system

Abstract

We provide the system. [Solution] Information input means for inputting image data and design data, An evaluation means that analyzes input image data and evaluates the deterioration state of an item, A determination means that determines whether to repair or replace based on the evaluation results from the evaluation means, A proposal generation means that generates and outputs proposal content according to the judgment result, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult to quickly and reasonably make a decision on repair or replacement due to deterioration of an article. In particular, in the conventional method, it takes a lot of time and specialized knowledge to accurately evaluate the deterioration state of an article and determine an appropriate countermeasure method, resulting in a decrease in cost efficiency and a delay in decision-making.

Means for Solving the Problems

[0005] This invention supports rapid and accurate decision-making by providing a system that automatically analyzes the deterioration state of an item based on image data and design data, and proposes the optimal method for repair or replacement based on the results. Specifically, it has a configuration that includes means for evaluating the input data, determines a course of action based on that evaluation, and generates and outputs a proposed course of action. This enables rational and cost-effective responses according to the deterioration state.

[0006] "Image data" refers to digital data acquired to visually capture the external appearance and internal condition of an object.

[0007] "Design data" refers to information that includes drawings and specifications related to the structure and function of an item.

[0008] "Deterioration" refers to a state in which an item's performance or function has deteriorated due to age or environmental conditions.

[0009] "Evaluation means" refers to devices or programs that analyze the condition of an item from input data and determine its degree of deterioration.

[0010] "Determination means" refers to a process or apparatus for determining an appropriate method of repair or replacement based on the results of the evaluation means.

[0011] A "proposal generation means" is a process or apparatus that generates information for proposing the optimal course of action based on the judgment result.

[0012] "Information input means" refers to an interface or device for receiving image data and design data related to an item. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a system for evaluating the deterioration state of an article and proposing appropriate repair or replacement. This system incorporates various means for accurately determining the deterioration state by inputting image data and design data of the article and analyzing them.

[0035] The basic procedure for implementing the system is as follows: First, the user uses a terminal to take images of the items they wish to observe for deterioration and prepares relevant data such as design drawings. This data is then input into the system via the terminal. The terminal organizes the input data and sends it to the server.

[0036] The server uses an evaluation tool to analyze the received data. The evaluation tool uses an AI-powered image analysis algorithm to quantify and evaluate the deterioration state of the item. Based on this evaluation result, the server uses a judgment tool to determine whether repair is appropriate or whether replacement is necessary.

[0037] For example, when evaluating the deterioration of a box housing wireless equipment, the server recognizes rust and crack patterns from photographs and quantifies the degree of deterioration. Taking into account the number of years since installation and environmental conditions, it calculates the specific repair methods, costs, and life extension if repair is appropriate. If replacement is necessary, it presents options for a new housing.

[0038] The suggestions generated by the server are presented to the user via the terminal. The user considers the suggested information and makes the most reasonable choice. This system makes it possible to carry out repairs and replacements due to deterioration in a rational and rapid manner.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses a terminal to prepare image data and design data of the item. This includes taking photographs to visually capture the item's exterior and internal condition, and digitizing design drawings. The user enters the necessary information according to the terminal's input interface.

[0042] Step 2:

[0043] The terminal consolidates the input data and converts it into an appropriate format for analysis. This data unifies the formats of image data and design data and is organized into packets optimized for communication with the server.

[0044] Step 3:

[0045] The terminal sends the organized data to the server. The crucial point here is to perform error checking based on the communication protocol to ensure the data is received accurately.

[0046] Step 4:

[0047] The server receives the data and starts the analysis using the evaluation tool. The data is fed into the AI ​​model, and image analysis is performed to quantify the deterioration state of the item. At this time, the evaluation tool compares it with past deterioration patterns and calculates a specific deterioration index.

[0048] Step 5:

[0049] Based on the evaluation results, the server uses a decision-making mechanism to determine whether repair is possible or replacement is necessary. Here, the optimal choice is made based on pre-set thresholds. A high degradation index suggests replacement, while a low degradation index suggests repair.

[0050] Step 6:

[0051] Based on the assessment results, the server uses a proposal generation mechanism to generate specific content to propose to the user. If repair is proposed, specific repair methods, costs, and possible life extensions are presented. If replacement is proposed, options for new items and their specifications are suggested.

[0052] Step 7:

[0053] The server sends the generated proposals to the terminal. The terminal organizes the received information in an easy-to-understand format and displays it on the user's dashboard. Based on this information, the user makes a final decision and takes the necessary actions.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern times, deterioration of goods due to long-term use is unavoidable, and it is necessary to accurately assess the degree of deterioration and make appropriate decisions regarding repair or replacement. However, conventional methods require specialized knowledge and experience to determine the extent of deterioration, and this process is time-consuming and costly. There is a need for a means to quickly and accurately assess deterioration and determine the optimal countermeasures.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes an information input means for inputting image data and design data; an evaluation means for analyzing the input image data using high-performance recognition processing and quantifying and evaluating the deterioration state of the article; and a determination means for determining the necessity of repair or replacement based on the evaluation results from the evaluation means. This makes it possible to quickly evaluate the deterioration state of the article and, if necessary, propose a reasonable and efficient repair or replacement.

[0059] "Information input means" refers to a device or function for receiving image data and design data from a user.

[0060] "Evaluation means" refers to a function or process that analyzes input image data and quantifies the deterioration state of an item for quantitative evaluation.

[0061] "Determination means" refers to a function or process that determines the need for repair or replacement of an item based on the evaluation results from evaluation means.

[0062] "Proposal generation means" refers to a function or process that generates information, including repair methods and replacement options, based on the judgment result and presents it to the user.

[0063] "Information provision means" refers to a mechanism or function for notifying users of the generated proposal content via a terminal.

[0064] This system is designed to assess the deterioration of items and suggest appropriate repair or replacement. Specific embodiments are described below.

[0065] First, the user uses their device to take pictures of the item they want to observe for deterioration. For example, they might use a smartphone camera app to take photos of the deteriorated area and prepare design drawings and data about the usage environment. This data is then centralized and organized by the device's application before being sent to the server.

[0066] The terminal ensures data format consistency by converting image data and design data to JSON format, and then sends them to the server. The HTTP protocol is used for data transmission, and encoding can be applied to ensure security.

[0067] The server receives the transmitted data and analyzes the deterioration state of the items using an AI image analysis algorithm. Software used for this analysis includes, for example, an image recognition model using a convolutional neural network (CNN). The server detects features such as rust and cracks, quantifies them, and generates a deterioration score.

[0068] Based on the analysis results, the server evaluates the degree of deterioration and determines whether the deterioration exceeds a set standard value. This determination then determines whether the item needs repair or replacement.

[0069] For example, in the case of a box housing wireless equipment, the server recognizes rust and crack patterns from photographs and calculates a deterioration score. Then, considering the number of years since installation and the surrounding environmental conditions, it calculates specific repair methods, costs, and the lifespan extension if repair is appropriate. If replacement is necessary, it proposes the most suitable new housing box.

[0070] The generated suggestions are sent from the server to the terminal and notified to the user. The user can then review this information through the terminal and make the most rational and efficient choice.

[0071] Examples of prompts to input into a generative AI model are as follows:

[0072] "Based on the image data and design data of the storage case, please conduct a deterioration assessment and propose repair or replacement."

[0073] This system makes it possible to quickly and accurately assess the deterioration status of items and propose appropriate countermeasures as needed.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user takes images of the item whose deterioration they want to check using a device and prepares the design data. The input data consists of image files of the item and design data in PDF format. This data is centralized on the device, and the images are saved in JPEG format. Specifically, the user uses a camera app on their smartphone or tablet to take images of the item from multiple angles and scans or digitally copies the design drawings.

[0077] Step 2:

[0078] The terminal organizes the image data and design data collected by the user and prepares them for transmission to the server. The input image files and design data are converted to JSON format and organized into a single data package. Specifically, the terminal application aggregates each data into a specific folder and executes scripts to convert the data format. It also verifies the data's integrity and makes corrections as needed.

[0079] Step 3:

[0080] The terminal sends the organized data package to the server. As output, it sends the data to the server in an encoded state using the HTTP protocol. Specifically, the terminal verifies that it is connected to the network, generates a send request using the API, and sends the data. After the transmission is complete, the terminal notifies the user of the successful transmission.

[0081] Step 4:

[0082] The server receives data from the terminal and performs an integrity check. The input data is in JSON format, and the server deserializes it to expand the data. Specifically, after confirming the integrity of the data, the server saves the necessary information to the database and checks for any errors.

[0083] Step 5:

[0084] The server processes the unfolded image data using an AI image analysis algorithm to quantify the deterioration state of the items. The input data is an image of the item, and the output data is the deterioration score. Specifically, the server runs an image recognition program using a CNN to extract features such as rust and cracks. Based on this, it quantifies each attribute and finally scores the deterioration.

[0085] Step 6:

[0086] The server determines whether an item is repairable or needs replacement based on the results of the evaluation. Inputs are the degradation score and user design data. The output is a judgment result indicating whether repair or replacement is necessary. Specifically, the server compares the score to a predetermined threshold and applies a judgment logic to determine the result.

[0087] Step 7:

[0088] The server generates a proposal, including specific repair methods and replacement options, based on the assessment result. Inputs include the assessment result, past cases, and reference data. Output is the proposed repair or replacement content presented to the user. Specifically, the server extracts the optimal solution from the database, organizes the information, and creates a proposal document.

[0089] Step 8:

[0090] The server sends the generated suggestions to the terminal and notifies the user. The output is the suggestion information displayed on the terminal's user interface. Specifically, the server encrypts the data while considering security and sends it using the HTTP protocol. After receiving the data, the terminal executes a notification function for the user and displays the information on the screen.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Conventional methods for evaluating deterioration primarily rely on manual visual inspection, which is time-consuming and labor-intensive, and often lacks accuracy and consistency. Furthermore, managing and notifying about the deterioration of equipment and facilities across a wide range of factory spaces is difficult, potentially leading to inadequate repairs or replacements and ultimately decreased production efficiency. Therefore, a more efficient and reliable system is needed.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes data collection means for inputting image information and design information, evaluation means for analyzing the input image information and evaluating the deterioration state of the structure, and determination means for determining whether to perform repair or replacement based on the evaluation results. This enables autonomous mobile machines within a factory to efficiently patrol a wide range of equipment, quickly and accurately determine the deterioration state, and notify the manager of the optimal timing for repair or replacement.

[0096] "Image information" refers to digital data acquired visually, which records the appearance of objects or structures.

[0097] "Design information" refers to data that includes technical specifications for items and structures, and includes information related to product design drawings and manufacturing processes.

[0098] "Data acquisition means" refers to a mechanism for acquiring image information and design information of an object and inputting them into a system.

[0099] "Evaluation means" refers to the process or mechanism that analyzes input image information and determines the state of deterioration of an item or structure.

[0100] "Decision-making means" refers to a method for determining appropriate repair or replacement measures based on the evaluation results obtained by the evaluation means.

[0101] "Proposal generation means" refers to a process or system that generates and outputs proposals regarding repairs or replacements based on the judgment results.

[0102] "Communication means" refers to technology that analyzes information acquired by autonomous mobile machines within a factory and notifies the administrator of the results.

[0103] An "autonomous mobile machine" refers to a device that has the ability to patrol a factory without specific instructions and collect necessary data.

[0104] The system for implementing this invention begins with an autonomous mobile machine operating within a factory to collect image information of target items or structures. The image information is acquired by the camera of the autonomous mobile machine. The acquired image information is transmitted to a server via a terminal and input into the system along with design information by a data collection means.

[0105] The server receives this input information and uses an AI-powered image analysis algorithm as an evaluation tool to quantify the state of deterioration. This analysis utilizes a deep learning framework such as Keras to normalize image size and extract features. If deterioration is detected as a result of the evaluation, the server determines whether repair or replacement is necessary using a decision-making tool.

[0106] The determined findings are then generated by a proposal generation system into a report that specifically describes recommended repair methods and replacements. This report is then communicated to the administrator via a communication system. Based on the report, the administrator can efficiently plan and implement factory maintenance work. This system makes it possible to detect equipment deterioration in the factory in advance and take appropriate countermeasures.

[0107] A concrete example is the regular inspection of pipelines in a manufacturing plant. An automated machine acquires images, analyzes them for corrosion and cracks, and notifies the administrator. An example of a prompt message would be: "Enter the latest images of the pipeline and evaluate its condition. Determine whether appropriate repairs or replacements are necessary, and generate a report."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user acquires images of designated equipment and facilities within the factory via an autonomous mobile machine. This image acquisition function is performed by an autonomous machine equipped with a camera, and the image data is transmitted to a terminal. The input is image data taken within the factory, which is temporarily stored as output on the terminal.

[0111] Step 2:

[0112] The terminal sends the acquired image data to the server. The transmitted data includes image information and related design information. The terminal organizes this data and uploads it to the server in a standard format. The input is the image data acquired in the previous step, which is sent to the server as output.

[0113] Step 3:

[0114] The server analyzes the received image and design information using an AI model as an evaluation tool. During this analysis process, the image data is normalized and features are extracted. A deep learning framework such as Keras is used, and the input is the image data sent to the server. The output is a numerical evaluation of the degradation state based on that data.

[0115] Step 4:

[0116] The server determines the need for repair or replacement based on the evaluation results using a decision-making mechanism. This decision algorithm determines the appropriate action based on the degree of deterioration. The input is the evaluation result of the deterioration state, and the output is the decision information for repair or replacement.

[0117] Step 5:

[0118] The server uses a proposal generation mechanism to generate specific repair or replacement proposals based on the decision information. In this generation process, the proposal content is output in document format. The input is decision information regarding repair or replacement, and the output is a report of the final proposal content.

[0119] Step 6:

[0120] The server notifies the administrator of the proposed content generated via communication means. This notification is sent via email or a notification system, prompting the administrator to review the information and take appropriate action. The input is a report of the proposed content, and the output is a notification message sent to the administrator.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] The present invention is a system that, in addition to evaluating the deterioration of an article and proposing repair or replacement based on that evaluation, presents information while taking into account the user's emotions. This system includes information input means, evaluation means, determination means, proposal generation means, and an emotion engine.

[0123] The user uses a terminal to input image data and design data related to the item. The terminal then sends the data to a server, initiating the analysis process. The server uses an evaluation tool to analyze the photographic data and quantify the item's deterioration state. Furthermore, based on the evaluation results, a decision tool determines the optimal course of action, whether repair or replacement.

[0124] The suggestion generation means generates repair methods and replacement options for the user based on the results of the determination means. In this process, the emotion engine analyzes the user's emotions in real time and creates suggestions that reflect their psychological state. Specifically, if the user is feeling anxious, the suggestion generation means is adjusted to present detailed and reassuring information.

[0125] For example, even if the system suggests replacing a storage container due to deterioration, if the emotion engine detects anxiety from the user's voice or input, it will provide more specific explanations about the necessity and benefits of the replacement. Furthermore, images and infographics are used to supplement the information visually, aiding user understanding.

[0126] This system allows users to receive not only technical judgments but also flexible and empathetic suggestions that take their emotions into consideration. This makes the user's decision-making process more comfortable and smoother, enabling them to make the optimal choice.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user uses a terminal to prepare image data and design data related to the item and inputs the data into the input interface. This includes taking detailed photographs of the item's exterior and interior.

[0130] Step 2:

[0131] The terminal organizes the input data and converts it into a format for transmission to the server. This data transfer includes the integration of image data and design data, which are then efficiently sent to the server.

[0132] Step 3:

[0133] The server uses evaluation tools to analyze the received data. AI algorithms are employed to analyze and evaluate the deterioration state of items from image data, and this information is quantified.

[0134] Step 4:

[0135] Based on the evaluation results, the server uses a determination tool to decide whether it should be repaired or replaced. The established criteria determine which option is more cost-effective and necessary.

[0136] Step 5:

[0137] Based on the assessment results, the server uses a proposal generation mechanism to create specific proposals. These proposals include specific methods, costs, and expected benefits corresponding to the repair or replacement options.

[0138] Step 6:

[0139] The emotion engine analyzes user input and responses in real time to evaluate the user's emotional state. Based on this, it understands what kind of feedback the user needs.

[0140] Step 7:

[0141] The server adjusts its suggestions based on the results of the emotion engine. It designs content that takes user emotions into consideration and supplements it with information that provides a sense of security.

[0142] Step 8:

[0143] The server sends the final proposal to the terminal. The terminal displays this information in an easy-to-understand format to help the user make better decisions.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] Conventional technologies have a problem in that they cannot take into account the user's feelings when appropriately evaluating the deterioration state of goods and proposing the optimal repair or replacement, and therefore cannot adequately address the user's anxieties and questions. Furthermore, they only provide technical proposals and do not provide sufficient detailed information for the user to make informed decisions.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes data acquisition means for inputting image information and structural information, condition evaluation means for analyzing the acquired image information and evaluating the deterioration state of the item, and emotion processing means for analyzing the user's emotional state and adjusting the proposed content according to the emotion. This makes it possible to make specific and convincing proposals while taking the user's emotions into consideration.

[0149] "Image information" refers to digital data that captures the visual characteristics of an object.

[0150] "Structural information" refers to technical data that describes the design and specifications of an item.

[0151] "Data acquisition means" refers to devices and methods for acquiring image information and structural information.

[0152] A "condition evaluation means" is a device or method for analyzing and quantifying the deterioration state of an item based on acquired image information.

[0153] A "decision-making mechanism" is a device or method for determining the optimal action of repair or replacement based on the evaluated state of deterioration.

[0154] A "proposal generation means" is a device or method for creating and presenting specific methods for repair or replacement to a user based on the results of their action decision.

[0155] "Emotional processing means" refers to devices or methods for analyzing a user's emotional state and adjusting suggested content according to that emotion.

[0156] A "system" is an overall configuration that integrates these means to perform deterioration assessment and make recommendations for items.

[0157] This invention is a system for evaluating the deterioration of articles and generating optimal repair or replacement suggestions. The system goes through the processes of inputting image and structural information, data analysis, and suggestion generation. It also adjusts the suggestion content to take user sentiment into consideration.

[0158] The user inputs image and structural information of items via the terminal. The terminal then sends the collected data, using its camera and image upload functions, to the server using the HTTP protocol.

[0159] The server uses image recognition software such as TENSORFLOW® to analyze image information. Based on the analysis, it evaluates the deterioration state of the item and records it as numerical data. Subsequently, based on the evaluation results, the server uses an action decision mechanism to determine the optimal action, whether repair or replacement. This decision is made based on past data and established rules.

[0160] The suggestion generation mechanism generates specific repair methods and replacement options for the user based on the results of their action decision. The suggestions include detailed procedures and cost information. Furthermore, the server uses emotion processing tools to analyze the user's emotions using natural language processing. For example, if it detects user anxiety, it adds reassuring detailed information or infographics.

[0161] As a concrete example, a user takes a photograph of a deteriorated storage box and inputs it into the system. In this case, an example of a prompt message would be: "Evaluate the deterioration of the storage box based on the image data submitted by the user, suggest repair or replacement, and provide additional information that takes the user's feelings into consideration."

[0162] This system allows users to receive not only technical guidance but also detailed suggestions that take emotions into consideration, making decision-making smoother and more satisfying.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The user inputs image data and design data of an item using a device. Specifically, the user takes a picture of the item with their smartphone camera and uploads the image along with the design data to the application, thereby importing the data into the device. The input data is then appropriately formatted for use in subsequent analysis.

[0166] Step 2:

[0167] The terminal transmits the input image data and design data to the server. The data is securely uploaded to the server using the HTTP protocol. During this transmission process, data format checks and communication stability are ensured.

[0168] Step 3:

[0169] The server analyzes the received image data. Specifically, it uses image recognition software (e.g., TensorFlow) to evaluate the deterioration state of the items. In this process, feature points are extracted from the image data, and a machine learning model is used to calculate a numerical value for the degree of deterioration. This result is stored in a database used in the next step.

[0170] Step 4:

[0171] The server uses an action decision mechanism to determine whether to repair or replace based on the condition assessment results. It automatically determines the optimal course of action by referring to the assessed degree of deterioration, past analysis data, or established rules. The determined information is then prepared for use by the suggestion generation mechanism.

[0172] Step 5:

[0173] The server uses a suggestion generation mechanism to propose specific repair methods and replacement options. For example, it generates a detailed suggestion such as, "Partial repair is possible, and the estimated cost is ¥XX." After this, the suggested content is sent to an emotion processing mechanism.

[0174] Step 6:

[0175] The server uses emotion processing tools to analyze the user's input data or communication and determine their current emotional state. It uses emotion analysis tools (e.g., natural language processing techniques) to determine whether the user is experiencing anxiety or doubt.

[0176] Step 7:

[0177] The server adjusts the suggestions based on the emotional data it receives. If the user is feeling anxious, the suggestion generation system provides more detailed explanations and adds relatable information. This allows the user to make decisions with confidence.

[0178] Step 8:

[0179] The terminal presents the user with the final proposal sent from the server. Based on this information, the user considers repair or replacement options and makes a decision on what to do.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0182] Conventional systems made repair and replacement suggestions for deteriorated objects based solely on technical aspects, failing to consider the user's emotions or psychological state. As a result, the suggestions could potentially cause anxiety or dissatisfaction among users, creating a need for a more user-friendly system that supports appropriate decision-making.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes an information input means for inputting image data and design data; an evaluation means for analyzing the input image data and evaluating the deterioration state of an object; a determination means for determining whether to repair or replace the object based on the evaluation results from the evaluation means; a proposal generation means for generating proposals according to the determination results and outputting them in a form adjusted to the user's emotional state; and an automatic generation means for proposing options to improve the condition of the device. This makes it possible to provide proposals that give a greater sense of security by considering not only the technical aspects but also the user's emotions and psychological state.

[0185] "Information input means" refers to a device or program for receiving image data and design data and inputting them into a system.

[0186] "Evaluation means" refers to a process or system that analyzes input image data to quantify or qualitatively evaluate the deterioration state of an object.

[0187] "Determination means" refers to a process or system that determines whether repair or replacement is the appropriate method based on the results obtained from evaluation means.

[0188] A "proposal generation means" is a device or program that generates repair or replacement suggestions for the user based on the judgment result and outputs them in accordance with the user's emotional state.

[0189] "Emotional analysis means" refers to a process or system for analyzing a user's emotional state and adjusting suggestions based on that data.

[0190] "Automatic generation means" refers to a process or system for automatically generating and proposing options related to improving the condition of a device.

[0191] The system for implementing this invention aims to perform image data and design data input, data analysis, degradation state evaluation, proposal generation, and sentiment analysis. The system is effectively operated through the interaction of a server, terminals, and users.

[0192] The server first receives image data and design data transmitted from the terminal. This data is then incorporated into the system by an information input means. The image data is analyzed using TensorFlow or other image processing software, and the deterioration state of the object is quantified. Based on the deterioration state results obtained by the evaluation means, the determination means determines the optimal repair or replacement option.

[0193] The suggestion generation mechanism utilizes these judgment results to provide users with information on appropriate repair methods and replacement equipment. Furthermore, the emotion analysis engine analyzes the user's emotional state and adjusts the suggested content to create reassuring explanations. Azure® Cognitive Services and similar technologies are used to read and analyze emotions from the user's voice and input data in real time.

[0194] For example, if a part of a factory's equipment is deteriorating, the system will suggest that the part needs to be replaced. If the user expresses concern, the system will reassure them by presenting specific benefits, such as, "This replacement work usually takes less than 30 minutes and will improve product performance by 5%."

[0195] An example of a prompt message for a generative AI model might be: "Analyze an image showing a state of deterioration and generate text that provides necessary countermeasures and reassurance."

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The terminal inputs image data and design data of an object and sends it to the server. The input data includes details of factory equipment and consumables. Once the data is sent from the terminal to the server, the data is incorporated into the system by the information input means.

[0199] Step 2:

[0200] The server analyzes the received image data using an evaluation tool. Image processing software such as TensorFlow supports this analysis. The degradation state is quantified from the input image data, and the result is output by the evaluation tool.

[0201] Step 3:

[0202] Based on the analysis results from the evaluation means, the server uses a determination means to determine the optimal action for repair or replacement. If the evaluation results indicate deterioration above a certain standard, the server makes a determination recommending the replacement of the part. This determination result is used in the next step.

[0203] Step 4:

[0204] Based on the results of the determination means, the server generates specific suggestions for the user using the suggestion generation means. Repair methods and replacement parts are selected, and the suggestion content, including detailed information, is constructed. The suggestion content is output to the user terminal so that the user can access it.

[0205] Step 5:

[0206] The server analyzes user input and voice tone using an emotion analysis engine with Azure Cognitive Services, etc., to understand the user's emotional state. If the user shows signs of anxiety, the suggestion generation system adjusts the suggestions and provides additional information to reassure the user. This information is intended to facilitate the user's decision-making process.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention relates to a system for evaluating the deterioration state of an article and proposing appropriate repair or replacement. This system incorporates various means for accurately determining the deterioration state by inputting image data and design data of the article and analyzing them.

[0224] The basic procedure for implementing the system is as follows: First, the user uses a terminal to take images of the items they wish to observe for deterioration and prepares relevant data such as design drawings. This data is then input into the system via the terminal. The terminal organizes the input data and sends it to the server.

[0225] The server uses an evaluation tool to analyze the received data. The evaluation tool uses an AI-powered image analysis algorithm to quantify and evaluate the deterioration state of the item. Based on this evaluation result, the server uses a judgment tool to determine whether repair is appropriate or whether replacement is necessary.

[0226] For example, when evaluating the deterioration of a box housing wireless equipment, the server recognizes rust and crack patterns from photographs and quantifies the degree of deterioration. Taking into account the number of years since installation and environmental conditions, it calculates the specific repair methods, costs, and life extension if repair is appropriate. If replacement is necessary, it presents options for a new housing.

[0227] The suggestions generated by the server are presented to the user via the terminal. The user considers the suggested information and makes the most reasonable choice. This system makes it possible to carry out repairs and replacements due to deterioration in a rational and rapid manner.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user uses a terminal to prepare image data and design data of the item. This includes taking photographs to visually capture the item's exterior and internal condition, and digitizing design drawings. The user enters the necessary information according to the terminal's input interface.

[0231] Step 2:

[0232] The terminal consolidates the input data and converts it into an appropriate format for analysis. This data unifies the formats of image data and design data and is organized into packets optimized for communication with the server.

[0233] Step 3:

[0234] The terminal sends the organized data to the server. The crucial point here is to perform error checking based on the communication protocol to ensure the data is received accurately.

[0235] Step 4:

[0236] The server receives the data and starts the analysis using the evaluation tool. The data is fed into the AI ​​model, and image analysis is performed to quantify the deterioration state of the item. At this time, the evaluation tool compares it with past deterioration patterns and calculates a specific deterioration index.

[0237] Step 5:

[0238] Based on the evaluation results, the server uses a decision-making mechanism to determine whether repair is possible or replacement is necessary. Here, the optimal choice is made based on pre-set thresholds. A high degradation index suggests replacement, while a low degradation index suggests repair.

[0239] Step 6:

[0240] Based on the assessment results, the server uses a proposal generation mechanism to generate specific content to propose to the user. If repair is proposed, specific repair methods, costs, and possible life extensions are presented. If replacement is proposed, options for new items and their specifications are suggested.

[0241] Step 7:

[0242] The server sends the generated proposals to the terminal. The terminal organizes the received information in an easy-to-understand format and displays it on the user's dashboard. Based on this information, the user makes a final decision and takes the necessary actions.

[0243] (Example 1)

[0244] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0245] In modern times, deterioration of goods due to long-term use is unavoidable, and it is necessary to accurately assess the degree of deterioration and make appropriate decisions regarding repair or replacement. However, conventional methods require specialized knowledge and experience to determine the extent of deterioration, and this process is time-consuming and costly. There is a need for a means to quickly and accurately assess deterioration and determine the optimal countermeasures.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes an information input means for inputting image data and design data; an evaluation means for analyzing the input image data using high-performance recognition processing and quantifying and evaluating the deterioration state of the article; and a determination means for determining the necessity of repair or replacement based on the evaluation results from the evaluation means. This makes it possible to quickly evaluate the deterioration state of the article and, if necessary, propose a reasonable and efficient repair or replacement.

[0248] "Information input means" refers to a device or function for receiving image data and design data from a user.

[0249] "Evaluation means" refers to a function or process that analyzes input image data and quantifies the deterioration state of an item for quantitative evaluation.

[0250] "Determination means" refers to a function or process that determines the need for repair or replacement of an item based on the evaluation results from evaluation means.

[0251] "Proposal generation means" refers to a function or process that generates information, including repair methods and replacement options, based on the judgment result and presents it to the user.

[0252] "Information provision means" refers to a mechanism or function for notifying users of the generated proposal content via a terminal.

[0253] This system is designed to assess the deterioration of items and suggest appropriate repair or replacement. Specific embodiments are described below.

[0254] First, the user uses their device to take pictures of the item they want to observe for deterioration. For example, they might use a smartphone camera app to take photos of the deteriorated area and prepare design drawings and data about the usage environment. This data is then centralized and organized by the device's application before being sent to the server.

[0255] The terminal ensures data format consistency by converting image data and design data to JSON format, and then sends them to the server. The HTTP protocol is used for data transmission, and encoding can be applied to ensure security.

[0256] The server receives the transmitted data and analyzes the deterioration state of the items using an AI image analysis algorithm. Software used for this analysis includes, for example, an image recognition model using a convolutional neural network (CNN). The server detects features such as rust and cracks, quantifies them, and generates a deterioration score.

[0257] Based on the analysis results, the server evaluates the degree of deterioration and determines whether the deterioration exceeds a set standard value. This determination then determines whether the item needs repair or replacement.

[0258] For example, in the case of a box housing wireless equipment, the server recognizes rust and crack patterns from photographs and calculates a deterioration score. Then, considering the number of years since installation and the surrounding environmental conditions, it calculates specific repair methods, costs, and the lifespan extension if repair is appropriate. If replacement is necessary, it proposes the most suitable new housing box.

[0259] The generated suggestions are sent from the server to the terminal and notified to the user. The user can then review this information through the terminal and make the most rational and efficient choice.

[0260] Examples of prompts to input into a generative AI model are as follows:

[0261] "Based on the image data and design data of the storage case, please conduct a deterioration assessment and propose repair or replacement."

[0262] This system makes it possible to quickly and accurately assess the deterioration status of items and propose appropriate countermeasures as needed.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The user takes images of the item whose deterioration they want to check using a device and prepares the design data. The input data consists of image files of the item and design data in PDF format. This data is centralized on the device, and the images are saved in JPEG format. Specifically, the user uses a camera app on their smartphone or tablet to take images of the item from multiple angles and scans or digitally copies the design drawings.

[0266] Step 2:

[0267] The terminal organizes the image data and design data collected by the user and prepares them for transmission to the server. The input image files and design data are converted to JSON format and organized into a single data package. Specifically, the terminal application aggregates each data into a specific folder and executes scripts to convert the data format. It also verifies the data's integrity and makes corrections as needed.

[0268] Step 3:

[0269] The terminal sends the organized data package to the server. As output, it sends the data to the server in an encoded state using the HTTP protocol. Specifically, the terminal verifies that it is connected to the network, generates a send request using the API, and sends the data. After the transmission is complete, the terminal notifies the user of the successful transmission.

[0270] Step 4:

[0271] The server receives data from the terminal and performs an integrity check. The input data is in JSON format, and the server deserializes it to expand the data. Specifically, after confirming the integrity of the data, the server saves the necessary information to the database and checks for any errors.

[0272] Step 5:

[0273] The server processes the unfolded image data using an AI image analysis algorithm to quantify the deterioration state of the items. The input data is an image of the item, and the output data is the deterioration score. Specifically, the server runs an image recognition program using a CNN to extract features such as rust and cracks. Based on this, it quantifies each attribute and finally scores the deterioration.

[0274] Step 6:

[0275] The server determines whether an item is repairable or needs replacement based on the results of the evaluation. Inputs are the degradation score and user design data. The output is a judgment result indicating whether repair or replacement is necessary. Specifically, the server compares the score to a predetermined threshold and applies a judgment logic to determine the result.

[0276] Step 7:

[0277] The server generates a proposal, including specific repair methods and replacement options, based on the assessment result. Inputs include the assessment result, past cases, and reference data. Output is the proposed repair or replacement content presented to the user. Specifically, the server extracts the optimal solution from the database, organizes the information, and creates a proposal document.

[0278] Step 8:

[0279] The server sends the generated suggestions to the terminal and notifies the user. The output is the suggestion information displayed on the terminal's user interface. Specifically, the server encrypts the data while considering security and sends it using the HTTP protocol. After receiving the data, the terminal executes a notification function for the user and displays the information on the screen.

[0280] (Application Example 1)

[0281] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0282] Conventional methods for evaluating deterioration primarily rely on manual visual inspection, which is time-consuming and labor-intensive, and often lacks accuracy and consistency. Furthermore, managing and notifying about the deterioration of equipment and facilities across a wide range of factory spaces is difficult, potentially leading to inadequate repairs or replacements and ultimately decreased production efficiency. Therefore, a more efficient and reliable system is needed.

[0283] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes data collection means for inputting image information and design information, evaluation means for analyzing the input image information and evaluating the deterioration state of the structure, and determination means for determining either repair or replacement processing based on the evaluation result. Thereby, the autonomous mobile machine in the factory can efficiently patrol a wide range of facilities, quickly and accurately determine the deterioration state, and notify the administrator of the optimal timing for repair and replacement.

[0285] "Image information" refers to data in digital form obtained visually and records the appearance of an article or structure.

[0286] "Design information" is data including the technical specifications of an article or structure, and refers to information related to the design drawings and manufacturing processes of a product.

[0287] "Data collection means" refers to a mechanism for acquiring the image information and design information of an object and inputting them into the system.

[0288] "Evaluation means" refers to a process or mechanism for analyzing the input image information and determining the deterioration state of an article or structure.

[0289] "Determination means" refers to a method for determining an appropriate measure of repair or replacement based on the evaluation result by the evaluation means.

[0290] "Proposal generation means" refers to a process or system for generating and outputting the content of a proposal regarding repair or replacement based on the determination result.

[0291] "Communication means" refers to a technology for analyzing the information acquired by the autonomous mobile machine in the factory and notifying the administrator of the result.

[0292] "Autonomous mobile machine" refers to a device that can patrol the factory without specific instructions and has the ability to collect necessary data.

[0293] The system for implementing this invention begins with an autonomous mobile machine operating within a factory to collect image information of target items or structures. The image information is acquired by the camera of the autonomous mobile machine. The acquired image information is transmitted to a server via a terminal and input into the system along with design information by a data collection means.

[0294] The server receives this input information and uses an AI-powered image analysis algorithm as an evaluation tool to quantify the state of deterioration. This analysis utilizes a deep learning framework such as Keras to normalize image size and extract features. If deterioration is detected as a result of the evaluation, the server determines whether repair or replacement is necessary using a decision-making tool.

[0295] The determined findings are then generated by a proposal generation system into a report that specifically describes recommended repair methods and replacements. This report is then communicated to the administrator via a communication system. Based on the report, the administrator can efficiently plan and implement factory maintenance work. This system makes it possible to detect equipment deterioration in the factory in advance and take appropriate countermeasures.

[0296] A concrete example is the regular inspection of pipelines in a manufacturing plant. An automated machine acquires images, analyzes them for corrosion and cracks, and notifies the administrator. An example of a prompt message would be: "Enter the latest images of the pipeline and evaluate its condition. Determine whether appropriate repairs or replacements are necessary, and generate a report."

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The user acquires images of designated equipment and facilities within the factory through an autonomous mobile machine. This image acquisition function is executed by an autonomous machine equipped with a camera, and the image data is transmitted to the terminal. The input is the image data captured within the factory, and it is temporarily stored on the terminal as the output.

[0300] Step 2:

[0301] The terminal transmits the acquired image data to the server. The data to be transmitted includes image information and related design information. The terminal organizes this data and uploads it to the server in a standard format. The input is the image data acquired in the previous step, and it is sent to the server as the output.

[0302] Step 3:

[0303] The server analyzes the received image information and design information using an AI model as the evaluation means. In this analysis process, the image data is normalized and feature extraction is performed. A deep learning framework such as Keras is used, and the input is the image data sent to the server. The output is the numerical evaluation result of the deterioration state based on that data.

[0304] Step 4:

[0305] Based on the evaluation result, the server determines the necessity of repair or replacement using the judgment means. This judgment algorithm determines appropriate actions based on the degree of deterioration. The input is the evaluation result of the deterioration state, and the output is the judgment information for repair or replacement.

[0306] Step 5:

[0307] The server uses the proposal generation means to generate specific repair methods or replacement proposals based on the judgment information. In this generation process, the proposal content is output in document form. The input is the judgment information for repair or replacement, and the output is the report of the final proposal content.

[0308] Step 6:

[0309] The server notifies the administrator of the proposed content generated via communication means. This notification is sent via email or a notification system, prompting the administrator to review the information and take appropriate action. The input is a report of the proposed content, and the output is a notification message sent to the administrator.

[0310] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0311] The present invention is a system that, in addition to evaluating the deterioration of an article and proposing repair or replacement based on that evaluation, presents information while taking into account the user's emotions. This system includes information input means, evaluation means, determination means, proposal generation means, and an emotion engine.

[0312] The user uses a terminal to input image data and design data related to the item. The terminal then sends the data to a server, initiating the analysis process. The server uses an evaluation tool to analyze the photographic data and quantify the item's deterioration state. Furthermore, based on the evaluation results, a decision tool determines the optimal course of action, whether repair or replacement.

[0313] The suggestion generation means generates repair methods and replacement options for the user based on the results of the determination means. In this process, the emotion engine analyzes the user's emotions in real time and creates suggestions that reflect their psychological state. Specifically, if the user is feeling anxious, the suggestion generation means is adjusted to present detailed and reassuring information.

[0314] For example, even if the system suggests replacing a storage container due to deterioration, if the emotion engine detects anxiety from the user's voice or input, it will provide more specific explanations about the necessity and benefits of the replacement. Furthermore, images and infographics are used to supplement the information visually, aiding user understanding.

[0315] This system allows users to receive not only technical judgments but also flexible and empathetic suggestions that take their emotions into consideration. This makes the user's decision-making process more comfortable and smoother, enabling them to make the optimal choice.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The user uses a terminal to prepare image data and design data related to the item and inputs the data into the input interface. This includes taking detailed photographs of the item's exterior and interior.

[0319] Step 2:

[0320] The terminal organizes the input data and converts it into a format for transmission to the server. This data transfer includes the integration of image data and design data, which are then efficiently sent to the server.

[0321] Step 3:

[0322] The server uses evaluation tools to analyze the received data. AI algorithms are employed to analyze and evaluate the deterioration state of items from image data, and this information is quantified.

[0323] Step 4:

[0324] Based on the evaluation results, the server uses a determination tool to decide whether it should be repaired or replaced. The established criteria determine which option is more cost-effective and necessary.

[0325] Step 5:

[0326] Based on the assessment results, the server uses a proposal generation mechanism to create specific proposals. These proposals include specific methods, costs, and expected benefits corresponding to the repair or replacement options.

[0327] Step 6:

[0328] The emotion engine analyzes user input and responses in real time to evaluate the user's emotional state. Based on this, it understands what kind of feedback the user needs.

[0329] Step 7:

[0330] The server adjusts its suggestions based on the results of the emotion engine. It designs content that takes user emotions into consideration and supplements it with information that provides a sense of security.

[0331] Step 8:

[0332] The server sends the final proposal to the terminal. The terminal displays this information in an easy-to-understand format to help the user make better decisions.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] Conventional technologies have a problem in that they cannot take into account the user's feelings when appropriately evaluating the deterioration state of goods and proposing the optimal repair or replacement, and therefore cannot adequately address the user's anxieties and questions. Furthermore, they only provide technical proposals and do not provide sufficient detailed information for the user to make informed decisions.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes data acquisition means for inputting image information and structural information, condition evaluation means for analyzing the acquired image information and evaluating the deterioration state of the item, and emotion processing means for analyzing the user's emotional state and adjusting the proposed content according to the emotion. This makes it possible to make specific and convincing proposals while taking the user's emotions into consideration.

[0338] "Image information" refers to digital data that captures the visual characteristics of an object.

[0339] "Structural information" refers to technical data that describes the design and specifications of an item.

[0340] "Data acquisition means" refers to devices and methods for acquiring image information and structural information.

[0341] A "condition evaluation means" is a device or method for analyzing and quantifying the deterioration state of an item based on acquired image information.

[0342] A "decision-making mechanism" is a device or method for determining the optimal action of repair or replacement based on the evaluated state of deterioration.

[0343] A "proposal generation means" is a device or method for creating and presenting specific methods for repair or replacement to a user based on the results of their action decision.

[0344] "Emotional processing means" refers to devices or methods for analyzing a user's emotional state and adjusting suggested content according to that emotion.

[0345] A "system" is an overall configuration that integrates these means to perform deterioration assessment and make recommendations for items.

[0346] This invention is a system for evaluating the deterioration of articles and generating optimal repair or replacement suggestions. The system goes through the processes of inputting image and structural information, data analysis, and suggestion generation. It also adjusts the suggestion content to take user sentiment into consideration.

[0347] The user inputs image and structural information of items via the terminal. The terminal then sends the collected data, using its camera and image upload functions, to the server using the HTTP protocol.

[0348] The server uses image recognition software such as TensorFlow to analyze image information. Based on the analysis, it evaluates the deterioration state of the item and records it as numerical data. Subsequently, based on the evaluation results, the server uses an action decision mechanism to determine the optimal action, whether to repair or replace the item. This decision is made based on past data and established rules.

[0349] The suggestion generation mechanism generates specific repair methods and replacement options for the user based on the results of their action decision. The suggestions include detailed procedures and cost information. Furthermore, the server uses emotion processing tools to analyze the user's emotions using natural language processing. For example, if it detects user anxiety, it adds reassuring detailed information or infographics.

[0350] As a concrete example, a user takes a photograph of a deteriorated storage box and inputs it into the system. In this case, an example of a prompt message would be: "Evaluate the deterioration of the storage box based on the image data submitted by the user, suggest repair or replacement, and provide additional information that takes the user's feelings into consideration."

[0351] This system allows users to receive not only technical guidance but also detailed suggestions that take emotions into consideration, making decision-making smoother and more satisfying.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The user inputs image data and design data of an item using a device. Specifically, the user takes a picture of the item with their smartphone camera and uploads the image along with the design data to the application, thereby importing the data into the device. The input data is then appropriately formatted for use in subsequent analysis.

[0355] Step 2:

[0356] The terminal transmits the input image data and design data to the server. The data is securely uploaded to the server using the HTTP protocol. During this transmission process, data format checks and communication stability are ensured.

[0357] Step 3:

[0358] The server analyzes the received image data. Specifically, it uses image recognition software (e.g., TensorFlow) to evaluate the deterioration state of the items. In this process, feature points are extracted from the image data, and a machine learning model is used to calculate a numerical value for the degree of deterioration. This result is stored in a database used in the next step.

[0359] Step 4:

[0360] The server uses an action decision mechanism to determine whether to repair or replace based on the condition assessment results. It automatically determines the optimal course of action by referring to the assessed degree of deterioration, past analysis data, or established rules. The determined information is then prepared for use by the suggestion generation mechanism.

[0361] Step 5:

[0362] The server uses a suggestion generation mechanism to propose specific repair methods and replacement options. For example, it generates a detailed suggestion such as, "Partial repair is possible, and the estimated cost is ¥XX." After this, the suggested content is sent to an emotion processing mechanism.

[0363] Step 6:

[0364] The server uses emotion processing tools to analyze the user's input data or communication and determine their current emotional state. It uses emotion analysis tools (e.g., natural language processing techniques) to determine whether the user is experiencing anxiety or doubt.

[0365] Step 7:

[0366] The server adjusts the suggestions based on the emotional data it receives. If the user is feeling anxious, the suggestion generation system provides more detailed explanations and adds relatable information. This allows the user to make decisions with confidence.

[0367] Step 8:

[0368] The terminal presents the user with the final proposal sent from the server. Based on this information, the user considers repair or replacement options and makes a decision on what to do.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] Conventional systems made repair and replacement suggestions for deteriorated objects based solely on technical aspects, failing to consider the user's emotions or psychological state. As a result, the suggestions could potentially cause anxiety or dissatisfaction among users, creating a need for a more user-friendly system that supports appropriate decision-making.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes an information input means for inputting image data and design data; an evaluation means for analyzing the input image data and evaluating the deterioration state of an object; a determination means for determining whether to repair or replace the object based on the evaluation results from the evaluation means; a proposal generation means for generating proposals according to the determination results and outputting them in a form adjusted to the user's emotional state; and an automatic generation means for proposing options to improve the condition of the device. This makes it possible to provide proposals that give a greater sense of security by considering not only the technical aspects but also the user's emotions and psychological state.

[0374] "Information input means" refers to a device or program for receiving image data and design data and inputting them into a system.

[0375] "Evaluation means" refers to a process or system that analyzes input image data to quantify or qualitatively evaluate the deterioration state of an object.

[0376] "Determination means" refers to a process or system that determines whether repair or replacement is the appropriate method based on the results obtained from evaluation means.

[0377] A "proposal generation means" is a device or program that generates repair or replacement suggestions for the user based on the judgment result and outputs them in accordance with the user's emotional state.

[0378] "Emotional analysis means" refers to a process or system for analyzing a user's emotional state and adjusting suggestions based on that data.

[0379] "Automatic generation means" refers to a process or system for automatically generating and proposing options related to improving the condition of a device.

[0380] The system for implementing this invention aims to perform image data and design data input, data analysis, degradation state evaluation, proposal generation, and sentiment analysis. The system is effectively operated through the interaction of a server, terminals, and users.

[0381] The server first receives image data and design data transmitted from the terminal. This data is then incorporated into the system by an information input means. The image data is analyzed using TensorFlow or other image processing software, and the deterioration state of the object is quantified. Based on the deterioration state results obtained by the evaluation means, the determination means determines the optimal repair or replacement option.

[0382] The suggestion generation mechanism utilizes these judgment results to provide users with information on appropriate repair methods and replacement equipment. Furthermore, the emotion analysis engine analyzes the user's emotional state and adjusts the suggested content to create reassuring explanations. Azure Cognitive Services or similar technologies are used to read and analyze emotions from the user's voice and input data in real time.

[0383] For example, if a part of a factory's equipment is deteriorating, the system will suggest that the part needs to be replaced. If the user expresses concern, the system will reassure them by presenting specific benefits, such as, "This replacement work usually takes less than 30 minutes and will improve product performance by 5%."

[0384] An example of a prompt message for a generative AI model might be: "Analyze an image showing a state of deterioration and generate text that provides necessary countermeasures and reassurance."

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The terminal inputs image data and design data of an object and sends it to the server. The input data includes details of factory equipment and consumables. Once the data is sent from the terminal to the server, the data is incorporated into the system by the information input means.

[0388] Step 2:

[0389] The server analyzes the received image data using an evaluation tool. Image processing software such as TensorFlow supports this analysis. The degradation state is quantified from the input image data, and the result is output by the evaluation tool.

[0390] Step 3:

[0391] Based on the analysis results from the evaluation means, the server uses a determination means to determine the optimal action for repair or replacement. If the evaluation results indicate deterioration above a certain standard, the server makes a determination recommending the replacement of the part. This determination result is used in the next step.

[0392] Step 4:

[0393] Based on the results of the determination means, the server generates specific suggestions for the user using the suggestion generation means. Repair methods and replacement parts are selected, and the suggestion content, including detailed information, is constructed. The suggestion content is output to the user terminal so that the user can access it.

[0394] Step 5:

[0395] The server analyzes user input and voice tone using an emotion analysis engine with Azure Cognitive Services, etc., to understand the user's emotional state. If the user shows signs of anxiety, the suggestion generation system adjusts the suggestions and provides additional information to reassure the user. This information is intended to facilitate the user's decision-making process.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0402] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0404] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0406] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0407] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0408] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0411] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0412] This invention relates to a system for evaluating the deterioration state of an article and proposing appropriate repair or replacement. This system incorporates various means for accurately determining the deterioration state by inputting image data and design data of the article and analyzing them.

[0413] The basic procedure for implementing the system is as follows: First, the user uses a terminal to take images of the items they wish to observe for deterioration and prepares relevant data such as design drawings. This data is then input into the system via the terminal. The terminal organizes the input data and sends it to the server.

[0414] The server uses an evaluation tool to analyze the received data. The evaluation tool uses an AI-powered image analysis algorithm to quantify and evaluate the deterioration state of the item. Based on this evaluation result, the server uses a judgment tool to determine whether repair is appropriate or whether replacement is necessary.

[0415] For example, when evaluating the deterioration of a box housing wireless equipment, the server recognizes rust and crack patterns from photographs and quantifies the degree of deterioration. Taking into account the number of years since installation and environmental conditions, it calculates the specific repair methods, costs, and life extension if repair is appropriate. If replacement is necessary, it presents options for a new housing.

[0416] The suggestions generated by the server are presented to the user via the terminal. The user considers the suggested information and makes the most reasonable choice. This system makes it possible to carry out repairs and replacements due to deterioration in a rational and rapid manner.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] The user uses a terminal to prepare image data and design data of the item. This includes taking photographs to visually capture the item's exterior and internal condition, and digitizing design drawings. The user enters the necessary information according to the terminal's input interface.

[0420] Step 2:

[0421] The terminal consolidates the input data and converts it into an appropriate format for analysis. This data unifies the formats of image data and design data and is organized into packets optimized for communication with the server.

[0422] Step 3:

[0423] The terminal sends the organized data to the server. The crucial point here is to perform error checking based on the communication protocol to ensure the data is received accurately.

[0424] Step 4:

[0425] The server receives the data and starts the analysis using the evaluation tool. The data is fed into the AI ​​model, and image analysis is performed to quantify the deterioration state of the item. At this time, the evaluation tool compares it with past deterioration patterns and calculates a specific deterioration index.

[0426] Step 5:

[0427] Based on the evaluation results, the server uses a decision-making mechanism to determine whether repair is possible or replacement is necessary. Here, the optimal choice is made based on pre-set thresholds. A high degradation index suggests replacement, while a low degradation index suggests repair.

[0428] Step 6:

[0429] Based on the assessment results, the server uses a proposal generation mechanism to generate specific content to propose to the user. If repair is proposed, specific repair methods, costs, and possible life extensions are presented. If replacement is proposed, options for new items and their specifications are suggested.

[0430] Step 7:

[0431] The server sends the generated proposals to the terminal. The terminal organizes the received information in an easy-to-understand format and displays it on the user's dashboard. Based on this information, the user makes a final decision and takes the necessary actions.

[0432] (Example 1)

[0433] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0434] In modern times, deterioration of goods due to long-term use is unavoidable, and it is necessary to accurately assess the degree of deterioration and make appropriate decisions regarding repair or replacement. However, conventional methods require specialized knowledge and experience to determine the extent of deterioration, and this process is time-consuming and costly. There is a need for a means to quickly and accurately assess deterioration and determine the optimal countermeasures.

[0435] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0436] In this invention, the server includes an information input means for inputting image data and design data; an evaluation means for analyzing the input image data using high-performance recognition processing and quantifying and evaluating the deterioration state of the article; and a determination means for determining the necessity of repair or replacement based on the evaluation results from the evaluation means. This makes it possible to quickly evaluate the deterioration state of the article and, if necessary, propose a reasonable and efficient repair or replacement.

[0437] "Information input means" refers to a device or function for receiving image data and design data from a user.

[0438] "Evaluation means" refers to a function or process that analyzes input image data and quantifies the deterioration state of an item for quantitative evaluation.

[0439] "Determination means" refers to a function or process that determines the need for repair or replacement of an item based on the evaluation results from evaluation means.

[0440] "Proposal generation means" refers to a function or process that generates information, including repair methods and replacement options, based on the judgment result and presents it to the user.

[0441] "Information provision means" refers to a mechanism or function for notifying users of the generated proposal content via a terminal.

[0442] This system is designed to assess the deterioration of items and suggest appropriate repair or replacement. Specific embodiments are described below.

[0443] First, the user uses their device to take pictures of the item they want to observe for deterioration. For example, they might use a smartphone camera app to take photos of the deteriorated area and prepare design drawings and data about the usage environment. This data is then centralized and organized by the device's application before being sent to the server.

[0444] The terminal ensures data format consistency by converting image data and design data to JSON format, and then sends them to the server. The HTTP protocol is used for data transmission, and encoding can be applied to ensure security.

[0445] The server receives the transmitted data and analyzes the deterioration state of the items using an AI image analysis algorithm. Software used for this analysis includes, for example, an image recognition model using a convolutional neural network (CNN). The server detects features such as rust and cracks, quantifies them, and generates a deterioration score.

[0446] Based on the analysis results, the server evaluates the degree of deterioration and determines whether the deterioration exceeds a set standard value. This determination then determines whether the item needs repair or replacement.

[0447] For example, in the case of a box housing wireless equipment, the server recognizes rust and crack patterns from photographs and calculates a deterioration score. Then, considering the number of years since installation and the surrounding environmental conditions, it calculates specific repair methods, costs, and the lifespan extension if repair is appropriate. If replacement is necessary, it proposes the most suitable new housing box.

[0448] The generated suggestions are sent from the server to the terminal and notified to the user. The user can then review this information through the terminal and make the most rational and efficient choice.

[0449] Examples of prompts to input into a generative AI model are as follows:

[0450] "Based on the image data and design data of the storage case, please conduct a deterioration assessment and propose repair or replacement."

[0451] This system makes it possible to quickly and accurately assess the deterioration status of items and propose appropriate countermeasures as needed.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The user takes images of the item whose deterioration they want to check using a device and prepares the design data. The input data consists of image files of the item and design data in PDF format. This data is centralized on the device, and the images are saved in JPEG format. Specifically, the user uses a camera app on their smartphone or tablet to take images of the item from multiple angles and scans or digitally copies the design drawings.

[0455] Step 2:

[0456] The terminal organizes the image data and design data collected by the user and prepares them for transmission to the server. The input image files and design data are converted to JSON format and organized into a single data package. Specifically, the terminal application aggregates each data into a specific folder and executes scripts to convert the data format. It also verifies the data's integrity and makes corrections as needed.

[0457] Step 3:

[0458] The terminal sends the organized data package to the server. As output, it sends the data to the server in an encoded state using the HTTP protocol. Specifically, the terminal verifies that it is connected to the network, generates a send request using the API, and sends the data. After the transmission is complete, the terminal notifies the user of the successful transmission.

[0459] Step 4:

[0460] The server receives data from the terminal and performs an integrity check. The input data is in JSON format, and the server deserializes it to expand the data. Specifically, after confirming the integrity of the data, the server saves the necessary information to the database and checks for any errors.

[0461] Step 5:

[0462] The server processes the unfolded image data using an AI image analysis algorithm to quantify the deterioration state of the items. The input data is an image of the item, and the output data is the deterioration score. Specifically, the server runs an image recognition program using a CNN to extract features such as rust and cracks. Based on this, it quantifies each attribute and finally scores the deterioration.

[0463] Step 6:

[0464] The server determines whether an item is repairable or needs replacement based on the results of the evaluation. Inputs are the degradation score and user design data. The output is a judgment result indicating whether repair or replacement is necessary. Specifically, the server compares the score to a predetermined threshold and applies a judgment logic to determine the result.

[0465] Step 7:

[0466] The server generates a proposal, including specific repair methods and replacement options, based on the assessment result. Inputs include the assessment result, past cases, and reference data. Output is the proposed repair or replacement content presented to the user. Specifically, the server extracts the optimal solution from the database, organizes the information, and creates a proposal document.

[0467] Step 8:

[0468] The server sends the generated suggestions to the terminal and notifies the user. The output is the suggestion information displayed on the terminal's user interface. Specifically, the server encrypts the data while considering security and sends it using the HTTP protocol. After receiving the data, the terminal executes a notification function for the user and displays the information on the screen.

[0469] (Application Example 1)

[0470] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0471] Conventional methods for evaluating deterioration primarily rely on manual visual inspection, which is time-consuming and labor-intensive, and often lacks accuracy and consistency. Furthermore, managing and notifying about the deterioration of equipment and facilities across a wide range of factory spaces is difficult, potentially leading to inadequate repairs or replacements and ultimately decreased production efficiency. Therefore, a more efficient and reliable system is needed.

[0472] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0473] In this invention, the server includes data collection means for inputting image information and design information, evaluation means for analyzing the input image information and evaluating the deterioration state of the structure, and determination means for determining whether to perform repair or replacement based on the evaluation results. This enables autonomous mobile machines within a factory to efficiently patrol a wide range of equipment, quickly and accurately determine the deterioration state, and notify the manager of the optimal timing for repair or replacement.

[0474] "Image information" refers to digital data acquired visually, which records the appearance of objects or structures.

[0475] "Design information" refers to data that includes technical specifications for items and structures, and includes information related to product design drawings and manufacturing processes.

[0476] "Data acquisition means" refers to a mechanism for acquiring image information and design information of an object and inputting them into a system.

[0477] "Evaluation means" refers to the process or mechanism that analyzes input image information and determines the state of deterioration of an item or structure.

[0478] "Decision-making means" refers to a method for determining appropriate repair or replacement measures based on the evaluation results obtained by the evaluation means.

[0479] "Proposal generation means" refers to a process or system that generates and outputs proposals regarding repairs or replacements based on the judgment results.

[0480] "Communication means" refers to technology that analyzes information acquired by autonomous mobile machines within a factory and notifies the administrator of the results.

[0481] An "autonomous mobile machine" refers to a device that has the ability to patrol a factory without specific instructions and collect necessary data.

[0482] The system for implementing this invention begins with an autonomous mobile machine operating within a factory to collect image information of target items or structures. The image information is acquired by the camera of the autonomous mobile machine. The acquired image information is transmitted to a server via a terminal and input into the system along with design information by a data collection means.

[0483] The server receives this input information and uses an AI-powered image analysis algorithm as an evaluation tool to quantify the state of deterioration. This analysis utilizes a deep learning framework such as Keras to normalize image size and extract features. If deterioration is detected as a result of the evaluation, the server determines whether repair or replacement is necessary using a decision-making tool.

[0484] The determined findings are then generated by a proposal generation system into a report that specifically describes recommended repair methods and replacements. This report is then communicated to the administrator via a communication system. Based on the report, the administrator can efficiently plan and implement factory maintenance work. This system makes it possible to detect equipment deterioration in the factory in advance and take appropriate countermeasures.

[0485] A concrete example is the regular inspection of pipelines in a manufacturing plant. An automated machine acquires images, analyzes them for corrosion and cracks, and notifies the administrator. An example of a prompt message would be: "Enter the latest images of the pipeline and evaluate its condition. Determine whether appropriate repairs or replacements are necessary, and generate a report."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The user acquires images of designated equipment and facilities within the factory via an autonomous mobile machine. This image acquisition function is performed by an autonomous machine equipped with a camera, and the image data is transmitted to a terminal. The input is image data taken within the factory, which is temporarily stored as output on the terminal.

[0489] Step 2:

[0490] The terminal sends the acquired image data to the server. The transmitted data includes image information and related design information. The terminal organizes this data and uploads it to the server in a standard format. The input is the image data acquired in the previous step, which is sent to the server as output.

[0491] Step 3:

[0492] The server analyzes the received image and design information using an AI model as an evaluation tool. During this analysis process, the image data is normalized and features are extracted. A deep learning framework such as Keras is used, and the input is the image data sent to the server. The output is a numerical evaluation of the degradation state based on that data.

[0493] Step 4:

[0494] The server determines the need for repair or replacement based on the evaluation results using a decision-making mechanism. This decision algorithm determines the appropriate action based on the degree of deterioration. The input is the evaluation result of the deterioration state, and the output is the decision information for repair or replacement.

[0495] Step 5:

[0496] The server uses a proposal generation mechanism to generate specific repair or replacement proposals based on the decision information. In this generation process, the proposal content is output in document format. The input is decision information regarding repair or replacement, and the output is a report of the final proposal content.

[0497] Step 6:

[0498] The server notifies the administrator of the proposed content generated via communication means. This notification is sent via email or a notification system, prompting the administrator to review the information and take appropriate action. The input is a report of the proposed content, and the output is a notification message sent to the administrator.

[0499] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0500] The present invention is a system that, in addition to evaluating the deterioration of an article and proposing repair or replacement based on that evaluation, presents information while taking into account the user's emotions. This system includes information input means, evaluation means, determination means, proposal generation means, and an emotion engine.

[0501] The user uses a terminal to input image data and design data related to the item. The terminal then sends the data to a server, initiating the analysis process. The server uses an evaluation tool to analyze the photographic data and quantify the item's deterioration state. Furthermore, based on the evaluation results, a decision tool determines the optimal course of action, whether repair or replacement.

[0502] The suggestion generation means generates repair methods and replacement options for the user based on the results of the determination means. In this process, the emotion engine analyzes the user's emotions in real time and creates suggestions that reflect their psychological state. Specifically, if the user is feeling anxious, the suggestion generation means is adjusted to present detailed and reassuring information.

[0503] For example, even if the system suggests replacing a storage container due to deterioration, if the emotion engine detects anxiety from the user's voice or input, it will provide more specific explanations about the necessity and benefits of the replacement. Furthermore, images and infographics are used to supplement the information visually, aiding user understanding.

[0504] This system allows users to receive not only technical judgments but also flexible and empathetic suggestions that take their emotions into consideration. This makes the user's decision-making process more comfortable and smoother, enabling them to make the optimal choice.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The user uses a terminal to prepare image data and design data related to the item and inputs the data into the input interface. This includes taking detailed photographs of the item's exterior and interior.

[0508] Step 2:

[0509] The terminal organizes the input data and converts it into a format for transmission to the server. This data transfer includes the integration of image data and design data, which are then efficiently sent to the server.

[0510] Step 3:

[0511] The server uses evaluation tools to analyze the received data. AI algorithms are employed to analyze and evaluate the deterioration state of items from image data, and this information is quantified.

[0512] Step 4:

[0513] Based on the evaluation results, the server uses a determination tool to decide whether it should be repaired or replaced. The established criteria determine which option is more cost-effective and necessary.

[0514] Step 5:

[0515] Based on the assessment results, the server uses a proposal generation mechanism to create specific proposals. These proposals include specific methods, costs, and expected benefits corresponding to the repair or replacement options.

[0516] Step 6:

[0517] The emotion engine analyzes user input and responses in real time to evaluate the user's emotional state. Based on this, it understands what kind of feedback the user needs.

[0518] Step 7:

[0519] The server adjusts its suggestions based on the results of the emotion engine. It designs content that takes user emotions into consideration and supplements it with information that provides a sense of security.

[0520] Step 8:

[0521] The server sends the final proposal to the terminal. The terminal displays this information in an easy-to-understand format to help the user make better decisions.

[0522] (Example 2)

[0523] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] Conventional technologies have a problem in that they cannot take into account the user's feelings when appropriately evaluating the deterioration state of goods and proposing the optimal repair or replacement, and therefore cannot adequately address the user's anxieties and questions. Furthermore, they only provide technical proposals and do not provide sufficient detailed information for the user to make informed decisions.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0526] In this invention, the server includes data acquisition means for inputting image information and structural information, condition evaluation means for analyzing the acquired image information and evaluating the deterioration state of the item, and emotion processing means for analyzing the user's emotional state and adjusting the proposed content according to the emotion. This makes it possible to make specific and convincing proposals while taking the user's emotions into consideration.

[0527] "Image information" refers to digital data that captures the visual characteristics of an object.

[0528] "Structural information" refers to technical data that describes the design and specifications of an item.

[0529] "Data acquisition means" refers to devices and methods for acquiring image information and structural information.

[0530] A "condition evaluation means" is a device or method for analyzing and quantifying the deterioration state of an item based on acquired image information.

[0531] A "decision-making mechanism" is a device or method for determining the optimal action of repair or replacement based on the evaluated state of deterioration.

[0532] A "proposal generation means" is a device or method for creating and presenting specific methods for repair or replacement to a user based on the results of their action decision.

[0533] "Emotional processing means" refers to devices or methods for analyzing a user's emotional state and adjusting suggested content according to that emotion.

[0534] A "system" is an overall configuration that integrates these means to perform deterioration assessment and make recommendations for items.

[0535] This invention is a system for evaluating the deterioration of articles and generating optimal repair or replacement suggestions. The system goes through the processes of inputting image and structural information, data analysis, and suggestion generation. It also adjusts the suggestion content to take user sentiment into consideration.

[0536] The user inputs image and structural information of items via the terminal. The terminal then sends the collected data, using its camera and image upload functions, to the server using the HTTP protocol.

[0537] The server uses image recognition software such as TensorFlow to analyze image information. Based on the analysis, it evaluates the deterioration state of the item and records it as numerical data. Subsequently, based on the evaluation results, the server uses an action decision mechanism to determine the optimal action, whether to repair or replace the item. This decision is made based on past data and established rules.

[0538] The suggestion generation mechanism generates specific repair methods and replacement options for the user based on the results of their action decision. The suggestions include detailed procedures and cost information. Furthermore, the server uses emotion processing tools to analyze the user's emotions using natural language processing. For example, if it detects user anxiety, it adds reassuring detailed information or infographics.

[0539] As a concrete example, a user takes a photograph of a deteriorated storage box and inputs it into the system. In this case, an example of a prompt message would be: "Evaluate the deterioration of the storage box based on the image data submitted by the user, suggest repair or replacement, and provide additional information that takes the user's feelings into consideration."

[0540] This system allows users to receive not only technical guidance but also detailed suggestions that take emotions into consideration, making decision-making smoother and more satisfying.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The user inputs image data and design data of an item using a device. Specifically, the user takes a picture of the item with their smartphone camera and uploads the image along with the design data to the application, thereby importing the data into the device. The input data is then appropriately formatted for use in subsequent analysis.

[0544] Step 2:

[0545] The terminal transmits the input image data and design data to the server. The data is securely uploaded to the server using the HTTP protocol. During this transmission process, data format checks and communication stability are ensured.

[0546] Step 3:

[0547] The server analyzes the received image data. Specifically, it uses image recognition software (e.g., TensorFlow) to evaluate the deterioration state of the items. In this process, feature points are extracted from the image data, and a machine learning model is used to calculate a numerical value for the degree of deterioration. This result is stored in a database used in the next step.

[0548] Step 4:

[0549] The server uses an action decision mechanism to determine whether to repair or replace based on the condition assessment results. It automatically determines the optimal course of action by referring to the assessed degree of deterioration, past analysis data, or established rules. The determined information is then prepared for use by the suggestion generation mechanism.

[0550] Step 5:

[0551] The server uses a suggestion generation mechanism to propose specific repair methods and replacement options. For example, it generates a detailed suggestion such as, "Partial repair is possible, and the estimated cost is ¥XX." After this, the suggested content is sent to an emotion processing mechanism.

[0552] Step 6:

[0553] The server uses emotion processing tools to analyze the user's input data or communication and determine their current emotional state. It uses emotion analysis tools (e.g., natural language processing techniques) to determine whether the user is experiencing anxiety or doubt.

[0554] Step 7:

[0555] The server adjusts the suggestions based on the emotional data it receives. If the user is feeling anxious, the suggestion generation system provides more detailed explanations and adds relatable information. This allows the user to make decisions with confidence.

[0556] Step 8:

[0557] The terminal presents the user with the final proposal sent from the server. Based on this information, the user considers repair or replacement options and makes a decision on what to do.

[0558] (Application Example 2)

[0559] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0560] Conventional systems made repair and replacement suggestions for deteriorated objects based solely on technical aspects, failing to consider the user's emotions or psychological state. As a result, the suggestions could potentially cause anxiety or dissatisfaction among users, creating a need for a more user-friendly system that supports appropriate decision-making.

[0561] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0562] In this invention, the server includes an information input means for inputting image data and design data; an evaluation means for analyzing the input image data and evaluating the deterioration state of an object; a determination means for determining whether to repair or replace the object based on the evaluation results from the evaluation means; a proposal generation means for generating proposals according to the determination results and outputting them in a form adjusted to the user's emotional state; and an automatic generation means for proposing options to improve the condition of the device. This makes it possible to provide proposals that give a greater sense of security by considering not only the technical aspects but also the user's emotions and psychological state.

[0563] "Information input means" refers to a device or program for receiving image data and design data and inputting them into a system.

[0564] "Evaluation means" refers to a process or system that analyzes input image data to quantify or qualitatively evaluate the deterioration state of an object.

[0565] "Determination means" refers to a process or system that determines whether repair or replacement is the appropriate method based on the results obtained from evaluation means.

[0566] A "proposal generation means" is a device or program that generates repair or replacement suggestions for the user based on the judgment result and outputs them in accordance with the user's emotional state.

[0567] "Emotional analysis means" refers to a process or system for analyzing a user's emotional state and adjusting suggestions based on that data.

[0568] "Automatic generation means" refers to a process or system for automatically generating and proposing options related to improving the condition of a device.

[0569] The system for implementing this invention aims to perform image data and design data input, data analysis, degradation state evaluation, proposal generation, and sentiment analysis. The system is effectively operated through the interaction of a server, terminals, and users.

[0570] The server first receives image data and design data transmitted from the terminal. This data is then incorporated into the system by an information input means. The image data is analyzed using TensorFlow or other image processing software, and the deterioration state of the object is quantified. Based on the deterioration state results obtained by the evaluation means, the determination means determines the optimal repair or replacement option.

[0571] The suggestion generation mechanism utilizes these judgment results to provide users with information on appropriate repair methods and replacement equipment. Furthermore, the emotion analysis engine analyzes the user's emotional state and adjusts the suggested content to create reassuring explanations. Azure Cognitive Services or similar technologies are used to read and analyze emotions from the user's voice and input data in real time.

[0572] For example, if a part of a factory's equipment is deteriorating, the system will suggest that the part needs to be replaced. If the user expresses concern, the system will reassure them by presenting specific benefits, such as, "This replacement work usually takes less than 30 minutes and will improve product performance by 5%."

[0573] An example of a prompt message for a generative AI model might be: "Analyze an image showing a state of deterioration and generate text that provides necessary countermeasures and reassurance."

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] The terminal inputs image data and design data of an object and sends it to the server. The input data includes details of factory equipment and consumables. Once the data is sent from the terminal to the server, the data is incorporated into the system by the information input means.

[0577] Step 2:

[0578] The server analyzes the received image data using an evaluation tool. Image processing software such as TensorFlow supports this analysis. The degradation state is quantified from the input image data, and the result is output by the evaluation tool.

[0579] Step 3:

[0580] Based on the analysis results from the evaluation means, the server uses a determination means to determine the optimal action for repair or replacement. If the evaluation results indicate deterioration above a certain standard, the server makes a determination recommending the replacement of the part. This determination result is used in the next step.

[0581] Step 4:

[0582] Based on the results of the determination means, the server generates specific suggestions for the user using the suggestion generation means. Repair methods and replacement parts are selected, and the suggestion content, including detailed information, is constructed. The suggestion content is output to the user terminal so that the user can access it.

[0583] Step 5:

[0584] The server analyzes user input and voice tone using an emotion analysis engine with Azure Cognitive Services, etc., to understand the user's emotional state. If the user shows signs of anxiety, the suggestion generation system adjusts the suggestions and provides additional information to reassure the user. This information is intended to facilitate the user's decision-making process.

[0585] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0587] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0591] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0592] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0593] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0595] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0596] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0597] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0598] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0599] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0601] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] This invention relates to a system for evaluating the deterioration state of an article and proposing appropriate repair or replacement. This system incorporates various means for accurately determining the deterioration state by inputting image data and design data of the article and analyzing them.

[0603] The basic procedure for implementing the system is as follows: First, the user uses a terminal to take images of the items they wish to observe for deterioration and prepares relevant data such as design drawings. This data is then input into the system via the terminal. The terminal organizes the input data and sends it to the server.

[0604] The server uses an evaluation tool to analyze the received data. The evaluation tool uses an AI-powered image analysis algorithm to quantify and evaluate the deterioration state of the item. Based on this evaluation result, the server uses a judgment tool to determine whether repair is appropriate or whether replacement is necessary.

[0605] For example, when evaluating the deterioration of a box housing wireless equipment, the server recognizes rust and crack patterns from photographs and quantifies the degree of deterioration. Taking into account the number of years since installation and environmental conditions, it calculates the specific repair methods, costs, and life extension if repair is appropriate. If replacement is necessary, it presents options for a new housing.

[0606] The suggestions generated by the server are presented to the user via the terminal. The user considers the suggested information and makes the most reasonable choice. This system makes it possible to carry out repairs and replacements due to deterioration in a rational and rapid manner.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The user uses a terminal to prepare image data and design data of the item. This includes taking photographs to visually capture the item's exterior and internal condition, and digitizing design drawings. The user enters the necessary information according to the terminal's input interface.

[0610] Step 2:

[0611] The terminal consolidates the input data and converts it into an appropriate format for analysis. This data unifies the formats of image data and design data and is organized into packets optimized for communication with the server.

[0612] Step 3:

[0613] The terminal sends the organized data to the server. The crucial point here is to perform error checking based on the communication protocol to ensure the data is received accurately.

[0614] Step 4:

[0615] The server receives the data and starts the analysis using the evaluation tool. The data is fed into the AI ​​model, and image analysis is performed to quantify the deterioration state of the item. At this time, the evaluation tool compares it with past deterioration patterns and calculates a specific deterioration index.

[0616] Step 5:

[0617] Based on the evaluation results, the server uses a decision-making mechanism to determine whether repair is possible or replacement is necessary. Here, the optimal choice is made based on pre-set thresholds. A high degradation index suggests replacement, while a low degradation index suggests repair.

[0618] Step 6:

[0619] Based on the assessment results, the server uses a proposal generation mechanism to generate specific content to propose to the user. If repair is proposed, specific repair methods, costs, and possible life extensions are presented. If replacement is proposed, options for new items and their specifications are suggested.

[0620] Step 7:

[0621] The server sends the generated proposals to the terminal. The terminal organizes the received information in an easy-to-understand format and displays it on the user's dashboard. Based on this information, the user makes a final decision and takes the necessary actions.

[0622] (Example 1)

[0623] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0624] In modern times, deterioration of goods due to long-term use is unavoidable, and it is necessary to accurately assess the degree of deterioration and make appropriate decisions regarding repair or replacement. However, conventional methods require specialized knowledge and experience to determine the extent of deterioration, and this process is time-consuming and costly. There is a need for a means to quickly and accurately assess deterioration and determine the optimal countermeasures.

[0625] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0626] In this invention, the server includes an information input means for inputting image data and design data; an evaluation means for analyzing the input image data using high-performance recognition processing and quantifying and evaluating the deterioration state of the article; and a determination means for determining the necessity of repair or replacement based on the evaluation results from the evaluation means. This makes it possible to quickly evaluate the deterioration state of the article and, if necessary, propose a reasonable and efficient repair or replacement.

[0627] "Information input means" refers to a device or function for receiving image data and design data from a user.

[0628] "Evaluation means" refers to a function or process that analyzes input image data and quantifies the deterioration state of an item for quantitative evaluation.

[0629] "Determination means" refers to a function or process that determines the need for repair or replacement of an item based on the evaluation results from evaluation means.

[0630] "Proposal generation means" refers to a function or process that generates information, including repair methods and replacement options, based on the judgment result and presents it to the user.

[0631] "Information provision means" refers to a mechanism or function for notifying users of the generated proposal content via a terminal.

[0632] This system is designed to assess the deterioration of items and suggest appropriate repair or replacement. Specific embodiments are described below.

[0633] First, the user uses their device to take pictures of the item they want to observe for deterioration. For example, they might use a smartphone camera app to take photos of the deteriorated area and prepare design drawings and data about the usage environment. This data is then centralized and organized by the device's application before being sent to the server.

[0634] The terminal ensures data format consistency by converting image data and design data to JSON format, and then sends them to the server. The HTTP protocol is used for data transmission, and encoding can be applied to ensure security.

[0635] The server receives the transmitted data and analyzes the deterioration state of the items using an AI image analysis algorithm. Software used for this analysis includes, for example, an image recognition model using a convolutional neural network (CNN). The server detects features such as rust and cracks, quantifies them, and generates a deterioration score.

[0636] Based on the analysis results, the server evaluates the degree of deterioration and determines whether the deterioration exceeds a set standard value. This determination then determines whether the item needs repair or replacement.

[0637] For example, in the case of a box housing wireless equipment, the server recognizes rust and crack patterns from photographs and calculates a deterioration score. Then, considering the number of years since installation and the surrounding environmental conditions, it calculates specific repair methods, costs, and the lifespan extension if repair is appropriate. If replacement is necessary, it proposes the most suitable new housing box.

[0638] The generated suggestions are sent from the server to the terminal and notified to the user. The user can then review this information through the terminal and make the most rational and efficient choice.

[0639] Examples of prompts to input into a generative AI model are as follows:

[0640] "Based on the image data and design data of the storage case, please conduct a deterioration assessment and propose repair or replacement."

[0641] This system makes it possible to quickly and accurately assess the deterioration status of items and propose appropriate countermeasures as needed.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] The user takes images of the item whose deterioration they want to check using a device and prepares the design data. The input data consists of image files of the item and design data in PDF format. This data is centralized on the device, and the images are saved in JPEG format. Specifically, the user uses a camera app on their smartphone or tablet to take images of the item from multiple angles and scans or digitally copies the design drawings.

[0645] Step 2:

[0646] The terminal organizes the image data and design data collected by the user and prepares them for transmission to the server. The input image files and design data are converted to JSON format and organized into a single data package. Specifically, the terminal application aggregates each data into a specific folder and executes scripts to convert the data format. It also verifies the data's integrity and makes corrections as needed.

[0647] Step 3:

[0648] The terminal sends the organized data package to the server. As output, it sends the data to the server in an encoded state using the HTTP protocol. Specifically, the terminal verifies that it is connected to the network, generates a send request using the API, and sends the data. After the transmission is complete, the terminal notifies the user of the successful transmission.

[0649] Step 4:

[0650] The server receives data from the terminal and performs an integrity check. The input data is in JSON format, and the server deserializes it to expand the data. Specifically, after confirming the integrity of the data, the server saves the necessary information to the database and checks for any errors.

[0651] Step 5:

[0652] The server processes the unfolded image data using an AI image analysis algorithm to quantify the deterioration state of the items. The input data is an image of the item, and the output data is the deterioration score. Specifically, the server runs an image recognition program using a CNN to extract features such as rust and cracks. Based on this, it quantifies each attribute and finally scores the deterioration.

[0653] Step 6:

[0654] The server determines whether an item is repairable or needs replacement based on the results of the evaluation. Inputs are the degradation score and user design data. The output is a judgment result indicating whether repair or replacement is necessary. Specifically, the server compares the score to a predetermined threshold and applies a judgment logic to determine the result.

[0655] Step 7:

[0656] The server generates a proposal, including specific repair methods and replacement options, based on the assessment result. Inputs include the assessment result, past cases, and reference data. Output is the proposed repair or replacement content presented to the user. Specifically, the server extracts the optimal solution from the database, organizes the information, and creates a proposal document.

[0657] Step 8:

[0658] The server sends the generated suggestions to the terminal and notifies the user. The output is the suggestion information displayed on the terminal's user interface. Specifically, the server encrypts the data while considering security and sends it using the HTTP protocol. After receiving the data, the terminal executes a notification function for the user and displays the information on the screen.

[0659] (Application Example 1)

[0660] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] Conventional methods for evaluating deterioration primarily rely on manual visual inspection, which is time-consuming and labor-intensive, and often lacks accuracy and consistency. Furthermore, managing and notifying about the deterioration of equipment and facilities across a wide range of factory spaces is difficult, potentially leading to inadequate repairs or replacements and ultimately decreased production efficiency. Therefore, a more efficient and reliable system is needed.

[0662] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0663] In this invention, the server includes data collection means for inputting image information and design information, evaluation means for analyzing the input image information and evaluating the deterioration state of the structure, and determination means for determining whether to perform repair or replacement based on the evaluation results. This enables autonomous mobile machines within a factory to efficiently patrol a wide range of equipment, quickly and accurately determine the deterioration state, and notify the manager of the optimal timing for repair or replacement.

[0664] "Image information" refers to digital data acquired visually, which records the appearance of objects or structures.

[0665] "Design information" refers to data that includes technical specifications for items and structures, and includes information related to product design drawings and manufacturing processes.

[0666] "Data acquisition means" refers to a mechanism for acquiring image information and design information of an object and inputting them into a system.

[0667] "Evaluation means" refers to the process or mechanism that analyzes input image information and determines the state of deterioration of an item or structure.

[0668] "Decision-making means" refers to a method for determining appropriate repair or replacement measures based on the evaluation results obtained by the evaluation means.

[0669] "Proposal generation means" refers to a process or system that generates and outputs proposals regarding repairs or replacements based on the judgment results.

[0670] "Communication means" refers to technology that analyzes information acquired by autonomous mobile machines within a factory and notifies the administrator of the results.

[0671] An "autonomous mobile machine" refers to a device that has the ability to patrol a factory without specific instructions and collect necessary data.

[0672] The system for implementing this invention begins with an autonomous mobile machine operating within a factory to collect image information of target items or structures. The image information is acquired by the camera of the autonomous mobile machine. The acquired image information is transmitted to a server via a terminal and input into the system along with design information by a data collection means.

[0673] The server receives this input information and uses an AI-powered image analysis algorithm as an evaluation tool to quantify the state of deterioration. This analysis utilizes a deep learning framework such as Keras to normalize image size and extract features. If deterioration is detected as a result of the evaluation, the server determines whether repair or replacement is necessary using a decision-making tool.

[0674] The determined findings are then generated by a proposal generation system into a report that specifically describes recommended repair methods and replacements. This report is then communicated to the administrator via a communication system. Based on the report, the administrator can efficiently plan and implement factory maintenance work. This system makes it possible to detect equipment deterioration in the factory in advance and take appropriate countermeasures.

[0675] A concrete example is the regular inspection of pipelines in a manufacturing plant. An automated machine acquires images, analyzes them for corrosion and cracks, and notifies the administrator. An example of a prompt message would be: "Enter the latest images of the pipeline and evaluate its condition. Determine whether appropriate repairs or replacements are necessary, and generate a report."

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The user acquires images of designated equipment and facilities within the factory via an autonomous mobile machine. This image acquisition function is performed by an autonomous machine equipped with a camera, and the image data is transmitted to a terminal. The input is image data taken within the factory, which is temporarily stored as output on the terminal.

[0679] Step 2:

[0680] The terminal sends the acquired image data to the server. The transmitted data includes image information and related design information. The terminal organizes this data and uploads it to the server in a standard format. The input is the image data acquired in the previous step, which is sent to the server as output.

[0681] Step 3:

[0682] The server analyzes the received image and design information using an AI model as an evaluation tool. During this analysis process, the image data is normalized and features are extracted. A deep learning framework such as Keras is used, and the input is the image data sent to the server. The output is a numerical evaluation of the degradation state based on that data.

[0683] Step 4:

[0684] The server determines the need for repair or replacement based on the evaluation results using a decision-making mechanism. This decision algorithm determines the appropriate action based on the degree of deterioration. The input is the evaluation result of the deterioration state, and the output is the decision information for repair or replacement.

[0685] Step 5:

[0686] The server uses a proposal generation mechanism to generate specific repair or replacement proposals based on the decision information. In this generation process, the proposal content is output in document format. The input is decision information regarding repair or replacement, and the output is a report of the final proposal content.

[0687] Step 6:

[0688] The server notifies the administrator of the proposed content generated via communication means. This notification is sent via email or a notification system, prompting the administrator to review the information and take appropriate action. The input is a report of the proposed content, and the output is a notification message sent to the administrator.

[0689] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0690] The present invention is a system that, in addition to evaluating the deterioration of an article and proposing repair or replacement based on that evaluation, presents information while taking into account the user's emotions. This system includes information input means, evaluation means, determination means, proposal generation means, and an emotion engine.

[0691] The user uses a terminal to input image data and design data related to the item. The terminal then sends the data to a server, initiating the analysis process. The server uses an evaluation tool to analyze the photographic data and quantify the item's deterioration state. Furthermore, based on the evaluation results, a decision tool determines the optimal course of action, whether repair or replacement.

[0692] The suggestion generation means generates repair methods and replacement options for the user based on the results of the determination means. In this process, the emotion engine analyzes the user's emotions in real time and creates suggestions that reflect their psychological state. Specifically, if the user is feeling anxious, the suggestion generation means is adjusted to present detailed and reassuring information.

[0693] For example, even if the system suggests replacing a storage container due to deterioration, if the emotion engine detects anxiety from the user's voice or input, it will provide more specific explanations about the necessity and benefits of the replacement. Furthermore, images and infographics are used to supplement the information visually, aiding user understanding.

[0694] This system allows users to receive not only technical judgments but also flexible and empathetic suggestions that take their emotions into consideration. This makes the user's decision-making process more comfortable and smoother, enabling them to make the optimal choice.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The user uses a terminal to prepare image data and design data related to the item and inputs the data into the input interface. This includes taking detailed photographs of the item's exterior and interior.

[0698] Step 2:

[0699] The terminal organizes the input data and converts it into a format for transmission to the server. This data transfer includes the integration of image data and design data, which are then efficiently sent to the server.

[0700] Step 3:

[0701] The server uses evaluation tools to analyze the received data. AI algorithms are employed to analyze and evaluate the deterioration state of items from image data, and this information is quantified.

[0702] Step 4:

[0703] Based on the evaluation results, the server uses a determination tool to decide whether it should be repaired or replaced. The established criteria determine which option is more cost-effective and necessary.

[0704] Step 5:

[0705] Based on the assessment results, the server uses a proposal generation mechanism to create specific proposals. These proposals include specific methods, costs, and expected benefits corresponding to the repair or replacement options.

[0706] Step 6:

[0707] The emotion engine analyzes user input and responses in real time to evaluate the user's emotional state. Based on this, it understands what kind of feedback the user needs.

[0708] Step 7:

[0709] The server adjusts its suggestions based on the results of the emotion engine. It designs content that takes user emotions into consideration and supplements it with information that provides a sense of security.

[0710] Step 8:

[0711] The server sends the final proposal to the terminal. The terminal displays this information in an easy-to-understand format to help the user make better decisions.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] Conventional technologies have a problem in that they cannot take into account the user's feelings when appropriately evaluating the deterioration state of goods and proposing the optimal repair or replacement, and therefore cannot adequately address the user's anxieties and questions. Furthermore, they only provide technical proposals and do not provide sufficient detailed information for the user to make informed decisions.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] In this invention, the server includes data acquisition means for inputting image information and structural information, condition evaluation means for analyzing the acquired image information and evaluating the deterioration state of the item, and emotion processing means for analyzing the user's emotional state and adjusting the proposed content according to the emotion. This makes it possible to make specific and convincing proposals while taking the user's emotions into consideration.

[0717] "Image information" refers to digital data that captures the visual characteristics of an object.

[0718] "Structural information" refers to technical data that describes the design and specifications of an item.

[0719] "Data acquisition means" refers to devices and methods for acquiring image information and structural information.

[0720] A "condition evaluation means" is a device or method for analyzing and quantifying the deterioration state of an item based on acquired image information.

[0721] A "decision-making mechanism" is a device or method for determining the optimal action of repair or replacement based on the evaluated state of deterioration.

[0722] A "proposal generation means" is a device or method for creating and presenting specific methods for repair or replacement to a user based on the results of their action decision.

[0723] "Emotional processing means" refers to devices or methods for analyzing a user's emotional state and adjusting suggested content according to that emotion.

[0724] A "system" is an overall configuration that integrates these means to perform deterioration assessment and make recommendations for items.

[0725] This invention is a system for evaluating the deterioration of articles and generating optimal repair or replacement suggestions. The system goes through the processes of inputting image and structural information, data analysis, and suggestion generation. It also adjusts the suggestion content to take user sentiment into consideration.

[0726] The user inputs image and structural information of items via the terminal. The terminal then sends the collected data, using its camera and image upload functions, to the server using the HTTP protocol.

[0727] The server uses image recognition software such as TensorFlow to analyze image information. Based on the analysis, it evaluates the deterioration state of the item and records it as numerical data. Subsequently, based on the evaluation results, the server uses an action decision mechanism to determine the optimal action, whether to repair or replace the item. This decision is made based on past data and established rules.

[0728] The suggestion generation mechanism generates specific repair methods and replacement options for the user based on the results of their action decision. The suggestions include detailed procedures and cost information. Furthermore, the server uses emotion processing tools to analyze the user's emotions using natural language processing. For example, if it detects user anxiety, it adds reassuring detailed information or infographics.

[0729] As a concrete example, a user takes a photograph of a deteriorated storage box and inputs it into the system. In this case, an example of a prompt message would be: "Evaluate the deterioration of the storage box based on the image data submitted by the user, suggest repair or replacement, and provide additional information that takes the user's feelings into consideration."

[0730] This system allows users to receive not only technical guidance but also detailed suggestions that take emotions into consideration, making decision-making smoother and more satisfying.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] The user inputs image data and design data of an item using a device. Specifically, the user takes a picture of the item with their smartphone camera and uploads the image along with the design data to the application, thereby importing the data into the device. The input data is then appropriately formatted for use in subsequent analysis.

[0734] Step 2:

[0735] The terminal transmits the input image data and design data to the server. The data is securely uploaded to the server using the HTTP protocol. During this transmission process, data format checks and communication stability are ensured.

[0736] Step 3:

[0737] The server analyzes the received image data. Specifically, it uses image recognition software (e.g., TensorFlow) to evaluate the deterioration state of the items. In this process, feature points are extracted from the image data, and a machine learning model is used to calculate a numerical value for the degree of deterioration. This result is stored in a database used in the next step.

[0738] Step 4:

[0739] The server uses an action decision mechanism to determine whether to repair or replace based on the condition assessment results. It automatically determines the optimal course of action by referring to the assessed degree of deterioration, past analysis data, or established rules. The determined information is then prepared for use by the suggestion generation mechanism.

[0740] Step 5:

[0741] The server uses a suggestion generation mechanism to propose specific repair methods and replacement options. For example, it generates a detailed suggestion such as, "Partial repair is possible, and the estimated cost is ¥XX." After this, the suggested content is sent to an emotion processing mechanism.

[0742] Step 6:

[0743] The server uses emotion processing tools to analyze the user's input data or communication and determine their current emotional state. It uses emotion analysis tools (e.g., natural language processing techniques) to determine whether the user is experiencing anxiety or doubt.

[0744] Step 7:

[0745] The server adjusts the suggestions based on the emotional data it receives. If the user is feeling anxious, the suggestion generation system provides more detailed explanations and adds relatable information. This allows the user to make decisions with confidence.

[0746] Step 8:

[0747] The terminal presents the user with the final proposal sent from the server. Based on this information, the user considers repair or replacement options and makes a decision on what to do.

[0748] (Application Example 2)

[0749] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0750] Conventional systems made repair and replacement suggestions for deteriorated objects based solely on technical aspects, failing to consider the user's emotions or psychological state. As a result, the suggestions could potentially cause anxiety or dissatisfaction among users, creating a need for a more user-friendly system that supports appropriate decision-making.

[0751] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0752] In this invention, the server includes an information input means for inputting image data and design data; an evaluation means for analyzing the input image data and evaluating the deterioration state of an object; a determination means for determining whether to repair or replace the object based on the evaluation results from the evaluation means; a proposal generation means for generating proposals according to the determination results and outputting them in a form adjusted to the user's emotional state; and an automatic generation means for proposing options to improve the condition of the device. This makes it possible to provide proposals that give a greater sense of security by considering not only the technical aspects but also the user's emotions and psychological state.

[0753] "Information input means" refers to a device or program for receiving image data and design data and inputting them into a system.

[0754] "Evaluation means" refers to a process or system that analyzes input image data to quantify or qualitatively evaluate the deterioration state of an object.

[0755] "Determination means" refers to a process or system that determines whether repair or replacement is the appropriate method based on the results obtained from evaluation means.

[0756] A "proposal generation means" is a device or program that generates repair or replacement suggestions for the user based on the judgment result and outputs them in accordance with the user's emotional state.

[0757] "Emotional analysis means" refers to a process or system for analyzing a user's emotional state and adjusting suggestions based on that data.

[0758] "Automatic generation means" refers to a process or system for automatically generating and proposing options related to improving the condition of a device.

[0759] The system for implementing this invention aims to perform image data and design data input, data analysis, degradation state evaluation, proposal generation, and sentiment analysis. The system is effectively operated through the interaction of a server, terminals, and users.

[0760] The server first receives image data and design data transmitted from the terminal. This data is then incorporated into the system by an information input means. The image data is analyzed using TensorFlow or other image processing software, and the deterioration state of the object is quantified. Based on the deterioration state results obtained by the evaluation means, the determination means determines the optimal repair or replacement option.

[0761] The suggestion generation mechanism utilizes these judgment results to provide users with information on appropriate repair methods and replacement equipment. Furthermore, the emotion analysis engine analyzes the user's emotional state and adjusts the suggested content to create reassuring explanations. Azure Cognitive Services or similar technologies are used to read and analyze emotions from the user's voice and input data in real time.

[0762] For example, if a part of a factory's equipment is deteriorating, the system will suggest that the part needs to be replaced. If the user expresses concern, the system will reassure them by presenting specific benefits, such as, "This replacement work usually takes less than 30 minutes and will improve product performance by 5%."

[0763] An example of a prompt message for a generative AI model might be: "Analyze an image showing a state of deterioration and generate text that provides necessary countermeasures and reassurance."

[0764] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0765] Step 1:

[0766] The terminal inputs image data and design data of an object and sends it to the server. The input data includes details of factory equipment and consumables. Once the data is sent from the terminal to the server, the data is incorporated into the system by the information input means.

[0767] Step 2:

[0768] The server analyzes the received image data using an evaluation tool. Image processing software such as TensorFlow supports this analysis. The degradation state is quantified from the input image data, and the result is output by the evaluation tool.

[0769] Step 3:

[0770] Based on the analysis results from the evaluation means, the server uses a determination means to determine the optimal action for repair or replacement. If the evaluation results indicate deterioration above a certain standard, the server makes a determination recommending the replacement of the part. This determination result is used in the next step.

[0771] Step 4:

[0772] Based on the results of the determination means, the server generates specific suggestions for the user using the suggestion generation means. Repair methods and replacement parts are selected, and the suggestion content, including detailed information, is constructed. The suggestion content is output to the user terminal so that the user can access it.

[0773] Step 5:

[0774] The server analyzes user input and voice tone using an emotion analysis engine with Azure Cognitive Services, etc., to understand the user's emotional state. If the user shows signs of anxiety, the suggestion generation system adjusts the suggestions and provides additional information to reassure the user. This information is intended to facilitate the user's decision-making process.

[0775] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0776] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0777] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0778] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0779] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0780] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0781] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0782] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0783] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0784] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0785] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0786] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0787] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0788] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0789] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0790] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0791] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0792] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0793] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0794] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0795] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] Information input means for inputting image data and design data,

[0799] An evaluation means that analyzes input image data and evaluates the deterioration state of an item,

[0800] A determination means that determines whether to repair or replace based on the evaluation results from the evaluation means,

[0801] A proposal generation means that generates and outputs proposal content according to the judgment result,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, wherein the proposal generation means generates information including a specific repair method and an evaluation of its cost based on the determination result.

[0805] (Claim 3)

[0806] The system according to claim 1, wherein the proposal generation means generates information including the selection of an appropriate new article and its specifications and cost information based on the determination result.

[0807] "Example 1"

[0808] (Claim 1)

[0809] Information input means for inputting image data and design data,

[0810] An evaluation means that analyzes input image data using high-performance recognition processing and quantifies and evaluates the deterioration state of an item,

[0811] A determination means that determines the necessity of repair or replacement based on the evaluation results obtained by the evaluation means,

[0812] A proposal generation means that generates and outputs proposals including repair methods and replacement options according to the judgment result,

[0813] An information provision means for notifying the user of the above-generated proposal via a terminal,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, wherein the proposal generation means generates information including a specific repair method, an evaluation of its cost, and a predicted service life, based on the determination result, if repair is necessary.

[0817] (Claim 3)

[0818] The system according to claim 1, wherein the proposal generation means generates information including the selection of an appropriate new item, its specifications, and cost information, based on the determination result, if replacement is necessary.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A data collection means for inputting image information and design information,

[0822] An evaluation means that analyzes input image information and evaluates the deterioration state of the structure,

[0823] A determination means that determines whether to repair or replace based on the evaluation results from the evaluation means,

[0824] A proposal generation means that generates and outputs proposal content according to the judgment result,

[0825] A communication means that acquires image information using an autonomous mobile machine patrolling the factory and notifies the administrator based on the analysis results,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the proposal generation means generates information including a specific repair method and an evaluation of its cost based on the judgment result.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the proposal generation means generates information including the selection of an appropriate new structure and its specifications and cost information based on the judgment result.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A data acquisition means for inputting image information and structural information,

[0834] A condition evaluation means that analyzes acquired image information and evaluates the deterioration state of an item,

[0835] An action decision means that determines the optimal action of repair or replacement based on the evaluation results of the condition evaluation means,

[0836] A proposal generation means that generates and outputs proposals based on the action decision results,

[0837] An emotion processing means that analyzes the user's emotional state and adjusts the suggested content according to the emotion,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the proposal generation means generates information including a specific repair method and an evaluation of its cost based on the action decision result.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the proposal generation means generates information including the selection of an appropriate new item and its specifications and cost information based on the action decision result.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] Information input means for inputting image data and design data,

[0846] An evaluation means that analyzes input image data and evaluates the state of deterioration of an object,

[0847] A determination means that determines whether to repair or replace based on the evaluation results from the evaluation means,

[0848] A suggestion generation means that generates suggested content according to the judgment result and outputs it in a form adjusted to the user's emotional state,

[0849] An automated generation means for suggesting options to improve the condition of the device,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, wherein the proposal generation means generates information including a specific repair method and an evaluation of its cost based on the determination result.

[0853] (Claim 3)

[0854] The system according to claim 1, wherein the proposal generation means generates information including the selection of an appropriate new item and its specifications and cost information based on the determination result, and provides an explanation that provides a sense of security based on the sentiment analysis result. [Explanation of symbols]

[0855] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Information input means for inputting image data and design data, An evaluation means that analyzes input image data and evaluates the deterioration state of an item, A determination means that determines whether to repair or replace based on the evaluation results from the evaluation means, A proposal generation means that generates and outputs proposal content according to the judgment result, A system that includes this.

2. The system according to claim 1, wherein the proposal generation means generates information including a specific repair method and an evaluation of its cost based on the determination result.

3. The system according to claim 1, wherein the proposal generation means generates information including the selection of an appropriate new article and its specifications and cost information based on the determination result.

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