system

Abstract

The system according to this embodiment aims to understand the characteristics and condition of specimens, propose appropriate conservation measures, and automate the management and tracking of specimens. [Solution] The system according to the embodiment comprises a characteristic recognition unit, an evaluation proposal unit, an information management unit, and a management tracking unit. The characteristic recognition unit recognizes the characteristics and condition of the specimen. The evaluation proposal unit evaluates the preservation status based on the characteristics and condition recognized by the characteristic recognition unit and proposes appropriate preservation measures. The information management unit centrally manages specimen information based on the preservation measures proposed by the evaluation proposal unit. The management tracking unit automates the management and tracking of specimens based on the information managed by the information management 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the process of grasping the characteristics and states of specimens and proposing appropriate preservation measures is performed manually, there are problems of low efficiency and difficulty in specimen management and tracking.

[0005] The system according to the embodiment aims to grasp the characteristics and states of specimens, propose appropriate preservation measures, and automate specimen management and tracking.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a characteristics recognition unit, an evaluation proposal unit, an information management unit, and a management and tracking unit. The characteristics recognition unit recognizes the characteristics and condition of the specimen. The evaluation proposal unit evaluates the preservation status based on the characteristics and condition recognized by the characteristics recognition unit and proposes appropriate preservation measures. The information management unit centrally manages specimen information based on the preservation measures proposed by the evaluation proposal unit. The management and tracking unit automates the management and tracking of specimens based on the information managed by the information management unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the characteristics and condition of specimens, propose appropriate conservation measures, and automate the management and tracking of specimens. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied 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 reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[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) An AI system according to an embodiment of the present invention is a system that automates the preservation, tracking, and classification of specimens held by universities, museums, and research institutions. This AI system uses image recognition technology to understand the characteristics and condition of specimens, and uses machine learning to evaluate the preservation status and propose appropriate preservation measures. As a result, the management and tracking of specimens are automated, significantly reducing time and effort. Furthermore, long-term preservation of specimens is guaranteed by monitoring their condition and proposing appropriate preservation measures. For example, the AI ​​system takes an image of a specimen, and the AI ​​analyzes the image to identify the characteristics and condition of the specimen. For example, it identifies the shape, color, and surface condition of the specimen. This allows for an accurate understanding of the characteristics and condition of the specimen. Next, the AI ​​system evaluates the preservation status using machine learning. Based on past data, the AI ​​predicts the preservation status of the specimen and proposes appropriate preservation measures. For example, if the specimen is deteriorating, it proposes appropriate temperature and humidity control methods. This optimizes the preservation status of the specimen and guarantees long-term preservation. Furthermore, the AI ​​system centrally manages specimen information by linking with a database. The AI ​​registers and manages information such as the characteristics, condition, and preservation status of specimens in the database. This allows for centralized management and efficient tracking of specimen information. This system improves the efficiency and accuracy of specimen management. Automating specimen management and tracking significantly reduces time and effort. Furthermore, monitoring the specimen's condition and suggesting appropriate conservation measures ensures the long-term preservation of specimens. For example, if a specimen is deteriorating, the AI ​​can suggest appropriate conservation measures to optimize its preservation. This AI system can improve efficiency and accuracy in specimen management at universities, museums, and research institutions, thereby enhancing the quality of scientific research and education. For instance, automating specimen management and tracking allows researchers to accurately understand the condition of specimens and conduct research more efficiently. Optimizing specimen preservation also ensures the long-term preservation of valuable specimens. Thus, the AI ​​system automates specimen characterization, preservation assessment, information management, and management tracking, enabling efficient specimen management.

[0029] The AI ​​system according to this embodiment comprises a characteristic recognition unit, an evaluation proposal unit, an information management unit, and a management tracking unit. The characteristic recognition unit recognizes the characteristics and condition of the specimen. For example, the characteristic recognition unit takes an image of the specimen, and the AI ​​analyzes the image to identify the characteristics and condition of the specimen. For example, the characteristic recognition unit identifies the shape, color, surface condition, etc. of the specimen. For example, the characteristic recognition unit can recognize the characteristics and condition of the specimen using image recognition technology. The evaluation proposal unit evaluates the preservation status based on the characteristics and condition recognized by the characteristic recognition unit and proposes appropriate preservation measures. For example, the evaluation proposal unit predicts the preservation status of the specimen based on past data and proposes appropriate preservation measures. For example, if the specimen is deteriorating, the evaluation proposal unit proposes appropriate temperature and humidity control methods. For example, the evaluation proposal unit can evaluate the preservation status using machine learning and propose appropriate preservation measures. The information management unit centrally manages specimen information based on the preservation measures proposed by the evaluation proposal unit. For example, the information management unit registers and manages information such as the characteristics and condition of the specimen and the preservation status in a database. For example, the Information Management Department can centrally manage and efficiently track specimen information. The Information Management Department can centrally manage specimen information, for example, by linking with a database. The Management and Tracking Department automates the management and tracking of specimens based on the information managed by the Information Management Department. The Management and Tracking Department significantly reduces time and effort by automating the management and tracking of specimens, for example. The Management and Tracking Department monitors the condition of specimens and proposes appropriate conservation measures. The Management and Tracking Department can centrally manage and efficiently track specimen information, for example. As a result, the AI ​​system according to the embodiment can automate the understanding of specimen characteristics, evaluation of preservation status, information management, and management tracking, thereby achieving efficient specimen management.

[0030] The characteristic recognition unit understands the characteristics and condition of the specimen. For example, the characteristic recognition unit takes an image of the specimen, and the AI ​​analyzes the image to identify the characteristics and condition of the specimen. Specifically, it uses image recognition technology to identify the shape, color, and surface condition of the specimen. For example, an image of the specimen is taken with a high-resolution camera and input into the AI. The AI ​​uses an image analysis algorithm based on deep learning to analyze the shape, color, and surface condition of the specimen in detail. Shape analysis measures the contour and dimensions of the specimen, and color analysis identifies the color tone and color distribution of the specimen. Surface condition analysis detects minute scratches, stains, and signs of deterioration on the surface of the specimen. This allows the characteristic recognition unit to accurately understand the detailed characteristics and condition of the specimen. Furthermore, in order to quantitatively evaluate the characteristics and condition of the specimen, the characteristic recognition unit can take multiple images and perform analysis from different angles and under different lighting conditions. This allows for a multifaceted evaluation of the characteristics and condition of the specimen, providing more accurate information. The characteristic recognition unit stores these analysis results in a database so that they can be used by subsequent evaluation proposal units and information management units. This allows the characteristic recognition unit to grasp the characteristics and condition of the specimen in detail, supporting efficient specimen management.

[0031] The evaluation and proposal unit evaluates the preservation status based on the characteristics and condition identified by the characteristics recognition unit and proposes appropriate preservation measures. Specifically, it predicts the preservation status of specimens based on past data and proposes appropriate preservation measures. For example, if a specimen is deteriorating, it proposes appropriate temperature and humidity control methods. The evaluation and proposal unit can use machine learning to evaluate the preservation status and propose appropriate preservation measures. The machine learning model learns from past preservation data and specimen characteristic data to predict the deterioration pattern of the specimen and the impact of the preservation environment. For example, if a specimen is deteriorating, the evaluation and proposal unit proposes optimal temperature and humidity settings and provides specific means to improve the preservation environment. In addition, the evaluation and proposal unit can use a rule-based system to select appropriate preservation measures according to the characteristics and condition of the specimen. This allows the evaluation and proposal unit to propose more accurate preservation measures by combining machine learning and rule-based approaches. Furthermore, the evaluation and proposal unit can evaluate the effectiveness of the proposed preservation measures and modify them as needed. This allows the evaluation and proposal unit to continuously monitor the preservation status of specimens and provide optimal preservation measures.

[0032] The Information Management Department centrally manages specimen information based on the conservation measures proposed by the Evaluation and Proposal Department. Specifically, it registers and manages information such as the characteristics, condition, and preservation status of specimens in a database. The Information Management Department can centrally manage and efficiently track specimen information. For example, it can register data on the characteristics, condition, and preservation status of specimens in the database and search and update it as needed. The Information Management Department can centrally manage specimen information by linking with the database. This allows the Information Management Department to efficiently manage specimen information and quickly provide necessary information. Furthermore, the Information Management Department can visualize specimen information and provide tools for evaluating preservation status and the effectiveness of conservation measures. For example, it can display the preservation status of specimens in graphs and charts, allowing for visual confirmation of changes in the preservation environment and the effectiveness of conservation measures. This allows the Information Management Department to efficiently manage specimen information and quickly grasp the evaluation of preservation status and the effectiveness of conservation measures.

[0033] The Management and Tracking Unit automates the management and tracking of specimens based on information managed by the Information Management Unit. Specifically, automating the management and tracking of specimens significantly reduces time and effort. For example, the Management and Tracking Unit monitors the condition of specimens and proposes appropriate conservation measures. The Management and Tracking Unit can centrally manage and efficiently track specimen information. For example, it can monitor the condition of specimens in real time and immediately propose conservation measures if an abnormality is detected. The Management and Tracking Unit can register specimen information in a database and perform searches and updates as needed. This allows the Management and Tracking Unit to efficiently manage specimen information and quickly grasp the evaluation of preservation status and the effectiveness of conservation measures. Furthermore, the Management and Tracking Unit can visualize specimen information and provide tools for evaluating preservation status and the effectiveness of conservation measures. For example, it can display the preservation status of specimens in graphs and charts, allowing for visual confirmation of changes in the preservation environment and the effectiveness of conservation measures. This allows the Management and Tracking Unit to efficiently manage specimen information and quickly grasp the evaluation of preservation status and the effectiveness of conservation measures.

[0034] The characteristic recognition unit can capture images of specimens, and AI can analyze these images to identify the characteristics and condition of the specimens. For example, the characteristic recognition unit can scan an image of a specimen and save it as image data. Then, the characteristic recognition unit analyzes the image data using image recognition technology to identify the characteristics and condition of the specimens. For example, the characteristic recognition unit can identify the shape, color, and surface condition of the specimens. The characteristic recognition unit can also use image recognition technology to understand the characteristics and condition of specimens. For example, the characteristic recognition unit can use an image recognition algorithm to identify the characteristics and condition of the specimens. This allows for accurate understanding of the characteristics and condition of specimens through image analysis. Some or all of the above-described processes in the characteristic recognition unit may be performed using AI, or they may not be performed using AI. For example, the characteristic recognition unit can input the image data of the specimens into a generating AI, and have the generating AI perform the identification of characteristics and condition from the image data.

[0035] The evaluation and proposal unit can predict the preservation status of specimens based on past data and propose appropriate preservation measures. For example, the evaluation and proposal unit can collect past preservation data and predict the preservation status using machine learning algorithms. For example, if the specimen is deteriorating, the evaluation and proposal unit can propose appropriate temperature and humidity control methods. The evaluation and proposal unit can also predict the preservation status based on past data and propose appropriate preservation measures. For example, the evaluation and proposal unit can analyze past preservation data and predict changes in the preservation status. This allows it to predict the preservation status using past data and propose appropriate preservation measures. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input past preservation data into a generating AI and have the generating AI perform the prediction of the preservation status and propose preservation measures.

[0036] The Information Management Department can register and manage information such as the characteristics, condition, and preservation status of specimens in a database. For example, the Information Management Department can register information such as the characteristics, condition, and preservation status of specimens in a database and manage it centrally. For example, the Information Management Department can register specimen information in a database and track it efficiently. The Information Management Department can also centrally manage specimen information by linking with the database. For example, the Information Management Department manages and tracks specimens based on the information registered in the database. This allows for centralized management and efficient tracking of specimen information. Some or all of the above processes in the Information Management Department may be performed using AI, for example, or without AI. For example, the Information Management Department can input specimen information into a generating AI and have the generating AI perform the registration and management of the information.

[0037] The management and tracking unit can automate the management and tracking of specimens. For example, by automating the management and tracking of specimens, the management and tracking unit significantly reduces time and effort. For instance, the management and tracking unit monitors the condition of specimens and proposes appropriate conservation measures. Furthermore, the management and tracking unit can centrally manage and efficiently track specimen information. For example, the management and tracking unit registers specimen information in a database and performs management and tracking. This automates and efficiently manages and tracks specimens. Some or all of the above processes in the management and tracking unit may be performed using AI, or not. For example, the management and tracking unit can input specimen information into a generating AI and have the generating AI perform the automated management and tracking.

[0038] The evaluation and proposal unit can propose appropriate temperature and humidity control methods if the specimen is deteriorating. For example, the evaluation and proposal unit can analyze the deterioration status of the specimen and propose the optimal temperature and humidity control method. The evaluation and proposal unit can also propose appropriate preservation measures if the specimen is deteriorating. For example, the evaluation and proposal unit proposes appropriate preservation measures based on the deterioration status of the specimen. This allows for the proposal of appropriate preservation measures for specimens that are deteriorating. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input the deterioration status of the specimen into a generating AI and have the generating AI propose appropriate temperature and humidity control methods.

[0039] The characteristic recognition unit can improve the accuracy of characteristic recognition by considering past state changes of the sample. For example, the characteristic recognition unit can adjust the characteristic recognition algorithm based on past state change data to improve accuracy. For example, the characteristic recognition unit can analyze the past degradation patterns of the sample and consider them when characteristic recognition. The characteristic recognition unit can also optimize the timing of characteristic recognition using past state change data. This improves the accuracy of characteristic recognition by considering past state changes. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input past state change data into a generating AI and have the generating AI perform the characteristic recognition accuracy improvement.

[0040] The characteristic recognition unit can apply different image analysis algorithms depending on the type of specimen. For example, for plant specimens, the characteristic recognition unit can apply an algorithm that analyzes the shape and color of the leaves. For example, for animal specimens, the characteristic recognition unit can apply an algorithm that analyzes the texture of the fur and the shape of the body. Furthermore, for mineral specimens, the characteristic recognition unit can apply an algorithm that analyzes the surface luster and crystal structure. This improves the accuracy of characteristic recognition by applying an image analysis algorithm appropriate to the type of specimen. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input image data appropriate to the type of specimen into a generating AI and have the generating AI execute the application of an image analysis algorithm.

[0041] The characteristic recognition unit can perform characteristic recognition while considering the geographical distribution of the sample. For example, the characteristic recognition unit adjusts the characteristic recognition algorithm based on the geographical distribution data of the sample. For example, the characteristic recognition unit optimizes the timing of characteristic recognition based on geographical distribution. The characteristic recognition unit can also use geographical distribution data to determine the priority of characteristic recognition. This improves the accuracy of characteristic recognition by considering geographical distribution. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input geographical distribution data into a generating AI and have the generating AI perform improvements to the accuracy of characteristic recognition.

[0042] The characteristic recognition unit can improve the accuracy of characteristic recognition by referring to relevant literature for the sample. For example, the characteristic recognition unit adjusts the characteristic recognition algorithm based on relevant literature for the sample. For example, the characteristic recognition unit optimizes the timing of characteristic recognition using data from relevant literature. The characteristic recognition unit can also refer to relevant literature to determine the priority of characteristic recognition. This improves the accuracy of characteristic recognition by referring to relevant literature. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input relevant literature data into a generating AI and have the generating AI perform the characteristic recognition accuracy improvement.

[0043] The evaluation and proposal unit can improve the accuracy of its proposals by considering the preservation history of the specimen when proposing preservation measures. For example, the evaluation and proposal unit adjusts the preservation measure proposal algorithm based on the preservation history data. For example, the evaluation and proposal unit analyzes the preservation history and proposes the optimal preservation measure. The evaluation and proposal unit can also optimize the timing of preservation measure proposals using the preservation history data. This improves the accuracy of preservation measure proposals by considering the preservation history. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input preservation history data into a generating AI and have the generating AI perform the task of improving the accuracy of preservation measure proposals.

[0044] The evaluation and proposal unit can apply different proposal algorithms depending on the type of specimen when proposing conservation measures. For example, for plant specimens, the evaluation and proposal unit can apply an algorithm that proposes appropriate temperature and humidity control methods. For example, for animal specimens, the evaluation and proposal unit can apply an algorithm that proposes appropriate storage containers and preservation methods. Furthermore, for mineral specimens, the evaluation and proposal unit can also apply an algorithm that proposes appropriate storage environments and handling methods. This improves the accuracy of proposed conservation measures by applying proposal algorithms tailored to the type of specimen. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input data corresponding to the type of specimen into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0045] The evaluation and proposal unit can consider the specimen's preservation environment when proposing preservation measures. For example, the evaluation and proposal unit can adjust the preservation measure proposal algorithm based on preservation environment data. For example, the evaluation and proposal unit can analyze the preservation environment and propose the optimal preservation measure. The evaluation and proposal unit can also optimize the timing of preservation measure proposals using preservation environment data. This improves the accuracy of preservation measure proposals by considering the preservation environment. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input preservation environment data into a generating AI and have the generating AI perform the task of improving the accuracy of preservation measure proposals.

[0046] The evaluation and proposal unit can improve the accuracy of its proposals by referring to relevant literature for the sample when proposing maintenance measures. For example, the evaluation and proposal unit adjusts the algorithm for proposing maintenance measures based on relevant literature for the sample. For example, the evaluation and proposal unit optimizes the timing of proposals for maintenance measures using data from relevant literature. The evaluation and proposal unit can also determine the priority of maintenance measures by referring to relevant literature. This improves the accuracy of proposals for maintenance measures by referring to relevant literature. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of proposals for maintenance measures.

[0047] The Information Management Department can improve the accuracy of information management by considering the past management history of samples during information management. For example, the Information Management Department can adjust the information management algorithm based on management history data. For example, the Information Management Department can analyze management history and provide the optimal information management method. The Information Management Department can also optimize the timing of information management using management history data. This improves the accuracy of information management by considering past management history. Some or all of the above processes in the Information Management Department may be performed using AI, for example, or without AI. For example, the Information Management Department can input management history data into a generating AI and have the generating AI perform the improvement of information management accuracy.

[0048] The Information Management Department can manage information while considering the geographical distribution of the sample. For example, the Information Management Department can adjust its information management algorithm based on geographical distribution data. For example, the Information Management Department can analyze geographical distribution and provide the optimal information management method. The Information Management Department can also optimize the timing of information management using geographical distribution data. This improves the accuracy of information management by considering geographical distribution. Some or all of the above processes in the Information Management Department may be performed using AI, for example, or without AI. For example, the Information Management Department can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of information management.

[0049] The management tracking unit can improve tracking accuracy by considering the sample's past tracking history during management tracking. For example, the management tracking unit adjusts the management tracking algorithm based on tracking history data. For example, the management tracking unit analyzes the tracking history and provides the optimal management tracking method. The management tracking unit can also optimize the timing of management tracking using tracking history data. This improves the accuracy of management tracking by considering past tracking history. Some or all of the above processing in the management tracking unit may be performed using AI, for example, or without AI. For example, the management tracking unit can input tracking history data into a generating AI and have the generating AI perform the improvement of management tracking accuracy.

[0050] The management tracking unit can perform tracking while considering the geographical distribution of the sample. For example, the management tracking unit can adjust the management tracking algorithm based on geographical distribution data. For example, the management tracking unit can analyze the geographical distribution and provide the optimal management tracking method. The management tracking unit can also optimize the timing of management tracking using geographical distribution data. This improves the accuracy of management tracking by considering geographical distribution. Some or all of the above processing in the management tracking unit may be performed using AI, for example, or without AI. For example, the management tracking unit can input geographical distribution data into a generating AI and have the generating AI perform improvements to the accuracy of management tracking.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The characteristic assessment unit can also analyze the acoustic properties of a specimen to understand its characteristics and condition. For example, by irradiating the specimen with sound waves and analyzing the reflected sound, the characteristic assessment unit can identify the specimen's internal structure and material. Furthermore, the characteristic assessment unit can measure the specimen's vibration characteristics and evaluate its condition. In addition, the characteristic assessment unit can register the specimen's acoustic properties in a database and compare them with other specimens. This allows for a more accurate understanding of the specimen's characteristics and condition.

[0053] The evaluation proposal unit can consider the chemical properties of a specimen when evaluating its preservation status. For example, the evaluation proposal unit can analyze chemical substances adhering to the surface of the specimen to assess its preservation status. It can also measure the chemical components contained within the specimen to assess its preservation status. Furthermore, the evaluation proposal unit can register the chemical properties of a specimen in a database and compare them with other specimens. This allows for a more accurate assessment of the specimen's preservation status.

[0054] The Information Management Department can consider the physical characteristics of specimens when managing their information. For example, it can measure the weight and dimensions of specimens and register them in a database. It can also measure the hardness and elasticity of specimens and evaluate their preservation status. Furthermore, it can register the physical characteristics of specimens in a database and compare them with other specimens. This allows for more accurate management of specimen information.

[0055] The management and tracking unit can consider the biological characteristics of specimens when managing and tracking them. For example, the management and tracking unit can analyze the DNA of specimens and evaluate their preservation status. It can also detect the presence of microorganisms in specimens and evaluate their preservation status. Furthermore, the management and tracking unit can register the biological characteristics of specimens in a database and compare them with other specimens. This allows for more accurate management and tracking of specimens.

[0056] The characteristic analysis unit can also analyze the thermal properties of a sample to understand its characteristics and condition. For example, by applying heat to a sample and analyzing the reaction, the characteristic analysis unit can identify the internal structure and material of the sample. Furthermore, the characteristic analysis unit can measure the thermal conductivity and specific heat of a sample to evaluate its condition. In addition, the characteristic analysis unit can register the thermal properties of a sample in a database and compare them with other samples. This allows for a more accurate understanding of the sample's characteristics and condition.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The characteristic recognition unit understands the characteristics and condition of the specimen. For example, it takes an image of the specimen, and the AI ​​analyzes the image to identify the characteristics and condition of the specimen. The characteristic recognition unit can identify the shape, color, surface condition, etc. of the specimen and understand its characteristics and condition using image recognition technology. Step 2: The evaluation and proposal unit evaluates the preservation status based on the characteristics and condition identified by the characteristics recognition unit and proposes appropriate preservation measures. For example, it predicts the preservation status of the specimen based on past data and proposes appropriate preservation measures. If the specimen is deteriorating, the evaluation and proposal unit proposes appropriate temperature and humidity control methods. The evaluation and proposal unit can use machine learning to evaluate the preservation status and propose appropriate preservation measures. Step 3: The Information Management Department centrally manages specimen information based on the conservation measures proposed by the Evaluation Proposal Department. For example, it registers and manages information such as the characteristics, condition, and preservation status of specimens in a database. The Information Management Department can centrally manage and efficiently track specimen information. The Information Management Department can centrally manage specimen information by linking with the database. Step 4: The Management and Tracking Unit automates the management and tracking of specimens based on the information managed by the Information Management Unit. For example, automating the management and tracking of specimens significantly reduces time and effort. The Management and Tracking Unit monitors the condition of specimens and proposes appropriate conservation measures. The Management and Tracking Unit can centrally manage and efficiently track specimen information.

[0059] (Example of form 2) An AI system according to an embodiment of the present invention is a system that automates the preservation, tracking, and classification of specimens held by universities, museums, and research institutions. This AI system uses image recognition technology to understand the characteristics and condition of specimens, and uses machine learning to evaluate the preservation status and propose appropriate preservation measures. As a result, the management and tracking of specimens are automated, significantly reducing time and effort. Furthermore, long-term preservation of specimens is guaranteed by monitoring their condition and proposing appropriate preservation measures. For example, the AI ​​system takes an image of a specimen, and the AI ​​analyzes the image to identify the characteristics and condition of the specimen. For example, it identifies the shape, color, and surface condition of the specimen. This allows for an accurate understanding of the characteristics and condition of the specimen. Next, the AI ​​system evaluates the preservation status using machine learning. Based on past data, the AI ​​predicts the preservation status of the specimen and proposes appropriate preservation measures. For example, if the specimen is deteriorating, it proposes appropriate temperature and humidity control methods. This optimizes the preservation status of the specimen and guarantees long-term preservation. Furthermore, the AI ​​system centrally manages specimen information by linking with a database. The AI ​​registers and manages information such as the characteristics, condition, and preservation status of specimens in the database. This allows for centralized management and efficient tracking of specimen information. This system improves the efficiency and accuracy of specimen management. Automating specimen management and tracking significantly reduces time and effort. Furthermore, monitoring the specimen's condition and suggesting appropriate conservation measures ensures the long-term preservation of specimens. For example, if a specimen is deteriorating, the AI ​​can suggest appropriate conservation measures to optimize its preservation. This AI system can improve efficiency and accuracy in specimen management at universities, museums, and research institutions, thereby enhancing the quality of scientific research and education. For instance, automating specimen management and tracking allows researchers to accurately understand the condition of specimens and conduct research more efficiently. Optimizing specimen preservation also ensures the long-term preservation of valuable specimens. Thus, the AI ​​system automates specimen characterization, preservation assessment, information management, and management tracking, enabling efficient specimen management.

[0060] The AI ​​system according to this embodiment comprises a characteristic recognition unit, an evaluation proposal unit, an information management unit, and a management tracking unit. The characteristic recognition unit recognizes the characteristics and condition of the specimen. For example, the characteristic recognition unit takes an image of the specimen, and the AI ​​analyzes the image to identify the characteristics and condition of the specimen. For example, the characteristic recognition unit identifies the shape, color, surface condition, etc. of the specimen. For example, the characteristic recognition unit can recognize the characteristics and condition of the specimen using image recognition technology. The evaluation proposal unit evaluates the preservation status based on the characteristics and condition recognized by the characteristic recognition unit and proposes appropriate preservation measures. For example, the evaluation proposal unit predicts the preservation status of the specimen based on past data and proposes appropriate preservation measures. For example, if the specimen is deteriorating, the evaluation proposal unit proposes appropriate temperature and humidity control methods. For example, the evaluation proposal unit can evaluate the preservation status using machine learning and propose appropriate preservation measures. The information management unit centrally manages specimen information based on the preservation measures proposed by the evaluation proposal unit. For example, the information management unit registers and manages information such as the characteristics and condition of the specimen and the preservation status in a database. For example, the Information Management Department can centrally manage and efficiently track specimen information. The Information Management Department can centrally manage specimen information, for example, by linking with a database. The Management and Tracking Department automates the management and tracking of specimens based on the information managed by the Information Management Department. The Management and Tracking Department significantly reduces time and effort by automating the management and tracking of specimens, for example. The Management and Tracking Department monitors the condition of specimens and proposes appropriate conservation measures. The Management and Tracking Department can centrally manage and efficiently track specimen information, for example. As a result, the AI ​​system according to the embodiment can automate the understanding of specimen characteristics, evaluation of preservation status, information management, and management tracking, thereby achieving efficient specimen management.

[0061] The characteristic recognition unit understands the characteristics and condition of the specimen. For example, the characteristic recognition unit takes an image of the specimen, and the AI ​​analyzes the image to identify the characteristics and condition of the specimen. Specifically, it uses image recognition technology to identify the shape, color, and surface condition of the specimen. For example, an image of the specimen is taken with a high-resolution camera and input into the AI. The AI ​​uses an image analysis algorithm based on deep learning to analyze the shape, color, and surface condition of the specimen in detail. Shape analysis measures the contour and dimensions of the specimen, and color analysis identifies the color tone and color distribution of the specimen. Surface condition analysis detects minute scratches, stains, and signs of deterioration on the surface of the specimen. This allows the characteristic recognition unit to accurately understand the detailed characteristics and condition of the specimen. Furthermore, in order to quantitatively evaluate the characteristics and condition of the specimen, the characteristic recognition unit can take multiple images and perform analysis from different angles and under different lighting conditions. This allows for a multifaceted evaluation of the characteristics and condition of the specimen, providing more accurate information. The characteristic recognition unit stores these analysis results in a database so that they can be used by subsequent evaluation proposal units and information management units. This allows the characteristic recognition unit to grasp the characteristics and condition of the specimen in detail, supporting efficient specimen management.

[0062] The evaluation and proposal unit evaluates the preservation status based on the characteristics and condition identified by the characteristics recognition unit and proposes appropriate preservation measures. Specifically, it predicts the preservation status of specimens based on past data and proposes appropriate preservation measures. For example, if a specimen is deteriorating, it proposes appropriate temperature and humidity control methods. The evaluation and proposal unit can use machine learning to evaluate the preservation status and propose appropriate preservation measures. The machine learning model learns from past preservation data and specimen characteristic data to predict the deterioration pattern of the specimen and the impact of the preservation environment. For example, if a specimen is deteriorating, the evaluation and proposal unit proposes optimal temperature and humidity settings and provides specific means to improve the preservation environment. In addition, the evaluation and proposal unit can use a rule-based system to select appropriate preservation measures according to the characteristics and condition of the specimen. This allows the evaluation and proposal unit to propose more accurate preservation measures by combining machine learning and rule-based approaches. Furthermore, the evaluation and proposal unit can evaluate the effectiveness of the proposed preservation measures and modify them as needed. This allows the evaluation and proposal unit to continuously monitor the preservation status of specimens and provide optimal preservation measures.

[0063] The Information Management Department centrally manages specimen information based on the conservation measures proposed by the Evaluation and Proposal Department. Specifically, it registers and manages information such as the characteristics, condition, and preservation status of specimens in a database. The Information Management Department can centrally manage and efficiently track specimen information. For example, it can register data on the characteristics, condition, and preservation status of specimens in the database and search and update it as needed. The Information Management Department can centrally manage specimen information by linking with the database. This allows the Information Management Department to efficiently manage specimen information and quickly provide necessary information. Furthermore, the Information Management Department can visualize specimen information and provide tools for evaluating preservation status and the effectiveness of conservation measures. For example, it can display the preservation status of specimens in graphs and charts, allowing for visual confirmation of changes in the preservation environment and the effectiveness of conservation measures. This allows the Information Management Department to efficiently manage specimen information and quickly grasp the evaluation of preservation status and the effectiveness of conservation measures.

[0064] The Management and Tracking Unit automates the management and tracking of specimens based on information managed by the Information Management Unit. Specifically, automating the management and tracking of specimens significantly reduces time and effort. For example, the Management and Tracking Unit monitors the condition of specimens and proposes appropriate conservation measures. The Management and Tracking Unit can centrally manage and efficiently track specimen information. For example, it can monitor the condition of specimens in real time and immediately propose conservation measures if an abnormality is detected. The Management and Tracking Unit can register specimen information in a database and perform searches and updates as needed. This allows the Management and Tracking Unit to efficiently manage specimen information and quickly grasp the evaluation of preservation status and the effectiveness of conservation measures. Furthermore, the Management and Tracking Unit can visualize specimen information and provide tools for evaluating preservation status and the effectiveness of conservation measures. For example, it can display the preservation status of specimens in graphs and charts, allowing for visual confirmation of changes in the preservation environment and the effectiveness of conservation measures. This allows the Management and Tracking Unit to efficiently manage specimen information and quickly grasp the evaluation of preservation status and the effectiveness of conservation measures.

[0065] The characteristic recognition unit can capture images of specimens, and AI can analyze these images to identify the characteristics and condition of the specimens. For example, the characteristic recognition unit can scan an image of a specimen and save it as image data. Then, the characteristic recognition unit analyzes the image data using image recognition technology to identify the characteristics and condition of the specimens. For example, the characteristic recognition unit can identify the shape, color, and surface condition of the specimens. The characteristic recognition unit can also use image recognition technology to understand the characteristics and condition of specimens. For example, the characteristic recognition unit can use an image recognition algorithm to identify the characteristics and condition of the specimens. This allows for accurate understanding of the characteristics and condition of specimens through image analysis. Some or all of the above-described processes in the characteristic recognition unit may be performed using AI, or they may not be performed using AI. For example, the characteristic recognition unit can input the image data of the specimens into a generating AI, and have the generating AI perform the identification of characteristics and condition from the image data.

[0066] The evaluation and proposal unit can predict the preservation status of specimens based on past data and propose appropriate preservation measures. For example, the evaluation and proposal unit can collect past preservation data and predict the preservation status using machine learning algorithms. For example, if the specimen is deteriorating, the evaluation and proposal unit can propose appropriate temperature and humidity control methods. The evaluation and proposal unit can also predict the preservation status based on past data and propose appropriate preservation measures. For example, the evaluation and proposal unit can analyze past preservation data and predict changes in the preservation status. This allows it to predict the preservation status using past data and propose appropriate preservation measures. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input past preservation data into a generating AI and have the generating AI perform the prediction of the preservation status and propose preservation measures.

[0067] The Information Management Department can register and manage information such as the characteristics, condition, and preservation status of specimens in a database. For example, the Information Management Department can register information such as the characteristics, condition, and preservation status of specimens in a database and manage it centrally. For example, the Information Management Department can register specimen information in a database and track it efficiently. The Information Management Department can also centrally manage specimen information by linking with the database. For example, the Information Management Department manages and tracks specimens based on the information registered in the database. This allows for centralized management and efficient tracking of specimen information. Some or all of the above processes in the Information Management Department may be performed using AI, for example, or without AI. For example, the Information Management Department can input specimen information into a generating AI and have the generating AI perform the registration and management of the information.

[0068] The management and tracking unit can automate the management and tracking of specimens. For example, by automating the management and tracking of specimens, the management and tracking unit significantly reduces time and effort. For instance, the management and tracking unit monitors the condition of specimens and proposes appropriate conservation measures. Furthermore, the management and tracking unit can centrally manage and efficiently track specimen information. For example, the management and tracking unit registers specimen information in a database and performs management and tracking. This automates and efficiently manages and tracks specimens. Some or all of the above processes in the management and tracking unit may be performed using AI, or not. For example, the management and tracking unit can input specimen information into a generating AI and have the generating AI perform the automated management and tracking.

[0069] The evaluation and proposal unit can propose appropriate temperature and humidity control methods if the specimen is deteriorating. For example, the evaluation and proposal unit can analyze the deterioration status of the specimen and propose the optimal temperature and humidity control method. The evaluation and proposal unit can also propose appropriate preservation measures if the specimen is deteriorating. For example, the evaluation and proposal unit proposes appropriate preservation measures based on the deterioration status of the specimen. This allows for the proposal of appropriate preservation measures for specimens that are deteriorating. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input the deterioration status of the specimen into a generating AI and have the generating AI propose appropriate temperature and humidity control methods.

[0070] The characteristic recognition unit can estimate the user's emotions and adjust the timing of characteristic recognition of the sample based on the estimated user emotions. For example, if the user is stressed, the characteristic recognition unit can reduce the frequency of characteristic recognition to alleviate the user's burden. For example, if the user is relaxed, the characteristic recognition unit can increase the frequency of characteristic recognition to collect more detailed data. The characteristic recognition unit can also adjust the timing of characteristic recognition to quickly acquire data if the user is in a hurry. This allows the timing of characteristic recognition to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the timing of characteristic recognition.

[0071] The characteristic recognition unit can improve the accuracy of characteristic recognition by considering past state changes of the sample. For example, the characteristic recognition unit can adjust the characteristic recognition algorithm based on past state change data to improve accuracy. For example, the characteristic recognition unit can analyze the past degradation patterns of the sample and consider them when characteristic recognition. The characteristic recognition unit can also optimize the timing of characteristic recognition using past state change data. This improves the accuracy of characteristic recognition by considering past state changes. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input past state change data into a generating AI and have the generating AI perform the characteristic recognition accuracy improvement.

[0072] The characteristic recognition unit can apply different image analysis algorithms depending on the type of specimen. For example, for plant specimens, the characteristic recognition unit can apply an algorithm that analyzes the shape and color of the leaves. For example, for animal specimens, the characteristic recognition unit can apply an algorithm that analyzes the texture of the fur and the shape of the body. Furthermore, for mineral specimens, the characteristic recognition unit can apply an algorithm that analyzes the surface luster and crystal structure. This improves the accuracy of characteristic recognition by applying an image analysis algorithm appropriate to the type of specimen. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input image data appropriate to the type of specimen into a generating AI and have the generating AI execute the application of an image analysis algorithm.

[0073] The characteristic recognition unit can estimate the user's emotions and determine the priority of characteristic recognition based on the estimated user emotions. For example, if the user is stressed, the characteristic recognition unit will postpone the recognition of less important samples. For example, if the user is relaxed, the characteristic recognition unit will prioritize the recognition of high-importance samples. The characteristic recognition unit can also adjust the priority of characteristic recognition and quickly acquire data if the user is in a hurry. This allows the priority of characteristic recognition to be determined 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 characteristic recognition unit may be performed using AI, for example, or not using AI. For example, the characteristic recognition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determination of characteristic recognition priority.

[0074] The characteristic recognition unit can perform characteristic recognition while considering the geographical distribution of the sample. For example, the characteristic recognition unit adjusts the characteristic recognition algorithm based on the geographical distribution data of the sample. For example, the characteristic recognition unit optimizes the timing of characteristic recognition based on geographical distribution. The characteristic recognition unit can also use geographical distribution data to determine the priority of characteristic recognition. This improves the accuracy of characteristic recognition by considering geographical distribution. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input geographical distribution data into a generating AI and have the generating AI perform improvements to the accuracy of characteristic recognition.

[0075] The characteristic recognition unit can improve the accuracy of characteristic recognition by referring to relevant literature for the sample. For example, the characteristic recognition unit adjusts the characteristic recognition algorithm based on relevant literature for the sample. For example, the characteristic recognition unit optimizes the timing of characteristic recognition using data from relevant literature. The characteristic recognition unit can also refer to relevant literature to determine the priority of characteristic recognition. This improves the accuracy of characteristic recognition by referring to relevant literature. Some or all of the above processing in the characteristic recognition unit may be performed using AI, for example, or without AI. For example, the characteristic recognition unit can input relevant literature data into a generating AI and have the generating AI perform the characteristic recognition accuracy improvement.

[0076] The evaluation and suggestion unit can estimate the user's emotions and adjust the suggested methods for safeguarding based on the estimated emotions. For example, if the user is stressed, the evaluation and suggestion unit can provide simple suggestions to reduce the user's burden. For example, if the user is relaxed, the evaluation and suggestion unit can provide detailed suggestions to deepen the user's understanding. The evaluation and suggestion unit can also quickly suggest safeguarding if the user is in a hurry. This allows the suggested methods for safeguarding to be adjusted 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 evaluation and suggestion unit may be performed using AI or not. For example, the evaluation and suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the suggested methods for safeguarding.

[0077] The evaluation and proposal unit can improve the accuracy of its proposals by considering the preservation history of the specimen when proposing preservation measures. For example, the evaluation and proposal unit adjusts the preservation measure proposal algorithm based on the preservation history data. For example, the evaluation and proposal unit analyzes the preservation history and proposes the optimal preservation measure. The evaluation and proposal unit can also optimize the timing of preservation measure proposals using the preservation history data. This improves the accuracy of preservation measure proposals by considering the preservation history. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input preservation history data into a generating AI and have the generating AI perform the task of improving the accuracy of preservation measure proposals.

[0078] The evaluation and proposal unit can apply different proposal algorithms depending on the type of specimen when proposing conservation measures. For example, for plant specimens, the evaluation and proposal unit can apply an algorithm that proposes appropriate temperature and humidity control methods. For example, for animal specimens, the evaluation and proposal unit can apply an algorithm that proposes appropriate storage containers and preservation methods. Furthermore, for mineral specimens, the evaluation and proposal unit can also apply an algorithm that proposes appropriate storage environments and handling methods. This improves the accuracy of proposed conservation measures by applying proposal algorithms tailored to the type of specimen. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input data corresponding to the type of specimen into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0079] The evaluation and suggestion unit can estimate the user's emotions and determine the priority of safety measures based on the estimated emotions. For example, if the user is stressed, the evaluation and suggestion unit will postpone less important safety measures. For example, if the user is relaxed, the evaluation and suggestion unit will prioritize more important safety measures. The evaluation and suggestion unit can also adjust the priority of safety measures and make suggestions quickly if the user is in a hurry. This allows the priority of safety measures to be determined 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 evaluation and suggestion unit may be performed using AI or not using AI. For example, the evaluation and suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determination of safety measure priorities.

[0080] The evaluation and proposal unit can consider the specimen's preservation environment when proposing preservation measures. For example, the evaluation and proposal unit can adjust the preservation measure proposal algorithm based on preservation environment data. For example, the evaluation and proposal unit can analyze the preservation environment and propose the optimal preservation measure. The evaluation and proposal unit can also optimize the timing of preservation measure proposals using preservation environment data. This improves the accuracy of preservation measure proposals by considering the preservation environment. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input preservation environment data into a generating AI and have the generating AI perform the task of improving the accuracy of preservation measure proposals.

[0081] The evaluation and proposal unit can improve the accuracy of its proposals by referring to relevant literature for the sample when proposing maintenance measures. For example, the evaluation and proposal unit adjusts the algorithm for proposing maintenance measures based on relevant literature for the sample. For example, the evaluation and proposal unit optimizes the timing of proposals for maintenance measures using data from relevant literature. The evaluation and proposal unit can also determine the priority of maintenance measures by referring to relevant literature. This improves the accuracy of proposals for maintenance measures by referring to relevant literature. Some or all of the above processing in the evaluation and proposal unit may be performed using AI, for example, or without AI. For example, the evaluation and proposal unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of proposals for maintenance measures.

[0082] The information management unit can estimate the user's emotions and adjust the information management method based on the estimated emotions. For example, if the user is stressed, the information management unit can provide a simple information management method to reduce the user's burden. For example, if the user is relaxed, the information management unit can provide a detailed information management method to deepen the user's understanding. The information management unit can also provide a method for quickly managing information if the user is in a hurry. This allows the information management method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information management unit may be performed using AI, or not using AI. For example, the information management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the information management method.

[0083] The Information Management Department can improve the accuracy of information management by considering the past management history of samples during information management. For example, the Information Management Department can adjust the information management algorithm based on management history data. For example, the Information Management Department can analyze management history and provide the optimal information management method. The Information Management Department can also optimize the timing of information management using management history data. This improves the accuracy of information management by considering past management history. Some or all of the above processes in the Information Management Department may be performed using AI, for example, or without AI. For example, the Information Management Department can input management history data into a generating AI and have the generating AI perform the improvement of information management accuracy.

[0084] The information management department can estimate the user's emotions and determine the priority of information management based on the estimated emotions. For example, if the user is stressed, the information management department will postpone less important information management. For example, if the user is relaxed, the information management department will prioritize more important information management. The information management department can also adjust the priority of information management and manage it quickly if the user is in a hurry. This allows the information management department to determine the priority of information management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information management department may be performed using AI, or not using AI. For example, the information management department can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine the priority of information management.

[0085] The Information Management Department can manage information while considering the geographical distribution of the sample. For example, the Information Management Department can adjust its information management algorithm based on geographical distribution data. For example, the Information Management Department can analyze geographical distribution and provide the optimal information management method. The Information Management Department can also optimize the timing of information management using geographical distribution data. This improves the accuracy of information management by considering geographical distribution. Some or all of the above processes in the Information Management Department may be performed using AI, for example, or without AI. For example, the Information Management Department can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the accuracy of information management.

[0086] The management tracking unit can estimate the user's emotions and adjust the management tracking method based on the estimated user emotions. For example, if the user is stressed, the management tracking unit can provide a simple management tracking method to reduce the user's burden. For example, if the user is relaxed, the management tracking unit can provide a detailed management tracking method to deepen the user's understanding. The management tracking unit can also provide a method for rapid management tracking if the user is in a hurry. This allows the management tracking method to be adjusted 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 management tracking unit may be performed using AI, for example, or not using AI. For example, the management tracking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the management tracking method.

[0087] The management tracking unit can improve tracking accuracy by considering the sample's past tracking history during management tracking. For example, the management tracking unit adjusts the management tracking algorithm based on tracking history data. For example, the management tracking unit analyzes the tracking history and provides the optimal management tracking method. The management tracking unit can also optimize the timing of management tracking using tracking history data. This improves the accuracy of management tracking by considering past tracking history. Some or all of the above processing in the management tracking unit may be performed using AI, for example, or without AI. For example, the management tracking unit can input tracking history data into a generating AI and have the generating AI perform the improvement of management tracking accuracy.

[0088] The management tracking unit can estimate the user's emotions and determine the priority of management tracking based on the estimated user emotions. For example, if the user is stressed, the management tracking unit will postpone less important management tracking. For example, if the user is relaxed, the management tracking unit will prioritize more important management tracking. The management tracking unit can also adjust the priority of management tracking to track quickly if the user is in a hurry. This allows the management tracking priority to be determined 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 management tracking unit may be performed using AI or not using AI. For example, the management tracking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determination of management tracking priority.

[0089] The management tracking unit can perform tracking while considering the geographical distribution of the sample. For example, the management tracking unit can adjust the management tracking algorithm based on geographical distribution data. For example, the management tracking unit can analyze the geographical distribution and provide the optimal management tracking method. The management tracking unit can also optimize the timing of management tracking using geographical distribution data. This improves the accuracy of management tracking by considering geographical distribution. Some or all of the above processing in the management tracking unit may be performed using AI, for example, or without AI. For example, the management tracking unit can input geographical distribution data into a generating AI and have the generating AI perform improvements to the accuracy of management tracking.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The characteristic assessment unit can also analyze the acoustic properties of a specimen to understand its characteristics and condition. For example, by irradiating the specimen with sound waves and analyzing the reflected sound, the characteristic assessment unit can identify the specimen's internal structure and material. Furthermore, the characteristic assessment unit can measure the specimen's vibration characteristics and evaluate its condition. In addition, the characteristic assessment unit can register the specimen's acoustic properties in a database and compare them with other specimens. This allows for a more accurate understanding of the specimen's characteristics and condition.

[0092] The evaluation proposal unit can consider the chemical properties of a specimen when evaluating its preservation status. For example, the evaluation proposal unit can analyze chemical substances adhering to the surface of the specimen to assess its preservation status. It can also measure the chemical components contained within the specimen to assess its preservation status. Furthermore, the evaluation proposal unit can register the chemical properties of a specimen in a database and compare them with other specimens. This allows for a more accurate assessment of the specimen's preservation status.

[0093] The Information Management Department can consider the physical characteristics of specimens when managing their information. For example, it can measure the weight and dimensions of specimens and register them in a database. It can also measure the hardness and elasticity of specimens and evaluate their preservation status. Furthermore, it can register the physical characteristics of specimens in a database and compare them with other specimens. This allows for more accurate management of specimen information.

[0094] The management and tracking unit can consider the biological characteristics of specimens when managing and tracking them. For example, the management and tracking unit can analyze the DNA of specimens and evaluate their preservation status. It can also detect the presence of microorganisms in specimens and evaluate their preservation status. Furthermore, the management and tracking unit can register the biological characteristics of specimens in a database and compare them with other specimens. This allows for more accurate management and tracking of specimens.

[0095] The characteristic analysis unit can also analyze the thermal properties of a sample to understand its characteristics and condition. For example, by applying heat to a sample and analyzing the reaction, the characteristic analysis unit can identify the internal structure and material of the sample. Furthermore, the characteristic analysis unit can measure the thermal conductivity and specific heat of a sample to evaluate its condition. In addition, the characteristic analysis unit can register the thermal properties of a sample in a database and compare them with other samples. This allows for a more accurate understanding of the sample's characteristics and condition.

[0096] The characteristic assessment unit can estimate the user's emotions and adjust the method of characteristic assessment of the sample based on the estimated user emotions. For example, if the user is stressed, the characteristic assessment unit will perform characteristic assessment in a simplified manner to reduce the user's burden. If the user is relaxed, the characteristic assessment unit will perform characteristic assessment in a more detailed manner to collect more data. Also, if the user is in a hurry, the characteristic assessment unit can choose a method for rapid characteristic assessment. In this way, the characteristic assessment method can be adjusted according to the user's emotions.

[0097] The evaluation and suggestion unit can estimate the user's emotions and adjust the suggested safety measures based on those emotions. For example, if the user is stressed, the evaluation and suggestion unit will provide concise suggestions to reduce the user's burden. If the user is relaxed, the evaluation and suggestion unit will provide detailed suggestions to deepen the user's understanding. Furthermore, if the user is in a hurry, the evaluation and suggestion unit can provide suggestions quickly. This allows for the adjustment of suggested safety measures according to the user's emotions.

[0098] The information management department can estimate the user's emotions and adjust the information management interface based on those emotions. For example, if the user is stressed, the information management department can provide a simple interface to reduce the user's burden. If the user is relaxed, the information management department can provide a detailed interface to deepen the user's understanding. Furthermore, if the user is in a hurry, the information management department can provide an interface that allows for quick information management. This allows the information management interface to be adjusted according to the user's emotions.

[0099] The management tracking unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the management tracking unit provides a simple notification method to reduce the user's burden. If the user is relaxed, the management tracking unit provides a detailed notification method to deepen the user's understanding. Also, if the user is in a hurry, the management tracking unit can provide a method to provide a quick notification. In this way, the management tracking notification method can be adjusted according to the user's emotions.

[0100] The characteristics assessment unit can estimate the user's emotions and adjust the display method of the characteristics assessment results based on the estimated emotions. For example, if the user is stressed, the characteristics assessment unit will display the results concisely to reduce the user's burden. If the user is relaxed, the characteristics assessment unit will display the results in detail to deepen the user's understanding. Also, if the user is in a hurry, the characteristics assessment unit can choose a method to display the results quickly. In this way, the display method of the characteristics assessment results can be adjusted according to the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The characteristic recognition unit understands the characteristics and condition of the specimen. For example, it takes an image of the specimen, and the AI ​​analyzes the image to identify the characteristics and condition of the specimen. The characteristic recognition unit can identify the shape, color, surface condition, etc. of the specimen and understand its characteristics and condition using image recognition technology. Step 2: The evaluation and proposal unit evaluates the preservation status based on the characteristics and condition identified by the characteristics recognition unit and proposes appropriate preservation measures. For example, it predicts the preservation status of the specimen based on past data and proposes appropriate preservation measures. If the specimen is deteriorating, the evaluation and proposal unit proposes appropriate temperature and humidity control methods. The evaluation and proposal unit can use machine learning to evaluate the preservation status and propose appropriate preservation measures. Step 3: The Information Management Department centrally manages specimen information based on the conservation measures proposed by the Evaluation Proposal Department. For example, it registers and manages information such as the characteristics, condition, and preservation status of specimens in a database. The Information Management Department can centrally manage and efficiently track specimen information. The Information Management Department can centrally manage specimen information by linking with the database. Step 4: The Management and Tracking Unit automates the management and tracking of specimens based on the information managed by the Information Management Unit. For example, automating the management and tracking of specimens significantly reduces time and effort. The Management and Tracking Unit monitors the condition of specimens and proposes appropriate conservation measures. The Management and Tracking Unit can centrally manage and efficiently track specimen information.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the characteristic recognition unit, evaluation proposal unit, information management unit, and management tracking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the characteristic recognition unit uses the camera 42 of the smart device 14 to capture an image of the specimen, and the control unit 46A analyzes the image to identify the characteristics and condition of the specimen. The evaluation proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, predicts the preservation status of the specimen based on past data, and proposes appropriate preservation measures. The information management unit registers specimen information in the database 24 of the data processing unit 12 and manages it centrally. The management tracking unit can automate the management and tracking of specimens in the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the characteristic recognition unit, evaluation proposal unit, information management unit, and management tracking unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the characteristic recognition unit uses the camera 42 of the smart glasses 214 to capture an image of the specimen, and the control unit 46A analyzes the image to identify the characteristics and condition of the specimen. The evaluation proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which predicts the preservation status of the specimen based on past data and proposes appropriate preservation measures. The information management unit registers specimen information in the database 24 of the data processing unit 12 and manages it centrally. The management tracking unit can automate the management and tracking of specimens, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0138] Each of the multiple elements described above, including the characteristic recognition unit, evaluation proposal unit, information management unit, and management tracking unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the characteristic recognition unit uses the camera 42 of the headset terminal 314 to capture an image of the specimen, and the control unit 46A analyzes the image to identify the characteristics and condition of the specimen. The evaluation proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which predicts the preservation status of the specimen based on past data and proposes appropriate preservation measures. The information management unit registers specimen information in the database 24 of the data processing unit 12 and manages it centrally. The management tracking unit can automate the management and tracking of specimens by, for example, the control unit 46A of the headset terminal 314. 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[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 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.

[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 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).

[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] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the characteristic recognition unit, evaluation proposal unit, information management unit, and management tracking unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the characteristic recognition unit uses the camera 42 of the robot 414 to capture images of the specimen and the control unit 46A analyzes the images to identify the characteristics and condition of the specimen. The evaluation proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, predicts the preservation status of the specimen based on past data, and proposes appropriate preservation measures. The information management unit registers specimen information in the database 24 of the data processing unit 12 and manages it centrally. The management tracking unit can automate the management and tracking of specimens by the control unit 46A of the robot 414. 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A characteristic recognition unit that grasps the characteristics and condition of the specimen, Based on the characteristics and conditions identified by the characteristic recognition unit, an evaluation and proposal unit evaluates the preservation status and proposes appropriate preservation measures. An information management unit centrally manages specimen information based on the preservation measures proposed by the aforementioned evaluation proposal unit, The system includes a management and tracking unit that automates the management and tracking of specimens based on information managed by the aforementioned information management unit. A system characterized by the following features. (Note 2) The characteristic recognition unit is, Images of the specimen are taken, and AI analyzes those images to identify the specimen's characteristics and condition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned evaluation proposal unit, Based on past data, we predict the preservation status of specimens and propose appropriate conservation measures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned information management department, Information such as the characteristics, condition, and preservation status of specimens is registered and managed in a database. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management and tracking unit is: Automate the management and tracking of specimens. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned evaluation proposal unit, If the specimen is deteriorating, we will suggest appropriate temperature and humidity control methods. The system described in Appendix 3, characterized by the features described herein. (Note 7) The characteristic recognition unit is, We estimate the user's emotions and adjust the timing of sample characterization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The characteristic recognition unit is, Improve the accuracy of characteristic assessment by considering past state changes of the sample. The system described in Appendix 1, characterized by the features described herein. (Note 9) The characteristic recognition unit is, Apply different image analysis algorithms depending on the type of sample. The system described in Appendix 1, characterized by the features described herein. (Note 10) The characteristic recognition unit is, The system estimates the user's emotions and determines the priority of characteristic identification based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The characteristic recognition unit is, Characterization should take into account the geographical distribution of the sample. The system described in Appendix 1, characterized by the features described herein. (Note 12) The characteristic recognition unit is, Improve the accuracy of characteristic assessment by referring to relevant literature for the specimen. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned evaluation proposal unit, The system estimates the user's emotions and adjusts the proposed method of protection measures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned evaluation proposal unit, When proposing conservation measures, consider the preservation history of the specimens to improve the accuracy of the proposals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned evaluation proposal unit, When proposing conservation measures, different proposed algorithms are applied depending on the type of specimen. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned evaluation proposal unit, The system estimates user sentiment and prioritizes security measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned evaluation proposal unit, When proposing conservation measures, the preservation environment of the specimens should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned evaluation proposal unit, When proposing conservation measures, refer to relevant literature on the specimen to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned information management department, We estimate the user's emotions and adjust the information management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned information management department, When managing information, consider the past management history of the specimens to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned information management department, It estimates user sentiment and determines information management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned information management department, When managing information, the geographical distribution of the specimens should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management and tracking unit is: We estimate user sentiment and adjust management and tracking methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management and tracking unit is: During management and tracking, consider the specimen's past tracking history to improve tracking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management and tracking unit is: It estimates user sentiment and determines management tracking priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management and tracking unit is: When tracking and managing samples, consider the geographical distribution of the specimens. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 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 characteristic recognition unit that grasps the characteristics and condition of the specimen, Based on the characteristics and conditions identified by the characteristic recognition unit, an evaluation and proposal unit evaluates the preservation status and proposes appropriate preservation measures. An information management unit centrally manages specimen information based on the preservation measures proposed by the aforementioned evaluation proposal unit, The system includes a management and tracking unit that automates the management and tracking of specimens based on information managed by the aforementioned information management unit. A system characterized by the following features.

2. The characteristic recognition unit is, Images of the specimen are taken, and AI analyzes those images to identify the specimen's characteristics and condition. The system according to feature 1.

3. The aforementioned evaluation proposal unit, Based on past data, we predict the preservation status of specimens and propose appropriate conservation measures. The system according to feature 1.

4. The aforementioned information management department, Information such as the characteristics, condition, and preservation status of specimens is registered and managed in a database. The system according to feature 1.

5. The aforementioned management and tracking unit is: Automate the management and tracking of specimens. The system according to feature 1.

6. The aforementioned evaluation proposal unit, If the specimen is deteriorating, we will suggest appropriate temperature and humidity control methods. The system according to claim 3.

7. The characteristic recognition unit is, We estimate the user's emotions and adjust the timing of sample characterization based on the estimated user emotions. The system according to feature 1.

8. The characteristic recognition unit is, Improve the accuracy of characteristic assessment by considering past state changes of the sample. The system according to feature 1.

9. The characteristic recognition unit is, Apply different image analysis algorithms depending on the type of sample. The system according to feature 1.

10. The characteristic recognition unit is, The system estimates the user's emotions and determines the priority of characteristic identification based on the estimated user emotions. The system according to feature 1.

Images