] Output: Alert notification to administrator

[0722] Specific operation: Based on the loss prediction results, the central management system will send an alert to the administrator via email, SMS, etc.

[0723] Example 2

[0724] 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."

[0725] In conventional fixed asset management systems, asset information was often updated and tracked manually, resulting in delays in updating information when personnel were transferred or retired. Furthermore, there were insufficient mechanisms for preventing the loss of fixed assets, making asset management cumbersome. This increased the risk of fixed assets being lost or misused.

[0726] 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.

[0727] In this invention, the server includes means for generating and printing QR codes, means for reading QR codes, means for sending acquired fixed asset information by email, means for analyzing received emails and saving the information in a database, and means for periodically checking the information in the database and issuing an alert if an abnormality is detected. This makes it possible to centrally manage fixed asset information, quickly update information when personnel are transferred or resign, and prevent the loss of fixed assets.

[0728] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[0729] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0730] "Reading means" refers to a device or software that scans the QR code and obtains its contents as digital data.

[0731] The "means of acquisition" refers to a device or software for extracting the necessary information from the contents of the scanned QR code.

[0732] "Aggregating means" refers to a device or software that stores and organizes acquired information in a database for centralized management.

[0733] A "tracking instrument" is a device or software that monitors and manages the location and status of fixed assets based on aggregated information.

[0734] "Means for predicting and preventing loss" refers to devices or software that detect signs of fixed asset data not being updated or not being operational, predict the risk of loss, and take measures to prevent it.

[0735] A "generating and printing means" is a device or software that converts fixed asset information into a QR code and physically prints the QR code.

[0736] The "means for sending by e-mail" is a device or software for sending the acquired information to the server via e-mail.

[0737] The "means for analyzing and storing in a database" refers to a device or software for analyzing the content of received e-mail and storing the information in a database.

[0738] An "alert issuing means" is a device or software that periodically checks the information in the database and notifies the administrator if an abnormality is detected.

[0739] This invention is a system for efficiently managing fixed assets owned by companies and organizations and preventing their loss. This system manages asset information using QR codes affixed to fixed assets.

[0740] The server hosts a fixed asset management system, and uses a Python script to convert each computer's asset information (asset number, purchase date, person in charge, etc.) into a QR code, which is then printed using a dedicated label printer and affixed to each computer.

[0741] The user (new person in charge) uses the smartphone camera to scan the QR code attached to the computer. The device (smartphone) reads the contents of the QR code using a dedicated app. The asset information obtained from the QR code is displayed on the smartphone app screen. The user checks the displayed information and enters the new person in charge information.

[0742] The acquired asset information is sent to the server via email via a smartphone app. The device checks the integrity of the information before sending it and converts it into the required format (e.g., JSON). The server parses the received email and uses a Python script to save the asset information to a database. The server compares it with existing data and updates the contact information as necessary.

[0743] The server tracks fixed assets based on information collected in a database. The server periodically runs a Python script to check asset information. If an abnormality is detected, the server sends an alert email to the administrator.

[0744] As a concrete example, the following scenario can be considered.

[0745] Person A in the IT department of a certain company is retiring. Person B is to take over his computer. Person B uses the camera on his smartphone to scan the QR code attached to the computer. The QR code contains the computer's asset number "PC-12345," the purchase date "2022-01-15," and information about the previous person in charge, "A." Person B then sends the scanned information to the server via email using an app on his smartphone. The server saves the received information in a database and updates the person in charge information to "B."

[0746] An example of a prompt sentence might be:

[0747] "How can I use my smartphone camera to scan the QR code attached to a company's fixed asset computer and send the acquired asset information to the server via email?"

[0748] In this way, information on fixed assets can be managed centrally, and information can be updated quickly when personnel are transferred or leave the company, making it possible to prevent the loss of fixed assets.

[0749] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0750] Step 1: Generate and paste a QR code

[0751] The server uses the fixed asset management system to convert the asset information of each computer (asset number, purchase date, person in charge, etc.) into a QR code. Specifically, a Python script is used to encode the asset information into QR code format. The input is the asset information of each computer, and the output is a QR code image. The generated QR code is printed using a dedicated label printer and affixed to each computer.

[0752] Step 2: Scan the QR code

[0753] The user uses the camera on their smartphone to scan the QR code attached to their computer. The device (smartphone) then reads the contents of the QR code using a dedicated app. The input is the QR code image, and the output is the asset information extracted from the QR code. Specifically, the user launches the camera app on their smartphone and scans the QR code.

[0754] Step 3: Obtain asset information

[0755] The terminal (smartphone) displays the asset information obtained from the QR code. The input is the asset information extracted from the QR code, and the output is the asset information displayed on the smartphone screen. The user checks the displayed information and enters new contact information as necessary. Specifically, the user checks the asset information on the dedicated app screen and enters new contact information in the form.

[0756] Step 4: Submit asset information

[0757] The user sends asset information obtained through a smartphone app to the server by email. The device checks the integrity of the information before sending and converts it into the required format (e.g., JSON format). The input is the asset information, including the new contact information entered by the user, and the output is data in email format. Specifically, the user taps the "Send" button on the dedicated app.

[0758] Step 5: Aggregate and update asset information

[0759] The server parses the received email and saves the asset information in a database. The server compares it with existing data and updates the contact information as needed. The input is the asset information in email format, and the output is the updated asset information saved in the database. Specifically, the server uses a Python script to parse the email and save the information in the database.

[0760] Step 6: Track fixed assets

[0761] The server tracks fixed assets based on information aggregated in a database. The server periodically runs a Python script to check asset information. The input is the asset information stored in the database, and the output is an alert notification if an abnormality is detected. Specifically, the server periodically scans the database, and if an abnormality is detected, it sends an alert email to the administrator.

[0762] In this way, information on fixed assets can be managed centrally, and information can be updated quickly when personnel are transferred or leave the company, making it possible to prevent the loss of fixed assets.

[0763] (Application example 2)

[0764] 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."

[0765] The traditional method of managing machines and robots used in factories is time-consuming and difficult to do efficiently. Furthermore, due to insufficient tracking of machine maintenance history and personnel information, machines can be lost or improperly managed. This leads to issues such as reduced factory operational efficiency and increased costs.

[0766] 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.

[0767] In this invention, the server includes means for reading QR codes affixed to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, means for reading QR codes affixed to machines in the factory, means for sending the read machine information by email, and means for notifying the manager of the sent information. This allows for efficient management of machines and robots in the factory, makes it easy to track maintenance history and information about the person in charge, and makes it possible to prevent loss or improper management of machines.

[0768] "Fixed assets" are assets that a company or organization holds for long-term use, and include buildings, machinery, equipment, etc.

[0769] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0770] "Reading means" refers to a device or method for scanning a QR code and obtaining its contents as digital data.

[0771] "Means for acquiring information" refers to a device or method for extracting the necessary information from the data in the scanned QR code.

[0772] "Means for aggregating information" refers to a device or method for centrally organizing and managing acquired information.

[0773] A "tracking means" is a device or method for monitoring and managing the location and status of fixed assets based on aggregated information.

[0774] "Means for predicting and preventing loss" refers to devices and methods for analyzing signs such as non-updating of fixed asset data or non-operation, predicting the risk of loss, and taking measures.

[0775] "Machinery in a factory" refers to machinery and equipment such as production equipment and robots used in a factory.

[0776] "Means for sending by e-mail" refers to a device or method for sending the acquired information to a designated recipient via e-mail.

[0777] The "means for notifying the administrator" refers to a device or method for notifying the administrator of the transmitted information.

[0778] A system for carrying out this invention is designed to improve the efficiency of managing machines and robots in a factory. The system includes: means for reading QR codes affixed to fixed assets; means for acquiring information about the fixed assets based on the read QR codes; means for aggregating the acquired information about the fixed assets; means for tracking the fixed assets based on the aggregated information about the fixed assets; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; means for reading QR codes affixed to machines in a factory; means for sending the read information about the machines by email; and means for notifying an administrator of the sent information.

[0779] Hardware and software used

[0780] Hardware: Smartphone or tablet with camera

[0781] Software: Python, qrcode library (QR code generation), OpenCV (QR code reading), smtplib (email sending)

[0782] Data processing and calculation

[0783] 1. QR code generation: The server uses the qrcode library to generate a QR code containing the machine's asset information (asset number, purchase date, contact person, etc.).

[0784] 2. QR Code Reading: Using a smartphone camera, we read the QR code attached to a machine in the factory. We use OpenCV to extract data from the QR code.

[0785] 3. Information Acquisition and Aggregation: The smartphone processes the scanned data and extracts the necessary information. The extracted information is sent via email to a server where it is centrally aggregated.

[0786] 4. Information tracking and loss prediction: The server uses the collected information to monitor and manage the location and status of machines. It also analyzes signs of data non-update or non-operation, predicts the risk of loss, and takes appropriate measures.

[0787] 5. Notification: The transmitted information is sent to the administrator via email, allowing the administrator to understand the machine status in real time and take appropriate action.

[0788] Specific examples

[0789] For example, by attaching a QR code to a robot in a factory and having a maintenance person read the QR code with their smartphone, they can instantly obtain the robot's maintenance history and information about the person in charge and report it to the manager. This system allows for efficient management of machines and robots in a factory, makes it easy to track maintenance history and information about the person in charge, and makes it possible to prevent loss of machines or improper management.

[0790] Prompt Sentence Examples

[0791] Develop an application that reads the QR code attached to a robot in a factory with a smartphone, obtains the robot's asset information (asset number, purchase date, person in charge, maintenance history, etc.), and sends it to the manager by email.

[0792] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0793] Step 1:

[0794] The server uses the qrcode library to generate a QR code containing the machine's asset information (asset number, purchase date, contact person, etc.). The machine's asset information is required as input, and a QR code image is generated as output. This QR code is then affixed to the machine.

[0795] Step 2:

[0796] A user uses a smartphone camera to read a QR code attached to a machine in a factory. The QR code image is required as input, and the QR code data is obtained as output. This includes the specific actions of scanning a QR code using a smartphone camera app.

[0797] Step 3:

[0798] The device uses OpenCV to extract data from the QR code. The input is a QR code image, and the output is the asset information contained in the QR code. Specifically, the device analyzes the QR code image and decodes the data.

[0799] Step 4:

[0800] The device sends the extracted asset information to the server via email. The input requires the asset information extracted from the QR code, and the output is an email. Specific operations include creating an email using smtplib and sending it to the specified address.

[0801] Step 5:

[0802] The server aggregates asset information from received emails. The email content is required as input, and the aggregated asset information is obtained as output. Specifically, the server analyzes the email content and stores it in a database.

[0803] Step 6:

[0804] The server monitors and manages the location and status of the machines based on the aggregated information. The aggregated asset information is required as input, and the monitoring results of the machine's location and status are obtained as output. Specific operations include retrieving information from the database and inputting it into the monitoring system.

[0805] Step 7:

[0806] The server analyzes signs of data inactivity or non-operation, predicts the risk of loss, and takes countermeasures. Aggregated asset information is required as input, and the predicted risk of loss is obtained as output. Specific operations include using data analysis algorithms to assess risk and propose countermeasures.

[0807] Step 8:

[0808] The server notifies the administrator of the sent information by email. The input is the aggregated asset information, and the output is a notification to the administrator. Specific operations include creating a notification email using smtplib and sending it to the administrator.

[0809] 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.

[0810] "Example 1"

[0811] One embodiment of the present invention is a system that combines a fixed asset management system with an emotion engine. This system recognizes a user's emotions and adjusts the method of managing fixed assets based on those emotions. Specifically, the emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. For example, if the system recognizes that the user is stressed about losing a fixed asset, it takes measures such as strengthening the tracking of fixed assets. On the other hand, if the system recognizes that the user is indifferent to managing fixed assets, it takes measures such as automating the management of fixed assets. This enables optimal fixed asset management according to the user's emotions.

[0812] "Example 2"

[0813] Another embodiment of the present invention provides specific means for the emotion engine to recognize the user's emotion. Specifically, the user's facial expression is photographed with a camera, and the emotion is recognized from the image data. The user's voice is also recorded with a microphone, and the emotion is recognized from the voice data. Furthermore, if the user inputs text, the emotion is recognized from the text data. By combining these means, it is possible to recognize the user's emotion more accurately.

[0814] The processing flow of each embodiment will be described below.

[0815] "Example 1"

[0816] Step 1: The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. Step 2: Based on the emotions recognized by the emotion engine, the method of managing fixed assets is adjusted. For example, if the system recognizes that the user is feeling stressed about losing a fixed asset, it will take measures such as strengthening tracking of the fixed asset.

[0817] Step 3: If the system recognizes that the user is uninterested in managing fixed assets, it will take measures such as automating the management of fixed assets. This will enable optimal fixed asset management according to the user's feelings.

[0818] "Example 2"

[0819] Step 1: The user's facial expression is captured by a camera, and the emotion engine recognizes the emotion from the image data.

[0820] Step 2: The user's voice is recorded with a microphone, and the emotion engine recognizes emotions from the voice data.

[0821] Step 3: When the user enters text, the emotion engine recognizes the emotion from the text data. By combining these methods, it is possible to recognize the user's emotion more accurately.

[0822] Example 1

[0823] 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."

[0824] Conventional fixed asset management systems require manual input of fixed asset information, making management cumbersome. It is also difficult to detect early signs of loss or non-operation of fixed assets, making it difficult to take appropriate measures. Furthermore, management methods are not adjusted to take into account user emotions, resulting in insufficient support for user stress and apathy. To solve these issues, a system that automatically acquires, aggregates, and tracks fixed asset information is needed.

[0825] 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.

[0826] In this invention, the server includes: means for reading QR codes attached to fixed assets; means for acquiring fixed asset information based on the read QR codes; means for sending the acquired fixed asset information by email; means for aggregating the transmitted fixed asset information; means for tracking fixed assets based on the aggregated fixed asset information; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; and means for recognizing user emotions and adjusting the fixed asset management method based on the emotions. This enables automatic acquisition, aggregation, and tracking of fixed asset information, enabling early detection of signs of loss or non-operation and appropriate countermeasures. It also enables optimal fixed asset management based on user emotions.

[0827] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[0828] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[0829] A "means of reading" is a device or software that analyzes the QR code and obtains the information contained within it.

[0830] The "means of obtaining information" refers to devices or software for collecting detailed information about fixed assets based on data read from QR codes.

[0831] The "means for sending by e-mail" refers to a device or software for sending information about the acquired fixed assets to a specified address via e-mail.

[0832] The "means for aggregating information" refers to a device or software for centrally collecting and managing the transmitted information on fixed assets.

[0833] "Tracking means" refers to devices or software that monitor the location and usage of fixed assets based on aggregated fixed asset information.

[0834] "Means to predict and prevent loss" refers to devices and software that detect signs of fixed asset data not being updated or not being used, predict the possibility of loss, and take measures to prevent it.

[0835] "Means for recognizing emotions" refers to devices or software that analyze emotions from a user's facial expressions, voice, text input, etc.

[0836] The "means for adjusting the management method" is a device or software for optimizing the management method of fixed assets based on the recognized user's emotions.

[0837] The present invention provides a system for improving the efficiency of fixed asset management and reducing the risk of loss or non-operation. Specific embodiments of this system will be described below.

[0838] System configuration

[0839] 1. Scan the QR code

[0840] The user uses the camera of a mobile information terminal (e.g., a smartphone) to read the QR code attached to the fixed asset.

[0841] The terminal uses a QR code reader app (e.g., QR Code Reader) to analyze the QR code and obtain information about the fixed asset.

[0842] 2. Acquisition of information

[0843] The terminal temporarily stores the fixed asset information (e.g., serial number, purchase date, installation location) obtained from the QR code in its memory.

[0844] 3. Transmission of Information

[0845] The terminal automatically inputs the acquired information about the fixed assets into an email and sends it to the email address of the specified server.

[0846] The device uses an app that provides email sending functionality (e.g., Gmail).

[0847] 4. Receiving and collecting information

[0848] The server analyzes the received email and extracts information about the fixed assets.

[0849] The server stores the extracted information in a database and manages it centrally using a database management system (e.g., MySQL).

[0850] 5. Fixed asset tracking

[0851] The server periodically checks the information on fixed assets stored in the database and monitors their location and usage.

[0852] The server records the movement history and frequency of use of fixed assets and checks for any abnormalities.

[0853] 6. Loss prediction and prevention

[0854] The server uses machine learning models (e.g., Scikit-learn) to detect signs of fixed asset data not being updated or not being used.

[0855] If the server determines that there is a high possibility of loss, it will send an alert email to the administrator.

[0856] 7. Emotional Recognition

[0857] The user inputs emotions using the camera, microphone, and keyboard of the mobile information terminal.

[0858] The device uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) to analyze emotions from the user's facial expressions, voice, and text.

[0859] 8. Sending Emotional Data

[0860] The device sends the analyzed emotion data to the server using the HTTPS protocol.

[0861] 9. Adjusting Emotional Management

[0862] The server analyzes the received emotion data and determines the user's emotional state.

[0863] If the server determines that the user is stressed about losing an asset, it will strengthen the tracking of the asset, such as by increasing the frequency of real-time location updates.

[0864] If the server determines that the user is not interested in managing fixed assets, it automates the management of fixed assets by periodically performing automatic checks and sending reminders.

[0865] Specific examples

[0866] When a user wants to read a QR code attached to an office computer with their smartphone, they launch the smartphone's camera app and select the QR code reader app.

[0867] The terminal analyzes the QR code and obtains the serial number "ABC123", purchase date "2022-01-01", and installation location "Office A".

[0868] The device automatically enters the acquired information into the email app and sends it to the server's email address "asset_management@example.com."

[0869] The server analyzes the received email and stores it in the database as "PC, serial number: ABC123, purchase date: 2022-01-01, installation location: Office A."

[0870] The server periodically checks the database, and if a computer has not been used for a long period of time, it sends an alert email to the administrator stating, "The computer (serial number: ABC123) has not been used for a long period of time. Please check."

[0871] Prompt Sentence Examples

[0872] "Please scan the QR code attached to the office computer with your smartphone and send the obtained information by email."

[0873] "If a user is stressed about losing a fixed asset, what action will the system take?"

[0874] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0875] Step 1:

[0876] The user activates the camera on their mobile information terminal and reads the QR code attached to the fixed asset.

[0877] Input: QR code attached to fixed asset

[0878] Output: QR code data

[0879] Specific operation: The user launches the camera app on their smartphone, selects the QR code reader app, and scans the QR code.

[0880] Step 2:

[0881] The terminal uses a QR code reader app to analyze the QR code and obtain information about the fixed asset.

[0882] Input: QR code data

[0883] Output: Fixed asset information (e.g., serial number, purchase date, installation location)

[0884] Specific operation: The device analyzes the QR code through a QR code reader app and extracts information such as the serial number "ABC123", purchase date "2022-01-01", and installation location "Office A".

[0885] Step 3:

[0886] The terminal automatically enters the acquired fixed asset information into an email and sends it to the specified server email address.

[0887] Input: Fixed asset information

[0888] Output: Email

[0889] Specific operation: The device launches the email application, automatically enters the acquired fixed asset information into the email body, and sends it to the server's email address "asset_management@example.com."

[0890] Step 4:

[0891] The server analyzes the received email and extracts information about the fixed assets.

[0892] Input: Email

[0893] Output: Fixed asset information

[0894] Specific operation: The server analyzes the email received through the email server and extracts fixed asset information from the email body.

[0895] Step 5:

[0896] The server stores the extracted information in a database and manages it centrally.

[0897] Input: Fixed asset information

[0898] Output: Information stored in the database

[0899] Specific operation: The server uses a database management system (e.g., MySQL) to store information about fixed assets in a database.

[0900] Step 6:

[0901] The server periodically checks the information on fixed assets stored in the database and monitors their location and usage.

[0902] Input: Information stored in a database

[0903] Output: Monitoring results

[0904] Specific operation: The server sets up a scheduled task to periodically query the database to check the location and usage status of fixed assets.

[0905] Step 7:

[0906] The server uses machine learning models to detect signs of fixed asset data not being updated or not being used.

[0907] Input: Monitoring results

[0908] Output: Anomaly detection results

[0909] Specific operation: The server uses a machine learning model (e.g., Scikit-learn) to analyze fixed asset data for signs of non-update or non-operation and detect anomalies.

[0910] Step 8:

[0911] If the server determines that there is a high possibility of loss, it will send an alert email to the administrator.

[0912] Input: Anomaly detection result

[0913] Output: Alert email

[0914] Specific operation: Based on the anomaly detection results, the server sends an alert email to the administrator's email address.

[0915] Step 9:

[0916] The user inputs emotions using the camera, microphone, and keyboard of the mobile information terminal.

[0917] Input: User facial expressions, voice, and text input

[0918] Output: Emotion data

[0919] Specific operations: The user captures their facial expressions with the smartphone camera, records audio with the microphone, and enters text with the keyboard.

[0920] Step 10:

[0921] The device uses an emotion recognition engine to analyze emotions from the user's facial expressions, voice, and text.

[0922] Input: User facial expressions, voice, and text input

[0923] Output: Parsed emotion data

[0924] Specific operation: The device uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotions.

[0925] Step 11:

[0926] The device transmits the analyzed emotion data to the server.

[0927] Input: Parsed emotion data

[0928] Output: Emotion data sent to the server

[0929] Specific operation: The device uses the HTTPS protocol to send the analyzed emotion data to the server.

[0930] Step 12:

[0931] The server analyzes the received emotion data and determines the user's emotional state.

[0932] Input: Emotion data sent to the server

[0933] Output: User's emotional state

[0934] Specific operation: The server analyzes the received emotional data and determines whether the user is stressed or indifferent.

[0935] Step 13:

[0936] The server adjusts how the fixed assets are managed based on the user's emotional state.

[0937] Input: User's emotional state

[0938] Output: Coordinated management

[0939] Specific behavior: The server tightens the tracking of fixed assets when the user is stressed, and automates management when the user is disinterested.

[0940] (Application example 1)

[0941] 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."

[0942] In conventional fixed asset management systems, fixed asset information is managed manually, which can lead to delays in updating information and an increased risk of loss. In addition, the management method is not adjusted according to the user's emotions, which can lead to user stress and apathy, reducing the efficiency of fixed asset management.

[0943] 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.

[0944] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, and means for recognizing user emotions and adjusting the management method for the fixed assets based on the emotions. This enables efficient management of fixed assets and flexible adjustment of the management method according to the user's emotions.

[0945] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[0946] A "QR code" is a two-dimensional barcode for storing information, and the information can be obtained using a reading device such as a camera.

[0947] "Reading means" refers to a device or method for obtaining information such as a QR code.

[0948] "Means for acquiring information" refers to a device or method for extracting data related to fixed assets from the scanned QR code.

[0949] "Means for aggregating information" refers to devices and methods for centrally managing information on acquired fixed assets.

[0950] A "tracking means" is a device or method for monitoring the location and status of an asset based on aggregated information about the asset.

[0951] "Means for predicting and preventing loss" refers to devices and methods for predicting the risk of loss based on signs such as non-updating of fixed asset data or non-operation, and preventing it.

[0952] "Means for recognizing emotions and adjusting management methods based on those emotions" refers to a device or method for recognizing emotions from a user's facial expressions, voice, text input, etc., and changing the management method for fixed assets in accordance with those emotions.

[0953] A system for carrying out the present invention includes an application installed on a robot that manages fixed assets in a factory. A specific embodiment of this system will be described below.

[0954] Generating a Program

[0955] The program of this system includes the following main functions:

[0956] 1. Ability to read QR codes attached to fixed assets

[0957] 2. Ability to obtain fixed asset information based on scanned QR codes

[0958] 3. A function to consolidate information on acquired fixed assets

[0959] 4. Ability to track fixed assets based on aggregated fixed asset information

[0960] 5. A function that predicts and prevents loss based on signs of non-updating or non-operating fixed asset data

[0961] 6. The ability to recognize user emotions and adjust how fixed assets are managed based on those emotions

[0962] Program processing explanation

[0963] Scanning QR codes

[0964] The robot uses the OpenCV library to read QR codes that are affixed to each fixed asset in the factory, and the robot periodically makes rounds to read them.

[0965] Obtaining and sending fixed asset information

[0966] The fixed asset information is obtained from the scanned QR code and sent to the administrator via email using smtplib and email.mime.text. This allows the fixed asset information to be centrally collected.

[0967] Fixed asset tracking and loss prediction

[0968] Based on the aggregated information, the location and status of fixed assets are tracked, signs of non-updating or non-operating fixed asset data are detected, and the risk of loss is predicted and prevented.

[0969] Adjusting emotion recognition and management strategies

[0970] Use the emotion_recognition library to recognize user emotions. Analyze emotions from facial expressions, voice, and text inputs to enhance asset tracking if the user is stressed, or automate management if the user is apathetic.

[0971] Specific examples

[0972] For example, a robot in a factory periodically patrols the factory and reads the QR code attached to each machine. The information read is sent to the manager via email, and if the manager's emotions are recognized as stress, the robot will strengthen its tracking.

[0973] Prompt Sentence Examples

[0974] Develop a robot application that manages fixed assets in a factory. This application reads QR codes, obtains information about fixed assets, and sends it via email. It also includes an emotion engine that adjusts management methods according to the manager's emotions. The libraries used are OpenCV, smtplib, and emotion_recognition.

[0975] In this way, the present invention realizes efficient management of fixed assets and flexible adjustment of management methods according to the user's feelings.

[0976] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0977] Step 1:

[0978] The robot patrols the factory and uses a camera to read QR codes attached to fixed assets. The input is an image of the QR code, and the output is the information about the fixed asset extracted from the QR code. The QR code data is decoded using the OpenCV library.

[0979] Step 2:

[0980] The robot temporarily saves the information about the fixed asset obtained from the scanned QR code. The input is the information about the fixed asset extracted from the QR code, and the output is the temporarily saved information about the fixed asset. The information is stored in a database or memory.

[0981] Step 3:

[0982] The robot sends the acquired fixed asset information to the administrator via email. The input is the temporarily saved fixed asset information, and the output is the email sent to the administrator. The robot uses smtplib and email.mime.text to compose and send the email.

[0983] Step 4:

[0984] The server aggregates the fixed asset information received from the administrator. The input is the email received from the administrator, and the output is the aggregated fixed asset information. The information is integrated into a database and managed centrally.

[0985] Step 5:

[0986] The server tracks fixed assets based on the aggregated fixed asset information. The input is the aggregated fixed asset information, and the output is the tracking results of the fixed asset's location and status. The location information and operating status are monitored and recorded in a database.

[0987] Step 6:

[0988] The server detects signs of fixed asset data not being updated or not being used, and performs loss prediction. The input is the fixed asset tracking results, and the output is the loss prediction results. Data analysis algorithms are used to detect abnormal patterns.

[0989] Step 7:

[0990] The robot uses the emotion_recognition library to recognize the user's emotions. The input is the user's facial expressions, voice, and text input, and the output is the recognized emotion. The data is collected using a camera and microphone and analyzed with an emotion recognition model.

[0991] Step 8:

[0992] The server adjusts the method of managing fixed assets based on the recognized emotion. The input is the recognized emotion, and the output is the adjusted management method. Depending on the emotion, the tracking frequency can be changed or settings can be made to automate management.

[0993] In this way, the system realizes efficient management of fixed assets and flexible adjustment of management methods according to the user's feelings.

[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] In conventional fixed asset management systems, fixed asset information is often managed manually, making updating and tracking the information cumbersome. Furthermore, there is a lack of effective means to prevent the loss of fixed assets. Furthermore, there is no integrated system for recognizing user emotions, making it difficult to accurately grasp the user's status. To solve these issues, a system that integrates fixed asset management and user emotion recognition is needed.

[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 means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, means for photographing a user's facial expression and recognizing emotions from the image data, means for recording the user's voice and recognizing emotions from the voice data, means for recognizing emotions from text data entered by the user, and means for integrating these emotion data to more accurately recognize the user's emotions. This integrates fixed asset management and user emotion recognition, making it possible to prevent loss of fixed assets and accurately grasp the user's condition.

[0999] "Fixed assets" are assets owned by a company or organization that are used over a long period of time, and include buildings, machinery, equipment, computers, etc.

[1000] A "QR code" is a type of two-dimensional barcode that visually encodes information and can be read by a camera or scanner.

[1001] "Means of reading" refers to devices or technologies used to capture QR codes or other information, including cameras and scanners.

[1002] "Means of obtaining information" refers to the technology or process used to extract the necessary information from the scanned QR code or other data.

[1003] "Means of aggregating information" refers to technologies and systems for centrally compiling and managing acquired information.

[1004] "Tracking measures" refers to the technologies and processes used to monitor and manage the location and condition of fixed assets based on aggregated information.

[1005] "Means to predict and prevent loss" refers to technologies and systems that analyze signs of fixed asset data not being updated or not being operational, and predict and prevent the possibility of loss.

[1006] "Means for capturing facial expressions" refers to technology or devices for capturing images of a user's facial expressions with a camera and acquiring the image data.

[1007] "Means for recognizing emotions" refers to technologies and systems for analyzing and recognizing a user's emotions from acquired image data, audio data, and text data.

[1008] "Means for recording audio" refers to the technology or device for recording the user's audio with a microphone and acquiring the audio data.

[1009] "Means for recognizing emotions from text data" refers to technologies and systems that analyze text data entered by a user and recognize emotions from its content.

[1010] "Means for integrating emotion data" refers to technologies and systems that integrate data obtained from multiple emotion recognition means to more accurately recognize a user's emotions.

[1011] MODE FOR CARRYING OUT THE INVENTION

[1012] The present invention is a system that integrates fixed asset management and user emotion recognition. A specific embodiment of this system will be described below.

[1013] Fixed Asset Management System

[1014] 1. Generate and attach a QR code

[1015] The server generates a QR code based on the information (asset number, purchase date, person in charge, etc.) from the computer registered in the fixed asset management system. To generate the QR code, for example, use the Python qrcode library.

[1016] The user prints out the generated QR code and attaches it to their computer.

[1017] 2. Scan the QR code

[1018] The new employee scans the QR code using the camera on their smartphone, which has either a camera app or a dedicated QR code reader app installed.

[1019] The terminal (smartphone) analyzes the contents of the QR code and obtains the computer's asset information.

[1020] 3. Transmission of Information

[1021] The device will then send the acquired asset information via email, automatically entering the information into the body of the email using the smartphone's email app.

[1022] The server receives the information at the specified email address.

[1023] 4. Information collection and management

[1024] The server analyzes the content of the received email and registers the information in the database of the fixed asset management system. For example, the Python imaplib library is used for email analysis.

[1025] The server periodically updates the database (e.g., MySQL) to keep the asset information up to date.

[1026] 5. Fixed asset tracking

[1027] The server uses a database to track changes in the location and ownership of fixed assets, and issues alerts in the event of unusual activity or possible loss.

[1028] Emotion Engine System

[1029] 1. Photographing facial expressions

[1030] The user stands in front of the camera and shows their facial expression, which is then captured by the camera and image data is acquired.

[1031] 2. Recording Audio

[1032] A user speaks into a microphone, which records the voice and captures the audio data.

[1033] 3. Enter text

[1034] The user inputs text using a keyboard or a touch screen, and the terminal acquires the input text data.

[1035] 4. Emotion Analysis

[1036] The server analyzes the image data and recognizes the user's emotions from their facial expressions. For image analysis, the OpenCV library is used, for example.

[1037] The server analyzes the voice data and recognizes emotions from the user's voice. For voice analysis, Google Cloud Speech-to-Text is used, for example.

[1038] The server analyzes the text data and recognizes emotions from the user's text, using natural language processing, such as the NLTK library.

[1039] 5. Emotional Data Integration

[1040] The server integrates these emotion data and determines the final emotion. For example, if the facial expression, voice, and text are "joy," the final emotion will be "joy."

[1041] The server notifies the user of the result.

[1042] Examples and prompts

[1043] Example: A company's IT department purchases a new computer and affixes a QR code to it. When the person in charge is transferred, the new person scans the QR code with their smartphone to retrieve the information. The information is then sent via email to the IT department's management system.

[1044] Example prompt: "Scan the QR code on your new computer and email us the information you get."

[1045] Example: A user smiles in front of a camera. The system analyzes the facial expression and recognizes that the user is "happy." Also, if the user enters text such as "Today was a fun day," the system will recognize the emotion of "happy" from that text as well.

[1046] Example prompt: "Smile for the camera and text us how you're feeling today."

[1047] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1048] Fixed Asset Management System Processing Steps

[1049] Step 1: Generate and paste a QR code

[1050] The server generates a QR code based on the information on the computer registered in the fixed asset management system (asset number, purchase date, person in charge, etc.). The computer information is required as input, and a QR code is generated as output.

[1051] Specific operation: The server generates a QR code using a QR code generation library (e.g., qrcode for Python), and outputs the generated QR code as an image file.

[1052] The user prints the generated QR code and pastes it on their computer. An image file of the generated QR code is required as input, and the QR code is pasted on the computer as output.

[1053] Step 2: Scan the QR code

[1054] The new employee reads the QR code using the camera on their smartphone. The QR code is required as input, and the content of the QR code is obtained as output.

[1055] How it works: Scan the QR code using your smartphone's camera app or a dedicated QR code reader app. The app will analyze the contents of the QR code and display the asset information.

[1056] Step 3: Submit your information

[1057] The terminal sends the acquired asset information by email. The acquired asset information is required as input, and an email is sent as output.

[1058] Specific operation: The smartphone launches the email app and automatically enters the acquired asset information into the email body. The user presses the send button to send the email. The server receives the email and saves the contents in a database.

[1059] Step 4: Aggregating and managing information

[1060] The server analyzes the content of the received email and registers the information in the database of the fixed asset management system. The received email is required as input, and the information is registered in the database as output.

[1061] Specific operation: The server analyzes received emails using an email analysis library (e.g., imaplib for Python). It stores the analyzed information in a database (e.g., MySQL). It periodically cleans the database to keep it up to date.

[1062] Step 5: Track fixed assets

[1063] The server tracks changes in the location and ownership of fixed assets based on a database. It requires database information as input and provides tracking results as output.

[1064] Specific behavior: The server executes database queries to retrieve asset information. If it detects any abnormal activity (e.g., a change in the person in charge within a short period of time), it sends an email or notification to the administrator.

[1065] Emotion engine system processing steps

[1066] Step 1: Capture facial expressions

[1067] The user stands in front of the camera and shows their facial expression. The user's facial expression is required as input, and image data is obtained as output.

[1068] Specific operation: The user shows a smile or an angry expression in front of the camera. The camera captures that moment and saves the image data.

[1069] Step 2: Record your audio

[1070] The user speaks into a microphone: the user's voice is required as input and audio data is obtained as output.

[1071] What happens: The user says, "Today was a fun day." The microphone records the speech and saves it as an audio file.

[1072] Step 3: Entering text

[1073] The user inputs text using a keyboard or a touch screen. The user's text is required as input, and text data is obtained as output.

[1074] Specific operation: The user types the text "Today was a fun day" into their smartphone or computer. The device saves the text data.

[1075] Step 4: Sentiment Analysis

[1076] The server analyzes the image data and recognizes the user's emotions from their facial expressions. Image data is required as input, and emotion recognition results are obtained as output.

[1077] Specific operation: The server analyzes facial expressions using an image analysis library (e.g., OpenCV).

[1078] The server analyzes the voice data and recognizes the emotion from the user's voice. The voice data is required as input, and the emotion recognition result is obtained as output.

[1079] What happens: The server analyzes the audio using a speech analysis library (e.g., Google Cloud Speech-to-Text).

[1080] The server analyzes the text data and recognizes the emotion from the user's text. The text data is required as input, and the emotion recognition result is obtained as output.

[1081] What happens: The server parses the text using a natural language processing library (e.g., NLTK).

[1082] Step 5: Integrating Emotional Data

[1083] The server integrates these emotion data and determines the final emotion. Each emotion recognition result is required as input, and the integrated emotion recognition result is obtained as output.

[1084] Specific operation: The server integrates the results of each analysis and determines the user's overall emotion. For example, if the facial expression, voice, and text are all "happy," the final emotion will be "happy." The server then notifies the user of the result.

[1085] (Application example 2)

[1086] 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."

[1087] Conventional fixed asset management systems require manual management of fixed asset information, resulting in delays in updating information and an increased risk of loss. Furthermore, they do not take into account the emotions of workers, which can lead to reduced work efficiency. To solve these problems, a system is needed that automatically acquires and manages fixed asset information, and also recognizes the emotions of workers to provide appropriate support.

[1088] 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.

[1089] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs such as non-updating of fixed asset data or non-operation, means for recognizing the emotions of workers, and means for providing appropriate support to the workers based on the recognized emotions. This not only makes it possible to automatically manage information about fixed assets and prevent loss, but also to provide appropriate support that takes into account the emotions of the workers.

[1090] "Fixed assets" are assets owned by a company or organization that are used for a long period of time, and include buildings, machinery, equipment, vehicles, etc.

[1091] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1092] "Means for reading" refers to the device or software used to scan the QR code and obtain its information.

[1093] "Means of obtaining information" refers to the equipment or software used to analyze the data read from the QR code and extract the necessary information.

[1094] "Means of aggregating information" refers to databases or software for centrally managing acquired information.

[1095] "Tracking instruments" refers to devices and software used to monitor the location and condition of fixed assets based on aggregated information.

[1096] "Means to predict and prevent loss" refers to devices and software that detect signs of fixed asset data not being updated or not being used, predict the risk of loss, and take measures to prevent it.

[1097] "Means for recognizing emotions" refers to devices and software for analyzing emotions from workers' facial expressions, voice, and text data.

[1098] "Means for providing appropriate support" refers to devices and software for providing necessary assistance and advice to workers based on the recognized emotions.

[1099] "Smart devices" refers to mobile information terminals with advanced functions, such as smartphones and tablets.

[1100] "Email" is a means of communication for sending and receiving text and files over the Internet.

[1101] As an embodiment of the present invention, a method for installing an application that combines a fixed asset management system using QR codes and an emotion engine on a robot in a factory will be described.

[1102] System Program

[1103] This system operates using the following hardware and software:

[1104] Hardware: Camera, microphone

[1105] Software: OpenCV (QR code reading), smtplib (email sending), EmotionRecognizer (emotion recognition)

[1106] Program processing

[1107] The server first reads the QR code from the image captured by the camera. It uses OpenCV to scan the QR code and obtain the information. It then analyzes the obtained fixed asset information and extracts the necessary data. This data is then sent to the administrator via email using smtplib. The sent information is then aggregated in a database and managed centrally.

[1108] Furthermore, the server recognizes the worker's emotions from images captured by the camera and audio recorded by the microphone. Using EmotionRecognizer, it analyzes the image and audio data to identify the worker's emotions. Based on the recognized emotions, it provides appropriate support to the worker. For example, if a worker is feeling stressed, it can respond by displaying a message encouraging them to take a break.

[1109] Specific examples

[1110] For example, a robot in a factory can periodically read the QR code on each machine and send fixed asset information to the manager. Also, if a worker is feeling stressed, the robot can recognize that emotion and provide appropriate support. In this way, fixed asset management and worker support can be performed simultaneously.

[1111] Prompt Sentence Examples

[1112] "Develop an application that allows a robot in a factory to read QR codes to manage information on fixed assets, and recognizes the emotions of workers to provide appropriate support. Use OpenCV to read QR codes and EmotionRecognizer to recognize emotions."

[1113] The above is a specific embodiment for carrying out the present invention. This system not only automatically manages information on fixed assets and prevents loss, but also provides appropriate support that takes into account the emotions of workers.

[1114] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1115] Step 1:

[1116] The server reads the QR code from the image captured by the camera. As input, it receives the image data captured by the camera and scans the QR code using OpenCV. As output, it obtains the fixed asset information contained in the QR code.

[1117] Step 2:

[1118] The server analyzes the fixed asset information based on the scanned QR code. As input, it receives and analyzes the data obtained from the QR code. As output, it extracts detailed information such as the fixed asset number, purchase date, and person in charge.

[1119] Step 3:

[1120] The server sends the acquired fixed asset information by email. As input, it receives the parsed fixed asset information and sends it to the administrator's email address using smtplib. As output, the fixed asset information is notified to the administrator.

[1121] Step 4:

[1122] The server aggregates the transmitted fixed asset information in a database. As input, it receives the fixed asset information transmitted by email and stores it in the database. As output, the fixed asset information is managed in a unified manner.

[1123] Step 5:

[1124] The server recognizes the worker's emotions from images captured by a camera and audio recorded by a microphone. It receives image and audio data as input and analyzes emotions using EmotionRecognizer. The output identifies the worker's emotional state.

[1125] Step 6:

[1126] The server provides appropriate support to the worker based on the recognized emotion. As input, it receives the identified emotional state and determines the appropriate support content. As output, it displays a message or advice encouraging the worker to take a break.

[1127] These are the specific processing steps of this system. By clarifying the operations and inputs / outputs performed at each step, it is easier to understand the flow of the entire system.

[1128] 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.

[1129] 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.

[1130] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1131] 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.

[1132] [Third embodiment]

[1133] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1135] 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).

[1136] 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.

[1137] 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.

[1138] 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).

[1139] 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.

[1140] 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.

[1141] 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.

[1142] 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.

[1143] 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.

[1144] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1145] "Example 1"

[1146] In one embodiment of the present invention, a QR code attached to a fixed asset is read using a smartphone camera. Information about the fixed asset is obtained based on the read QR code, and the obtained information about the fixed asset is sent via email via the smartphone. The transmitted information about the fixed asset is centrally aggregated. The fixed asset is tracked based on the aggregated information about the fixed asset. Loss is predicted based on signs of fixed asset data not being updated or not being used, and loss is prevented before it occurs.

[1147] "Example 2"

[1148] As a specific example, a company affixes a QR code to a computer, which is a fixed asset. This QR code contains information such as the computer's asset number, purchase date, and the person in charge. When the person in charge is transferred or leaves the company, the new person can read the QR code using their smartphone camera to obtain information about the fixed asset. The obtained information is sent via email via the smartphone and centrally aggregated. Based on the aggregated information, the fixed asset can be tracked to prevent loss.

[1149] The processing flow of each embodiment will be described below.

[1150] "Example 1"

[1151] Step 1: Use your smartphone camera to read the QR code attached to the fixed asset.

[1152] Step 2: Obtain fixed asset information based on the scanned QR code.

[1153] Step 3: Information about the acquired fixed assets is sent via email via smartphone. Step 4: The sent information about the fixed assets is centrally consolidated.

[1154] Step 5: Track fixed assets based on the aggregated fixed asset information.

[1155] Step 6: Predict losses based on signs such as non-updating of fixed asset data or non-operation, and prevent them from occurring.

[1156] "Example 2"

[1157] Step 1: A QR code is attached to a computer, which is a fixed asset of a company. This QR code contains information such as the computer's asset number, purchase date, and person in charge.

[1158] Step 2: If the person in charge is transferred or resigns, the new person in charge will read the QR code using the smartphone camera.

[1159] Step 3: Obtain fixed asset information based on the scanned QR code.

[1160] Step 4: The information obtained is sent via email via your smartphone.

[1161] Step 5: The submitted information on fixed assets is centrally aggregated.

[1162] Step 6: Use the aggregated information to track fixed assets and prevent loss.

[1163] Example 1

[1164] 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."

[1165] In the area of ​​fixed asset management, it is difficult to efficiently acquire, consolidate, and track information on fixed assets. In addition, there is a lack of methods to predict and prevent loss based on signs such as inactivity or non-operation of fixed asset data.

[1166] 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.

[1167] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring fixed asset information based on the read QR codes, means for sending the acquired fixed asset information by email, means for aggregating the transmitted fixed asset information, means for tracking fixed assets based on the aggregated fixed asset information, and means for predicting and preventing loss based on signs such as non-updating of fixed asset data or non-operation. This allows for efficient management of fixed asset information and makes it possible to predict and prevent loss.

[1168] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[1169] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1170] A "mobile information terminal" is a portable information processing device such as a smartphone or tablet.

[1171] A "camera" is a device for taking images or videos.

[1172] "Email" is a means of communication for sending and receiving text and files over the Internet.

[1173] A "server" is a computer system that provides services to other computers on a network.

[1174] A "database" is a system for efficiently storing, searching, and managing data.

[1175] "Tracking" is the act of continuously monitoring the location and status of an object or information.

[1176] "Not updated" refers to a state in which data has not been updated for a certain period of time.

[1177] "Inoperation" refers to a state in which equipment or systems are not in use for a certain period of time.

[1178] "Loss prediction" is the act of predicting the possibility of loss of an item based on data analysis.

[1179] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and make predictions and classifications.

[1180] This invention is a system for efficiently managing fixed assets, and includes means for reading QR codes affixed to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for sending the acquired information about the fixed assets by email, means for aggregating the transmitted information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, and means for predicting loss based on signs such as non-updating of fixed asset data or non-operation, and for preventing loss before it occurs.

[1181] The user uses the smartphone camera to read the QR code attached to the fixed asset. Using a QR code reader app (e.g., QR Code Reader, Google Lens), the user places the QR code within the camera's frame, and the app automatically scans the QR code and acquires the data.

[1182] The data from the QR code read by the device is sent to the server. The server accesses a database (e.g., MySQL, PostgreSQL) and retrieves information about the fixed asset corresponding to the QR code. The retrieved information includes the name, location, purchase date, current condition, etc. of the fixed asset.

[1183] The information about the fixed assets acquired by the terminal is sent by email. The email is sent using the SMTP protocol and an email service such as Gmail or Outlook. The body of the email contains detailed information about the fixed assets.

[1184] The server analyzes the received emails and aggregates the information about fixed assets into a centralized database. The aggregated data is then stored in a fixed asset management system (e.g., SAP, Oracle Asset Management).

[1185] The server tracks fixed assets based on the aggregated information on fixed assets. Tracking includes the movement history and current location information of fixed assets. The server periodically updates the database to keep the information up to date.

[1186] The server analyzes fixed asset data and detects signs of non-renewal, non-operation, etc. A generative AI model (e.g., TensorFlow, PyTorch) is used to predict loss and send alerts to administrators to prevent it from happening.

[1187] For example, a user uses their smartphone to scan a QR code attached to an office printer. The QR code contains the printer's ID, which the device sends to a server. The server retrieves the printer's details from a database, and the device sends the information to an administrator via email. The server aggregates the received information into a database and tracks printer usage. If the printer has not been used for a long period of time, a generative AI model is used to predict the possibility of loss and send an alert to the administrator.

[1188] Example prompt sentence:

[1189] "Scan the QR code attached to the office printer with your smartphone. The printer information will be acquired based on the scanned QR code and sent to you by email. The information will be aggregated on the server and used to track the printer and predict if it has been lost."

[1190] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1191] Step 1:

[1192] The user activates the smartphone camera and reads the QR code attached to the fixed asset using a QR code reader app (e.g., QR Code Reader, Google Lens). When the user holds the QR code within the camera's frame, the app automatically scans the QR code and acquires the data.

[1193] Input: QR code

[1194] Output: QR code data

[1195] Specific operation: The user opens the camera app on their smartphone and points the camera at a QR code. The app recognizes the QR code and reads the data.

[1196] Step 2:

[1197] The terminal sends the data from the QR code it reads to the server, which then accesses a database (e.g., MySQL, PostgreSQL) to retrieve information about the fixed asset corresponding to the QR code.

[1198] Input: QR code data

[1199] Output: Fixed asset information

[1200] What happens: The device sends the QR code data to the server, which queries the database to retrieve the fixed asset information.

[1201] Step 3:

[1202] The information about the fixed assets acquired by the terminal is sent by email. The email is sent using the SMTP protocol and an email service such as Gmail or Outlook. The body of the email contains detailed information about the fixed assets.

[1203] Input: Fixed asset information

[1204] Output: Email

[1205] Specific operation: The terminal inserts the fixed asset information into the email body, and then sends the email through the SMTP server.

[1206] Step 4:

[1207] The server analyzes the received emails and aggregates the information about fixed assets into a centralized database. The aggregated data is then stored in a fixed asset management system (e.g., SAP, Oracle Asset Management).

[1208] Input: Email

[1209] Output: Aggregated fixed asset information

[1210] Specific operation: The server analyzes the received email and saves the analysis results in a database.

[1211] Step 5:

[1212] The server tracks fixed assets based on the aggregated information on fixed assets. Tracking includes the movement history and current location information of fixed assets. The server periodically updates the database to keep the information up to date.

[1213] Input: Aggregated fixed asset information

[1214] Output: Fixed asset tracking information

[1215] Specific operation: The server records the fixed asset movement history in a database. The server updates the database periodically.

[1216] Step 6:

[1217] The server analyzes fixed asset data and detects signs of non-renewal, non-operation, etc. A generative AI model (e.g., TensorFlow, PyTorch) is used to predict loss and send alerts to administrators to prevent it from happening.

[1218] Input: Fixed asset tracking information

[1219] Output: Loss prediction alert

[1220] How it works: The server analyzes the data using the generated AI model. The server predicts the possibility of loss and generates an alert. The server sends the alert to an administrator.

[1221] (Application example 1)

[1222] 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."

[1223] With conventional fixed asset management systems, it was difficult to monitor the location and status of fixed assets in real time, which increased the risk of loss or non-operation, especially in large-scale factory asset management. Furthermore, the time-consuming task of manually aggregating fixed asset information made efficient management difficult.

[1224] 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.

[1225] In this invention, the server includes: means for reading QR codes affixed to fixed assets; means for acquiring information about the fixed assets based on the read QR codes; means for aggregating the acquired information about the fixed assets; means for tracking the fixed assets based on the aggregated information about the fixed assets; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; means installed on robots in the factory for transmitting information about the fixed assets in real time to the central management system; and means for monitoring the location and status of the fixed assets based on the information transmitted to the central management system. This makes it possible to monitor the location and status of fixed assets in the factory in real time and reduce the risk of loss or non-operation.

[1226] "Fixed assets" are physical assets that a business or organization holds for long-term use.

[1227] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1228] "Means of reading" refers to the equipment or technology used to decode the QR code and obtain its information.

[1229] "Means of obtaining information" refers to devices and technologies for collecting detailed information about fixed assets based on data read from QR codes.

[1230] "Means of aggregating information" refers to devices or technologies for centrally compiling information on acquired fixed assets.

[1231] "Tracking means" refers to devices and technologies that monitor the location and status of assets based on aggregated fixed asset information.

[1232] "Means for predicting and preventing loss" refers to devices and technologies that analyze signs of fixed asset data not being updated or not being operational, and predict and prevent the risk of loss.

[1233] A "factory robot" is an automated mechanical device used to perform tasks in a factory.

[1234] A "central management system" is a system for centrally managing and monitoring fixed asset information within a factory.

[1235] "Real-time transmission means" refers to devices or technologies for instantly transmitting fixed asset information to the central management system.

[1236] "Means for monitoring location and status" refers to devices and technologies for continuously monitoring the location and status of assets based on information about fixed assets sent to the central management system.

[1237] The present invention relates to a system for improving the efficiency of fixed asset management within a factory. A specific embodiment of this system will be described below.

[1238] First, QR codes are attached to fixed assets within the factory. The QR codes contain the identification information of the fixed assets. Robots within the factory are equipped with cameras and periodically patrol the factory, reading the QR codes attached to the fixed assets.

[1239] The robot uses the OpenCV library to decode the QR code and retrieve information, such as the asset's identification and location. The robot then transmits the information to a central management system in real time using wireless communication technologies such as Wi-Fi and Bluetooth.

[1240] The central management system centrally aggregates the received information on fixed assets. The aggregated information is stored in a database and used to monitor the location and status of fixed assets in real time. A relational database management system (RDBMS) such as MySQL or PostgreSQL is used for database management.

[1241] Furthermore, the central management system analyzes signs of fixed asset data inactivity and non-operation to predict loss, using machine learning algorithms such as the Python scikit-learn library to analyze fixed asset data and predict the risk of loss.

[1242] This system makes it possible to monitor the location and status of fixed assets within a factory in real time, reducing the risk of loss or non-operation. It also eliminates the need to manually collect information on fixed assets, enabling more efficient management.

[1243] For example, a robot in a factory periodically patrols the area and reads the QR codes attached to each machine and tool. The robot sends the read information to a central management system, which monitors the status of the assets in real time. If it detects signs of asset inactivity or loss, it sends an alert to the manager.

[1244] Example prompt for a generative AI model:

[1245] To streamline fixed asset management within the factory, develop an application that allows a robot to read QR codes and send asset information to a central management system. This application will have the function of monitoring the location and status of assets in real time, reducing the risk of loss or non-operation.

[1246] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1247] Step 1:

[1248] The robot patrols the factory and uses a camera to take pictures of QR codes attached to fixed assets.

[1249] Input: QR code attached to fixed assets in the factory

[1250] Output: QR code image data

[1251] Specific operation: The robot periodically moves around the factory and uses a camera to photograph QR codes affixed to fixed assets.

[1252] Step 2:

[1253] The robot uses the OpenCV library to decode the QR code and obtain the fixed asset's identifying information.

[1254] Input: QR code image data

[1255] Output: Fixed asset identification information (text data)

[1256] Specific operation: The robot inputs the image data of the captured QR code into the OpenCV library, decodes the QR code, and obtains the fixed asset identification information.

[1257] Step 3:

[1258] The robot transmits the identification information of fixed assets acquired in real time to a central management system.

[1259] Input: Fixed asset identification information (text data)

[1260] Output: Fixed asset identification information sent to the central management system

[1261] Specific operation: The robot uses wireless communication technologies such as Wi-Fi and Bluetooth to transmit the identification information of the acquired fixed assets to the central management system.

[1262] Step 4:

[1263] The central management system centrally collects the fixed asset information it receives and stores it in a database.

[1264] Input: Fixed asset identification information sent by the robot

[1265] Output: Fixed asset information stored in the database

[1266] Specific operation: The central management system stores the received fixed asset information in a relational database management system (RDBMS).

[1267] Step 5:

[1268] The central management system monitors the location and status of fixed assets in real time based on aggregated fixed asset information.

[1269] Input: Fixed asset information stored in the database

[1270] Output: Fixed asset location and status monitoring results

[1271] How it works: The central management system periodically checks the information stored in the database and monitors the location and status of fixed assets.

[1272] Step 6:

[1273] The central management system analyzes signs such as non-updating or non-operating fixed asset data and predicts losses.

[1274] Input: Fixed asset information stored in the database

[1275] Output: Loss prediction result

[1276] How it works: The central management system uses machine learning algorithms to analyze fixed asset data and predict the risk of loss.

[1277] Step 7:

[1278] The central management system sends an alert to the administrator based on the loss prediction results.

[1279] Input: Loss prediction result

[1280] Output: Alert notification to administrator

[1281] Specific operation: Based on the loss prediction results, the central management system will send an alert to the administrator via email, SMS, etc.

[1282] Example 2

[1283] 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."

[1284] In conventional fixed asset management systems, asset information was often updated and tracked manually, resulting in delays in updating information when personnel were transferred or retired. Furthermore, there were insufficient mechanisms for preventing the loss of fixed assets, making asset management cumbersome. This increased the risk of fixed assets being lost or misused.

[1285] 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.

[1286] In this invention, the server includes means for generating and printing QR codes, means for reading QR codes, means for sending acquired fixed asset information by email, means for analyzing received emails and saving the information in a database, and means for periodically checking the information in the database and issuing an alert if an abnormality is detected. This makes it possible to centrally manage fixed asset information, quickly update information when personnel are transferred or resign, and prevent the loss of fixed assets.

[1287] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[1288] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1289] "Reading means" refers to a device or software that scans the QR code and obtains its contents as digital data.

[1290] The "means of acquisition" refers to a device or software for extracting the necessary information from the contents of the scanned QR code.

[1291] "Aggregating means" refers to a device or software that stores and organizes acquired information in a database for centralized management.

[1292] A "tracking instrument" is a device or software that monitors and manages the location and status of fixed assets based on aggregated information.

[1293] "Means for predicting and preventing loss" refers to devices or software that detect signs of fixed asset data not being updated or not being operational, predict the risk of loss, and take measures to prevent it.

[1294] A "generating and printing means" is a device or software that converts fixed asset information into a QR code and physically prints the QR code.

[1295] The "means for sending by e-mail" is a device or software for sending the acquired information to the server via e-mail.

[1296] The "means for analyzing and storing in a database" refers to a device or software for analyzing the content of received e-mail and storing the information in a database.

[1297] An "alert issuing means" is a device or software that periodically checks the information in the database and notifies the administrator if an abnormality is detected.

[1298] This invention is a system for efficiently managing fixed assets owned by companies and organizations and preventing their loss. This system manages asset information using QR codes affixed to fixed assets.

[1299] The server hosts a fixed asset management system, and uses a Python script to convert each computer's asset information (asset number, purchase date, person in charge, etc.) into a QR code, which is then printed using a dedicated label printer and affixed to each computer.

[1300] The user (new person in charge) uses the smartphone camera to scan the QR code attached to the computer. The device (smartphone) reads the contents of the QR code using a dedicated app. The asset information obtained from the QR code is displayed on the smartphone app screen. The user checks the displayed information and enters the new person in charge information.

[1301] The acquired asset information is sent to the server via email via a smartphone app. The device checks the integrity of the information before sending it and converts it into the required format (e.g., JSON). The server parses the received email and uses a Python script to save the asset information to a database. The server compares it with existing data and updates the contact information as necessary.

[1302] The server tracks fixed assets based on information collected in a database. The server periodically runs a Python script to check asset information. If an abnormality is detected, the server sends an alert email to the administrator.

[1303] As a concrete example, the following scenario can be considered.

[1304] Person A in the IT department of a certain company is retiring. Person B is to take over his computer. Person B uses the camera on his smartphone to scan the QR code attached to the computer. The QR code contains the computer's asset number "PC-12345," the purchase date "2022-01-15," and information about the previous person in charge, "A." Person B then sends the scanned information to the server via email using an app on his smartphone. The server saves the received information in a database and updates the person in charge information to "B."

[1305] An example of a prompt sentence might be:

[1306] "How can I use my smartphone camera to scan the QR code attached to a company's fixed asset computer and send the acquired asset information to the server via email?"

[1307] In this way, information on fixed assets can be managed centrally, and information can be updated quickly when personnel are transferred or leave the company, making it possible to prevent the loss of fixed assets.

[1308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1309] Step 1: Generate and paste a QR code

[1310] The server uses the fixed asset management system to convert the asset information of each computer (asset number, purchase date, person in charge, etc.) into a QR code. Specifically, a Python script is used to encode the asset information into QR code format. The input is the asset information of each computer, and the output is a QR code image. The generated QR code is printed using a dedicated label printer and affixed to each computer.

[1311] Step 2: Scan the QR code

[1312] The user uses the camera on their smartphone to scan the QR code attached to their computer. The device (smartphone) then reads the contents of the QR code using a dedicated app. The input is the QR code image, and the output is the asset information extracted from the QR code. Specifically, the user launches the camera app on their smartphone and scans the QR code.

[1313] Step 3: Obtain asset information

[1314] The terminal (smartphone) displays the asset information obtained from the QR code. The input is the asset information extracted from the QR code, and the output is the asset information displayed on the smartphone screen. The user checks the displayed information and enters new contact information as necessary. Specifically, the user checks the asset information on the dedicated app screen and enters new contact information in the form.

[1315] Step 4: Submit asset information

[1316] The user sends asset information obtained through a smartphone app to the server by email. The device checks the integrity of the information before sending and converts it into the required format (e.g., JSON format). The input is the asset information, including the new contact information entered by the user, and the output is data in email format. Specifically, the user taps the "Send" button on the dedicated app.

[1317] Step 5: Aggregate and update asset information

[1318] The server parses the received email and saves the asset information in a database. The server compares it with existing data and updates the contact information as needed. The input is the asset information in email format, and the output is the updated asset information saved in the database. Specifically, the server uses a Python script to parse the email and save the information in the database.

[1319] Step 6: Track fixed assets

[1320] The server tracks fixed assets based on information aggregated in a database. The server periodically runs a Python script to check asset information. The input is the asset information stored in the database, and the output is an alert notification if an abnormality is detected. Specifically, the server periodically scans the database, and if an abnormality is detected, it sends an alert email to the administrator.

[1321] In this way, information on fixed assets can be managed centrally, and information can be updated quickly when personnel are transferred or leave the company, making it possible to prevent the loss of fixed assets.

[1322] (Application example 2)

[1323] 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."

[1324] The traditional method of managing machines and robots used in factories is time-consuming and difficult to do efficiently. Furthermore, due to insufficient tracking of machine maintenance history and personnel information, machines can be lost or improperly managed. This leads to issues such as reduced factory operational efficiency and increased costs.

[1325] 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.

[1326] In this invention, the server includes means for reading QR codes affixed to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, means for reading QR codes affixed to machines in the factory, means for sending the read machine information by email, and means for notifying the manager of the sent information. This allows for efficient management of machines and robots in the factory, makes it easy to track maintenance history and information about the person in charge, and makes it possible to prevent loss or improper management of machines.

[1327] "Fixed assets" are assets that a company or organization holds for long-term use, and include buildings, machinery, equipment, etc.

[1328] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1329] "Reading means" refers to a device or method for scanning a QR code and obtaining its contents as digital data.

[1330] "Means for acquiring information" refers to a device or method for extracting the necessary information from the data in the scanned QR code.

[1331] "Means for aggregating information" refers to a device or method for centrally organizing and managing acquired information.

[1332] A "tracking means" is a device or method for monitoring and managing the location and status of fixed assets based on aggregated information.

[1333] "Means for predicting and preventing loss" refers to devices and methods for analyzing signs such as non-updating of fixed asset data or non-operation, predicting the risk of loss, and taking measures.

[1334] "Machinery in a factory" refers to machinery and equipment such as production equipment and robots used in a factory.

[1335] "Means for sending by e-mail" refers to a device or method for sending the acquired information to a designated recipient via e-mail.

[1336] The "means for notifying the administrator" refers to a device or method for notifying the administrator of the transmitted information.

[1337] A system for carrying out this invention is designed to improve the efficiency of managing machines and robots in a factory. The system includes: means for reading QR codes affixed to fixed assets; means for acquiring information about the fixed assets based on the read QR codes; means for aggregating the acquired information about the fixed assets; means for tracking the fixed assets based on the aggregated information about the fixed assets; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; means for reading QR codes affixed to machines in a factory; means for sending the read information about the machines by email; and means for notifying an administrator of the sent information.

[1338] Hardware and software used

[1339] Hardware: Smartphone or tablet with camera

[1340] Software: Python, qrcode library (QR code generation), OpenCV (QR code reading), smtplib (email sending)

[1341] Data processing and calculation

[1342] 1. QR code generation: The server uses the qrcode library to generate a QR code containing the machine's asset information (asset number, purchase date, contact person, etc.).

[1343] 2. QR Code Reading: Using a smartphone camera, we read the QR code attached to a machine in the factory. We use OpenCV to extract data from the QR code.

[1344] 3. Information Acquisition and Aggregation: The smartphone processes the scanned data and extracts the necessary information. The extracted information is sent via email to a server where it is centrally aggregated.

[1345] 4. Information tracking and loss prediction: The server uses the collected information to monitor and manage the location and status of machines. It also analyzes signs of data non-update or non-operation, predicts the risk of loss, and takes appropriate measures.

[1346] 5. Notification: The transmitted information is sent to the administrator via email, allowing the administrator to understand the machine status in real time and take appropriate action.

[1347] Specific examples

[1348] For example, by attaching a QR code to a robot in a factory and having a maintenance person read the QR code with their smartphone, they can instantly obtain the robot's maintenance history and information about the person in charge and report it to the manager. This system allows for efficient management of machines and robots in a factory, makes it easy to track maintenance history and information about the person in charge, and makes it possible to prevent loss of machines or improper management.

[1349] Prompt Sentence Examples

[1350] Develop an application that reads the QR code attached to a robot in a factory with a smartphone, obtains the robot's asset information (asset number, purchase date, person in charge, maintenance history, etc.), and sends it to the manager by email.

[1351] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1352] Step 1:

[1353] The server uses the qrcode library to generate a QR code containing the machine's asset information (asset number, purchase date, contact person, etc.). The machine's asset information is required as input, and a QR code image is generated as output. This QR code is then affixed to the machine.

[1354] Step 2:

[1355] A user uses a smartphone camera to read a QR code attached to a machine in a factory. The QR code image is required as input, and the QR code data is obtained as output. This includes the specific actions of scanning a QR code using a smartphone camera app.

[1356] Step 3:

[1357] The device uses OpenCV to extract data from the QR code. The input is a QR code image, and the output is the asset information contained in the QR code. Specifically, the device analyzes the QR code image and decodes the data.

[1358] Step 4:

[1359] The device sends the extracted asset information to the server via email. The input requires the asset information extracted from the QR code, and the output is an email. Specific operations include creating an email using smtplib and sending it to the specified address.

[1360] Step 5:

[1361] The server aggregates asset information from received emails. The email content is required as input, and the aggregated asset information is obtained as output. Specifically, the server analyzes the email content and stores it in a database.

[1362] Step 6:

[1363] The server monitors and manages the location and status of the machines based on the aggregated information. The aggregated asset information is required as input, and the monitoring results of the machine's location and status are obtained as output. Specific operations include retrieving information from the database and inputting it into the monitoring system.

[1364] Step 7:

[1365] The server analyzes signs of data inactivity or non-operation, predicts the risk of loss, and takes countermeasures. Aggregated asset information is required as input, and the predicted risk of loss is obtained as output. Specific operations include using data analysis algorithms to assess risk and propose countermeasures.

[1366] Step 8:

[1367] The server notifies the administrator of the sent information by email. The input is the aggregated asset information, and the output is a notification to the administrator. Specific operations include creating a notification email using smtplib and sending it to the administrator.

[1368] 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.

[1369] "Example 1"

[1370] One embodiment of the present invention is a system that combines a fixed asset management system with an emotion engine. This system recognizes a user's emotions and adjusts the method of managing fixed assets based on those emotions. Specifically, the emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. For example, if the system recognizes that the user is stressed about losing a fixed asset, it takes measures such as strengthening the tracking of fixed assets. On the other hand, if the system recognizes that the user is indifferent to managing fixed assets, it takes measures such as automating the management of fixed assets. This enables optimal fixed asset management according to the user's emotions.

[1371] "Example 2"

[1372] Another embodiment of the present invention provides specific means for the emotion engine to recognize the user's emotion. Specifically, the user's facial expression is photographed with a camera, and the emotion is recognized from the image data. The user's voice is also recorded with a microphone, and the emotion is recognized from the voice data. Furthermore, if the user inputs text, the emotion is recognized from the text data. By combining these means, it is possible to recognize the user's emotion more accurately.

[1373] The processing flow of each embodiment will be described below.

[1374] "Example 1"

[1375] Step 1: The emotion engine recognizes emotions from the user's facial expressions, voice, text input, etc. Step 2: Based on the emotions recognized by the emotion engine, the method of managing fixed assets is adjusted. For example, if the system recognizes that the user is feeling stressed about losing a fixed asset, it will take measures such as strengthening tracking of the fixed asset.

[1376] Step 3: If the system recognizes that the user is uninterested in managing fixed assets, it will take measures such as automating the management of fixed assets. This will enable optimal fixed asset management according to the user's feelings.

[1377] "Example 2"

[1378] Step 1: The user's facial expression is captured by a camera, and the emotion engine recognizes the emotion from the image data.

[1379] Step 2: The user's voice is recorded with a microphone, and the emotion engine recognizes emotions from the voice data.

[1380] Step 3: When the user enters text, the emotion engine recognizes the emotion from the text data. By combining these methods, it is possible to recognize the user's emotion more accurately.

[1381] Example 1

[1382] 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."

[1383] Conventional fixed asset management systems require manual input of fixed asset information, making management cumbersome. It is also difficult to detect early signs of loss or non-operation of fixed assets, making it difficult to take appropriate measures. Furthermore, management methods are not adjusted to take into account user emotions, resulting in insufficient support for user stress and apathy. To solve these issues, a system that automatically acquires, aggregates, and tracks fixed asset information is needed.

[1384] 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.

[1385] In this invention, the server includes: means for reading QR codes attached to fixed assets; means for acquiring fixed asset information based on the read QR codes; means for sending the acquired fixed asset information by email; means for aggregating the transmitted fixed asset information; means for tracking fixed assets based on the aggregated fixed asset information; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; and means for recognizing user emotions and adjusting the fixed asset management method based on the emotions. This enables automatic acquisition, aggregation, and tracking of fixed asset information, enabling early detection of signs of loss or non-operation and appropriate countermeasures. It also enables optimal fixed asset management based on user emotions.

[1386] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[1387] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1388] A "means of reading" is a device or software that analyzes the QR code and obtains the information contained within it.

[1389] The "means of obtaining information" refers to devices or software for collecting detailed information about fixed assets based on data read from QR codes.

[1390] The "means for sending by e-mail" refers to a device or software for sending information about the acquired fixed assets to a specified address via e-mail.

[1391] The "means for aggregating information" refers to a device or software for centrally collecting and managing the transmitted information on fixed assets.

[1392] "Tracking means" refers to devices or software that monitor the location and usage of fixed assets based on aggregated fixed asset information.

[1393] "Means to predict and prevent loss" refers to devices and software that detect signs of fixed asset data not being updated or not being used, predict the possibility of loss, and take measures to prevent it.

[1394] "Means for recognizing emotions" refers to devices or software that analyze emotions from a user's facial expressions, voice, text input, etc.

[1395] The "means for adjusting the management method" is a device or software for optimizing the management method of fixed assets based on the recognized user's emotions.

[1396] The present invention provides a system for improving the efficiency of fixed asset management and reducing the risk of loss or non-operation. Specific embodiments of this system will be described below.

[1397] System configuration

[1398] 1. Scan the QR code

[1399] The user uses the camera of a mobile information terminal (e.g., a smartphone) to read the QR code attached to the fixed asset.

[1400] The terminal uses a QR code reader app (e.g., QR Code Reader) to analyze the QR code and obtain information about the fixed asset.

[1401] 2. Acquisition of information

[1402] The terminal temporarily stores the fixed asset information (e.g., serial number, purchase date, installation location) obtained from the QR code in its memory.

[1403] 3. Transmission of Information

[1404] The terminal automatically inputs the acquired information about the fixed assets into an email and sends it to the email address of the specified server.

[1405] The device uses an app that provides email sending functionality (e.g., Gmail).

[1406] 4. Receiving and collecting information

[1407] The server analyzes the received email and extracts information about the fixed assets.

[1408] The server stores the extracted information in a database and manages it centrally using a database management system (e.g., MySQL).

[1409] 5. Fixed asset tracking

[1410] The server periodically checks the information on fixed assets stored in the database and monitors their location and usage.

[1411] The server records the movement history and frequency of use of fixed assets and checks for any abnormalities.

[1412] 6. Loss prediction and prevention

[1413] The server uses machine learning models (e.g., Scikit-learn) to detect signs of fixed asset data not being updated or not being used.

[1414] If the server determines that there is a high possibility of loss, it will send an alert email to the administrator.

[1415] 7. Emotional Recognition

[1416] The user inputs emotions using the camera, microphone, and keyboard of the mobile information terminal.

[1417] The device uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) to analyze emotions from the user's facial expressions, voice, and text.

[1418] 8. Sending Emotional Data

[1419] The device sends the analyzed emotion data to the server using the HTTPS protocol.

[1420] 9. Adjusting Emotional Management

[1421] The server analyzes the received emotion data and determines the user's emotional state.

[1422] If the server determines that the user is stressed about losing an asset, it will strengthen the tracking of the asset, such as by increasing the frequency of real-time location updates.

[1423] If the server determines that the user is not interested in managing fixed assets, it automates the management of fixed assets by periodically performing automatic checks and sending reminders.

[1424] Specific examples

[1425] When a user wants to read a QR code attached to an office computer with their smartphone, they launch the smartphone's camera app and select the QR code reader app.

[1426] The terminal analyzes the QR code and obtains the serial number "ABC123", purchase date "2022-01-01", and installation location "Office A".

[1427] The device automatically enters the acquired information into the email app and sends it to the server's email address "asset_management@example.com."

[1428] The server analyzes the received email and stores it in the database as "PC, serial number: ABC123, purchase date: 2022-01-01, installation location: Office A."

[1429] The server periodically checks the database, and if a computer has not been used for a long period of time, it sends an alert email to the administrator stating, "The computer (serial number: ABC123) has not been used for a long period of time. Please check."

[1430] Prompt Sentence Examples

[1431] "Please scan the QR code attached to the office computer with your smartphone and send the obtained information by email."

[1432] "If a user is stressed about losing a fixed asset, what action will the system take?"

[1433] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1434] Step 1:

[1435] The user activates the camera on their mobile information terminal and reads the QR code attached to the fixed asset.

[1436] Input: QR code attached to fixed asset

[1437] Output: QR code data

[1438] Specific operation: The user launches the camera app on their smartphone, selects the QR code reader app, and scans the QR code.

[1439] Step 2:

[1440] The terminal uses a QR code reader app to analyze the QR code and obtain information about the fixed asset.

[1441] Input: QR code data

[1442] Output: Fixed asset information (e.g., serial number, purchase date, installation location)

[1443] Specific operation: The device analyzes the QR code through a QR code reader app and extracts information such as the serial number "ABC123", purchase date "2022-01-01", and installation location "Office A".

[1444] Step 3:

[1445] The terminal automatically enters the acquired fixed asset information into an email and sends it to the specified server email address.

[1446] Input: Fixed asset information

[1447] Output: Email

[1448] Specific operation: The device launches the email application, automatically enters the acquired fixed asset information into the email body, and sends it to the server's email address "asset_management@example.com."

[1449] Step 4:

[1450] The server analyzes the received email and extracts information about the fixed assets.

[1451] Input: Email

[1452] Output: Fixed asset information

[1453] Specific operation: The server analyzes the email received through the email server and extracts fixed asset information from the email body.

[1454] Step 5:

[1455] The server stores the extracted information in a database and manages it centrally.

[1456] Input: Fixed asset information

[1457] Output: Information stored in the database

[1458] Specific operation: The server uses a database management system (e.g., MySQL) to store information about fixed assets in a database.

[1459] Step 6:

[1460] The server periodically checks the information on fixed assets stored in the database and monitors their location and usage.

[1461] Input: Information stored in a database

[1462] Output: Monitoring results

[1463] Specific operation: The server sets up a scheduled task to periodically query the database to check the location and usage status of fixed assets.

[1464] Step 7:

[1465] The server uses machine learning models to detect signs of fixed asset data not being updated or not being used.

[1466] Input: Monitoring results

[1467] Output: Anomaly detection results

[1468] Specific operation: The server uses a machine learning model (e.g., Scikit-learn) to analyze fixed asset data for signs of non-update or non-operation and detect anomalies.

[1469] Step 8:

[1470] If the server determines that there is a high possibility of loss, it will send an alert email to the administrator.

[1471] Input: Anomaly detection result

[1472] Output: Alert email

[1473] Specific operation: Based on the anomaly detection results, the server sends an alert email to the administrator's email address.

[1474] Step 9:

[1475] The user inputs emotions using the camera, microphone, and keyboard of the mobile information terminal.

[1476] Input: User facial expressions, voice, and text input

[1477] Output: Emotion data

[1478] Specific operations: The user captures their facial expressions with the smartphone camera, records audio with the microphone, and enters text with the keyboard.

[1479] Step 10:

[1480] The device uses an emotion recognition engine to analyze emotions from the user's facial expressions, voice, and text.

[1481] Input: User facial expressions, voice, and text input

[1482] Output: Parsed emotion data

[1483] Specific operation: The device uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) to analyze the user's emotions.

[1484] Step 11:

[1485] The device transmits the analyzed emotion data to the server.

[1486] Input: Parsed emotion data

[1487] Output: Emotion data sent to the server

[1488] Specific operation: The device uses the HTTPS protocol to send the analyzed emotion data to the server.

[1489] Step 12:

[1490] The server analyzes the received emotion data and determines the user's emotional state.

[1491] Input: Emotion data sent to the server

[1492] Output: User's emotional state

[1493] Specific operation: The server analyzes the received emotional data and determines whether the user is stressed or indifferent.

[1494] Step 13:

[1495] The server adjusts how the fixed assets are managed based on the user's emotional state.

[1496] Input: User's emotional state

[1497] Output: Coordinated management

[1498] Specific behavior: The server tightens the tracking of fixed assets when the user is stressed, and automates management when the user is disinterested.

[1499] (Application example 1)

[1500] 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."

[1501] In conventional fixed asset management systems, fixed asset information is managed manually, which can lead to delays in updating information and an increased risk of loss. In addition, the management method is not adjusted according to the user's emotions, which can lead to user stress and apathy, reducing the efficiency of fixed asset management.

[1502] 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.

[1503] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, and means for recognizing user emotions and adjusting the management method for the fixed assets based on the emotions. This enables efficient management of fixed assets and flexible adjustment of the management method according to the user's emotions.

[1504] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[1505] A "QR code" is a two-dimensional barcode for storing information, and the information can be obtained using a reading device such as a camera.

[1506] "Reading means" refers to a device or method for obtaining information such as a QR code.

[1507] "Means for acquiring information" refers to a device or method for extracting data related to fixed assets from the scanned QR code.

[1508] "Means for aggregating information" refers to devices and methods for centrally managing information on acquired fixed assets.

[1509] A "tracking means" is a device or method for monitoring the location and status of an asset based on aggregated information about the asset.

[1510] "Means for predicting and preventing loss" refers to devices and methods for predicting the risk of loss based on signs such as non-updating of fixed asset data or non-operation, and preventing it.

[1511] "Means for recognizing emotions and adjusting management methods based on those emotions" refers to a device or method for recognizing emotions from a user's facial expressions, voice, text input, etc., and changing the management method for fixed assets in accordance with those emotions.

[1512] A system for carrying out the present invention includes an application installed on a robot that manages fixed assets in a factory. A specific embodiment of this system will be described below.

[1513] Generating a Program

[1514] The program of this system includes the following main functions:

[1515] 1. Ability to read QR codes attached to fixed assets

[1516] 2. Ability to obtain fixed asset information based on scanned QR codes

[1517] 3. A function to consolidate information on acquired fixed assets

[1518] 4. Ability to track fixed assets based on aggregated fixed asset information

[1519] 5. A function that predicts and prevents loss based on signs of non-updating or non-operating fixed asset data

[1520] 6. The ability to recognize user emotions and adjust how fixed assets are managed based on those emotions

[1521] Program processing explanation

[1522] Scanning QR codes

[1523] The robot uses the OpenCV library to read QR codes that are affixed to each fixed asset in the factory, and the robot periodically makes rounds to read them.

[1524] Obtaining and sending fixed asset information

[1525] The fixed asset information is obtained from the scanned QR code and sent to the administrator via email using smtplib and email.mime.text. This allows the fixed asset information to be centrally collected.

[1526] Fixed asset tracking and loss prediction

[1527] Based on the aggregated information, the location and status of fixed assets are tracked, signs of non-updating or non-operating fixed asset data are detected, and the risk of loss is predicted and prevented.

[1528] Adjusting emotion recognition and management strategies

[1529] Use the emotion_recognition library to recognize user emotions. Analyze emotions from facial expressions, voice, and text inputs to enhance asset tracking if the user is stressed, or automate management if the user is apathetic.

[1530] Specific examples

[1531] For example, a robot in a factory periodically patrols the factory and reads the QR code attached to each machine. The information read is sent to the manager via email, and if the manager's emotions are recognized as stress, the robot will strengthen its tracking.

[1532] Prompt Sentence Examples

[1533] Develop a robot application that manages fixed assets in a factory. This application reads QR codes, obtains information about fixed assets, and sends it via email. It also includes an emotion engine that adjusts management methods according to the manager's emotions. The libraries used are OpenCV, smtplib, and emotion_recognition.

[1534] In this way, the present invention realizes efficient management of fixed assets and flexible adjustment of management methods according to the user's feelings.

[1535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1536] Step 1:

[1537] The robot patrols the factory and uses a camera to read QR codes attached to fixed assets. The input is an image of the QR code, and the output is the information about the fixed asset extracted from the QR code. The QR code data is decoded using the OpenCV library.

[1538] Step 2:

[1539] The robot temporarily saves the information about the fixed asset obtained from the scanned QR code. The input is the information about the fixed asset extracted from the QR code, and the output is the temporarily saved information about the fixed asset. The information is stored in a database or memory.

[1540] Step 3:

[1541] The robot sends the acquired fixed asset information to the administrator via email. The input is the temporarily saved fixed asset information, and the output is the email sent to the administrator. The robot uses smtplib and email.mime.text to compose and send the email.

[1542] Step 4:

[1543] The server aggregates the fixed asset information received from the administrator. The input is the email received from the administrator, and the output is the aggregated fixed asset information. The information is integrated into a database and managed centrally.

[1544] Step 5:

[1545] The server tracks fixed assets based on the aggregated fixed asset information. The input is the aggregated fixed asset information, and the output is the tracking results of the fixed asset's location and status. The location information and operating status are monitored and recorded in a database.

[1546] Step 6:

[1547] The server detects signs of fixed asset data not being updated or not being used, and performs loss prediction. The input is the fixed asset tracking results, and the output is the loss prediction results. Data analysis algorithms are used to detect abnormal patterns.

[1548] Step 7:

[1549] The robot uses the emotion_recognition library to recognize the user's emotions. The input is the user's facial expressions, voice, and text input, and the output is the recognized emotion. The data is collected using a camera and microphone and analyzed with an emotion recognition model.

[1550] Step 8:

[1551] The server adjusts the method of managing fixed assets based on the recognized emotion. The input is the recognized emotion, and the output is the adjusted management method. Depending on the emotion, the tracking frequency can be changed or settings can be made to automate management.

[1552] In this way, the system realizes efficient management of fixed assets and flexible adjustment of management methods according to the user's feelings.

[1553] Example 2

[1554] 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."

[1555] In conventional fixed asset management systems, fixed asset information is often managed manually, making updating and tracking the information cumbersome. Furthermore, there is a lack of effective means to prevent the loss of fixed assets. Furthermore, there is no integrated system for recognizing user emotions, making it difficult to accurately grasp the user's status. To solve these issues, a system that integrates fixed asset management and user emotion recognition is needed.

[1556] 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.

[1557] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation, means for photographing a user's facial expression and recognizing emotions from the image data, means for recording the user's voice and recognizing emotions from the voice data, means for recognizing emotions from text data entered by the user, and means for integrating these emotion data to more accurately recognize the user's emotions. This integrates fixed asset management and user emotion recognition, making it possible to prevent loss of fixed assets and accurately grasp the user's condition.

[1558] "Fixed assets" are assets owned by a company or organization that are used over a long period of time, and include buildings, machinery, equipment, computers, etc.

[1559] A "QR code" is a type of two-dimensional barcode that visually encodes information and can be read by a camera or scanner.

[1560] "Means of reading" refers to devices or technologies used to capture QR codes or other information, including cameras and scanners.

[1561] "Means of obtaining information" refers to the technology or process used to extract the necessary information from the scanned QR code or other data.

[1562] "Means of aggregating information" refers to technologies and systems for centrally compiling and managing acquired information.

[1563] "Tracking measures" refers to the technologies and processes used to monitor and manage the location and condition of fixed assets based on aggregated information.

[1564] "Means to predict and prevent loss" refers to technologies and systems that analyze signs of fixed asset data not being updated or not being operational, and predict and prevent the possibility of loss.

[1565] "Means for capturing facial expressions" refers to technology or devices for capturing images of a user's facial expressions with a camera and acquiring the image data.

[1566] "Means for recognizing emotions" refers to technologies and systems for analyzing and recognizing a user's emotions from acquired image data, audio data, and text data.

[1567] "Means for recording audio" refers to the technology or device for recording the user's audio with a microphone and acquiring the audio data.

[1568] "Means for recognizing emotions from text data" refers to technologies and systems that analyze text data entered by a user and recognize emotions from its content.

[1569] "Means for integrating emotion data" refers to technologies and systems that integrate data obtained from multiple emotion recognition means to more accurately recognize a user's emotions.

[1570] MODE FOR CARRYING OUT THE INVENTION

[1571] The present invention is a system that integrates fixed asset management and user emotion recognition. A specific embodiment of this system will be described below.

[1572] Fixed Asset Management System

[1573] 1. Generate and attach a QR code

[1574] The server generates a QR code based on the information (asset number, purchase date, person in charge, etc.) from the computer registered in the fixed asset management system. To generate the QR code, for example, use the Python qrcode library.

[1575] The user prints out the generated QR code and attaches it to their computer.

[1576] 2. Scan the QR code

[1577] The new employee scans the QR code using the camera on their smartphone, which has either a camera app or a dedicated QR code reader app installed.

[1578] The terminal (smartphone) analyzes the contents of the QR code and obtains the computer's asset information.

[1579] 3. Transmission of Information

[1580] The device will then send the acquired asset information via email, automatically entering the information into the body of the email using the smartphone's email app.

[1581] The server receives the information at the specified email address.

[1582] 4. Information collection and management

[1583] The server analyzes the content of the received email and registers the information in the database of the fixed asset management system. For example, the Python imaplib library is used for email analysis.

[1584] The server periodically updates the database (e.g., MySQL) to keep the asset information up to date.

[1585] 5. Fixed asset tracking

[1586] The server uses a database to track changes in the location and ownership of fixed assets, and issues alerts in the event of unusual activity or possible loss.

[1587] Emotion Engine System

[1588] 1. Photographing facial expressions

[1589] The user stands in front of the camera and shows their facial expression, which is then captured by the camera and image data is acquired.

[1590] 2. Recording Audio

[1591] A user speaks into a microphone, which records the voice and captures the audio data.

[1592] 3. Enter text

[1593] The user inputs text using a keyboard or a touch screen, and the terminal acquires the input text data.

[1594] 4. Emotion Analysis

[1595] The server analyzes the image data and recognizes the user's emotions from their facial expressions. For image analysis, the OpenCV library is used, for example.

[1596] The server analyzes the voice data and recognizes emotions from the user's voice. For voice analysis, Google Cloud Speech-to-Text is used, for example.

[1597] The server analyzes the text data and recognizes emotions from the user's text, using natural language processing, such as the NLTK library.

[1598] 5. Emotional Data Integration

[1599] The server integrates these emotion data and determines the final emotion. For example, if the facial expression, voice, and text are "joy," the final emotion will be "joy."

[1600] The server notifies the user of the result.

[1601] Examples and prompts

[1602] Example: A company's IT department purchases a new computer and affixes a QR code to it. When the person in charge is transferred, the new person scans the QR code with their smartphone to retrieve the information. The information is then sent via email to the IT department's management system.

[1603] Example prompt: "Scan the QR code on your new computer and email us the information you get."

[1604] Example: A user smiles in front of a camera. The system analyzes the facial expression and recognizes that the user is "happy." Also, if the user enters text such as "Today was a fun day," the system will recognize the emotion of "happy" from that text as well.

[1605] Example prompt: "Smile for the camera and text us how you're feeling today."

[1606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1607] Fixed Asset Management System Processing Steps

[1608] Step 1: Generate and paste a QR code

[1609] The server generates a QR code based on the information on the computer registered in the fixed asset management system (asset number, purchase date, person in charge, etc.). The computer information is required as input, and a QR code is generated as output.

[1610] Specific operation: The server generates a QR code using a QR code generation library (e.g., qrcode for Python), and outputs the generated QR code as an image file.

[1611] The user prints the generated QR code and pastes it on their computer. An image file of the generated QR code is required as input, and the QR code is pasted on the computer as output.

[1612] Step 2: Scan the QR code

[1613] The new employee reads the QR code using the camera on their smartphone. The QR code is required as input, and the content of the QR code is obtained as output.

[1614] How it works: Scan the QR code using your smartphone's camera app or a dedicated QR code reader app. The app will analyze the contents of the QR code and display the asset information.

[1615] Step 3: Submit your information

[1616] The terminal sends the acquired asset information by email. The acquired asset information is required as input, and an email is sent as output.

[1617] Specific operation: The smartphone launches the email app and automatically enters the acquired asset information into the email body. The user presses the send button to send the email. The server receives the email and saves the contents in a database.

[1618] Step 4: Aggregating and managing information

[1619] The server analyzes the content of the received email and registers the information in the database of the fixed asset management system. The received email is required as input, and the information is registered in the database as output.

[1620] Specific operation: The server analyzes received emails using an email analysis library (e.g., imaplib for Python). It stores the analyzed information in a database (e.g., MySQL). It periodically cleans the database to keep it up to date.

[1621] Step 5: Track fixed assets

[1622] The server tracks changes in the location and ownership of fixed assets based on a database. It requires database information as input and provides tracking results as output.

[1623] Specific behavior: The server executes database queries to retrieve asset information. If it detects any abnormal activity (e.g., a change in the person in charge within a short period of time), it sends an email or notification to the administrator.

[1624] Emotion engine system processing steps

[1625] Step 1: Capture facial expressions

[1626] The user stands in front of the camera and shows their facial expression. The user's facial expression is required as input, and image data is obtained as output.

[1627] Specific operation: The user shows a smile or an angry expression in front of the camera. The camera captures that moment and saves the image data.

[1628] Step 2: Record your audio

[1629] The user speaks into a microphone: the user's voice is required as input and audio data is obtained as output.

[1630] What happens: The user says, "Today was a fun day." The microphone records the speech and saves it as an audio file.

[1631] Step 3: Entering text

[1632] The user inputs text using a keyboard or a touch screen. The user's text is required as input, and text data is obtained as output.

[1633] Specific operation: The user types the text "Today was a fun day" into their smartphone or computer. The device saves the text data.

[1634] Step 4: Sentiment Analysis

[1635] The server analyzes the image data and recognizes the user's emotions from their facial expressions. Image data is required as input, and emotion recognition results are obtained as output.

[1636] Specific operation: The server analyzes facial expressions using an image analysis library (e.g., OpenCV).

[1637] The server analyzes the voice data and recognizes the emotion from the user's voice. The voice data is required as input, and the emotion recognition result is obtained as output.

[1638] What happens: The server analyzes the audio using a speech analysis library (e.g., Google Cloud Speech-to-Text).

[1639] The server analyzes the text data and recognizes the emotion from the user's text. The text data is required as input, and the emotion recognition result is obtained as output.

[1640] What happens: The server parses the text using a natural language processing library (e.g., NLTK).

[1641] Step 5: Integrating Emotional Data

[1642] The server integrates these emotion data and determines the final emotion. Each emotion recognition result is required as input, and the integrated emotion recognition result is obtained as output.

[1643] Specific operation: The server integrates the results of each analysis and determines the user's overall emotion. For example, if the facial expression, voice, and text are all "happy," the final emotion will be "happy." The server then notifies the user of the result.

[1644] (Application example 2)

[1645] 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."

[1646] Conventional fixed asset management systems require manual management of fixed asset information, resulting in delays in updating information and an increased risk of loss. Furthermore, they do not take into account the emotions of workers, which can lead to reduced work efficiency. To solve these problems, a system is needed that automatically acquires and manages fixed asset information, and also recognizes the emotions of workers to provide appropriate support.

[1647] 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.

[1648] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for aggregating the acquired information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, means for predicting and preventing loss based on signs such as non-updating of fixed asset data or non-operation, means for recognizing the emotions of workers, and means for providing appropriate support to the workers based on the recognized emotions. This not only makes it possible to automatically manage information about fixed assets and prevent loss, but also to provide appropriate support that takes into account the emotions of the workers.

[1649] "Fixed assets" are assets owned by a company or organization that are used for a long period of time, and include buildings, machinery, equipment, vehicles, etc.

[1650] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1651] "Means for reading" refers to the device or software used to scan the QR code and obtain its information.

[1652] "Means of obtaining information" refers to the equipment or software used to analyze the data read from the QR code and extract the necessary information.

[1653] "Means of aggregating information" refers to databases or software for centrally managing acquired information.

[1654] "Tracking instruments" refers to devices and software used to monitor the location and condition of fixed assets based on aggregated information.

[1655] "Means to predict and prevent loss" refers to devices and software that detect signs of fixed asset data not being updated or not being used, predict the risk of loss, and take measures to prevent it.

[1656] "Means for recognizing emotions" refers to devices and software for analyzing emotions from workers' facial expressions, voice, and text data.

[1657] "Means for providing appropriate support" refers to devices and software for providing necessary assistance and advice to workers based on the recognized emotions.

[1658] "Smart devices" refers to mobile information terminals with advanced functions, such as smartphones and tablets.

[1659] "Email" is a means of communication for sending and receiving text and files over the Internet.

[1660] As an embodiment of the present invention, a method for installing an application that combines a fixed asset management system using QR codes and an emotion engine on a robot in a factory will be described.

[1661] System Program

[1662] This system operates using the following hardware and software:

[1663] Hardware: Camera, microphone

[1664] Software: OpenCV (QR code reading), smtplib (email sending), EmotionRecognizer (emotion recognition)

[1665] Program processing

[1666] The server first reads the QR code from the image captured by the camera. It uses OpenCV to scan the QR code and obtain the information. It then analyzes the obtained fixed asset information and extracts the necessary data. This data is then sent to the administrator via email using smtplib. The sent information is then aggregated in a database and managed centrally.

[1667] Furthermore, the server recognizes the worker's emotions from images captured by the camera and audio recorded by the microphone. Using EmotionRecognizer, it analyzes the image and audio data to identify the worker's emotions. Based on the recognized emotions, it provides appropriate support to the worker. For example, if a worker is feeling stressed, it can respond by displaying a message encouraging them to take a break.

[1668] Specific examples

[1669] For example, a robot in a factory can periodically read the QR code on each machine and send fixed asset information to the manager. Also, if a worker is feeling stressed, the robot can recognize that emotion and provide appropriate support. In this way, fixed asset management and worker support can be performed simultaneously.

[1670] Prompt Sentence Examples

[1671] "Develop an application that allows a robot in a factory to read QR codes to manage information on fixed assets, and recognizes the emotions of workers to provide appropriate support. Use OpenCV to read QR codes and EmotionRecognizer to recognize emotions."

[1672] The above is a specific embodiment for carrying out the present invention. This system not only automatically manages information on fixed assets and prevents loss, but also provides appropriate support that takes into account the emotions of workers.

[1673] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1674] Step 1:

[1675] The server reads the QR code from the image captured by the camera. As input, it receives the image data captured by the camera and scans the QR code using OpenCV. As output, it obtains the fixed asset information contained in the QR code.

[1676] Step 2:

[1677] The server analyzes the fixed asset information based on the scanned QR code. As input, it receives and analyzes the data obtained from the QR code. As output, it extracts detailed information such as the fixed asset number, purchase date, and person in charge.

[1678] Step 3:

[1679] The server sends the acquired fixed asset information by email. As input, it receives the parsed fixed asset information and sends it to the administrator's email address using smtplib. As output, the fixed asset information is notified to the administrator.

[1680] Step 4:

[1681] The server aggregates the transmitted fixed asset information in a database. As input, it receives the fixed asset information transmitted by email and stores it in the database. As output, the fixed asset information is managed in a unified manner.

[1682] Step 5:

[1683] The server recognizes the worker's emotions from images captured by a camera and audio recorded by a microphone. It receives image and audio data as input and analyzes emotions using EmotionRecognizer. The output identifies the worker's emotional state.

[1684] Step 6:

[1685] The server provides appropriate support to the worker based on the recognized emotion. As input, it receives the identified emotional state and determines the appropriate support content. As output, it displays a message or advice encouraging the worker to take a break.

[1686] These are the specific processing steps of this system. By clarifying the operations and inputs / outputs performed at each step, it is easier to understand the flow of the entire system.

[1687] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1688] 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.

[1689] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1690] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1691] [Fourth embodiment]

[1692] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1693] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1694] 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).

[1695] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1696] 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.

[1697] 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).

[1698] 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.

[1699] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1700] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1701] 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.

[1702] 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.

[1703] In the robot 414, the processor 46 performs the reception output process. 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.

[1704] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1705] "Example 1"

[1706] In one embodiment of the present invention, a QR code attached to a fixed asset is read using a smartphone camera. Information about the fixed asset is obtained based on the read QR code, and the obtained information about the fixed asset is sent via email via the smartphone. The transmitted information about the fixed asset is centrally aggregated. The fixed asset is tracked based on the aggregated information about the fixed asset. Loss is predicted based on signs of fixed asset data not being updated or not being used, and loss is prevented before it occurs.

[1707] "Example 2"

[1708] As a specific example, a company affixes a QR code to a computer, which is a fixed asset. This QR code contains information such as the computer's asset number, purchase date, and the person in charge. When the person in charge is transferred or leaves the company, the new person can read the QR code using their smartphone camera to obtain information about the fixed asset. The obtained information is sent via email via the smartphone and centrally aggregated. Based on the aggregated information, the fixed asset can be tracked to prevent loss.

[1709] The processing flow of each embodiment will be described below.

[1710] "Example 1"

[1711] Step 1: Use your smartphone camera to read the QR code attached to the fixed asset.

[1712] Step 2: Obtain fixed asset information based on the scanned QR code.

[1713] Step 3: Information about the acquired fixed assets is sent via email via smartphone. Step 4: The sent information about the fixed assets is centrally consolidated.

[1714] Step 5: Track fixed assets based on the aggregated fixed asset information.

[1715] Step 6: Predict losses based on signs such as non-updating of fixed asset data or non-operation, and prevent them from occurring.

[1716] "Example 2"

[1717] Step 1: A QR code is attached to a computer, which is a fixed asset of a company. This QR code contains information such as the computer's asset number, purchase date, and person in charge.

[1718] Step 2: If the person in charge is transferred or resigns, the new person in charge will read the QR code using the smartphone camera.

[1719] Step 3: Obtain fixed asset information based on the scanned QR code.

[1720] Step 4: The information obtained is sent via email via your smartphone.

[1721] Step 5: The submitted information on fixed assets is centrally aggregated.

[1722] Step 6: Use the aggregated information to track fixed assets and prevent loss.

[1723] Example 1

[1724] 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 robot 414 will be referred to as a "terminal."

[1725] In the area of ​​fixed asset management, it is difficult to efficiently acquire, consolidate, and track information on fixed assets. In addition, there is a lack of methods to predict and prevent loss based on signs such as inactivity or non-operation of fixed asset data.

[1726] 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.

[1727] In this invention, the server includes means for reading QR codes attached to fixed assets, means for acquiring fixed asset information based on the read QR codes, means for sending the acquired fixed asset information by email, means for aggregating the transmitted fixed asset information, means for tracking fixed assets based on the aggregated fixed asset information, and means for predicting and preventing loss based on signs such as non-updating of fixed asset data or non-operation. This allows for efficient management of fixed asset information and makes it possible to predict and prevent loss.

[1728] A "fixed asset" is a physical asset that a business or organization owns for long-term use.

[1729] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1730] A "mobile information terminal" is a portable information processing device such as a smartphone or tablet.

[1731] A "camera" is a device for taking images or videos.

[1732] "Email" is a means of communication for sending and receiving text and files over the Internet.

[1733] A "server" is a computer system that provides services to other computers on a network.

[1734] A "database" is a system for efficiently storing, searching, and managing data.

[1735] "Tracking" is the act of continuously monitoring the location and status of an object or information.

[1736] "Not updated" refers to a state in which data has not been updated for a certain period of time.

[1737] "Inoperation" refers to a state in which equipment or systems are not in use for a certain period of time.

[1738] "Loss prediction" is the act of predicting the possibility of loss of an item based on data analysis.

[1739] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and make predictions and classifications.

[1740] This invention is a system for efficiently managing fixed assets, and includes means for reading QR codes affixed to fixed assets, means for acquiring information about the fixed assets based on the read QR codes, means for sending the acquired information about the fixed assets by email, means for aggregating the transmitted information about the fixed assets, means for tracking the fixed assets based on the aggregated information about the fixed assets, and means for predicting loss based on signs such as non-updating of fixed asset data or non-operation, and for preventing loss before it occurs.

[1741] The user uses the smartphone camera to read the QR code attached to the fixed asset. Using a QR code reader app (e.g., QR Code Reader, Google Lens), the user places the QR code within the camera's frame, and the app automatically scans the QR code and acquires the data.

[1742] The data from the QR code read by the device is sent to the server. The server accesses a database (e.g., MySQL, PostgreSQL) and retrieves information about the fixed asset corresponding to the QR code. The retrieved information includes the name, location, purchase date, current condition, etc. of the fixed asset.

[1743] The information about the fixed assets acquired by the terminal is sent by email. The email is sent using the SMTP protocol and an email service such as Gmail or Outlook. The body of the email contains detailed information about the fixed assets.

[1744] The server analyzes the received emails and aggregates the information about fixed assets into a centralized database. The aggregated data is then stored in a fixed asset management system (e.g., SAP, Oracle Asset Management).

[1745] The server tracks fixed assets based on the aggregated information on fixed assets. Tracking includes the movement history and current location information of fixed assets. The server periodically updates the database to keep the information up to date.

[1746] The server analyzes fixed asset data and detects signs of non-renewal, non-operation, etc. A generative AI model (e.g., TensorFlow, PyTorch) is used to predict loss and send alerts to administrators to prevent it from happening.

[1747] For example, a user uses their smartphone to scan a QR code attached to an office printer. The QR code contains the printer's ID, which the device sends to a server. The server retrieves the printer's details from a database, and the device sends the information to an administrator via email. The server aggregates the received information into a database and tracks printer usage. If the printer has not been used for a long period of time, a generative AI model is used to predict the possibility of loss and send an alert to the administrator.

[1748] Example prompt sentence:

[1749] "Scan the QR code attached to the office printer with your smartphone. The printer information will be acquired based on the scanned QR code and sent to you by email. The information will be aggregated on the server and used to track the printer and predict if it has been lost."

[1750] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1751] Step 1:

[1752] The user activates the smartphone camera and reads the QR code attached to the fixed asset using a QR code reader app (e.g., QR Code Reader, Google Lens). When the user holds the QR code within the camera's frame, the app automatically scans the QR code and acquires the data.

[1753] Input: QR code

[1754] Output: QR code data

[1755] Specific operation: The user opens the camera app on their smartphone and points the camera at a QR code. The app recognizes the QR code and reads the data.

[1756] Step 2:

[1757] The terminal sends the data from the QR code it reads to the server, which then accesses a database (e.g., MySQL, PostgreSQL) to retrieve information about the fixed asset corresponding to the QR code.

[1758] Input: QR code data

[1759] Output: Fixed asset information

[1760] What happens: The device sends the QR code data to the server, which queries the database to retrieve the fixed asset information.

[1761] Step 3:

[1762] The information about the fixed assets acquired by the terminal is sent by email. The email is sent using the SMTP protocol and an email service such as Gmail or Outlook. The body of the email contains detailed information about the fixed assets.

[1763] Input: Fixed asset information

[1764] Output: Email

[1765] Specific operation: The terminal inserts the fixed asset information into the email body, and then sends the email through the SMTP server.

[1766] Step 4:

[1767] The server analyzes the received emails and aggregates the information about fixed assets into a centralized database. The aggregated data is then stored in a fixed asset management system (e.g., SAP, Oracle Asset Management).

[1768] Input: Email

[1769] Output: Aggregated fixed asset information

[1770] Specific operation: The server analyzes the received email and saves the analysis results in a database.

[1771] Step 5:

[1772] The server tracks fixed assets based on the aggregated information on fixed assets. Tracking includes the movement history and current location information of fixed assets. The server periodically updates the database to keep the information up to date.

[1773] Input: Aggregated fixed asset information

[1774] Output: Fixed asset tracking information

[1775] Specific operation: The server records the fixed asset movement history in a database. The server updates the database periodically.

[1776] Step 6:

[1777] The server analyzes fixed asset data and detects signs of non-renewal, non-operation, etc. A generative AI model (e.g., TensorFlow, PyTorch) is used to predict loss and send alerts to administrators to prevent it from happening.

[1778] Input: Fixed asset tracking information

[1779] Output: Loss prediction alert

[1780] How it works: The server analyzes the data using the generated AI model. The server predicts the possibility of loss and generates an alert. The server sends the alert to an administrator.

[1781] (Application example 1)

[1782] 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 robot 414 will be referred to as a "terminal."

[1783] With conventional fixed asset management systems, it was difficult to monitor the location and status of fixed assets in real time, which increased the risk of loss or non-operation, especially in large-scale factory asset management. Furthermore, the time-consuming task of manually aggregating fixed asset information made efficient management difficult.

[1784] 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.

[1785] In this invention, the server includes: means for reading QR codes affixed to fixed assets; means for acquiring information about the fixed assets based on the read QR codes; means for aggregating the acquired information about the fixed assets; means for tracking the fixed assets based on the aggregated information about the fixed assets; means for predicting and preventing loss based on signs of non-updating of fixed asset data or non-operation; means installed on robots in the factory for transmitting information about the fixed assets in real time to the central management system; and means for monitoring the location and status of the fixed assets based on the information transmitted to the central management system. This makes it possible to monitor the location and status of fixed assets in the factory in real time and reduce the risk of loss or non-operation.

[1786] "Fixed assets" are physical assets that a business or organization holds for long-term use.

[1787] A "QR code" is a type of two-dimensional barcode, a matrix-format code for storing information.

[1788] "Means of reading" refers to the equipment or technology used to decode the QR code and obtain its information.

[1789] "Means of obtaining information" refers to devices and technologies for collecting detailed information about fixed assets based on data read from QR codes.

[1790] "Means of aggregating information" refers to devices or technologies for centrally compiling information on acquired fixed assets.

[1791] "Tracking means" refers to devices and technologies that monitor the location and status of assets based on aggregated fixed asset information.

[1792] "Means for predicting and preventing loss" refers to devices and technologies that analyze signs of fixed asset data not being updated or not being operational, and predict and prevent the risk of loss.

[1793] A "factory robot" is an automated mechanical device used to perform tasks in a factory.

[1794] A "central management system" is a system for centrally managing and monitoring fixed asset information within a factory.

[1795] "Real-time transmission means" refers to devices or technologies for instantly transmitting fixed asset information to the central management system.

[1796] "Means for monitoring location and status" refers to devices and technologies for continuously monitoring the location and status of assets based on information about fixed assets sent to the central management system.

[1797] The present invention relates to a system for improving the efficiency of fixed asset management within a factory. A specific embodiment of this system will be described below.

[1798] First, QR codes are attached to fixed assets within the factory. The QR codes contain the identification information of the fixed assets. Robots within the factory are equipped with cameras and periodically patrol the factory, reading the QR codes attached to the fixed assets.

[1799] The robot uses the OpenCV library to decode the QR code and retrieve information, such as the asset's identification and location. The robot then transmits the information to a central management system in real time using wireless communication technologies such as Wi-Fi and Bluetooth.

[1800] The central management system centrally aggregates the received information on fixed assets. The aggregated information is stored in a database and used to monitor the location and status of fixed assets in real time. A relational database management system (RDBMS) such as MySQL or PostgreSQL is used for database management.

[1801] Furthermore, the central management system analyzes signs of fixed asset data inactivity and non-operation to predict loss, using machine learning algorithms such as the Python scikit-learn library to analyze fixed asset data and predict the risk of loss.

[1802] This system makes it possible to monitor the location and status of fixed assets within a factory in real time, reducing the risk of loss or non-operation. It also eliminates the need to manually collect information on fixed assets, enabling more efficient management.

[1803] For example, a robot in a factory periodically patrols the area and reads the QR codes attached to each machine and tool. The robot sends the read information to a central management system, which monitors the status of the assets in real time. If it detects signs of asset inactivity or loss, it sends an alert to the manager.

[1804] Example prompt for a generative AI model:

[1805] To streamline fixed asset management within the factory, develop an application that allows a robot to read QR codes and send asset information to a central management system. This application will have the function of monitoring the location and status of assets in real time, reducing the risk of loss or non-operation.

[1806] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1807] Step 1:

[1808] The robot patrols the factory a...

Claims

1. A system for managing fixed assets, comprising: A means for reading a QR code attached to the fixed asset; A means for acquiring information on the fixed asset based on the read QR code; a means for transmitting the acquired information on the fixed assets by email; a means for aggregating the information on the fixed assets transmitted by the email; a means for tracking the fixed assets based on the aggregated information of the fixed assets; A means for predicting loss based on signs of non-update or non-operation of the data of the fixed assets and preventing loss in advance; A means for recognizing an emotion of a user who uses the fixed asset and adjusting a management method of the fixed asset based on the emotion; A system including:

2. 2. The system according to claim 1, wherein the QR code is read using a camera of a portable information terminal.

3. 2. The system according to claim 1, wherein the acquired information on the fixed assets is transmitted by electronic mail via a portable information terminal and is then consolidated.

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