system

Abstract

The system according to this embodiment aims to predict the failure rate based on data and determine whether or not it can be repaired. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, a prediction unit, a determination unit, and a provision unit. The input unit receives data. The analysis unit analyzes the data input by the input unit. The prediction unit predicts the failure rate based on the data analyzed by the analysis unit. The determination unit determines whether repair is possible based on the results predicted by the prediction unit. The provision unit provides the results obtained by the determination unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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] In the conventional technology, there is a problem that it is difficult to predict the failure rate and only short-term prediction based on actual data can be performed.

[0005] The system according to the embodiment aims to predict the failure rate based on data and determine whether it can be repaired.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, an analysis unit, a prediction unit, a determination unit, and a provision unit. The input unit receives data. The analysis unit analyzes the data input by the input unit. The prediction unit predicts the failure rate based on the data analyzed by the analysis unit. The determination unit determines whether repair is possible based on the results predicted by the prediction unit. The provision unit provides the results obtained by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict the failure rate based on data and determine whether it is repairable. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

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

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The failure rate prediction system according to an embodiment of the present invention is a system that uses AI to predict the failure rate of components and supports inventory management and repair response. The failure rate prediction system takes data such as the number of component failures and inventory levels as input, and the AI ​​analyzes this data to predict failure rate trends and inventory lifespan. Furthermore, it provides information on whether repairs are possible and supports the decision-making process for responding before inventory runs out. This mechanism streamlines component management and contributes to resource conservation and reduction of environmental impact. For example, data such as the number of component failures and inventory levels are input. In this case, if long-term data is available, more accurate predictions can be made. For example, the number of failures (units / month) and inventory level of component A are input. This allows the AI ​​to predict failure rate trends and inventory lifespan. Next, the AI ​​analyzes the input data and predicts failure rate trends and inventory lifespan. For example, based on past data, it predicts future failure rate trends and calculates how long inventory will last. This allows support for deciding on a response strategy before inventory runs out. Furthermore, it provides information on whether repairs are possible. For example, if repair is possible, repairs are promoted; if repair is not possible, an operational policy is decided before inventory runs out. In this way, component management is made more efficient, contributing to resource conservation and a reduction in environmental impact. By repairing faulty products, resources can be recycled, contributing to the achievement of goals such as the SDGs. Furthermore, by deciding on a response policy before inventory runs out, component management is made more efficient, and unnecessary inventory can be reduced. Thus, the failure rate prediction system can make component management more efficient, contributing to resource conservation and a reduction in environmental impact.

[0029] The failure rate prediction system according to the embodiment comprises an input unit, an analysis unit, a prediction unit, a determination unit, and a provision unit. The input unit receives data. The data includes, but is not limited to, the number of component failures and inventory levels. For example, the input unit can receive data on the number of component failures (units / month) and inventory levels. The analysis unit analyzes the data received by the input unit. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The prediction unit predicts the failure rate based on the data analyzed by the analysis unit. For example, the prediction unit can predict future failure rate trends based on past data. The determination unit determines whether repair is possible based on the results predicted by the prediction unit. For example, the determination unit can determine whether repair is possible by considering the availability of necessary parts and the repair time. The provision unit provides the results obtained by the determination unit. For example, the provision unit can provide the prediction results and determination results to the user. As a result, the failure rate prediction system according to the embodiment can efficiently perform a series of processes from data input to analysis, prediction, determination, and provision. For example, the input unit can input data such as the number of component failures and inventory levels. The analysis unit can analyze the input data and predict failure rate trends and inventory lifespan. The prediction unit can predict future failure rate trends based on past data. The determination unit can determine whether or not a component is repairable. The provision unit can provide prediction results and determination results. As a result, the failure rate prediction system according to this embodiment can efficiently perform a series of processes from data input to analysis, prediction, determination, and provision.

[0030] The input unit takes in data. This data includes, but is not limited to, the number of component failures or inventory levels. For example, the input unit can input the number of component failures (units / month) or inventory levels. The input unit not only provides an interface for users to manually input data, but also has the capability to automatically retrieve data from external systems and databases. For example, it can integrate with manufacturing line management systems or inventory management systems to obtain the latest data in real time. This allows the input unit to eliminate the need for manual input and ensure data accuracy and consistency. Furthermore, the input unit has the capability to verify the format and content of the data, automatically detecting and correcting inaccurate or missing data. For example, if the input data contains abnormal values, the input unit will warn the user and prompt them to correct it. If there is missing data, it can be supplemented based on past data or other relevant data. This allows the input unit to maintain data quality and provide a foundation for the analysis and forecasting units to perform accurate analysis and forecasting. Additionally, the input unit has the capability to record the data input history for later reference. This allows users to review past data input history and make corrections or re-entries as needed. This allows the input unit to efficiently perform the entire process from data entry to management.

[0031] The analysis unit analyzes the data input by the input unit. For example, the analysis unit can analyze data using statistical analysis or machine learning algorithms. Specifically, statistical analysis calculates basic statistical indicators (mean, median, standard deviation, etc.) to understand the distribution and trends of the data, clarifying its characteristics. Correlation analysis and regression analysis can also be performed to identify factors influencing failure rates and inventory levels. Machine learning algorithms build models based on past data and perform predictions and classifications on new data. For example, regression models and time series analysis models can be used to predict failure rates, and clustering and association rules can be applied to fluctuations in inventory levels. Furthermore, the analysis unit also performs data preprocessing and feature engineering. Data preprocessing involves imputing missing values, removing outliers, and normalizing and standardizing the data to prepare it for analysis. Feature engineering extracts useful features from the data to improve model performance. For example, features such as component usage frequency and environmental conditions can be added to predict failure rates. This allows the analysis unit to analyze the input data from multiple perspectives and accurately grasp trends in failure rates and inventory levels.

[0032] The prediction unit predicts the failure rate based on the data analyzed by the analysis unit. For example, the prediction unit can predict future failure rate trends based on past data. Specifically, it uses time series analysis models and regression models to predict future failure rates from past failure data. In time series analysis models, the unit learns the temporal patterns of past data and predicts future data. For example, moving average models and autoregressive models can be used to capture seasonal variations and trends in failure rates. In regression models, factors that affect the failure rate are used as explanatory variables to predict the failure rate. For example, a regression model can be constructed to predict the failure rate using the frequency of use of components and environmental conditions as explanatory variables. Furthermore, the prediction unit can also perform ensemble learning by combining multiple models using machine learning algorithms. This allows for the integration of prediction results from individual models, enabling more accurate predictions. For example, predictions can be made by combining multiple decision tree models using random forests or gradient boosting. As a result, the prediction unit can accurately predict future failure rates based on past data and provide information for taking appropriate countermeasures.

[0033] The determination unit determines whether repair is possible based on the results predicted by the prediction unit. For example, the determination unit can determine whether repair is possible by considering factors such as the availability of necessary parts and the repair time. Specifically, it checks the inventory and supply status of necessary parts based on the predicted failure rate and determines whether repair is possible. It also considers the time and cost required for repair to determine whether the repair is economically feasible. For example, if the necessary parts are not in stock or the repair time is too long, it is determined that repair is difficult. On the other hand, if there is sufficient stock of parts and the repair time is short, it is determined that repair is possible. Furthermore, the determination unit also has a function to determine the priority of repairs. For example, it can determine that components with a high impact from failure or components that require urgent repair should be repaired first. As a result, the determination unit can quickly and accurately determine the feasibility and priority of repairs based on the predicted failure rate, and formulate an efficient repair plan.

[0034] The service provider provides the results obtained by the judgment provider. For example, the service provider can provide users with prediction results and judgment results. Specifically, it displays results in a dashboard or report format that users can access. The dashboard visually displays prediction and judgment results that are updated in real time, making them easy for users to understand intuitively. For example, it uses graphs and charts to display trends in failure rates and repair priorities. In addition, the report format documents detailed analysis results and prediction results so that users can refer to them later. Furthermore, the service provider has an alert function that can send notifications to users when important information or urgent action is required. For example, it can send notifications via email or SMS if the failure rate suddenly increases or if parts that need repair are identified. This allows the service provider to provide users with quick and accurate information and support appropriate responses. Furthermore, the service provider also has a function to collect user feedback and use it to improve the system. For example, users can comment on and evaluate the information provided, and the accuracy and usability of the system can be improved based on that feedback. This allows the service provider to provide users with high-quality information and improve the overall performance of the system.

[0035] The input unit can input data such as the number of component failures and inventory levels. For example, the input unit can input the number of component failures (units / month) and inventory levels. For example, the input unit can input the number of component failures. The input unit can also input inventory levels. Furthermore, the input unit can input long-term data. For example, the input unit can input data for the past year. This makes it possible to predict failure rates and manage inventory by inputting data such as the number of component failures and inventory levels. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input data such as the number of component failures and inventory levels into a generating AI and have the generating AI perform data analysis.

[0036] The analysis unit can analyze the input data and predict failure rate trends and inventory lifespan. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze the data using statistical analysis. Furthermore, the analysis unit can analyze the data using machine learning algorithms. In addition, the analysis unit can analyze the data using time series analysis or trend analysis. By analyzing the data, it is possible to predict failure rate trends and inventory lifespan. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the input data into a generating AI and have the generating AI perform the data analysis.

[0037] The prediction unit can predict future failure rate trends based on past data. For example, the prediction unit can predict future failure rate trends based on data from the past year. The prediction unit can also predict future failure rate trends based on data from a specific period. Furthermore, the prediction unit can analyze past data to predict future failure rate trends. For example, the prediction unit can analyze past data to predict future failure rate trends. This allows for the estimation of future failure rates by predicting failure rate trends based on past data. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past data into a generating AI and have the generating AI perform the prediction of failure rate trends.

[0038] The determination unit can determine whether or not a repair is possible. The determination unit can determine whether or not a repair is possible by considering, for example, the availability of necessary parts and the time required for the repair. For example, the determination unit can determine whether or not a repair is possible by considering the availability of necessary parts. The determination unit can also determine whether or not a repair is possible by considering the time required for the repair. Furthermore, the determination unit can determine whether or not a repair is possible by considering the cost required for the repair. For example, the determination unit can determine whether or not a repair is possible by considering the cost required for the repair. In this way, by determining whether or not a repair is possible, it is possible to determine whether or not a repair can be performed. Some or all of the above processing in the determination unit may be performed using, for example, AI, or not. For example, the determination unit can input the availability of necessary parts, the time required for the repair, and the cost of the repair into a generating AI, and have the generating AI perform a determination of whether or not a repair is possible.

[0039] The service provider can provide prediction results and judgment results. For example, the service provider can provide prediction results to the user. For example, the service provider can provide prediction results. The service provider can also provide judgment results. Furthermore, the service provider can display prediction results and judgment results in graphs or text. For example, the service provider can display prediction results in graphs. The service provider can also display judgment results in text. This allows the user to take appropriate action by providing prediction results and judgment results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input prediction results and judgment results into a generating AI and have the generating AI perform the information provision.

[0040] The input unit can analyze the user's past data input history and select the optimal input method. For example, the input unit can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). The input unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the input unit can analyze the user's past input history and select a method that minimizes input errors. In this way, the optimal input method can be selected by analyzing past data input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past data input history into a generating AI and have the generating AI select the optimal input method.

[0041] The input unit can filter data based on the user's current work situation and areas of interest during data entry. For example, the input unit can prioritize inputting data related to the project the user is currently working on. The input unit can also filter and input highly relevant data based on the user's areas of interest. Furthermore, the input unit can filter data to input only the necessary data according to the user's work situation. This enables efficient data entry by filtering data based on the user's work situation and areas of interest. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input data on the user's current work situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0042] The input unit can prioritize inputting highly relevant data by considering the user's geographical location information during data entry. For example, if the user is in a specific region, the input unit can prioritize inputting data related to that region. For example, if the user is in a specific region, the input unit can prioritize inputting data related to that region. Furthermore, if the user is on the move, the input unit can prioritize inputting highly relevant data based on their current location. For example, if the user is in a specific location, the input unit can prioritize inputting data related to that location. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location information into a generating AI and have the generating AI perform the input of highly relevant data.

[0043] The input unit can analyze the user's social media activity and input relevant data during data entry. For example, the input unit can analyze the content of the user's social media posts and input relevant data. The input unit can also analyze the activities of the user's social media followers and friends and input relevant data. Furthermore, the input unit can analyze the user's social media trends and input relevant data. This allows for efficient input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input data on the user's social media activity into a generating AI and have the generating AI input the relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm specialized in failure rate prediction to failure data. For example, the analysis unit can apply an algorithm specialized in inventory management to inventory data. For example, the analysis unit can apply an algorithm specialized in inventory management to inventory data. Furthermore, the analysis unit can apply an algorithm that evaluates repairability to repair data. For example, the analysis unit can apply an algorithm that evaluates repairability to repair data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationships between failure data and inventory data. The prediction unit can also make predictions by considering the interrelationships between repair data and failure data. Furthermore, the prediction unit can analyze the interrelationships between data to improve the accuracy of its predictions. In this way, the accuracy of predictions can be improved by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0049] The prediction unit can make predictions by considering the attribute information of the data submitter. For example, the prediction unit can make predictions by considering the position and job duties of the data submitter. The prediction unit can also make predictions by considering the past performance of the data submitter. Furthermore, the prediction unit can analyze the attribute information of the data submitter to improve the accuracy of the prediction. For example, the prediction unit can analyze the attribute information of the data submitter to improve the accuracy of the prediction. In this way, the accuracy of the prediction can be improved by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the prediction.

[0050] The prediction unit can make predictions while considering the geographical distribution of the data. For example, the prediction unit can predict failure rates by region while considering the geographical distribution of the data. The prediction unit can also predict the shelf life of inventory based on the geographical distribution. Furthermore, the prediction unit can analyze the geographical distribution to improve the accuracy of the prediction. This allows for improved prediction accuracy by region by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the prediction.

[0051] The prediction unit can improve the accuracy of its predictions by referring to relevant literature during the prediction process. For example, the prediction unit can improve the accuracy of its failure rate predictions by referring to relevant literature. The prediction unit can also predict the shelf life of inventory based on relevant literature. Furthermore, the prediction unit can analyze relevant literature to improve the accuracy of its predictions. In this way, the accuracy of predictions can be improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature into a generating AI and have the generating AI perform the prediction.

[0052] The judgment unit can optimize the current judgment by referring to past judgment data during the judgment process. For example, the judgment unit can optimize the current judgment based on past judgment data. The judgment unit can also analyze past judgment data to improve the accuracy of the judgment. For example, the judgment unit can analyze past judgment data to improve the accuracy of the judgment. Furthermore, the judgment unit can maintain consistency in judgments by referring to past judgment data. For example, the judgment unit can maintain consistency in judgments by referring to past judgment data. This allows the current judgment to be optimized by referring to past judgment data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input past judgment data into a generating AI and have the generating AI perform the optimization of the current judgment.

[0053] The judgment unit can apply different judgment methods to each data category during the judgment process. For example, the judgment unit can apply a judgment method specialized in failure rate prediction to failure data. For example, the judgment unit can apply a judgment method specialized in failure rate prediction to failure data. The judgment unit can also apply a judgment method specialized in inventory management to inventory data. For example, the judgment unit can apply a judgment method specialized in inventory management to inventory data. Furthermore, the judgment unit can apply a judgment method that evaluates repairability to repair data. For example, the judgment unit can apply a judgment method that evaluates repairability to repair data. By applying an appropriate judgment method according to the data category, the accuracy of the judgment can be improved. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the data category into a generating AI and have the generating AI execute the application of the judgment method.

[0054] The judgment unit can analyze changes in the judgment based on the data submission time. For example, the judgment unit can make a judgment based on the latest data and analyze the changes by comparing it with past data. The judgment unit can also prioritize the latest data and postpone older data submissions. Furthermore, the judgment unit can analyze changes in the judgment in stages according to the submission time. This enables efficient judgment by analyzing changes in the judgment based on the data submission time. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the data submission time into a generating AI and have the generating AI perform the analysis of changes in the judgment.

[0055] The determination unit can analyze the determination by referring to relevant market data at the time of determination. For example, the determination unit can improve the accuracy of the failure rate determination by referring to relevant market data. The determination unit can also determine the inventory shelf life based on relevant market data. For example, the determination unit can determine the inventory shelf life based on relevant market data. Furthermore, the determination unit can analyze the relevant market data to improve the accuracy of the determination. For example, the determination unit can analyze the relevant market data to improve the accuracy of the determination. In this way, the accuracy of the determination can be improved by referring to relevant market data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input relevant market data into a generating AI and have the generating AI perform the determination.

[0056] The data delivery unit can improve the accuracy of its delivery by considering the interrelationships between data. For example, the data delivery unit can provide information by considering the interrelationships between failure data and inventory data. The data delivery unit can also provide information by considering the interrelationships between repair data and failure data. Furthermore, the data delivery unit can analyze the interrelationships between data to improve the accuracy of its delivery. By considering the interrelationships between data, the accuracy of the delivery can be improved. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without AI. For example, the data delivery unit can input the interrelationships between data into a generating AI and have the generating AI perform the information delivery.

[0057] The data provisioning unit can provide data while considering the attribute information of the data submitter. For example, the data provisioning unit can provide information while considering the position and job duties of the data submitter. The data provisioning unit can also provide information while considering the past performance of the data submitter. Furthermore, the data provisioning unit can analyze the attribute information of the data submitter to improve the accuracy of the provision. For example, the data provisioning unit can analyze the attribute information of the data submitter to improve the accuracy of the provision. This allows for improved accuracy of provision by considering the attribute information of the data submitter. Some or all of the above processing in the data provisioning unit may be performed using AI, for example, or without AI. For example, the data provisioning unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the provision of information.

[0058] The data delivery unit can provide data while considering its geographical distribution. For example, the data delivery unit can provide region-specific information while considering the geographical distribution of the data. The data delivery unit can also provide inventory duration based on geographical distribution. Furthermore, the data delivery unit can analyze geographical distribution to improve the accuracy of the delivery. This makes it possible to provide region-specific information by considering the geographical distribution of the data. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without AI. For example, the data delivery unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the information delivery.

[0059] The data provider can improve the accuracy of its data provision by referring to relevant literature at the time of provision. For example, the data provider can improve the accuracy of failure rate information provision by referring to relevant literature. The data provider can also provide inventory shelf life based on relevant literature. For example, the data provider can provide inventory shelf life based on relevant literature. Furthermore, the data provider can analyze relevant literature to improve the accuracy of its data provision. For example, the data provider can analyze relevant literature to improve the accuracy of its data provision. This allows for improved accuracy of data provision by referring to relevant literature. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input relevant literature into a generating AI and have the generating AI provide the information.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The input unit can analyze the user's past data entry history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). It can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, it can analyze the user's past input history and select a method that minimizes input errors. In this way, the optimal input method can be selected by analyzing past data entry history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past data entry history into a generating AI and have the generating AI select the optimal input method.

[0062] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, it can make predictions by considering the interrelationships between failure data and inventory data. It can also make predictions by considering the interrelationships between repair data and failure data. Furthermore, it can analyze the interrelationships between data to improve the accuracy of predictions. In this way, the accuracy of predictions can be improved by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of predictions.

[0063] The data delivery unit can improve the accuracy of its delivery by considering the interrelationships between data. For example, it can provide information by considering the interrelationships between failure data and inventory data. It can also provide information by considering the interrelationships between repair data and failure data. Furthermore, it can analyze the interrelationships between data to improve the accuracy of its delivery. In this way, the accuracy of delivery can be improved by considering the interrelationships between data. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without using AI. For example, the data delivery unit can input the interrelationships between data into a generating AI and have the generating AI perform the information delivery.

[0064] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data, and a simplified analysis on less important data. Furthermore, it can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The input unit is used to input data. The data may include, but is not limited to, the number of component failures or inventory levels. For example, the input unit can input the number of component failures (units / month) or inventory levels. Step 2: The analysis unit analyzes the data input by the input unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Step 3: The prediction unit predicts the failure rate based on the data analyzed by the analysis unit. For example, the prediction unit can predict future failure rate trends based on past data. Step 4: The determination unit determines whether the item is repairable based on the results predicted by the prediction unit. The determination unit can determine whether the item is repairable by considering, for example, the availability of necessary parts and the time required for the repair. Step 5: The providing unit provides the results obtained by the determination unit. The providing unit can, for example, provide the user with prediction results or determination results.

[0067] (Example of form 2) The failure rate prediction system according to an embodiment of the present invention is a system that uses AI to predict the failure rate of components and supports inventory management and repair response. The failure rate prediction system takes data such as the number of component failures and inventory levels as input, and the AI ​​analyzes this data to predict failure rate trends and inventory lifespan. Furthermore, it provides information on whether repairs are possible and supports the decision-making process for responding before inventory runs out. This mechanism streamlines component management and contributes to resource conservation and reduction of environmental impact. For example, data such as the number of component failures and inventory levels are input. In this case, if long-term data is available, more accurate predictions can be made. For example, the number of failures (units / month) and inventory level of component A are input. This allows the AI ​​to predict failure rate trends and inventory lifespan. Next, the AI ​​analyzes the input data and predicts failure rate trends and inventory lifespan. For example, based on past data, it predicts future failure rate trends and calculates how long inventory will last. This allows support for deciding on a response strategy before inventory runs out. Furthermore, it provides information on whether repairs are possible. For example, if repair is possible, repairs are promoted; if repair is not possible, an operational policy is decided before inventory runs out. In this way, component management is made more efficient, contributing to resource conservation and a reduction in environmental impact. By repairing faulty products, resources can be recycled, contributing to the achievement of goals such as the SDGs. Furthermore, by deciding on a response policy before inventory runs out, component management is made more efficient, and unnecessary inventory can be reduced. Thus, the failure rate prediction system can make component management more efficient, contributing to resource conservation and a reduction in environmental impact.

[0068] The failure rate prediction system according to the embodiment comprises an input unit, an analysis unit, a prediction unit, a determination unit, and a provision unit. The input unit receives data. The data includes, but is not limited to, the number of component failures and inventory levels. For example, the input unit can receive data on the number of component failures (units / month) and inventory levels. The analysis unit analyzes the data received by the input unit. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The prediction unit predicts the failure rate based on the data analyzed by the analysis unit. For example, the prediction unit can predict future failure rate trends based on past data. The determination unit determines whether repair is possible based on the results predicted by the prediction unit. For example, the determination unit can determine whether repair is possible by considering the availability of necessary parts and the repair time. The provision unit provides the results obtained by the determination unit. For example, the provision unit can provide the prediction results and determination results to the user. As a result, the failure rate prediction system according to the embodiment can efficiently perform a series of processes from data input to analysis, prediction, determination, and provision. For example, the input unit can input data such as the number of component failures and inventory levels. The analysis unit can analyze the input data and predict failure rate trends and inventory lifespan. The prediction unit can predict future failure rate trends based on past data. The determination unit can determine whether or not a component is repairable. The provision unit can provide prediction results and determination results. As a result, the failure rate prediction system according to this embodiment can efficiently perform a series of processes from data input to analysis, prediction, determination, and provision.

[0069] The input unit takes in data. This data includes, but is not limited to, the number of component failures or inventory levels. For example, the input unit can input the number of component failures (units / month) or inventory levels. The input unit not only provides an interface for users to manually input data, but also has the capability to automatically retrieve data from external systems and databases. For example, it can integrate with manufacturing line management systems or inventory management systems to obtain the latest data in real time. This allows the input unit to eliminate the need for manual input and ensure data accuracy and consistency. Furthermore, the input unit has the capability to verify the format and content of the data, automatically detecting and correcting inaccurate or missing data. For example, if the input data contains abnormal values, the input unit will warn the user and prompt them to correct it. If there is missing data, it can be supplemented based on past data or other relevant data. This allows the input unit to maintain data quality and provide a foundation for the analysis and forecasting units to perform accurate analysis and forecasting. Additionally, the input unit has the capability to record the data input history for later reference. This allows users to review past data input history and make corrections or re-entries as needed. This allows the input unit to efficiently perform the entire process from data entry to management.

[0070] The analysis unit analyzes the data input by the input unit. For example, the analysis unit can analyze data using statistical analysis or machine learning algorithms. Specifically, statistical analysis calculates basic statistical indicators (mean, median, standard deviation, etc.) to understand the distribution and trends of the data, clarifying its characteristics. Correlation analysis and regression analysis can also be performed to identify factors influencing failure rates and inventory levels. Machine learning algorithms build models based on past data and perform predictions and classifications on new data. For example, regression models and time series analysis models can be used to predict failure rates, and clustering and association rules can be applied to fluctuations in inventory levels. Furthermore, the analysis unit also performs data preprocessing and feature engineering. Data preprocessing involves imputing missing values, removing outliers, and normalizing and standardizing the data to prepare it for analysis. Feature engineering extracts useful features from the data to improve model performance. For example, features such as component usage frequency and environmental conditions can be added to predict failure rates. This allows the analysis unit to analyze the input data from multiple perspectives and accurately grasp trends in failure rates and inventory levels.

[0071] The prediction unit predicts the failure rate based on the data analyzed by the analysis unit. For example, the prediction unit can predict future failure rate trends based on past data. Specifically, it uses time series analysis models and regression models to predict future failure rates from past failure data. In time series analysis models, the unit learns the temporal patterns of past data and predicts future data. For example, moving average models and autoregressive models can be used to capture seasonal variations and trends in failure rates. In regression models, factors that affect the failure rate are used as explanatory variables to predict the failure rate. For example, a regression model can be constructed to predict the failure rate using the frequency of use of components and environmental conditions as explanatory variables. Furthermore, the prediction unit can also perform ensemble learning by combining multiple models using machine learning algorithms. This allows for the integration of prediction results from individual models, enabling more accurate predictions. For example, predictions can be made by combining multiple decision tree models using random forests or gradient boosting. As a result, the prediction unit can accurately predict future failure rates based on past data and provide information for taking appropriate countermeasures.

[0072] The determination unit determines whether repair is possible based on the results predicted by the prediction unit. For example, the determination unit can determine whether repair is possible by considering factors such as the availability of necessary parts and the repair time. Specifically, it checks the inventory and supply status of necessary parts based on the predicted failure rate and determines whether repair is possible. It also considers the time and cost required for repair to determine whether the repair is economically feasible. For example, if the necessary parts are not in stock or the repair time is too long, it is determined that repair is difficult. On the other hand, if there is sufficient stock of parts and the repair time is short, it is determined that repair is possible. Furthermore, the determination unit also has a function to determine the priority of repairs. For example, it can determine that components with a high impact from failure or components that require urgent repair should be repaired first. As a result, the determination unit can quickly and accurately determine the feasibility and priority of repairs based on the predicted failure rate, and formulate an efficient repair plan.

[0073] The service provider provides the results obtained by the judgment provider. For example, the service provider can provide users with prediction results and judgment results. Specifically, it displays results in a dashboard or report format that users can access. The dashboard visually displays prediction and judgment results that are updated in real time, making them easy for users to understand intuitively. For example, it uses graphs and charts to display trends in failure rates and repair priorities. In addition, the report format documents detailed analysis results and prediction results so that users can refer to them later. Furthermore, the service provider has an alert function that can send notifications to users when important information or urgent action is required. For example, it can send notifications via email or SMS if the failure rate suddenly increases or if parts that need repair are identified. This allows the service provider to provide users with quick and accurate information and support appropriate responses. Furthermore, the service provider also has a function to collect user feedback and use it to improve the system. For example, users can comment on and evaluate the information provided, and the accuracy and usability of the system can be improved based on that feedback. This allows the service provider to provide users with high-quality information and improve the overall performance of the system.

[0074] The input unit can input data such as the number of component failures and inventory levels. For example, the input unit can input the number of component failures (units / month) and inventory levels. For example, the input unit can input the number of component failures. The input unit can also input inventory levels. Furthermore, the input unit can input long-term data. For example, the input unit can input data for the past year. This makes it possible to predict failure rates and manage inventory by inputting data such as the number of component failures and inventory levels. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input data such as the number of component failures and inventory levels into a generating AI and have the generating AI perform data analysis.

[0075] The analysis unit can analyze the input data and predict failure rate trends and inventory lifespan. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, the analysis unit can analyze the data using statistical analysis. Furthermore, the analysis unit can analyze the data using machine learning algorithms. In addition, the analysis unit can analyze the data using time series analysis or trend analysis. By analyzing the data, it is possible to predict failure rate trends and inventory lifespan. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the input data into a generating AI and have the generating AI perform the data analysis.

[0076] The prediction unit can predict future failure rate trends based on past data. For example, the prediction unit can predict future failure rate trends based on data from the past year. The prediction unit can also predict future failure rate trends based on data from a specific period. Furthermore, the prediction unit can analyze past data to predict future failure rate trends. For example, the prediction unit can analyze past data to predict future failure rate trends. This allows for the estimation of future failure rates by predicting failure rate trends based on past data. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past data into a generating AI and have the generating AI perform the prediction of failure rate trends.

[0077] The determination unit can determine whether or not a repair is possible. The determination unit can determine whether or not a repair is possible by considering, for example, the availability of necessary parts and the time required for the repair. For example, the determination unit can determine whether or not a repair is possible by considering the availability of necessary parts. The determination unit can also determine whether or not a repair is possible by considering the time required for the repair. Furthermore, the determination unit can determine whether or not a repair is possible by considering the cost required for the repair. For example, the determination unit can determine whether or not a repair is possible by considering the cost required for the repair. In this way, by determining whether or not a repair is possible, it is possible to determine whether or not a repair can be performed. Some or all of the above processing in the determination unit may be performed using, for example, AI, or not. For example, the determination unit can input the availability of necessary parts, the time required for the repair, and the cost of the repair into a generating AI, and have the generating AI perform a determination of whether or not a repair is possible.

[0078] The service provider can provide prediction results and judgment results. For example, the service provider can provide prediction results to the user. For example, the service provider can provide prediction results. The service provider can also provide judgment results. Furthermore, the service provider can display prediction results and judgment results in graphs or text. For example, the service provider can display prediction results in graphs. The service provider can also display judgment results in text. This allows the user to take appropriate action by providing prediction results and judgment results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input prediction results and judgment results into a generating AI and have the generating AI perform the information provision.

[0079] The input unit can estimate the user's emotions and adjust the timing of data entry based on the estimated emotions. For example, if the user is stressed, the input unit can delay the timing of data entry to allow for more relaxed input. The input unit can also speed up the timing of data entry if the user is focused, enabling more efficient input. Furthermore, if the user is tired, the input unit can adjust the timing of data entry to allow for breaks during input. By adjusting the timing of data entry according to the user's emotions, more efficient data entry becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data input.

[0080] The input unit can analyze the user's past data input history and select the optimal input method. For example, the input unit can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). The input unit can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, the input unit can analyze the user's past input history and select a method that minimizes input errors. In this way, the optimal input method can be selected by analyzing past data input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past data input history into a generating AI and have the generating AI select the optimal input method.

[0081] The input unit can filter data based on the user's current work situation and areas of interest during data entry. For example, the input unit can prioritize inputting data related to the project the user is currently working on. The input unit can also filter and input highly relevant data based on the user's areas of interest. Furthermore, the input unit can filter data to input only the necessary data according to the user's work situation. This enables efficient data entry by filtering data based on the user's work situation and areas of interest. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input data on the user's current work situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0082] The input unit can estimate the user's emotions and determine the priority of the data to be input based on the estimated emotions. For example, if the user is stressed, the input unit can prioritize inputting high-importance data and postpone inputting low-importance data. For example, if the user is stressed, the input unit can postpone inputting low-importance data. Also, if the user is relaxed, the input unit can input all data evenly. For example, if the user is relaxed, the input unit can input all data evenly. Furthermore, if the user is in a hurry, the input unit can prioritize inputting the most important data. For example, if the user is in a hurry, the input unit can prioritize inputting the most important data. This enables efficient data input by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input user emotion data into a generating AI, and have the generating AI determine the priority of the data.

[0083] The input unit can prioritize inputting highly relevant data by considering the user's geographical location information during data entry. For example, if the user is in a specific region, the input unit can prioritize inputting data related to that region. For example, if the user is in a specific region, the input unit can prioritize inputting data related to that region. Furthermore, if the user is on the move, the input unit can prioritize inputting highly relevant data based on their current location. For example, if the user is in a specific location, the input unit can prioritize inputting data related to that location. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location information into a generating AI and have the generating AI perform the input of highly relevant data.

[0084] The input unit can analyze the user's social media activity and input relevant data during data entry. For example, the input unit can analyze the content of the user's social media posts and input relevant data. The input unit can also analyze the activities of the user's social media followers and friends and input relevant data. Furthermore, the input unit can analyze the user's social media trends and input relevant data. This allows for efficient input of relevant data by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input data on the user's social media activity into a generating AI and have the generating AI input the relevant data.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide highly visual analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an algorithm specialized in failure rate prediction to failure data. For example, the analysis unit can apply an algorithm specialized in inventory management to inventory data. For example, the analysis unit can apply an algorithm specialized in inventory management to inventory data. Furthermore, the analysis unit can apply an algorithm that evaluates repairability to repair data. For example, the analysis unit can apply an algorithm that evaluates repairability to repair data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is excited, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for efficient analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis in stages according to the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data submission date into a generating AI and have the generating AI determine the priority of analysis.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0091] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is relaxed, the prediction unit can provide detailed prediction results. For example, if the user is relaxed, the prediction unit can provide detailed prediction results. For example, if the user is in a hurry, the prediction unit can provide concise prediction results. For example, if the user is excited, the prediction unit can provide visually stimulating prediction results. For example, if the user is excited, the prediction unit can provide visually stimulating prediction results. This allows for efficient prediction results by adjusting the prediction criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generating AI and have the generating AI adjust the prediction criteria.

[0092] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationships between failure data and inventory data. The prediction unit can also make predictions by considering the interrelationships between repair data and failure data. Furthermore, the prediction unit can analyze the interrelationships between data to improve the accuracy of its predictions. In this way, the accuracy of predictions can be improved by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0093] The prediction unit can make predictions by considering the attribute information of the data submitter. For example, the prediction unit can make predictions by considering the position and job duties of the data submitter. The prediction unit can also make predictions by considering the past performance of the data submitter. Furthermore, the prediction unit can analyze the attribute information of the data submitter to improve the accuracy of the prediction. For example, the prediction unit can analyze the attribute information of the data submitter to improve the accuracy of the prediction. In this way, the accuracy of the prediction can be improved by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the prediction.

[0094] The prediction unit can estimate the user's emotions and adjust the order in which the prediction results are displayed based on the estimated emotions. For example, if the user is nervous, the prediction unit can display important prediction results first. For example, if the user is nervous, the prediction unit can display important prediction results first. For example, if the user is relaxed, the prediction unit can display all prediction results evenly. For example, if the user is relaxed, the prediction unit can display all prediction results evenly. Furthermore, if the user is in a hurry, the prediction unit can display the most important prediction results first. For example, if the user is in a hurry, the prediction unit can display the most important prediction results first. This allows for efficient information provision by adjusting the display order of prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generating AI and have the generating AI adjust the display order of the prediction results.

[0095] The prediction unit can make predictions while considering the geographical distribution of the data. For example, the prediction unit can predict failure rates by region while considering the geographical distribution of the data. The prediction unit can also predict the shelf life of inventory based on the geographical distribution. Furthermore, the prediction unit can analyze the geographical distribution to improve the accuracy of the prediction. This allows for improved prediction accuracy by region by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the prediction.

[0096] The prediction unit can improve the accuracy of its predictions by referring to relevant literature during the prediction process. For example, the prediction unit can improve the accuracy of its failure rate predictions by referring to relevant literature. The prediction unit can also predict the shelf life of inventory based on relevant literature. Furthermore, the prediction unit can analyze relevant literature to improve the accuracy of its predictions. In this way, the accuracy of predictions can be improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant literature into a generating AI and have the generating AI perform the prediction.

[0097] The judgment unit can estimate the user's emotions and adjust the display method of the judgment based on the estimated user's emotions. For example, if the user is nervous, the judgment unit can provide a simple and highly visible display method. For example, if the user is nervous, the judgment unit can provide a simple and highly visible display method. Also, if the user is relaxed, the judgment unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the judgment unit can provide a display method that gets straight to the point. For example, if the user is in a hurry, the judgment unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the judgment according to the user's emotions, it becomes possible to provide highly visible information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input user emotion data into the generating AI and have the generating AI adjust how the judgment is displayed.

[0098] The judgment unit can optimize the current judgment by referring to past judgment data during the judgment process. For example, the judgment unit can optimize the current judgment based on past judgment data. The judgment unit can also analyze past judgment data to improve the accuracy of the judgment. For example, the judgment unit can analyze past judgment data to improve the accuracy of the judgment. Furthermore, the judgment unit can maintain consistency in judgments by referring to past judgment data. For example, the judgment unit can maintain consistency in judgments by referring to past judgment data. This allows the current judgment to be optimized by referring to past judgment data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input past judgment data into a generating AI and have the generating AI perform the optimization of the current judgment.

[0099] The judgment unit can apply different judgment methods to each data category during the judgment process. For example, the judgment unit can apply a judgment method specialized in failure rate prediction to failure data. For example, the judgment unit can apply a judgment method specialized in failure rate prediction to failure data. The judgment unit can also apply a judgment method specialized in inventory management to inventory data. For example, the judgment unit can apply a judgment method specialized in inventory management to inventory data. Furthermore, the judgment unit can apply a judgment method that evaluates repairability to repair data. For example, the judgment unit can apply a judgment method that evaluates repairability to repair data. By applying an appropriate judgment method according to the data category, the accuracy of the judgment can be improved. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the data category into a generating AI and have the generating AI execute the application of the judgment method.

[0100] The judgment unit can estimate the user's emotions and adjust the importance of the judgments based on the estimated emotions. For example, if the user is nervous, the judgment unit can display the most important judgment results first. For example, if the user is nervous, the judgment unit can display the most important judgment results first. For example, if the user is relaxed, the judgment unit can display all judgment results equally. For example, if the user is relaxed, the judgment unit can display all judgment results equally. Furthermore, if the user is in a hurry, the judgment unit can display the most important judgment results first. For example, if the user is in a hurry, the judgment unit can display the most important judgment results first. This allows for efficient information provision by adjusting the importance of judgments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input user emotion data into the generating AI and have the generating AI adjust the importance of the judgment.

[0101] The judgment unit can analyze changes in the judgment based on the data submission time. For example, the judgment unit can make a judgment based on the latest data and analyze the changes by comparing it with past data. The judgment unit can also prioritize the latest data and postpone older data submissions. Furthermore, the judgment unit can analyze changes in the judgment in stages according to the submission time. This enables efficient judgment by analyzing changes in the judgment based on the data submission time. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the data submission time into a generating AI and have the generating AI perform the analysis of changes in the judgment.

[0102] The determination unit can analyze the determination by referring to relevant market data at the time of determination. For example, the determination unit can improve the accuracy of the failure rate determination by referring to relevant market data. The determination unit can also determine the inventory shelf life based on relevant market data. For example, the determination unit can determine the inventory shelf life based on relevant market data. Furthermore, the determination unit can analyze the relevant market data to improve the accuracy of the determination. For example, the determination unit can analyze the relevant market data to improve the accuracy of the determination. In this way, the accuracy of the determination can be improved by referring to relevant market data. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input relevant market data into a generating AI and have the generating AI perform the determination.

[0103] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is stressed, the provider can postpone less important information and prioritize more important information. For example, if the user is stressed, the provider can postpone less important information. The provider can also provide all information equally if the user is relaxed. For example, if the user is relaxed, the provider can provide all information equally. Furthermore, if the user is in a hurry, the provider can prioritize providing the most important information. For example, if the user is in a hurry, the provider can prioritize providing the most important information. This enables efficient information provision by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​determine the priority of the information.

[0104] The data delivery unit can improve the accuracy of its delivery by considering the interrelationships between data. For example, the data delivery unit can provide information by considering the interrelationships between failure data and inventory data. The data delivery unit can also provide information by considering the interrelationships between repair data and failure data. Furthermore, the data delivery unit can analyze the interrelationships between data to improve the accuracy of its delivery. By considering the interrelationships between data, the accuracy of the delivery can be improved. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without AI. For example, the data delivery unit can input the interrelationships between data into a generating AI and have the generating AI perform the information delivery.

[0105] The data provisioning unit can provide data while considering the attribute information of the data submitter. For example, the data provisioning unit can provide information while considering the position and job duties of the data submitter. The data provisioning unit can also provide information while considering the past performance of the data submitter. Furthermore, the data provisioning unit can analyze the attribute information of the data submitter to improve the accuracy of the provision. For example, the data provisioning unit can analyze the attribute information of the data submitter to improve the accuracy of the provision. This allows for improved accuracy of provision by considering the attribute information of the data submitter. Some or all of the above processing in the data provisioning unit may be performed using AI, for example, or without AI. For example, the data provisioning unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the provision of information.

[0106] The information provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is nervous, the provider can provide a simple and highly visible display method. For example, if the user is nervous, the provider can provide a simple and highly visible display method. Also, if the user is relaxed, the provider can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the provider can provide a display method that gets straight to the point. For example, if the user is in a hurry, the provider can provide a display method that gets straight to the point. This makes it possible to provide highly visible information by adjusting the way the information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​adjust how the information is displayed.

[0107] The data delivery unit can provide data while considering its geographical distribution. For example, the data delivery unit can provide region-specific information while considering the geographical distribution of the data. The data delivery unit can also provide inventory duration based on geographical distribution. Furthermore, the data delivery unit can analyze geographical distribution to improve the accuracy of the delivery. This makes it possible to provide region-specific information by considering the geographical distribution of the data. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without AI. For example, the data delivery unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the information delivery.

[0108] The data provider can improve the accuracy of its data provision by referring to relevant literature at the time of provision. For example, the data provider can improve the accuracy of failure rate information provision by referring to relevant literature. The data provider can also provide inventory shelf life based on relevant literature. For example, the data provider can provide inventory shelf life based on relevant literature. Furthermore, the data provider can analyze relevant literature to improve the accuracy of its data provision. For example, the data provider can analyze relevant literature to improve the accuracy of its data provision. This allows for improved accuracy of data provision by referring to relevant literature. Some or all of the above processing in the data provider may be performed using AI, for example, or without AI. For example, the data provider can input relevant literature into a generating AI and have the generating AI provide the information.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The failure rate prediction system can further estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the prediction results according to the user's emotions, highly visible information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the prediction results.

[0111] The input unit can analyze the user's past data entry history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). It can also suggest the optimal input method for a specific time period based on the user's past input history. Furthermore, it can analyze the user's past input history and select a method that minimizes input errors. In this way, the optimal input method can be selected by analyzing past data entry history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past data entry history into a generating AI and have the generating AI select the optimal input method.

[0112] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide an easy-to-understand analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0113] The prediction unit can improve the accuracy of its predictions by considering the interrelationships between data. For example, it can make predictions by considering the interrelationships between failure data and inventory data. It can also make predictions by considering the interrelationships between repair data and failure data. Furthermore, it can analyze the interrelationships between data to improve the accuracy of predictions. In this way, the accuracy of predictions can be improved by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of predictions.

[0114] The judgment unit can estimate the user's emotions and adjust the display method of the judgment based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the judgment according to the user's emotions, it becomes possible to provide highly visible information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the judgment.

[0115] The data delivery unit can improve the accuracy of its delivery by considering the interrelationships between data. For example, it can provide information by considering the interrelationships between failure data and inventory data. It can also provide information by considering the interrelationships between repair data and failure data. Furthermore, it can analyze the interrelationships between data to improve the accuracy of its delivery. In this way, the accuracy of delivery can be improved by considering the interrelationships between data. Some or all of the above processing in the data delivery unit may be performed using AI, for example, or without using AI. For example, the data delivery unit can input the interrelationships between data into a generating AI and have the generating AI perform the information delivery.

[0116] The input unit can estimate the user's emotions and adjust the timing of data entry based on the estimated emotions. For example, if the user is stressed, the timing of data entry can be delayed to allow for relaxed data entry. Conversely, if the user is focused, the timing of data entry can be sped up to enable efficient data entry. Furthermore, if the user is tired, the timing of data entry can be adjusted to allow for breaks during data entry. This allows for efficient data entry by adjusting the timing of data entry according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI, or not using AI. For example, the input unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data entry.

[0117] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data, and a simplified analysis on less important data. Furthermore, it can adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0118] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is relaxed, it can provide detailed prediction results. If the user is in a hurry, it can provide concise prediction results. Furthermore, if the user is excited, it can provide visually stimulating prediction results. In this way, by adjusting the prediction criteria according to the user's emotions, efficient prediction results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the prediction criteria.

[0119] The information provider can estimate the user's emotions and prioritize the information to be provided based on those emotions. For example, if the user is stressed, less important information can be postponed, and more important information can be prioritized. If the user is relaxed, all information can be provided equally. Furthermore, if the user is in a hurry, the most important information can be prioritized. This enables efficient information delivery by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The input unit is used to input data. The data may include, but is not limited to, the number of component failures or inventory levels. For example, the input unit can input the number of component failures (units / month) or inventory levels. Step 2: The analysis unit analyzes the data input by the input unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Step 3: The prediction unit predicts the failure rate based on the data analyzed by the analysis unit. For example, the prediction unit can predict future failure rate trends based on past data. Step 4: The determination unit determines whether the item is repairable based on the results predicted by the prediction unit. The determination unit can determine whether the item is repairable by considering, for example, the availability of necessary parts and the time required for the repair. Step 5: The providing unit provides the results obtained by the determination unit. The providing unit can, for example, provide the user with prediction results or determination results.

[0122] 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.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the input unit, analysis unit, prediction unit, determination unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit is implemented by the receiving device 38 of the smart device 14 and inputs data such as the number of component failures and inventory levels. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the failure rate based on the analyzed data. The determination unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines whether repair is possible based on the predicted results. The provision unit is implemented by the output device 40 of the smart device 14 and provides the prediction results and determination results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0129] 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.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0131] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] 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.

[0133] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the input unit, analysis unit, prediction unit, determination unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the smart glasses 214 and inputs data such as the number of component failures and inventory levels. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the input data. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and predicts the failure rate based on the analyzed data. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines whether repair is possible based on the predicted results. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the prediction results and determination results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0145] 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0147] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] 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.

[0149] 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.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the input unit, analysis unit, prediction unit, determination unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the headset terminal 314 and inputs data such as the number of component failures and inventory levels. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the failure rate based on the analyzed data. The determination unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines whether repair is possible based on the predicted results. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides the prediction results and determination results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0161] 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.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0163] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] 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.

[0165] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] 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.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the input unit, analysis unit, prediction unit, determination unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the robot 414 and inputs data such as the number of component failures and inventory levels. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the failure rate based on the analyzed data. The determination unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines whether repair is possible based on the predicted results. The provision unit is implemented by the speaker 240 of the robot 414 and provides the prediction results and determination results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] 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.

[0176] Figure 9 shows the 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.

[0177] 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.

[0178] 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.

[0179] 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, and motorcycles, 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 based, for example, 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.

[0180] 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."

[0181] 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.

[0182] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] 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 other things 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.

[0192] 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.

[0193] (Note 1) An input section for entering data, An analysis unit that analyzes the data input by the aforementioned input unit, A prediction unit predicts the failure rate based on the data analyzed by the aforementioned analysis unit, A determination unit that determines whether repair is possible based on the results predicted by the prediction unit, The system includes a providing unit that provides the results obtained by the determination unit. A system characterized by the following features. (Note 2) The aforementioned input unit is Enter data such as the number of component failures and inventory levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes the input data to predict failure rate trends and inventory shelf life. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Predicting future failure rate trends based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The determination unit, Determine whether it can be repaired. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provides prediction results and judgment results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is The system estimates the user's emotions and adjusts the timing of data entry based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is Analyze the user's past data entry history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is When entering data, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is It estimates the user's emotions and determines the priority of input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is When entering data, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is When entering data, analyze the user's social media activity and input relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, consider the interrelationships between data to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, the attribute information of the data submitters is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, refer to relevant literature to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The determination unit, The system estimates the user's emotions and adjusts the display method of the judgment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The determination unit, When making a judgment, the system optimizes the current judgment by referring to past judgment data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The determination unit, When making a determination, different determination methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The determination unit, The system estimates the user's emotions and adjusts the importance of the decision based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The determination unit, When making a decision, we analyze how the decision changes based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The determination unit, When making a decision, the relevant market data is referenced to analyze the decision. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing data, we improve the accuracy of the data by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing data, the data will be provided while taking into account the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing data, we will take into account its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing data, we refer to relevant literature to improve the accuracy of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering data, An analysis unit that analyzes the data input by the aforementioned input unit, A prediction unit predicts the failure rate based on the data analyzed by the aforementioned analysis unit, A determination unit that determines whether repair is possible based on the results predicted by the prediction unit, The system includes a providing unit that provides the results obtained by the determination unit. A system characterized by the following features.

2. The aforementioned input unit is Enter data such as the number of component failures and inventory levels. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes the input data to predict failure rate trends and inventory shelf life. The system according to feature 1.

4. The prediction unit, Predicting future failure rate trends based on past data. The system according to feature 1.

5. The determination unit, Determine whether it can be repaired. The system according to feature 1.

6. The aforementioned supply unit is, Provides prediction results and judgment results. The system according to feature 1.

7. The aforementioned input unit is The system estimates the user's emotions and adjusts the timing of data entry based on those emotions. The system according to feature 1.

8. The aforementioned input unit is Analyze the user's past data entry history and select the optimal input method. The system according to feature 1.

9. The aforementioned input unit is When entering data, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

10. The aforementioned input unit is It estimates the user's emotions and determines the priority of input data based on the estimated user emotions. The system according to feature 1.

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