system

Abstract

The system according to this embodiment aims to strengthen the management of company assets and prevent their accidental disposal or loss. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, a proposal unit, and a generation unit. The collection unit collects data such as assets. The analysis unit analyzes the data collected by the collection unit. The evaluation unit evaluates classification and useful life based on the data analyzed by the analysis unit. The proposal unit proposes procedures and countermeasures based on the results evaluated by the evaluation unit. The generation unit automatically generates related materials based on the procedures and countermeasures proposed by the proposal 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 character of the chatbot, 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 prior art, the management of company assets and the like becomes insufficient due to changes or resignations of the persons in charge, and there is a risk of assets being missing or being disposed of incorrectly.

[0005] The system according to the embodiment aims to strengthen the management of company assets and the like and prevent incorrect disposal and missing assets.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, a proposal unit, and a generation unit. The collection unit collects data such as assets. The analysis unit analyzes the data collected by the collection unit. The evaluation unit evaluates classification and useful life based on the data analyzed by the analysis unit. The proposal unit proposes procedures and countermeasures based on the results evaluated by the evaluation unit. The generation unit automatically generates related materials based on the procedures and countermeasures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can strengthen the management of company assets and prevent accidental disposal or loss of location. [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, and the like. The communication I / F manages 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 asset management system according to an embodiment of the present invention is a system using an AI agent for managing a company's fixed assets and equipment. This asset management system prevents the loss of location of assets that become inactive or sent to warehouses due to personnel changes, retirements, changes in responsibilities, or the completion of tasks or projects. The AI ​​agent supports the classification and understanding of useful life of assets. This prevents errors in procedures and registration information, and prevents accidental disposal or loss of location. Furthermore, the AI ​​agent clarifies the National Tax Agency's fixed asset classification and information sources, preventing errors in the realistic classification and useful life of assets actually used by the company, and reducing friction with the National Tax Agency regarding differences in depreciation. In addition, to ensure the liquidity of assets within the organization and to effectively utilize items that are effectively idle, the AI ​​agent provides procedures and proposes new asset uses. For example, the AI ​​agent reduces man-hours and workload by identifying assets, determining management levels, handling procedures, proposing solutions, and verifying and documenting related materials. This allows for future changes, impacts, concerns, and risk predictions, while considering loss, misconduct, accidents, etc. Specifically, the AI ​​agent analyzes the content and scope of assets, automatically identifying affected internal documents and operational rules. It also automatically evaluates the company's level of asset management and proposes corrections and improvements. Furthermore, it automatically detects changes in regulations due to legal revisions and automatically generates comparison tables and proposed updates. This system makes it easier for employees of companies and organizations to grasp the daily realities of frequent purchases, disposals, and diverse asset items, minimizing procedural discrepancies, losses, and the burden and omissions of inventory work. By readily consulting and using the AI ​​agent, early operational checks and internal procedural adjustments become possible. The system automatically identifies assets related to standard and legal revisions, and generates improvement measures and countermeasures, reducing man-hours and increasing efficiency. As a result, the asset management system can efficiently collect, analyze, evaluate, propose, and generate data on assets.

[0029] The asset management system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, a proposal unit, and a generation unit. The collection unit collects data such as assets. The collection unit can automatically collect data using, for example, sensors. The collection unit can also manually input data. Furthermore, the collection unit can acquire data from existing databases. For example, the collection unit collects asset location information in real time using sensors. In the case of manual input, a person in charge inputs asset information into the system. Data acquisition from existing databases is performed via API. The analysis unit analyzes the data collected by the collection unit. The analysis unit can perform, for example, statistical analysis. Furthermore, the analysis unit can analyze data using machine learning algorithms. Furthermore, the analysis unit can recognize patterns in the data. For example, the analysis unit analyzes the frequency of asset use based on the collected data. When using machine learning algorithms, past data is learned to predict future usage patterns. Data pattern recognition is used for anomaly detection. The evaluation unit evaluates classification and service life based on the data analyzed by the analysis unit. The valuation unit can, for example, define asset categories and classify them accordingly. It can also evaluate useful life based on statutory useful life and actual usage. Furthermore, the valuation unit can assess the value of assets using evaluation indicators. For example, the valuation unit classifies assets into categories such as real estate, machinery and equipment, and financial data. Useful life is evaluated by comparing statutory useful life with actual usage. Evaluation indicators are used to quantify the value of assets. The proposal unit proposes procedures and solutions based on the evaluation results from the valuation unit. For example, the proposal unit can propose maintenance procedures. It can also propose disposal methods. Furthermore, the proposal unit can propose asset reallocation. For example, the proposal unit proposes a schedule for regular asset maintenance. Disposal methods may include environmentally conscious methods. Asset reallocation aims to make effective use of idle assets. The generation unit automatically generates relevant documents based on the procedures and solutions proposed by the proposal unit. For example, the generation unit can generate documents using templates.Furthermore, the generation unit can generate documents using a generation algorithm. In addition, the generation unit can automatically save the generated documents. For example, the generation unit documents proposed maintenance procedures based on a template. The generation algorithm generates the most suitable documents according to the proposed content. The generated documents are automatically saved to cloud storage. This allows the asset management system according to the embodiment to efficiently collect, analyze, evaluate, propose, and generate documents for asset data, etc.

[0030] The data collection unit collects data on assets and other related information. For example, the data collection unit can automatically collect data using sensors. Specifically, it can collect real-time location and environmental information of assets using GPS sensors, RFID tags, temperature sensors, humidity sensors, etc. This allows for accurate tracking of asset movement and storage conditions. The data collection unit can also manually input data. For example, a person in charge can periodically input asset status and usage information into the system to maintain up-to-date information. Furthermore, the data collection unit can retrieve data from existing databases. For example, it can retrieve data from an in-house ERP system or financial system via API and integrate it into the asset management system. This allows for effective utilization of existing data and maintains data consistency and accuracy. By combining these diverse data collection methods, the data collection unit achieves comprehensive asset management.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can perform statistical analysis. Specifically, it can statistically analyze asset usage frequency, operating hours, and fluctuations in the storage environment to evaluate the asset's condition and performance. The analysis unit can also analyze data using machine learning algorithms. For example, it can learn from past usage data to predict future usage patterns and failure risks. Furthermore, the analysis unit can recognize data patterns. For instance, it can use anomaly detection algorithms to detect unusual usage patterns or environmental fluctuations, enabling early problem identification. This allows the analysis unit to analyze collected data from multiple perspectives and understand the asset's condition and risks in real time. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. This supports effective asset management and optimal operation.

[0032] The valuation unit evaluates classification and useful life based on data analyzed by the analysis unit. For example, the valuation unit can define asset categories and perform classification based on those categories. Specifically, it can classify assets into categories such as real estate, machinery and equipment, and financial data, and apply management methods appropriate to the characteristics of each. The valuation unit can also evaluate useful life based on statutory useful life and actual usage conditions. For example, it can calculate the actual useful life of an asset by comparing the statutory useful life with the actual frequency of use and environmental conditions. Furthermore, the valuation unit can evaluate the value of an asset using evaluation indicators. For example, it can quantify the overall value of an asset by considering its market value, depreciation expenses, and insurance value. This allows the valuation unit to accurately assess the condition and value of assets and support appropriate management and operation.

[0033] The proposal department proposes procedures and solutions based on the evaluation results from the evaluation department. For example, the proposal department can propose maintenance procedures. Specifically, it can create a regular maintenance schedule according to the condition and usage of the assets, and propose the necessary work and timing of parts replacement. The proposal department can also propose disposal methods. For example, it can propose environmentally friendly disposal methods and recycling procedures to minimize the environmental impact associated with asset disposal. Furthermore, the proposal department can propose asset reallocation. For example, it can reallocate idle assets to other departments or projects to make effective use of assets. In this way, the proposal department can support the optimal operation and management of assets and improve the efficiency and sustainability of the company.

[0034] The generation unit automatically generates relevant documents based on the procedures and solutions proposed by the proposal unit. For example, the generation unit can generate documents using templates. Specifically, it can document proposed maintenance procedures, disposal methods, and relocation plans based on templates and provide them in a standardized format. The generation unit can also generate documents using generation algorithms. For example, it can automatically generate the most suitable documents according to the proposal, creating comprehensive documents that include all necessary information. Furthermore, the generation unit can automatically save the generated documents. For example, it can save them to cloud storage, allowing stakeholders to access them at any time. This enables the generation unit to efficiently generate documents based on proposals and support information sharing and management.

[0035] The analysis unit can analyze collected data and learn patterns in asset classification and useful life. For example, the analysis unit can define asset categories based on collected data and perform classification based on those categories. The analysis unit can also use machine learning algorithms to learn from past data and predict future usage patterns. For example, the analysis unit can analyze asset usage frequency and operating hours to learn patterns in useful life. The analysis unit can also perform data pattern recognition and anomaly detection. For example, the analysis unit can detect abnormal asset usage patterns based on collected data. This improves the accuracy of the analysis by learning patterns in asset classification and useful life. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input collected data into a generative AI, which can learn data patterns and output classification and useful life patterns.

[0036] The analysis unit can automatically refer to relevant laws and guidelines. For example, the analysis unit can automatically acquire international standards and industry standards and reflect them in the analysis. The analysis unit can also automatically detect changes in laws and guidelines and reflect them in the analysis results. For example, the analysis unit can automatically detect changes in regulations due to legal revisions and generate a comparison table of old and new versions. This improves the accuracy of the analysis by automatically referring to relevant laws and guidelines. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input data on relevant laws and guidelines into a generating AI, which can then detect changes and reflect them in the analysis results.

[0037] The proposal department can monitor the usage status of assets, identify idle assets, and propose new uses. For example, the proposal department can monitor the frequency of asset use and operating hours, and identify assets that have not been used for a certain period as idle assets. The proposal department can also propose new uses for idle assets, such as reallocation or leasing. For example, the proposal department may propose reusing idle assets in other departments. The proposal department may also propose generating revenue by leasing idle assets. In this way, by identifying idle assets and proposing new uses, it becomes possible to effectively utilize assets. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input asset usage data into a generation AI, which can identify idle assets and propose new uses.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal data collection method when collecting asset data, etc. For example, the data collection unit can propose the optimal data collection method based on the data collection methods the user has used in the past. The data collection unit can also select an efficient data collection method from the user's past data collection history. The data collection unit can also analyze the user's past data collection history and propose improvements to the data collection method. For example, the data collection unit can propose the optimal data collection method based on the data collection methods the user has used in the past. The data collection unit can select an efficient data collection method from the user's past data collection history. The data collection unit can analyze the user's past data collection history and propose improvements to the data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, and the generating AI can propose the optimal data collection method.

[0039] The data collection unit can filter data such as assets based on the user's current work situation and areas of interest. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the user's current work situation. The data collection unit can also collect only the necessary data based on the user's areas of interest. The data collection unit can analyze the user's work situation and areas of interest and filter the collected data. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the user's current work situation. The data collection unit can collect only the necessary data based on the user's areas of interest. The data collection unit can analyze the user's work situation and areas of interest and filter the collected data. This allows for the collection of highly relevant data by filtering the data based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's work situation and areas of interest into a generating AI, which can then perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting asset data, etc. For example, the data collection unit prioritizes the collection of data on nearby assets, etc., based on the user's current location. The data collection unit can also collect highly relevant data by considering the user's geographical location information. The data collection unit can also suggest optimal data collection points based on the user's location information. For example, the data collection unit prioritizes the collection of data on nearby assets, etc., based on the user's current location. The data collection unit collects highly relevant data by considering the user's geographical location information. The data collection unit suggests optimal data collection points based on the user's location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data when collecting data such as assets. For example, the data collection unit analyzes a user's social media activity and collects data such as relevant assets. The data collection unit can also collect necessary data based on a user's statements and posts on social media. The data collection unit can also monitor a user's social media activity and collect relevant data in real time. For example, the data collection unit analyzes a user's social media activity and collects data such as relevant assets. The data collection unit collects necessary data based on a user's statements and posts on social media. The data collection unit monitors a user's social media activity and collects relevant data in real time. This allows for the collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI, which can then collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of assets, etc., during the analysis. For example, the analysis unit performs a detailed analysis for assets, etc. with high importance. The analysis unit can also perform a simplified analysis for assets, etc. with low importance. The analysis unit can also evaluate the importance of assets, etc., and adjust the level of detail of the analysis. For example, the analysis unit performs a detailed analysis for assets, etc. with high importance. The analysis unit performs a simplified analysis for assets, etc. with low importance. The analysis unit evaluates the importance of assets, etc., and adjusts the level of detail of the analysis. In this way, by adjusting the level of detail of the analysis based on the importance of assets, etc., the analysis can be performed efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input asset importance data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of assets, etc., during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the category of assets, etc. The analysis unit can also apply different analysis methods for each category of assets, etc. The analysis unit can also classify the categories of assets, etc., and apply an analysis algorithm suitable for each. For example, the analysis unit can select the optimal analysis algorithm depending on the category of assets, etc. The analysis unit can apply different analysis methods for each category of assets, etc. The analysis unit can classify the categories of assets, etc., and apply an analysis algorithm suitable for each. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of assets, etc. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input asset category data into a generating AI, and the generating AI can apply the optimal analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the submission dates of assets, etc., during the analysis. For example, the analysis unit will prioritize the analysis of assets, etc., whose submission dates are approaching. The analysis unit can also adjust the analysis schedule based on the submission dates. The analysis unit can also determine the priority of analysis considering the submission dates. For example, the analysis unit will prioritize the analysis of assets, etc., whose submission dates are approaching. The analysis unit will adjust the analysis schedule based on the submission dates. The analysis unit will determine the priority of analysis considering the submission dates. This allows for efficient analysis by determining the priority of analysis based on the submission dates of assets, etc. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input asset submission date data into a generating AI, and the generating AI can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relationships between assets, etc., during the analysis. For example, the analysis unit evaluates the relationships between assets, etc., and determines the order of analysis. The analysis unit can also prioritize the analysis of highly related assets, etc. The analysis unit can also adjust the analysis schedule based on the relationships between assets, etc. For example, the analysis unit evaluates the relationships between assets, etc., and determines the order of analysis. The analysis unit prioritizes the analysis of highly related assets, etc. The analysis unit adjusts the analysis schedule based on the relationships between assets, etc. This allows for efficient analysis by adjusting the order of analysis based on the relationships between assets, etc. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input asset relationship data into a generating AI, and the generating AI can adjust the order of analysis.

[0046] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships of assets, etc. The evaluation unit can, for example, analyze the interrelationships of assets, etc., to improve the accuracy of its evaluations. The evaluation unit can also adjust the evaluation criteria by considering the relationships between assets, etc. The evaluation unit can also improve the accuracy by reflecting the interrelationships of assets, etc., in its evaluations. For example, the evaluation unit can analyze the interrelationships of assets, etc., to improve the accuracy of its evaluations. The evaluation unit can adjust the evaluation criteria by considering the relationships between assets, etc. The evaluation unit can improve the accuracy by reflecting the interrelationships of assets, etc., in its evaluations. This allows the evaluation unit to improve the accuracy of its evaluations based on the interrelationships of assets, etc. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the interrelationships of assets, etc., into a generating AI, and the generating AI can improve the accuracy of its evaluations.

[0047] The evaluation unit can perform evaluations based on the attribute information of the submitter of assets, etc., during the evaluation process. For example, the evaluation unit can adjust the evaluation criteria based on the submitter's attribute information. The evaluation unit can also perform evaluations considering the submitter's past evaluation history. The evaluation unit can also improve the accuracy of the evaluation by analyzing the submitter's attribute information. For example, the evaluation unit can adjust the evaluation criteria based on the submitter's attribute information. The evaluation unit can perform evaluations considering the submitter's past evaluation history. The evaluation unit can improve the accuracy of the evaluation by analyzing the submitter's attribute information. As a result, the accuracy of the evaluation is improved by considering the submitter's attribute information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the submitter's attribute information data into a generating AI, and the generating AI can perform the evaluation.

[0048] The evaluation unit can perform evaluations based on the geographical distribution of assets, etc. The evaluation unit can, for example, analyze the geographical distribution of assets, etc. and adjust the evaluation criteria. The evaluation unit can also determine the priority of evaluations based on geographical distribution. The evaluation unit can also improve the accuracy of evaluations by considering the geographical distribution of assets, etc. For example, the evaluation unit can analyze the geographical distribution of assets, etc. and adjust the evaluation criteria. The evaluation unit can determine the priority of evaluations based on geographical distribution. The evaluation unit can improve the accuracy of evaluations by considering the geographical distribution of assets, etc. As a result, the accuracy of evaluations is improved by considering the geographical distribution of assets, etc. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input geographical distribution data of assets, etc. into a generating AI, and the generating AI can perform the evaluation.

[0049] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on assets, etc., during the evaluation process. For example, the evaluation unit can adjust the evaluation criteria by referring to relevant literature on assets, etc. The evaluation unit can also improve the accuracy of its evaluation based on relevant literature. The evaluation unit can also automatically refer to relevant literature when evaluating assets, etc. For example, the evaluation unit can adjust the evaluation criteria by referring to relevant literature on assets, etc. The evaluation unit improves the accuracy of its evaluation based on relevant literature. The evaluation unit automatically refers to relevant literature when evaluating assets, etc. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input relevant literature data on assets, etc., into a generating AI, and the generating AI can perform the evaluation.

[0050] The proposal department can adjust the level of detail of a proposal based on the importance of the procedures and solutions. For example, the proposal department will provide detailed proposals for procedures and solutions of high importance. The proposal department can also provide simplified proposals for procedures and solutions of low importance. The proposal department can also evaluate the importance of procedures and solutions and adjust the level of detail of the proposal. For example, the proposal department will provide detailed proposals for procedures and solutions of high importance. The proposal department will provide simplified proposals for procedures and solutions of low importance. The proposal department will evaluate the importance of procedures and solutions and adjust the level of detail of the proposal. This allows for efficient proposal creation by adjusting the level of detail of proposals based on the importance of procedures and solutions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input importance data of procedures and solutions into a generating AI, and the generating AI can adjust the level of detail of the proposal.

[0051] The proposal unit can apply different proposal algorithms depending on the category of procedure and solution when making a proposal. For example, the proposal unit can select the optimal proposal algorithm depending on the category of procedure or solution. The proposal unit can also apply different proposal methods for each category of procedure or solution. The proposal unit can also classify the categories of procedures and solutions and apply a proposal algorithm appropriate for each. For example, the proposal unit can select the optimal proposal algorithm depending on the category of procedure or solution. The proposal unit can apply different proposal methods for each category of procedure or solution. The proposal unit can classify the categories of procedures and solutions and apply a proposal algorithm appropriate for each. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of procedure or solution. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category data of procedures and solutions into a generating AI, and the generating AI can apply the optimal proposal algorithm.

[0052] The proposal department can determine the priority of proposals based on the submission timing of procedures and solutions when submitting proposals. For example, the proposal department will prioritize proposals for procedures and solutions with upcoming submission deadlines. The proposal department can also adjust the proposal schedule based on the submission deadlines. The proposal department can also determine the priority of proposals considering the submission deadlines. For example, the proposal department will prioritize proposals for procedures and solutions with upcoming submission deadlines. The proposal department will adjust the proposal schedule based on the submission deadlines. The proposal department will determine the priority of proposals considering the submission deadlines. This allows for efficient proposal submission by determining the priority of proposals based on the submission timing of procedures and solutions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the submission timing of procedures and solutions into a generating AI, which can then determine the priority of proposals.

[0053] The proposal department can adjust the order of proposals based on the relevance of procedures and solutions when making proposals. For example, the proposal department evaluates the relevance of procedures and solutions and determines the order of proposals. The proposal department can also prioritize proposing procedures and solutions that are highly relevant. The proposal department can also adjust the schedule of proposals based on the relevance of procedures and solutions. For example, the proposal department evaluates the relevance of procedures and solutions and determines the order of proposals. The proposal department prioritizes proposing procedures and solutions that are highly relevant. The proposal department adjusts the schedule of proposals based on the relevance of procedures and solutions. This allows for efficient proposals by adjusting the order of proposals based on the relevance of procedures and solutions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the relevance of procedures and solutions into a generating AI, which can then adjust the order of proposals.

[0054] The generation unit can optimize its generation algorithm by referring to past generation data when generating data. For example, the generation unit can analyze past generation data and select the optimal generation algorithm. The generation unit can also improve the generation algorithm based on past generation data. The generation unit can also improve the accuracy of the generation algorithm by referring to past generation data. For example, the generation unit can analyze past generation data and select the optimal generation algorithm. The generation unit can improve the generation algorithm based on past generation data. The generation unit can improve the accuracy of the generation algorithm by referring to past generation data. As a result, the accuracy of the generation algorithm is improved by referring to past generation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past generation data into a generation AI, and the generation AI can optimize the generation algorithm.

[0055] The generation unit can apply different generation methods to each category of assets, etc., when generating data. For example, the generation unit can select the optimal generation method according to the category of assets, etc. The generation unit can also apply different generation methods to each category of assets, etc. The generation unit can also classify the categories of assets, etc., and apply a generation method suitable for each. For example, the generation unit can select the optimal generation method according to the category of assets, etc. The generation unit can apply different generation methods to each category of assets, etc. The generation unit can classify the categories of assets, etc., and apply a generation method suitable for each. By applying the optimal generation method to each category of assets, etc., the accuracy of the generated data is improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input asset category data into a generation AI, and the generation AI can apply the optimal generation method.

[0056] The generation unit can weight the generated data based on the submission dates of assets, etc., when generating documents. For example, the generation unit can prioritize generating documents for assets, etc., whose submission dates are approaching. The generation unit can also weight the generated data based on the submission dates. The generation unit can also determine the priority of the generated data by considering the submission dates. For example, the generation unit can prioritize generating documents for assets, etc., whose submission dates are approaching. The generation unit weights the generated data based on the submission dates. The generation unit determines the priority of the generated data by considering the submission dates. This allows for efficient document generation by weighting the generated data based on the submission dates of assets, etc. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input asset submission date data into a generation AI, and the generation AI can weight the generated data.

[0057] The generation unit can improve the accuracy of data generation by referring to relevant literature on assets, etc., when generating data. For example, the generation unit can improve the accuracy of generated data by referring to literature related to assets, etc. The generation unit can also improve the accuracy of generated data based on relevant literature. The generation unit can also automatically refer to relevant literature when generating data on assets, etc. For example, the generation unit can improve the accuracy of generated data by referring to literature related to assets, etc. The generation unit can improve the accuracy of generated data based on relevant literature. The generation unit automatically refers to relevant literature when generating data on assets, etc. This improves the accuracy of generated data by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input data on relevant literature on assets, etc., into a generation AI, which can then improve the accuracy of the generation.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit can analyze the user's past data collection history when collecting asset data, etc., and select the optimal collection method. For example, the data collection unit can propose the optimal collection method based on the collection methods the user has used in the past. The data collection unit can also select an efficient collection method from the user's past data collection history. The data collection unit can also analyze the user's past data collection history and propose improvements to the collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the user's past data collection history into a generating AI, and the generating AI can propose the optimal collection method.

[0060] The proposal department can monitor the usage status of assets, identify idle assets, and propose new uses. For example, the proposal department can monitor the frequency of asset use and operating hours, and identify assets that have not been used for a certain period as idle assets. The proposal department can also propose new uses for idle assets, such as reallocation or leasing. For example, the proposal department may propose reusing idle assets in other departments. The proposal department may also propose generating revenue by leasing idle assets. In this way, by identifying idle assets and proposing new uses, it becomes possible to effectively utilize assets. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input asset usage data into a generation AI, which can identify idle assets and propose new uses.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the assets when analyzing asset data. For example, the analysis unit can perform a detailed analysis on highly important assets. The analysis unit can also perform a simplified analysis on less important assets. The analysis unit can also evaluate the importance of assets and adjust the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the assets. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input asset importance data into a generating AI, which can then adjust the level of detail of the analysis.

[0062] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships of assets, etc. The evaluation unit can, for example, analyze the interrelationships of assets, etc., to improve the accuracy of its evaluations. The evaluation unit can also adjust the evaluation criteria by considering the relationships between assets, etc. The evaluation unit can also reflect the interrelationships of assets, etc., in its evaluations to improve their accuracy. This allows the evaluation accuracy to be improved based on the interrelationships of assets, etc. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the interrelationships of assets, etc., into a generating AI, which can then improve the accuracy of its evaluations.

[0063] The generation unit can optimize its generation algorithm by referring to past generation data when generating data. For example, the generation unit can analyze past generation data and select the optimal generation algorithm. The generation unit can also improve its generation algorithm based on past generation data. The generation unit can also improve the accuracy of its generation algorithm by referring to past generation data. As a result, the accuracy of the generation algorithm is improved by referring to past generation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past generation data into a generation AI, and the generation AI can optimize the generation algorithm.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects data on assets, etc. The data collection unit can automatically collect data using, for example, sensors. The data collection unit can also manually input data. Furthermore, the data collection unit can retrieve data from existing databases. For example, the data collection unit collects asset location information in real time using sensors. In the case of manual input, a person in charge enters asset information into the system. Data retrieval from existing databases is done via API. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can perform statistical analysis, for example. It can also analyze the data using machine learning algorithms. Furthermore, the analysis unit can recognize patterns in the data. For example, the analysis unit analyzes the frequency of asset use based on the collected data. When using machine learning algorithms, it learns from past data to predict future usage patterns. Pattern recognition of data is used for anomaly detection. Step 3: The valuation unit evaluates classification and useful life based on the data analyzed by the analysis unit. The valuation unit can, for example, define asset categories and perform classification based on those categories. It can also evaluate useful life based on statutory useful life or actual usage. Furthermore, the valuation unit can evaluate the value of assets using evaluation indicators. For example, the valuation unit classifies assets into categories such as real estate, machinery and equipment, and financial data. The evaluation of useful life is performed by comparing statutory useful life with actual usage. Evaluation indicators are used to quantify the value of assets. Step 4: The proposal department proposes procedures and solutions based on the evaluation results from the evaluation department. For example, the proposal department may propose maintenance procedures. It may also propose disposal methods. Furthermore, the proposal department may propose asset reallocation. For example, the proposal department may propose a schedule for regular maintenance of assets. Proposed disposal methods may include environmentally friendly methods. Asset reallocation aims to make effective use of idle assets. Step 5: The generation unit automatically generates relevant documents based on the procedures and solutions proposed by the proposal unit. The generation unit can generate documents using templates, for example. It can also generate documents using a generation algorithm. Furthermore, the generation unit can automatically save the generated documents. For example, the generation unit documents the proposed maintenance procedures based on templates. The generation algorithm generates the most suitable documents according to the proposal. The generated documents are automatically saved to cloud storage.

[0066] (Example of form 2) The asset management system according to an embodiment of the present invention is a system using an AI agent for managing a company's fixed assets and equipment. This asset management system prevents the loss of location of assets that become inactive or sent to warehouses due to personnel changes, retirements, changes in responsibilities, or the completion of tasks or projects. The AI ​​agent supports the classification and understanding of useful life of assets. This prevents errors in procedures and registration information, and prevents accidental disposal or loss of location. Furthermore, the AI ​​agent clarifies the National Tax Agency's fixed asset classification and information sources, preventing errors in the realistic classification and useful life of assets actually used by the company, and reducing friction with the National Tax Agency regarding differences in depreciation. In addition, to ensure the liquidity of assets within the organization and to effectively utilize items that are effectively idle, the AI ​​agent provides procedures and proposes new asset uses. For example, the AI ​​agent reduces man-hours and workload by identifying assets, determining management levels, handling procedures, proposing solutions, and verifying and documenting related materials. This allows for future changes, impacts, concerns, and risk predictions, while considering loss, misconduct, accidents, etc. Specifically, the AI ​​agent analyzes the content and scope of assets, automatically identifying affected internal documents and operational rules. It also automatically evaluates the company's level of asset management and proposes corrections and improvements. Furthermore, it automatically detects changes in regulations due to legal revisions and automatically generates comparison tables and proposed updates. This system makes it easier for employees of companies and organizations to grasp the daily realities of frequent purchases, disposals, and diverse asset items, minimizing procedural discrepancies, losses, and the burden and omissions of inventory work. By readily consulting and using the AI ​​agent, early operational checks and internal procedural adjustments become possible. The system automatically identifies assets related to standard and legal revisions, and generates improvement measures and countermeasures, reducing man-hours and increasing efficiency. As a result, the asset management system can efficiently collect, analyze, evaluate, propose, and generate data on assets.

[0067] The asset management system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, a proposal unit, and a generation unit. The collection unit collects data such as assets. The collection unit can automatically collect data using, for example, sensors. The collection unit can also manually input data. Furthermore, the collection unit can acquire data from existing databases. For example, the collection unit collects asset location information in real time using sensors. In the case of manual input, a person in charge inputs asset information into the system. Data acquisition from existing databases is performed via API. The analysis unit analyzes the data collected by the collection unit. The analysis unit can perform, for example, statistical analysis. Furthermore, the analysis unit can analyze data using machine learning algorithms. Furthermore, the analysis unit can recognize patterns in the data. For example, the analysis unit analyzes the frequency of asset use based on the collected data. When using machine learning algorithms, past data is learned to predict future usage patterns. Data pattern recognition is used for anomaly detection. The evaluation unit evaluates classification and service life based on the data analyzed by the analysis unit. The valuation unit can, for example, define asset categories and classify them accordingly. It can also evaluate useful life based on statutory useful life and actual usage. Furthermore, the valuation unit can assess the value of assets using evaluation indicators. For example, the valuation unit classifies assets into categories such as real estate, machinery and equipment, and financial data. Useful life is evaluated by comparing statutory useful life with actual usage. Evaluation indicators are used to quantify the value of assets. The proposal unit proposes procedures and solutions based on the evaluation results from the valuation unit. For example, the proposal unit can propose maintenance procedures. It can also propose disposal methods. Furthermore, the proposal unit can propose asset reallocation. For example, the proposal unit proposes a schedule for regular asset maintenance. Disposal methods may include environmentally conscious methods. Asset reallocation aims to make effective use of idle assets. The generation unit automatically generates relevant documents based on the procedures and solutions proposed by the proposal unit. For example, the generation unit can generate documents using templates.Furthermore, the generation unit can generate documents using a generation algorithm. In addition, the generation unit can automatically save the generated documents. For example, the generation unit documents proposed maintenance procedures based on a template. The generation algorithm generates the most suitable documents according to the proposed content. The generated documents are automatically saved to cloud storage. This allows the asset management system according to the embodiment to efficiently collect, analyze, evaluate, propose, and generate documents for asset data, etc.

[0068] The data collection unit collects data on assets and other related information. For example, the data collection unit can automatically collect data using sensors. Specifically, it can collect real-time location and environmental information of assets using GPS sensors, RFID tags, temperature sensors, humidity sensors, etc. This allows for accurate tracking of asset movement and storage conditions. The data collection unit can also manually input data. For example, a person in charge can periodically input asset status and usage information into the system to maintain up-to-date information. Furthermore, the data collection unit can retrieve data from existing databases. For example, it can retrieve data from an in-house ERP system or financial system via API and integrate it into the asset management system. This allows for effective utilization of existing data and maintains data consistency and accuracy. By combining these diverse data collection methods, the data collection unit achieves comprehensive asset management.

[0069] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can perform statistical analysis. Specifically, it can statistically analyze asset usage frequency, operating hours, and fluctuations in the storage environment to evaluate the asset's condition and performance. The analysis unit can also analyze data using machine learning algorithms. For example, it can learn from past usage data to predict future usage patterns and failure risks. Furthermore, the analysis unit can recognize data patterns. For instance, it can use anomaly detection algorithms to detect unusual usage patterns or environmental fluctuations, enabling early problem identification. This allows the analysis unit to analyze collected data from multiple perspectives and understand the asset's condition and risks in real time. Additionally, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and risk assessment. This supports effective asset management and optimal operation.

[0070] The valuation unit evaluates classification and useful life based on data analyzed by the analysis unit. For example, the valuation unit can define asset categories and perform classification based on those categories. Specifically, it can classify assets into categories such as real estate, machinery and equipment, and financial data, and apply management methods appropriate to the characteristics of each. The valuation unit can also evaluate useful life based on statutory useful life and actual usage conditions. For example, it can calculate the actual useful life of an asset by comparing the statutory useful life with the actual frequency of use and environmental conditions. Furthermore, the valuation unit can evaluate the value of an asset using evaluation indicators. For example, it can quantify the overall value of an asset by considering its market value, depreciation expenses, and insurance value. This allows the valuation unit to accurately assess the condition and value of assets and support appropriate management and operation.

[0071] The proposal department proposes procedures and solutions based on the evaluation results from the evaluation department. For example, the proposal department can propose maintenance procedures. Specifically, it can create a regular maintenance schedule according to the condition and usage of the assets, and propose the necessary work and timing of parts replacement. The proposal department can also propose disposal methods. For example, it can propose environmentally friendly disposal methods and recycling procedures to minimize the environmental impact associated with asset disposal. Furthermore, the proposal department can propose asset reallocation. For example, it can reallocate idle assets to other departments or projects to make effective use of assets. In this way, the proposal department can support the optimal operation and management of assets and improve the efficiency and sustainability of the company.

[0072] The generation unit automatically generates relevant documents based on the procedures and solutions proposed by the proposal unit. For example, the generation unit can generate documents using templates. Specifically, it can document proposed maintenance procedures, disposal methods, and relocation plans based on templates and provide them in a standardized format. The generation unit can also generate documents using generation algorithms. For example, it can automatically generate the most suitable documents according to the proposal, creating comprehensive documents that include all necessary information. Furthermore, the generation unit can automatically save the generated documents. For example, it can save them to cloud storage, allowing stakeholders to access them at any time. This enables the generation unit to efficiently generate documents based on proposals and support information sharing and management.

[0073] The analysis unit can analyze collected data and learn patterns in asset classification and useful life. For example, the analysis unit can define asset categories based on collected data and perform classification based on those categories. The analysis unit can also use machine learning algorithms to learn from past data and predict future usage patterns. For example, the analysis unit can analyze asset usage frequency and operating hours to learn patterns in useful life. The analysis unit can also perform data pattern recognition and anomaly detection. For example, the analysis unit can detect abnormal asset usage patterns based on collected data. This improves the accuracy of the analysis by learning patterns in asset classification and useful life. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input collected data into a generative AI, which can learn data patterns and output classification and useful life patterns.

[0074] The analysis unit can automatically refer to relevant laws and guidelines. For example, the analysis unit can automatically acquire international standards and industry standards and reflect them in the analysis. The analysis unit can also automatically detect changes in laws and guidelines and reflect them in the analysis results. For example, the analysis unit can automatically detect changes in regulations due to legal revisions and generate a comparison table of old and new versions. This improves the accuracy of the analysis by automatically referring to relevant laws and guidelines. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input data on relevant laws and guidelines into a generating AI, which can then detect changes and reflect them in the analysis results.

[0075] The proposal department can monitor the usage status of assets, identify idle assets, and propose new uses. For example, the proposal department can monitor the frequency of asset use and operating hours, and identify assets that have not been used for a certain period as idle assets. The proposal department can also propose new uses for idle assets, such as reallocation or leasing. For example, the proposal department may propose reusing idle assets in other departments. The proposal department may also propose generating revenue by leasing idle assets. In this way, by identifying idle assets and proposing new uses, it becomes possible to effectively utilize assets. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input asset usage data into a generation AI, which can identify idle assets and propose new uses.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection, such as asset data, based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. If the user is relaxed, the data collection unit can also accelerate the collection timing to collect data efficiently. If the user is in a hurry, the data collection unit can set the collection timing immediately to collect data quickly. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. This reduces the user's burden by adjusting the data collection timing 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-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of the user captured by the camera into a generating AI, which can then perform the estimation of the user's emotions.

[0077] The data collection unit can analyze the user's past data collection history and select the optimal data collection method when collecting asset data, etc. For example, the data collection unit can propose the optimal data collection method based on the data collection methods the user has used in the past. The data collection unit can also select an efficient data collection method from the user's past data collection history. The data collection unit can also analyze the user's past data collection history and propose improvements to the data collection method. For example, the data collection unit can propose the optimal data collection method based on the data collection methods the user has used in the past. The data collection unit can select an efficient data collection method from the user's past data collection history. The data collection unit can analyze the user's past data collection history and propose improvements to the data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, and the generating AI can propose the optimal data collection method.

[0078] The data collection unit can filter data such as assets based on the user's current work situation and areas of interest. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the user's current work situation. The data collection unit can also collect only the necessary data based on the user's areas of interest. The data collection unit can analyze the user's work situation and areas of interest and filter the collected data. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the user's current work situation. The data collection unit can collect only the necessary data based on the user's areas of interest. The data collection unit can analyze the user's work situation and areas of interest and filter the collected data. This allows for the collection of highly relevant data by filtering the data based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's work situation and areas of interest into a generating AI, which can then perform the filtering.

[0079] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important data. If the user is relaxed, the data collection unit can also prioritize collecting more important data. If the user is in a hurry, the data collection unit can immediately collect the most important data. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. This allows for efficient data collection by prioritizing 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data of the user captured by the camera into a generating AI, which can then perform the estimation of the user's emotions.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting asset data, etc. For example, the data collection unit prioritizes the collection of data on nearby assets, etc., based on the user's current location. The data collection unit can also collect highly relevant data by considering the user's geographical location information. The data collection unit can also suggest optimal data collection points based on the user's location information. For example, the data collection unit prioritizes the collection of data on nearby assets, etc., based on the user's current location. The data collection unit collects highly relevant data by considering the user's geographical location information. The data collection unit suggests optimal data collection points based on the user's location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0081] The data collection unit can analyze a user's social media activity and collect relevant data when collecting data such as assets. For example, the data collection unit analyzes a user's social media activity and collects data such as relevant assets. The data collection unit can also collect necessary data based on a user's statements and posts on social media. The data collection unit can also monitor a user's social media activity and collect relevant data in real time. For example, the data collection unit analyzes a user's social media activity and collects data such as relevant assets. The data collection unit collects necessary data based on a user's statements and posts on social media. The data collection unit monitors a user's social media activity and collects relevant data in real time. This allows for the collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI, which can then collect relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit may select a simple analysis method. If the user is relaxed, the analysis unit may also apply a more detailed analysis method. If the user is in a hurry, the analysis unit may also select a method to obtain analysis results quickly. For example, the analysis unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit may also record the user's voice and estimate their emotions using voice analysis technology. This improves the accuracy of the analysis by adjusting the data analysis method 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI, allowing the AI ​​to estimate the user's emotions.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of assets, etc., during the analysis. For example, the analysis unit performs a detailed analysis for assets, etc. with high importance. The analysis unit can also perform a simplified analysis for assets, etc. with low importance. The analysis unit can also evaluate the importance of assets, etc., and adjust the level of detail of the analysis. For example, the analysis unit performs a detailed analysis for assets, etc. with high importance. The analysis unit performs a simplified analysis for assets, etc. with low importance. The analysis unit evaluates the importance of assets, etc., and adjusts the level of detail of the analysis. In this way, by adjusting the level of detail of the analysis based on the importance of assets, etc., the analysis can be performed efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input asset importance data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of assets, etc., during analysis. For example, the analysis unit can select the optimal analysis algorithm depending on the category of assets, etc. The analysis unit can also apply different analysis methods for each category of assets, etc. The analysis unit can also classify the categories of assets, etc., and apply an analysis algorithm suitable for each. For example, the analysis unit can select the optimal analysis algorithm depending on the category of assets, etc. The analysis unit can apply different analysis methods for each category of assets, etc. The analysis unit can classify the categories of assets, etc., and apply an analysis algorithm suitable for each. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of assets, etc. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input asset category data into a generating AI, and the generating AI can apply the optimal analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a concise display method. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. This improves visibility by adjusting the display method of the analysis results 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI, allowing the AI ​​to estimate the user's emotions.

[0086] The analysis unit can determine the priority of analysis based on the submission dates of assets, etc., during the analysis. For example, the analysis unit will prioritize the analysis of assets, etc., whose submission dates are approaching. The analysis unit can also adjust the analysis schedule based on the submission dates. The analysis unit can also determine the priority of analysis considering the submission dates. For example, the analysis unit will prioritize the analysis of assets, etc., whose submission dates are approaching. The analysis unit will adjust the analysis schedule based on the submission dates. The analysis unit will determine the priority of analysis considering the submission dates. This allows for efficient analysis by determining the priority of analysis based on the submission dates of assets, etc. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input asset submission date data into a generating AI, and the generating AI can determine the priority of analysis.

[0087] The analysis unit can adjust the order of analysis based on the relationships between assets, etc., during the analysis. For example, the analysis unit evaluates the relationships between assets, etc., and determines the order of analysis. The analysis unit can also prioritize the analysis of highly related assets, etc. The analysis unit can also adjust the analysis schedule based on the relationships between assets, etc. For example, the analysis unit evaluates the relationships between assets, etc., and determines the order of analysis. The analysis unit prioritizes the analysis of highly related assets, etc. The analysis unit adjusts the analysis schedule based on the relationships between assets, etc. This allows for efficient analysis by adjusting the order of analysis based on the relationships between assets, etc. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input asset relationship data into a generating AI, and the generating AI can adjust the order of analysis.

[0088] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is stressed, the evaluation unit may relax the evaluation criteria. If the user is relaxed, the evaluation unit may also apply strict evaluation criteria. If the user is in a hurry, the evaluation unit may set criteria for a quick evaluation. For example, the evaluation unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The evaluation unit may also record the user's voice and estimate their emotions using voice analysis technology. This improves the accuracy of the evaluation by adjusting the evaluation criteria 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 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 evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0089] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships of assets, etc. The evaluation unit can, for example, analyze the interrelationships of assets, etc., to improve the accuracy of its evaluations. The evaluation unit can also adjust the evaluation criteria by considering the relationships between assets, etc. The evaluation unit can also improve the accuracy by reflecting the interrelationships of assets, etc., in its evaluations. For example, the evaluation unit can analyze the interrelationships of assets, etc., to improve the accuracy of its evaluations. The evaluation unit can adjust the evaluation criteria by considering the relationships between assets, etc. The evaluation unit can improve the accuracy by reflecting the interrelationships of assets, etc., in its evaluations. This allows the evaluation unit to improve the accuracy of its evaluations based on the interrelationships of assets, etc. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the interrelationships of assets, etc., into a generating AI, and the generating AI can improve the accuracy of its evaluations.

[0090] The evaluation unit can perform evaluations based on the attribute information of the submitter of assets, etc., during the evaluation process. For example, the evaluation unit can adjust the evaluation criteria based on the submitter's attribute information. The evaluation unit can also perform evaluations considering the submitter's past evaluation history. The evaluation unit can also improve the accuracy of the evaluation by analyzing the submitter's attribute information. For example, the evaluation unit can adjust the evaluation criteria based on the submitter's attribute information. The evaluation unit can perform evaluations considering the submitter's past evaluation history. The evaluation unit can improve the accuracy of the evaluation by analyzing the submitter's attribute information. As a result, the accuracy of the evaluation is improved by considering the submitter's attribute information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the submitter's attribute information data into a generating AI, and the generating AI can perform the evaluation.

[0091] The evaluation unit can estimate the user's emotions and adjust the display order of evaluation results based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit will display important evaluation results first. If the user is relaxed, the evaluation unit can also display detailed evaluation results sequentially. If the user is in a hurry, the evaluation unit can also display concise evaluation results first. For example, the evaluation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The evaluation unit can also record the user's voice and estimate their emotions using voice analysis technology. This improves readability by adjusting the display order of evaluation results 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 evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user image data captured by a camera into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0092] The evaluation unit can perform evaluations based on the geographical distribution of assets, etc. The evaluation unit can, for example, analyze the geographical distribution of assets, etc. and adjust the evaluation criteria. The evaluation unit can also determine the priority of evaluations based on geographical distribution. The evaluation unit can also improve the accuracy of evaluations by considering the geographical distribution of assets, etc. For example, the evaluation unit can analyze the geographical distribution of assets, etc. and adjust the evaluation criteria. The evaluation unit can determine the priority of evaluations based on geographical distribution. The evaluation unit can improve the accuracy of evaluations by considering the geographical distribution of assets, etc. As a result, the accuracy of evaluations is improved by considering the geographical distribution of assets, etc. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input geographical distribution data of assets, etc. into a generating AI, and the generating AI can perform the evaluation.

[0093] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on assets, etc., during the evaluation process. For example, the evaluation unit can adjust the evaluation criteria by referring to relevant literature on assets, etc. The evaluation unit can also improve the accuracy of its evaluation based on relevant literature. The evaluation unit can also automatically refer to relevant literature when evaluating assets, etc. For example, the evaluation unit can adjust the evaluation criteria by referring to relevant literature on assets, etc. The evaluation unit improves the accuracy of its evaluation based on relevant literature. The evaluation unit automatically refers to relevant literature when evaluating assets, etc. This improves the accuracy of the evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input relevant literature data on assets, etc., into a generating AI, and the generating AI can perform the evaluation.

[0094] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will offer simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can offer more detailed suggestions. If the user is in a hurry, the suggestion unit can offer suggestions that can be quickly implemented. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. This improves the acceptability of suggestions by adjusting their presentation 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal unit can input image data of the user captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0095] The proposal department can adjust the level of detail of a proposal based on the importance of the procedures and solutions. For example, the proposal department will provide detailed proposals for procedures and solutions of high importance. The proposal department can also provide simplified proposals for procedures and solutions of low importance. The proposal department can also evaluate the importance of procedures and solutions and adjust the level of detail of the proposal. For example, the proposal department will provide detailed proposals for procedures and solutions of high importance. The proposal department will provide simplified proposals for procedures and solutions of low importance. The proposal department will evaluate the importance of procedures and solutions and adjust the level of detail of the proposal. This allows for efficient proposal creation by adjusting the level of detail of proposals based on the importance of procedures and solutions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input importance data of procedures and solutions into a generating AI, and the generating AI can adjust the level of detail of the proposal.

[0096] The proposal unit can apply different proposal algorithms depending on the category of procedure and solution when making a proposal. For example, the proposal unit can select the optimal proposal algorithm depending on the category of procedure or solution. The proposal unit can also apply different proposal methods for each category of procedure or solution. The proposal unit can also classify the categories of procedures and solutions and apply a proposal algorithm appropriate for each. For example, the proposal unit can select the optimal proposal algorithm depending on the category of procedure or solution. The proposal unit can apply different proposal methods for each category of procedure or solution. The proposal unit can classify the categories of procedures and solutions and apply a proposal algorithm appropriate for each. This improves the accuracy of the proposal by applying the optimal proposal algorithm according to the category of procedure or solution. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category data of procedures and solutions into a generating AI, and the generating AI can apply the optimal proposal algorithm.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion unit can make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can make a longer suggestion with more detailed explanations. If the user is in a hurry, the suggestion unit can make a short, actionable suggestion. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. This improves the acceptability of the suggestion by adjusting its length 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input image data of the user captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0098] The proposal department can determine the priority of proposals based on the submission timing of procedures and solutions when submitting proposals. For example, the proposal department will prioritize proposals for procedures and solutions with upcoming submission deadlines. The proposal department can also adjust the proposal schedule based on the submission deadlines. The proposal department can also determine the priority of proposals considering the submission deadlines. For example, the proposal department will prioritize proposals for procedures and solutions with upcoming submission deadlines. The proposal department will adjust the proposal schedule based on the submission deadlines. The proposal department will determine the priority of proposals considering the submission deadlines. This allows for efficient proposal submission by determining the priority of proposals based on the submission timing of procedures and solutions. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the submission timing of procedures and solutions into a generating AI, which can then determine the priority of proposals.

[0099] The proposal department can adjust the order of proposals based on the relevance of procedures and solutions when making proposals. For example, the proposal department evaluates the relevance of procedures and solutions and determines the order of proposals. The proposal department can also prioritize proposing procedures and solutions that are highly relevant. The proposal department can also adjust the schedule of proposals based on the relevance of procedures and solutions. For example, the proposal department evaluates the relevance of procedures and solutions and determines the order of proposals. The proposal department prioritizes proposing procedures and solutions that are highly relevant. The proposal department adjusts the schedule of proposals based on the relevance of procedures and solutions. This allows for efficient proposals by adjusting the order of proposals based on the relevance of procedures and solutions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the relevance of procedures and solutions into a generating AI, which can then adjust the order of proposals.

[0100] The generation unit can estimate the user's emotions and adjust the method of generating relevant materials based on the estimated user emotions. For example, if the user is stressed, the generation unit will generate simple and easy-to-understand materials. If the user is relaxed, the generation unit can also generate detailed materials. If the user is in a hurry, the generation unit can also generate materials that can be quickly executed. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate their emotions using voice analysis technology. This improves the readability of the materials by adjusting the method of generating relevant materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input image data of the user captured by the camera into the generation AI, and have the generation AI perform the estimation of the user's emotions.

[0101] The generation unit can optimize its generation algorithm by referring to past generation data when generating data. For example, the generation unit can analyze past generation data and select the optimal generation algorithm. The generation unit can also improve the generation algorithm based on past generation data. The generation unit can also improve the accuracy of the generation algorithm by referring to past generation data. For example, the generation unit can analyze past generation data and select the optimal generation algorithm. The generation unit can improve the generation algorithm based on past generation data. The generation unit can improve the accuracy of the generation algorithm by referring to past generation data. As a result, the accuracy of the generation algorithm is improved by referring to past generation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past generation data into a generation AI, and the generation AI can optimize the generation algorithm.

[0102] The generation unit can apply different generation methods to each category of assets, etc., when generating data. For example, the generation unit can select the optimal generation method according to the category of assets, etc. The generation unit can also apply different generation methods to each category of assets, etc. The generation unit can also classify the categories of assets, etc., and apply a generation method suitable for each. For example, the generation unit can select the optimal generation method according to the category of assets, etc. The generation unit can apply different generation methods to each category of assets, etc. The generation unit can classify the categories of assets, etc., and apply a generation method suitable for each. By applying the optimal generation method to each category of assets, etc., the accuracy of the generated data is improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input asset category data into a generation AI, and the generation AI can apply the optimal generation method.

[0103] The generation unit can estimate the user's emotions and determine the priority of the materials to be generated based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating important materials. If the user is relaxed, the generation unit can also generate detailed materials sequentially. If the user is in a hurry, the generation unit can also generate concise materials first. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate their emotions using voice analysis technology. This allows for efficient material generation by determining the priority of the materials to be generated according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input image data of the user captured by the camera into the generation AI, and have the generation AI perform the estimation of the user's emotions.

[0104] The generation unit can weight the generated data based on the submission dates of assets, etc., when generating documents. For example, the generation unit can prioritize generating documents for assets, etc., whose submission dates are approaching. The generation unit can also weight the generated data based on the submission dates. The generation unit can also determine the priority of the generated data by considering the submission dates. For example, the generation unit can prioritize generating documents for assets, etc., whose submission dates are approaching. The generation unit weights the generated data based on the submission dates. The generation unit determines the priority of the generated data by considering the submission dates. This allows for efficient document generation by weighting the generated data based on the submission dates of assets, etc. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input asset submission date data into a generation AI, and the generation AI can weight the generated data.

[0105] The generation unit can improve the accuracy of data generation by referring to relevant literature on assets, etc., when generating data. For example, the generation unit can improve the accuracy of generated data by referring to literature related to assets, etc. The generation unit can also improve the accuracy of generated data based on relevant literature. The generation unit can also automatically refer to relevant literature when generating data on assets, etc. For example, the generation unit can improve the accuracy of generated data by referring to literature related to assets, etc. The generation unit can improve the accuracy of generated data based on relevant literature. The generation unit automatically refers to relevant literature when generating data on assets, etc. This improves the accuracy of generated data by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input data on relevant literature on assets, etc., into a generation AI, which can then improve the accuracy of the generation.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The data collection unit can analyze the user's past data collection history when collecting asset data, etc., and select the optimal collection method. For example, the data collection unit can propose the optimal collection method based on the collection methods the user has used in the past. The data collection unit can also select an efficient collection method from the user's past data collection history. The data collection unit can also analyze the user's past data collection history and propose improvements to the collection method. In this way, the optimal collection method can be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input the user's past data collection history into a generating AI, and the generating AI can propose the optimal collection method.

[0108] The analysis unit can estimate the user's emotions when analyzing asset data, etc., and adjust the analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can select a simple analysis method. If the user is relaxed, the analysis unit can also apply a more detailed analysis method. If the user is in a hurry, the analysis unit can also select a method that allows for quick analysis results. By adjusting the data analysis method according to the user's emotions, the accuracy of the analysis is improved. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0109] The evaluation unit can estimate the user's emotions when evaluating assets, etc., and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is stressed, the evaluation unit may relax the evaluation criteria. If the user is relaxed, the evaluation unit may also apply strict evaluation criteria. If the user is in a hurry, the evaluation unit may also set criteria for a quick evaluation. By adjusting the evaluation criteria according to the user's emotions, the accuracy of the evaluation is improved. 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 evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0110] The proposal department can monitor the usage status of assets, identify idle assets, and propose new uses. For example, the proposal department can monitor the frequency of asset use and operating hours, and identify assets that have not been used for a certain period as idle assets. The proposal department can also propose new uses for idle assets, such as reallocation or leasing. For example, the proposal department may propose reusing idle assets in other departments. The proposal department may also propose generating revenue by leasing idle assets. In this way, by identifying idle assets and proposing new uses, it becomes possible to effectively utilize assets. Some or all of the above processing in the proposal department may be performed using, for example, a generation AI, or not using a generation AI. For example, the proposal department can input asset usage data into a generation AI, which can identify idle assets and propose new uses.

[0111] The data collection unit can estimate the user's emotions and adjust the timing of data collection, such as asset data, based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. If the user is relaxed, the data collection unit can also advance the collection timing to collect data efficiently. If the user is in a hurry, the data collection unit can set the collection timing immediately to collect data quickly. In this way, the user's burden is reduced by adjusting the data collection timing 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0112] The analysis unit can adjust the level of detail of the analysis based on the importance of the assets when analyzing asset data. For example, the analysis unit can perform a detailed analysis on highly important assets. The analysis unit can also perform a simplified analysis on less important assets. The analysis unit can also evaluate the importance of assets and adjust the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the assets. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input asset importance data into a generating AI, which can then adjust the level of detail of the analysis.

[0113] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships of assets, etc. The evaluation unit can, for example, analyze the interrelationships of assets, etc., to improve the accuracy of its evaluations. The evaluation unit can also adjust the evaluation criteria by considering the relationships between assets, etc. The evaluation unit can also reflect the interrelationships of assets, etc., in its evaluations to improve their accuracy. This allows the evaluation accuracy to be improved based on the interrelationships of assets, etc. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the interrelationships of assets, etc., into a generating AI, which can then improve the accuracy of its evaluations.

[0114] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will offer simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can offer more detailed suggestions. If the user is in a hurry, the suggestion unit can offer suggestions that can be quickly implemented. By adjusting the way suggestions are presented according to the user's emotions, the acceptance of the suggestions is improved. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user image data captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0115] The generation unit can optimize its generation algorithm by referring to past generation data when generating data. For example, the generation unit can analyze past generation data and select the optimal generation algorithm. The generation unit can also improve its generation algorithm based on past generation data. The generation unit can also improve the accuracy of its generation algorithm by referring to past generation data. As a result, the accuracy of the generation algorithm is improved by referring to past generation data. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past generation data into a generation AI, and the generation AI can optimize the generation algorithm.

[0116] The generation unit can estimate the user's emotions and determine the priority of the materials to be generated based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating important materials. If the user is relaxed, the generation unit can also generate detailed materials sequentially. If the user is in a hurry, the generation unit can also generate concise materials first. This allows for efficient material generation by determining the priority of materials to be generated according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, or not using AI. For example, the generation unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects data on assets, etc. The data collection unit can automatically collect data using, for example, sensors. The data collection unit can also manually input data. Furthermore, the data collection unit can retrieve data from existing databases. For example, the data collection unit collects asset location information in real time using sensors. In the case of manual input, a person in charge enters asset information into the system. Data retrieval from existing databases is done via API. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can perform statistical analysis, for example. It can also analyze the data using machine learning algorithms. Furthermore, the analysis unit can recognize patterns in the data. For example, the analysis unit analyzes the frequency of asset use based on the collected data. When using machine learning algorithms, it learns from past data to predict future usage patterns. Pattern recognition of data is used for anomaly detection. Step 3: The valuation unit evaluates classification and useful life based on the data analyzed by the analysis unit. The valuation unit can, for example, define asset categories and perform classification based on those categories. It can also evaluate useful life based on statutory useful life or actual usage. Furthermore, the valuation unit can evaluate the value of assets using evaluation indicators. For example, the valuation unit classifies assets into categories such as real estate, machinery and equipment, and financial data. The evaluation of useful life is performed by comparing statutory useful life with actual usage. Evaluation indicators are used to quantify the value of assets. Step 4: The proposal department proposes procedures and solutions based on the evaluation results from the evaluation department. For example, the proposal department may propose maintenance procedures. It may also propose disposal methods. Furthermore, the proposal department may propose asset reallocation. For example, the proposal department may propose a schedule for regular maintenance of assets. Proposed disposal methods may include environmentally friendly methods. Asset reallocation aims to make effective use of idle assets. Step 5: The generation unit automatically generates relevant documents based on the procedures and solutions proposed by the proposal unit. The generation unit can generate documents using templates, for example. It can also generate documents using a generation algorithm. Furthermore, the generation unit can automatically save the generated documents. For example, the generation unit documents the proposed maintenance procedures based on templates. The generation algorithm generates the most suitable documents according to the proposal. The generated documents are automatically saved to cloud storage.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, proposal unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects asset data using the sensors and manual input device of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the classification and useful life of the assets based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes procedures and countermeasures based on the evaluation results using the specific processing unit 290 of the data processing unit 12. The generation unit automatically generates related materials based on the procedures and countermeasures proposed by the specific processing unit 290 of the data processing unit 12. 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.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, proposal unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects asset data using the sensors of the smart glasses 214 or a manual input device. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the classification and useful life of the assets based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes procedures and countermeasures based on the evaluation results by the specific processing unit 290 of the data processing unit 12. The generation unit automatically generates related materials based on the procedures and countermeasures proposed by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, proposal unit, and generation unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the collection unit collects asset data using the sensors and manual input device of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the classification and useful life of assets based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes procedures and countermeasures based on the evaluation results using the specific processing unit 290 of the data processing unit 12. The generation unit automatically generates related materials based on the procedures and countermeasures proposed by the specific processing unit 290 of the data processing unit 12. 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.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, proposal unit, and generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects asset data using the sensors and manual input devices of the robot 414. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The evaluation unit evaluates the classification and useful life of the assets based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes procedures and countermeasures based on the evaluation results using the specific processing unit 290 of the data processing unit 12. The generation unit automatically generates related materials based on the procedures and countermeasures proposed by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A data collection unit that collects asset data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that evaluates classification and service life based on the data analyzed by the analysis unit, Based on the results evaluated by the aforementioned evaluation unit, a proposal unit proposes procedures and countermeasures, The system includes a generation unit that automatically generates related documents based on the procedures and countermeasures proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The collected data is analyzed to learn patterns in asset classification and useful life. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Automatically references relevant laws and guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We monitor the usage status of assets, identify idle assets, and propose new ways to utilize them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of asset and other data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting asset and other data, the system analyzes the user's past data collection history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting asset and other 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 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting asset and other data, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting asset and other data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of assets, etc. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of assets, etc. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the timing of asset submissions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between assets, etc. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, During the evaluation process, improve the accuracy of the evaluation based on the interrelationships between assets, etc. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During the evaluation process, the assessment will be based on the attribute information of the submitter of the assets, etc. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, The system estimates the user's emotions and adjusts the display order of evaluation results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the valuation process, the valuation will be based on the geographical distribution of assets, etc. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation process, improve the accuracy of the evaluation based on relevant literature regarding assets, etc. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the procedures and solutions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of procedure and solution. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When submitting a proposal, we will prioritize the proposals based on the timing of submission of procedures and countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the procedures and solutions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is We estimate the user's emotions and adjust how relevant materials are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating data, the generation algorithm is optimized based on past generated data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is When generating documents, different generation methods are applied for each category of asset, etc. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is It estimates the user's emotions and determines the priority of the materials to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is When generating documents, the generated data is weighted based on the submission date of assets, etc. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is When generating data, improve the accuracy of the generation based on relevant literature such as assets. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 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. A data collection unit that collects asset data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that evaluates classification and service life based on the data analyzed by the analysis unit, Based on the results evaluated by the aforementioned evaluation unit, a proposal unit proposes procedures and countermeasures, The system includes a generation unit that automatically generates related documents based on the procedures and countermeasures proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit, The collected data is analyzed to learn patterns in asset classification and useful life. The system according to feature 1.

3. The aforementioned analysis unit, Automatically references relevant laws and guidelines. The system according to feature 1.

4. The aforementioned proposal section is, We monitor the usage status of assets, identify idle assets, and propose new ways to utilize them. The system according to feature 1.

5. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of asset and other data collection based on the estimated user sentiment. The system according to feature 1.

6. The aforementioned collection unit is When collecting asset and other data, the system analyzes the user's past data collection history to select the most suitable collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting asset and other data, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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