system

Abstract

The system according to this embodiment aims to propose the most suitable method for disposing of or preserving the belongings of a deceased person while taking into consideration the feelings of the bereaved family during the process of sorting through the belongings of the deceased. [Solution] The system according to the embodiment comprises a recognition unit, an evaluation unit, an emotion analysis unit, and a proposal unit. The recognition unit recognizes the heirlooms. The evaluation unit evaluates the memories and value of the heirlooms recognized by the recognition unit. The emotion analysis unit reads the emotions of the bereaved family towards the heirlooms evaluated by the evaluation unit. The proposal unit proposes the most suitable method for disposing of or preserving the heirlooms based on the emotions read by the emotion analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

[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 performed by at least one processor, the method 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] [[ID=​​​​​​​​​​​​​​​​​​​​​​​The system according to this embodiment comprises a recognition unit, an evaluation unit, an emotion analysis unit, and a proposal unit. The recognition unit recognizes the personal belongings of the deceased. The evaluation unit evaluates the memories and value of the personal belongings recognized by the recognition unit. The emotion analysis unit reads the emotions of the bereaved family towards the personal belongings evaluated by the evaluation unit. The proposal unit proposes the most suitable method for disposing of or preserving the personal belongings based on the emotions read by the emotion analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose the most suitable method for disposing of or preserving the belongings of a deceased person while taking into consideration the feelings of the bereaved family during the sorting of the deceased's belongings. [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 controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The heirloom sorting system according to an embodiment of the present invention is a system that combines generative AI and image recognition technology to recognize and analyze heirlooms and evaluate their memories and values. This heirloom sorting system recognizes heirlooms, and the generative AI analyzes their memories and values from past photos and videos. Next, it reads emotions from the voices and expressions of the bereaved family members and proposes optimal methods for disposing of and preserving heirlooms. With this mechanism, it is possible to support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting, and to cherish and inherit the memories of the deceased. For example, the heirloom sorting system recognizes heirlooms through a camera. At this time, it analyzes the shape and characteristics of the heirlooms using image recognition technology to identify the types and conditions of the heirlooms. For example, heirlooms such as old photo albums, letters, and accessories are photographed with a camera, and the image recognition technology identifies them. Next, the heirloom sorting system has the generative AI analyze the memories and values of the heirlooms from past photos and videos. The generative AI analyzes past photos and videos related to the heirlooms and evaluates what memories and values the heirlooms have. For example, it analyzes the memories of the family members shown in the photo album and the content of the message written in the letter, and evaluates the emotional value of the heirloom. Furthermore, the heirloom sorting system reads emotions from the voices and expressions of the bereaved family members. The generative AI analyzes the voices and expressions of the bereaved family members while looking at the heirlooms to grasp their emotional states. For example, it analyzes the tone of the voice and changes in the expression of the bereaved family member looking at the heirloom, and evaluates the emotion towards the heirloom. Finally, the heirloom sorting system proposes optimal methods for disposing of and preserving heirlooms. The generative AI comprehensively evaluates the physical value and emotional value of the heirlooms and the emotional state of the bereaved family members, and proposes optimal methods for disposing of and preserving heirlooms. For example, it proposes preserving heirlooms with high emotional value and selling heirlooms with high physical value but low emotional value. In this way, it is possible to support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting. As a result, the emotional burden of heirloom sorting is reduced, and efficient sorting that takes into account the feelings of the bereaved family members is realized. The bereaved family members can carry out practical sorting while cherishing the memories of the deceased. Also, the entire process of heirloom sorting is made more efficient, and the burden of time and cost is also reduced. For example, the time and cost required for heirloom sorting are reduced. In this way, heirloom sorting proceeds more smoothly, and the burden on the bereaved family members is reduced.As a result, the heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms.

[0029] The heirloom sorting system according to the embodiment includes a recognition unit, an evaluation unit, an emotion analysis unit, and a proposal unit. The recognition unit recognizes heirlooms. The recognition unit, for example, recognizes heirlooms through a camera and identifies the types and conditions of the heirlooms. The recognition unit can analyze the shapes and features of the heirlooms using image recognition technology and identify the types and conditions of the heirlooms. For example, the recognition unit takes pictures of heirlooms such as old photo albums, letters, and accessories with a camera, and the image recognition technology identifies them. The recognition unit, for example, can analyze the shapes and features of the heirlooms and identify the types and conditions of the heirlooms. The recognition unit, for example, can analyze the shapes and features of the heirlooms and identify the types and conditions of the heirlooms. The evaluation unit evaluates the memories and values of the heirlooms recognized by the recognition unit. The evaluation unit uses generative AI to evaluate the memories and values of the heirlooms from past photos and videos. The evaluation unit, for example, has the generative AI analyze past photos and videos and evaluate what memories and values the heirloom has. The evaluation unit, for example, analyzes the memories of the family members shown in the photo album and the content of the message written in the letter, and evaluates the emotional value of the heirloom. The evaluation unit, for example, has the generative AI analyze past photos and videos and evaluate what memories and values the heirloom has. The evaluation unit, for example, has the generative AI analyze past photos and videos and evaluate what memories and values the heirloom has. The emotion analysis unit reads the emotions of the family members towards the heirlooms evaluated by the evaluation unit. The emotion analysis unit uses generative AI to read emotions from the voices and expressions of the family members. The emotion analysis unit, for example, analyzes the voice and expression of the family member while looking at the heirloom and grasps their emotional state. The emotion analysis unit, for example, analyzes the tone of the voice and changes in the expression of the family member looking at the heirloom and evaluates the emotion towards the heirloom. The emotion analysis unit, for example, analyzes the voice and expression of the family member while looking at the heirloom and grasps their emotional state. The emotion analysis unit, for example, analyzes the tone of the voice and changes in the expression of the family member looking at the heirloom and evaluates the emotion towards the heirloom. The proposal unit proposes an optimal method for disposing of or preserving the heirloom based on the emotions read by the emotion analysis unit. The proposal unit uses generative AI to comprehensively evaluate the physical value, emotional value of the heirloom, and the emotional state of the family members, and proposes an optimal method for disposing of or preserving the heirloom.The Proposal Department proposes, for example, to preserve relics with high emotional value and to sell relics with high physical value but low emotional value. The Proposal Department comprehensively evaluates, for example, the physical value and emotional value of relics and the emotional state of the bereaved family, and proposes optimal methods for disposing of and preserving relics. The Proposal Department proposes, for example, to preserve relics with high emotional value and to sell relics with high physical value but low emotional value. Thereby, the relic sorting system according to the embodiment can support efficient and considerate relic sorting through the recognition, evaluation, emotional analysis, and proposal of relics.

[0030] The Recognition Department recognizes relics. The Recognition Department recognizes relics through, for example, a camera and identifies the type and state of the relics. The Recognition Department can analyze the shape and characteristics of the relics using image recognition technology and identify the type and state of the relics. Specifically, the Recognition Department uses a high-resolution camera to obtain a detailed image of the relic and inputs it into an image recognition algorithm. The image recognition algorithm utilizes deep learning technology to analyze the characteristics such as the shape, color, and texture of the relic and identify the type and state of the relics based on these characteristics. For example, relics such as old photo albums, letters, and accessories are photographed with a camera, and the image recognition technology identifies them. The Recognition Department can analyze the shape and characteristics of the relics and identify the type and state of the relics. Furthermore, the Recognition Department can also analyze the presence or absence of damage and the preservation state in order to evaluate the state of the relics. For example, it detects whether the pages of a photo album are torn, whether the ink of a letter has faded, or whether the metal part of an accessory is rusted. Thereby, the Recognition Department can comprehensively grasp the type and state of the relics and provide them as input data to the next Evaluation Department and Emotional Analysis Department. The Recognition Department can periodically update the dataset and retrain the learning model in order to improve the recognition accuracy of the relics. Thereby, high recognition accuracy can be maintained even for new relics and different preservation states.

[0031] The evaluation unit assesses the memories and value of the mementos recognized by the recognition unit. The evaluation unit uses generative AI to evaluate the memories and value of mementos from past photographs and videos. Specifically, the generative AI analyzes past photographs and videos to assess what kind of memories and value the mementos hold. For example, it analyzes family memories captured in photo albums or the content of messages written in letters to assess the emotional value of the mementos. The generative AI uses natural language processing technology to analyze the content of letters and memos and extract emotional keywords and phrases. This allows for a quantitative assessment of the emotional value of the mementos. Furthermore, based on the analysis results of past photographs and videos, the generative AI can also assess the historical and cultural value of the mementos. For example, it can assess the historical background and cultural significance of mementos related to a specific era or region. The evaluation unit comprehensively judges these evaluation results to provide a detailed assessment of the memories and value of the mementos. The evaluation unit stores the evaluation results in a database so that subsequent emotion analysis and proposal units can access them. This allows the evaluation department to accurately assess the memories and value of the deceased's belongings and provide important information to the bereaved family.

[0032] The emotion analysis unit reads the emotions of the bereaved family towards the mementos evaluated by the evaluation unit. The emotion analysis unit uses generative AI to read emotions from the bereaved family's voice and facial expressions. Specifically, the emotion analysis unit analyzes the voice and facial expressions of the bereaved family as they speak while looking at the mementos, and grasps their emotional state. The generative AI uses speech recognition technology to analyze the tone, pitch, and speed of the bereaved family's voice and detect changes in emotion. It also uses facial recognition technology to analyze the bereaved family's facial expressions and identify emotions such as joy, sadness, and surprise. For example, it analyzes the tone of voice and changes in facial expressions of the bereaved family as they look at the mementos and evaluates their emotions towards those mementos. Based on these analysis results, the emotion analysis unit can grasp in detail the emotional reactions of the bereaved family towards the mementos. Furthermore, the emotion analysis unit can monitor the bereaved family's emotional state in real time and continuously track changes in emotion. As a result, the emotion analysis unit can accurately evaluate the emotional value of the mementos to the bereaved family and provide it as input data to the proposal unit. The emotion analysis unit can periodically update the dataset and retrain the learning model to improve the accuracy of emotion analysis. This allows it to maintain high accuracy in emotion analysis even for different bereaved families and situations.

[0033] The proposal department proposes the optimal disposal or preservation method for the deceased's belongings based on the emotions read by the emotion analysis department. Using generative AI, the proposal department comprehensively evaluates the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, to propose the most suitable disposal or preservation method. Specifically, it proposes preserving items with high emotional value and selling items with high physical value but low emotional value. The generative AI analyzes data such as market price, rarity, and condition to evaluate the physical value of the belongings. It also analyzes emotional data provided by the emotion analysis department and considers the emotional state of the bereaved family to evaluate emotional value. This allows the proposal department to comprehensively evaluate the physical and emotional value of the belongings and propose the optimal disposal or preservation method. For example, it proposes preserving items with high emotional value and selling items with high physical value but low emotional value. Furthermore, the proposal department can also make specific suggestions regarding the preservation of the belongings. For example, it might propose preserving items with high emotional value using dedicated storage cases or digital archives. Furthermore, for items of high physical value, the department will propose appropriate storage locations and methods, and provide advice on how to maintain their value. This allows the department to suggest the best disposal and preservation methods for the bereaved family, supporting efficient and thoughtful estate settlement.

[0034] The recognition unit can recognize the belongings of the deceased through the camera and identify their type and condition. The recognition unit can, for example, recognize the belongings of the deceased through the camera and identify their type and condition. The recognition unit can, for example, recognize the belongings of the deceased through the camera and identify their type and condition. The recognition unit can, for example, recognize the belongings of the deceased through the camera and identify their type and condition. This improves the accuracy of sorting through the belongings of the deceased by accurately identifying their type and condition. Some or all of the above processing in the recognition unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the recognition unit can input image data of the belongings captured by the camera into a generation AI and have the generation AI perform the identification of the type and condition of the belongings.

[0035] The evaluation unit can analyze past photographs and videos to assess the memories and value of the belongings of the deceased. The evaluation unit can, for example, analyze past photographs and videos to assess the memories and value of the belongings of the deceased. The evaluation unit can, for example, analyze past photographs and videos to assess the memories and value of the belongings of the deceased. The evaluation unit can, for example, analyze past photographs and videos to assess the memories and value of the belongings of the deceased. This improves the quality of sorting through the belongings of the deceased by accurately assessing the memories and value of the belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input past photograph and video data into a generative AI and have the generative AI perform the assessment of the memories and value of the belongings of the deceased.

[0036] The emotion analysis unit can analyze the voices and facial expressions of the bereaved family and understand their emotional state. The emotion analysis unit can, for example, analyze the voices and facial expressions of the bereaved family and understand their emotional state. The emotion analysis unit can, for example, analyze the voices and facial expressions of the bereaved family and understand their emotional state. The emotion analysis unit can, for example, analyze the voices and facial expressions of the bereaved family and understand their emotional state. By accurately understanding the emotional state of the bereaved family, appropriate suggestions for sorting through the deceased's belongings can be made. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the emotion analysis unit can input data on the bereaved family's voices and facial expressions into a generative AI and have the generative AI perform the task of understanding their emotional state.

[0037] The proposal department can comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and propose the most suitable method for disposing of or preserving the belongings. For example, the proposal department can comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and propose the most suitable method for disposing of or preserving the belongings. For example, the proposal department can comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and propose the most suitable method for disposing of or preserving the belongings. For example, the proposal department can comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and propose the most suitable method for disposing of or preserving the belongings. This ensures that proposals are made that consider both the value of the belongings and the emotions of the bereaved family, resulting in efficient and thoughtful handling of the belongings. Some or all of the above-described processes in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can input data on the physical and emotional value of the deceased's belongings, as well as the emotional state of the bereaved family, into a generating AI, which can then generate suggestions for the optimal disposal or preservation methods for those belongings.

[0038] The proposal unit can propose preserving items with high emotional value and selling items with high physical value but low emotional value. For example, the proposal unit can propose preserving items with high emotional value and selling items with high physical value but low emotional value. For example, the proposal unit can propose preserving items with high emotional value and selling items with high physical value but low emotional value. For example, the proposal unit can propose preserving items with high emotional value and selling items with high physical value but low emotional value. This allows for the proposal of appropriate disposal or preservation methods based on the value of the items. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input data on the emotional and physical value of the items into a generative AI and have the generative AI make a proposal for preservation or sale.

[0039] The recognition unit can improve recognition accuracy by taking into account the deterioration and damage of the belongings over time when recognizing them. The recognition unit can, for example, improve recognition accuracy by taking into account the deterioration and damage of the belongings over time when recognizing them. The recognition unit can, for example, improve recognition accuracy by taking into account the deterioration and damage of the belongings over time when recognizing them. The recognition unit can, for example, improve recognition accuracy by taking into account the deterioration and damage of the belongings over time when recognizing them. As a result, recognition accuracy is improved by taking into account the deterioration and damage of the belongings over time. Some or all of the above processing in the recognition unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the recognition unit can input data on the deterioration and damage of the belongings over time into a generation AI and have the generation AI perform the improvement of recognition accuracy.

[0040] The recognition unit can acquire additional information to identify the material and manufacturing date of an item when recognizing it. The recognition unit can acquire additional information to identify the material and manufacturing date of an item when recognizing it. The recognition unit can acquire additional information to identify the material and manufacturing date of an item when recognizing it. The recognition unit can acquire additional information to identify the material and manufacturing date of an item when recognizing it. By acquiring additional information to identify the material and manufacturing date of an item, the recognition accuracy is improved. Some or all of the above processing in the recognition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recognition unit can input data on the material and manufacturing date of an item into a generation AI and have the generation AI acquire the additional information.

[0041] The recognition unit can improve recognition accuracy by considering the storage location and method of the belongings when recognizing them. The recognition unit can, for example, improve recognition accuracy by considering the storage location and method of the belongings when recognizing them. The recognition unit can, for example, improve recognition accuracy by considering the storage location and method of the belongings when recognizing them. The recognition unit can, for example, improve recognition accuracy by considering the storage location and method of the belongings when recognizing them. As a result, recognition accuracy is improved by considering the storage location and method of the belongings. Some or all of the above processing in the recognition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recognition unit can input data on the storage location and method of the belongings into the generating AI and have the generating AI perform the improvement of recognition accuracy.

[0042] The recognition unit can improve recognition accuracy by referring to related literature and historical background when recognizing the belongings. The recognition unit can, for example, improve recognition accuracy by referring to related literature and historical background when recognizing the belongings. The recognition unit can, for example, improve recognition accuracy by referring to related literature and historical background when recognizing the belongings. The recognition unit can, for example, improve recognition accuracy by referring to related literature and historical background when recognizing the belongings. As a result, recognition accuracy is improved by referring to related literature and historical background of the belongings. Some or all of the above processing in the recognition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recognition unit can input data on related literature and historical background of the belongings into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0043] The evaluation unit can improve the accuracy of its evaluations by considering the frequency and condition of use of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering the frequency and condition of use of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering the frequency and condition of use of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering the frequency and condition of use of the deceased's belongings when analyzing past photographs and videos. As a result, the evaluation accuracy is improved by considering the frequency and condition of use of the deceased's belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past photograph and video data into a generating AI and have the generating AI perform an evaluation of the frequency and condition of use.

[0044] The evaluation unit can perform evaluations by considering the attribute information of the owner of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can perform evaluations by considering the attribute information of the owner of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can perform evaluations by considering the attribute information of the owner of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can perform evaluations by considering the attribute information of the owner of the deceased's belongings when analyzing past photographs and videos. This ensures that the evaluation is performed appropriately by considering the attribute information of the owner of the deceased's belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past photograph and video data into a generating AI and have the generating AI perform an evaluation that takes the owner's attribute information into account.

[0045] The evaluation unit can improve the accuracy of its evaluations by considering events and incidents related to the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering events and incidents related to the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering events and incidents related to the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering events and incidents related to the deceased's belongings when analyzing past photographs and videos. As a result, the evaluation accuracy is improved by considering events and incidents related to the deceased's belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past photograph and video data into a generating AI and have the generating AI perform an evaluation of related events and incidents.

[0046] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and historical background of the belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and historical background of the belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and historical background of the belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and historical background of the belongings when analyzing past photographs and videos. As a result, the accuracy of the evaluation is improved by referring to relevant literature and historical background of the belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past photograph and video data into a generating AI and have the generating AI perform an evaluation of the relevant literature and historical background.

[0047] The emotion analysis unit can improve the accuracy of its analysis by referring to the bereaved family's past emotional history when analyzing their voice and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by referring to the bereaved family's past emotional history when analyzing their voice and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by referring to the bereaved family's past emotional history when analyzing their voice and facial expressions. This improves the accuracy of the emotion analysis by referring to the bereaved family's past emotional history. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input data on the bereaved family's past emotional history into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0048] The emotion analysis unit can perform emotion analysis while considering the attribute information of the bereaved family when analyzing the voice and facial expressions of the bereaved family. For example, the emotion analysis unit can perform emotion analysis while considering the attribute information of the bereaved family when analyzing the voice and facial expressions of the bereaved family. For example, the emotion analysis unit can perform emotion analysis while considering the attribute information of the bereaved family when analyzing the voice and facial expressions of the bereaved family. For example, the emotion analysis unit can perform emotion analysis while considering the attribute information of the bereaved family when analyzing the voice and facial expressions of the bereaved family. This ensures that emotion analysis is performed appropriately by considering the attribute information of the bereaved family. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the emotion analysis unit can input data on the attribute information of the bereaved family into a generating AI and have the generating AI perform emotion analysis.

[0049] The emotion analysis unit can improve the accuracy of its analysis by considering the living situation and environment of the bereaved family when analyzing their voices and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by considering the living situation and environment of the bereaved family when analyzing their voices and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by considering the living situation and environment of the bereaved family when analyzing their voices and facial expressions. This improves the accuracy of the emotion analysis by considering the living situation and environment of the bereaved family. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input data on the bereaved family's living situation and environment into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0050] The emotion analysis unit can improve the accuracy of its analysis by referring to relevant literature and past events related to the bereaved family when analyzing their voices and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by referring to relevant literature and past events related to the bereaved family when analyzing their voices and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by referring to relevant literature and past events related to the bereaved family when analyzing their voices and facial expressions. This improves the accuracy of the emotion analysis by referring to relevant literature and past events related to the bereaved family. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input data on relevant literature and past events related to the bereaved family into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0051] The proposal unit can improve the accuracy of its suggestions by considering the market value and rarity of the items when evaluating their physical and emotional value. For example, the proposal unit can improve the accuracy of its suggestions by considering the market value and rarity of the items when evaluating their physical and emotional value. For example, the proposal unit can improve the accuracy of its suggestions by considering the market value and rarity of the items when evaluating their physical and emotional value. For example, the proposal unit can improve the accuracy of its suggestions by considering the market value and rarity of the items when evaluating their physical and emotional value. This improves the accuracy of suggestions by considering the market value and rarity of the items. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input data on the market value and rarity of the items into a generative AI and have the generative AI perform the improvement of the suggestion accuracy.

[0052] The proposal department can make suggestions when evaluating the physical and emotional value of the belongings, taking into account the methods of preservation and storage environment. The proposal department can make suggestions when evaluating the physical and emotional value of the belongings, taking into account the methods of preservation and storage environment. The proposal department can make suggestions when evaluating the physical and emotional value of the belongings, taking into account the methods of preservation and storage environment. The proposal department can make suggestions when evaluating the physical and emotional value of the belongings, taking into account the methods of preservation and storage environment. This ensures that appropriate suggestions are made by considering the methods of preservation and storage environment of the belongings. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input data on the methods of preservation and storage environment of the belongings into a generative AI and have the generative AI execute the suggestions.

[0053] The suggestion unit can improve the accuracy of its suggestions by considering events and incidents related to the belongings when evaluating their physical and emotional value. For example, the suggestion unit can improve the accuracy of its suggestions by considering events and incidents related to the belongings when evaluating their physical and emotional value. For example, the suggestion unit can improve the accuracy of its suggestions by considering events and incidents related to the belongings when evaluating their physical and emotional value. For example, the suggestion unit can improve the accuracy of its suggestions by considering events and incidents related to the belongings when evaluating their physical and emotional value. This improves the accuracy of suggestions by considering events and incidents related to the belongings. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input data on events and incidents related to the belongings into a generative AI and have the generative AI perform the improvement of suggestion accuracy.

[0054] The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature and historical background when evaluating the physical and emotional value of the belongings. The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature and historical background when evaluating the physical and emotional value of the belongings. The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature and historical background when evaluating the physical and emotional value of the belongings. The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature and historical background when evaluating the physical and emotional value of the belongings. As a result, the accuracy of the suggestions is improved by referring to relevant literature and historical background of the belongings. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input data on relevant literature and historical background of the belongings into a generative AI and have the generative AI perform the improvement of the suggestion accuracy.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0057] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0058] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0059] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal disposal and preservation methods for heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0060] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal disposal and preservation methods for heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0061] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal disposal and preservation methods for heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0062] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0063] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0064] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0065] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the type and condition of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to understand their emotional states. The proposal unit can comprehensively evaluate the physical value and emotional value of heirlooms and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it is possible to support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0066] The processing flow of Form Example 1 will be briefly described below.

[0067] Step 1: The recognition unit recognizes the heirloom. The recognition unit recognizes the heirloom through, for example, a camera and identifies the type and condition of the heirloom. The shape and characteristics of the heirloom can be analyzed using image recognition technology to identify the type and condition of the heirloom. For example, heirlooms such as old photo albums, letters, and accessories are photographed with a camera, and image recognition technology identifies them. Step 2: The evaluation unit evaluates the memories and values of the heirloom recognized by the recognition unit. The evaluation unit uses generative AI to evaluate the memories and values of the heirloom from past photos and videos. For example, it analyzes the memories of the family members shown in the photo album and the content of the message written in the letter to evaluate the emotional value of the heirloom. Step 3: The sentiment analysis unit reads the sentiment of the bereaved family members towards the heirloom evaluated by the evaluation unit. The sentiment analysis unit uses generative AI to read the sentiment from the voices and expressions of the bereaved family members. For example, it analyzes the voice and expression of the bereaved family member while looking at the heirloom to understand their emotional state. Step 4: The proposal department proposes the optimal disposal or preservation method for the deceased's belongings based on the emotions read by the emotion analysis department. The proposal department uses generative AI to comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and proposes the optimal disposal or preservation method. For example, it might propose preserving belongings with high emotional value, or selling belongings that have high physical value but low emotional value.

[0068] (Example of form 2) The heirloom sorting system according to an embodiment of the present invention is a system that combines generative AI and image recognition technology to recognize and analyze heirlooms and evaluate their memories and values. This heirloom sorting system recognizes heirlooms, and the generative AI analyzes their memories and values from past photos and videos. Next, it reads emotions from the voices and expressions of the bereaved family members and proposes optimal methods for disposing of and preserving heirlooms. With this mechanism, it is possible to support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting, and to cherish and inherit the memories of the deceased. For example, the heirloom sorting system recognizes heirlooms through a camera. At this time, it analyzes the shape and characteristics of the heirlooms using image recognition technology to identify the types and conditions of the heirlooms. For example, heirlooms such as old photo albums, letters, and accessories are photographed with a camera, and the image recognition technology identifies them. Next, the heirloom sorting system has the generative AI analyze the memories and values of the heirlooms from past photos and videos. The generative AI analyzes past photos and videos related to the heirlooms and evaluates what memories and values the heirlooms have. For example, it analyzes the memories of the family members shown in the photo album and the content of the message written in the letter to evaluate the emotional value of the heirloom. Furthermore, the heirloom sorting system reads emotions from the voices and expressions of the bereaved family members. The generative AI analyzes the voices and expressions of the bereaved family members while looking at the heirlooms to grasp their emotional states. For example, it analyzes the tone of voice and changes in expression of the bereaved family member looking at the heirloom to evaluate the emotion towards the heirloom. Finally, the heirloom sorting system proposes optimal methods for disposing of and preserving heirlooms. The generative AI comprehensively evaluates the physical value and emotional value of the heirlooms and the emotional state of the bereaved family members and proposes optimal methods for disposing of and preserving heirlooms. For example, it proposes preserving heirlooms with high emotional value and selling heirlooms with high physical value but low emotional value. In this way, it is possible to support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting. As a result, the emotional burden of heirloom sorting is reduced, and efficient sorting that takes into account the feelings of the bereaved family is realized. The bereaved family can carry out practical sorting while cherishing the memories of the deceased. Also, the entire process of heirloom sorting is made more efficient, and the burden of time and cost is also reduced. For example, the time and cost required for heirloom sorting are reduced. In this way, heirloom sorting proceeds more smoothly, and the burden on the bereaved family is reduced.As a result, the heirloom sorting system can support efficient and considerate heirloom sorting through heirloom recognition, evaluation, sentiment analysis, and proposal.

[0069] The heirloom sorting system according to the embodiment includes a recognition unit, an evaluation unit, an emotion analysis unit, and a proposal unit. The recognition unit recognizes heirlooms. The recognition unit, for example, recognizes heirlooms through a camera and identifies the types and conditions of the heirlooms. The recognition unit can analyze the shapes and features of the heirlooms using image recognition technology and identify the types and conditions of the heirlooms. For example, the recognition unit takes pictures of heirlooms such as old photo albums, letters, and accessories with a camera, and the image recognition technology identifies them. The recognition unit, for example, can analyze the shapes and features of the heirlooms and identify the types and conditions of the heirlooms. The recognition unit, for example, can analyze the shapes and features of the heirlooms and identify the types and conditions of the heirlooms. The evaluation unit evaluates the memories and values of the heirlooms recognized by the recognition unit. The evaluation unit uses generative AI to evaluate the memories and values of the heirlooms from past photos and videos. The evaluation unit, for example, has the generative AI analyze past photos and videos and evaluate what memories and values the heirloom has. The evaluation unit, for example, analyzes the memories of the family members shown in the photo album and the content of the message written in the letter, and evaluates the emotional value of the heirloom. The evaluation unit, for example, has the generative AI analyze past photos and videos and evaluate what memories and values the heirloom has. The evaluation unit, for example, has the generative AI analyze past photos and videos and evaluate what memories and values the heirloom has. The emotion analysis unit reads the emotions of the family members towards the heirlooms evaluated by the evaluation unit. The emotion analysis unit uses generative AI to read the emotions from the voices and expressions of the family members. The emotion analysis unit, for example, analyzes the voice and expression of the family member while looking at the heirloom and grasps their emotional state. The emotion analysis unit, for example, analyzes the tone of the voice and changes in the expression of the family member looking at the heirloom and evaluates the emotion towards the heirloom. The emotion analysis unit, for example, analyzes the voice and expression of the family member while looking at the heirloom and grasps their emotional state. The emotion analysis unit, for example, analyzes the tone of the voice and changes in the expression of the family member looking at the heirloom and evaluates the emotion towards the heirloom. The proposal unit proposes the optimal disposal and preservation methods for the heirlooms based on the emotions read by the emotion analysis unit. The proposal unit uses generative AI to comprehensively evaluate the physical value, emotional value of the heirlooms, and the emotional state of the family members, and proposes the optimal disposal and preservation methods for the heirlooms.The Proposal Department proposes, for example, to preserve heirlooms with high emotional value and to sell heirlooms with high physical value but low emotional value. The Proposal Department comprehensively evaluates, for example, the physical and emotional value of heirlooms and the emotional state of the family members, and proposes optimal disposal and preservation methods for heirlooms. The Proposal Department proposes, for example, to preserve heirlooms with high emotional value and to sell heirlooms with high physical value but low emotional value. Thereby, the heirloom sorting system according to the embodiment can support efficient and considerate heirloom sorting through the recognition, evaluation, emotional analysis, and proposal of heirlooms.

[0070] The Recognition Department recognizes heirlooms. The Recognition Department recognizes heirlooms through, for example, a camera and identifies the type and condition of the heirlooms. The Recognition Department can analyze the shape and features of heirlooms using image recognition technology and identify the type and condition of the heirlooms. Specifically, the Recognition Department uses a high-resolution camera to obtain a detailed image of the heirloom and inputs it into an image recognition algorithm. The image recognition algorithm utilizes deep learning technology to analyze features such as the shape, color, and texture of the heirloom and identify the type and condition of the heirloom based on these features. For example, heirlooms such as old photo albums, letters, and accessories are photographed with a camera, and the image recognition technology identifies them. The Recognition Department can analyze the shape and features of the heirloom and identify the type and condition of the heirloom. Furthermore, the Recognition Department can also analyze the presence or absence of damage and the preservation condition in order to evaluate the condition of the heirloom. For example, it detects whether the pages of a photo album are torn, whether the ink of a letter has faded, or whether the metal part of an accessory is rusted. Thereby, the Recognition Department can grasp the type and condition of the heirloom in detail and provide it as input data to the next Evaluation Department and Emotional Analysis Department. The Recognition Department can periodically update the dataset and retrain the learning model in order to improve the recognition accuracy of the heirloom. Thereby, high recognition accuracy can be maintained even for new heirlooms and different preservation conditions.

[0071] The evaluation unit assesses the memories and value of the mementos recognized by the recognition unit. The evaluation unit uses generative AI to evaluate the memories and value of mementos from past photographs and videos. Specifically, the generative AI analyzes past photographs and videos to assess what kind of memories and value the mementos hold. For example, it analyzes family memories captured in photo albums or the content of messages written in letters to assess the emotional value of the mementos. The generative AI uses natural language processing technology to analyze the content of letters and memos and extract emotional keywords and phrases. This allows for a quantitative assessment of the emotional value of the mementos. Furthermore, based on the analysis results of past photographs and videos, the generative AI can also assess the historical and cultural value of the mementos. For example, it can assess the historical background and cultural significance of mementos related to a specific era or region. The evaluation unit comprehensively judges these evaluation results to provide a detailed assessment of the memories and value of the mementos. The evaluation unit stores the evaluation results in a database so that subsequent emotion analysis and proposal units can access them. This allows the evaluation department to accurately assess the memories and value of the deceased's belongings and provide important information to the bereaved family.

[0072] The emotion analysis unit reads the emotions of the bereaved family towards the mementos evaluated by the evaluation unit. The emotion analysis unit uses generative AI to read emotions from the bereaved family's voice and facial expressions. Specifically, the emotion analysis unit analyzes the voice and facial expressions of the bereaved family as they speak while looking at the mementos, and grasps their emotional state. The generative AI uses speech recognition technology to analyze the tone, pitch, and speed of the bereaved family's voice and detect changes in emotion. It also uses facial recognition technology to analyze the bereaved family's facial expressions and identify emotions such as joy, sadness, and surprise. For example, it analyzes the tone of voice and changes in facial expressions of the bereaved family as they look at the mementos and evaluates their emotions towards those mementos. Based on these analysis results, the emotion analysis unit can grasp in detail the emotional reactions of the bereaved family towards the mementos. Furthermore, the emotion analysis unit can monitor the bereaved family's emotional state in real time and continuously track changes in emotion. As a result, the emotion analysis unit can accurately evaluate the emotional value of the mementos to the bereaved family and provide it as input data to the proposal unit. The emotion analysis unit can periodically update the dataset and retrain the learning model to improve the accuracy of emotion analysis. This allows it to maintain high accuracy in emotion analysis even for different bereaved families and situations.

[0073] The proposal department proposes the optimal disposal or preservation method for the deceased's belongings based on the emotions read by the emotion analysis department. Using generative AI, the proposal department comprehensively evaluates the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, to propose the most suitable disposal or preservation method. Specifically, it proposes preserving items with high emotional value and selling items with high physical value but low emotional value. The generative AI analyzes data such as market price, rarity, and condition to evaluate the physical value of the belongings. It also analyzes emotional data provided by the emotion analysis department and considers the emotional state of the bereaved family to evaluate emotional value. This allows the proposal department to comprehensively evaluate the physical and emotional value of the belongings and propose the optimal disposal or preservation method. For example, it proposes preserving items with high emotional value and selling items with high physical value but low emotional value. Furthermore, the proposal department can also make specific suggestions regarding the preservation of the belongings. For example, it might propose preserving items with high emotional value using dedicated storage cases or digital archives. Furthermore, for items of high physical value, the department will propose appropriate storage locations and methods, and provide advice on how to maintain their value. This allows the department to suggest the best disposal and preservation methods for the bereaved family, supporting efficient and thoughtful estate settlement.

[0074] The recognition unit can recognize the belongings of the deceased through the camera and identify their type and condition. The recognition unit can, for example, recognize the belongings of the deceased through the camera and identify their type and condition. The recognition unit can, for example, recognize the belongings of the deceased through the camera and identify their type and condition. The recognition unit can, for example, recognize the belongings of the deceased through the camera and identify their type and condition. This improves the accuracy of sorting through the belongings of the deceased by accurately identifying their type and condition. Some or all of the above processing in the recognition unit may be performed using, for example, a generation AI, or it may be performed without a generation AI. For example, the recognition unit can input image data of the belongings captured by the camera into a generation AI and have the generation AI perform the identification of the type and condition of the belongings.

[0075] The evaluation unit can analyze past photographs and videos to assess the memories and value of the belongings of the deceased. The evaluation unit can, for example, analyze past photographs and videos to assess the memories and value of the belongings of the deceased. The evaluation unit can, for example, analyze past photographs and videos to assess the memories and value of the belongings of the deceased. The evaluation unit can, for example, analyze past photographs and videos to assess the memories and value of the belongings of the deceased. This improves the quality of sorting through the belongings of the deceased by accurately assessing the memories and value of the belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input past photograph and video data into a generative AI and have the generative AI perform the assessment of the memories and value of the belongings of the deceased.

[0076] The emotion analysis unit can analyze the voices and facial expressions of the bereaved family and understand their emotional state. The emotion analysis unit can, for example, analyze the voices and facial expressions of the bereaved family and understand their emotional state. The emotion analysis unit can, for example, analyze the voices and facial expressions of the bereaved family and understand their emotional state. The emotion analysis unit can, for example, analyze the voices and facial expressions of the bereaved family and understand their emotional state. By accurately understanding the emotional state of the bereaved family, appropriate suggestions for sorting through the deceased's belongings can be made. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI. For example, the emotion analysis unit can input data on the bereaved family's voices and facial expressions into a generative AI and have the generative AI perform the task of understanding their emotional state.

[0077] The proposal department can comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and propose the most suitable method for disposing of or preserving the belongings. For example, the proposal department can comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and propose the most suitable method for disposing of or preserving the belongings. For example, the proposal department can comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and propose the most suitable method for disposing of or preserving the belongings. For example, the proposal department can comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and propose the most suitable method for disposing of or preserving the belongings. This ensures that proposals are made that consider both the value of the belongings and the emotions of the bereaved family, resulting in efficient and thoughtful handling of the belongings. Some or all of the above-described processes in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can input data on the physical and emotional value of the deceased's belongings, as well as the emotional state of the bereaved family, into a generating AI, which can then generate suggestions for the optimal disposal or preservation methods for those belongings.

[0078] The proposal unit can propose preserving items with high emotional value and selling items with high physical value but low emotional value. For example, the proposal unit can propose preserving items with high emotional value and selling items with high physical value but low emotional value. For example, the proposal unit can propose preserving items with high emotional value and selling items with high physical value but low emotional value. For example, the proposal unit can propose preserving items with high emotional value and selling items with high physical value but low emotional value. This allows for the proposal of appropriate disposal or preservation methods based on the value of the items. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input data on the emotional and physical value of the items into a generative AI and have the generative AI make a proposal for preservation or sale.

[0079] The recognition unit can estimate the emotions of the bereaved family and adjust the recognition accuracy of the belongings based on the estimated emotions of the bereaved family. The recognition unit can, for example, estimate the emotions of the bereaved family and adjust the recognition accuracy of the belongings based on the estimated emotions of the bereaved family. The recognition unit can, for example, estimate the emotions of the bereaved family and adjust the recognition accuracy of the belongings based on the estimated emotions of the bereaved family. The recognition unit can, for example, estimate the emotions of the bereaved family and adjust the recognition accuracy of the belongings based on the estimated emotions of the bereaved family. This ensures that the belongings are recognized appropriately by adjusting the recognition accuracy according to the emotions of the bereaved family. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recognition unit may be performed using a generative AI, for example, or without a generative AI. For example, the recognition unit can input the emotions of the bereaved family into a generative AI and have the generative AI perform the adjustment of the recognition accuracy of the belongings.

[0080] The recognition unit can improve recognition accuracy by taking into account the deterioration and damage of the belongings over time when recognizing them. The recognition unit can, for example, improve recognition accuracy by taking into account the deterioration and damage of the belongings over time when recognizing them. The recognition unit can, for example, improve recognition accuracy by taking into account the deterioration and damage of the belongings over time when recognizing them. The recognition unit can, for example, improve recognition accuracy by taking into account the deterioration and damage of the belongings over time when recognizing them. As a result, recognition accuracy is improved by taking into account the deterioration and damage of the belongings over time. Some or all of the above processing in the recognition unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the recognition unit can input data on the deterioration and damage of the belongings over time into a generation AI and have the generation AI perform the improvement of recognition accuracy.

[0081] The recognition unit can acquire additional information to identify the material and manufacturing date of an item when recognizing it. The recognition unit can acquire additional information to identify the material and manufacturing date of an item when recognizing it. The recognition unit can acquire additional information to identify the material and manufacturing date of an item when recognizing it. The recognition unit can acquire additional information to identify the material and manufacturing date of an item when recognizing it. By acquiring additional information to identify the material and manufacturing date of an item, the recognition accuracy is improved. Some or all of the above processing in the recognition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the recognition unit can input data on the material and manufacturing date of an item into a generation AI and have the generation AI acquire the additional information.

[0082] The recognition unit can estimate the emotions of the bereaved family and determine the priority of the belongings to be recognized based on the estimated emotions of the bereaved family. The recognition unit can, for example, estimate the emotions of the bereaved family and determine the priority of the belongings to be recognized based on the estimated emotions of the bereaved family. The recognition unit can, for example, estimate the emotions of the bereaved family and determine the priority of the belongings to be recognized based on the estimated emotions of the bereaved family. The recognition unit can, for example, estimate the emotions of the bereaved family and determine the priority of the belongings to be recognized based on the estimated emotions of the bereaved family. This allows for efficient sorting of belongings by determining the priority of belongings according to the emotions of the bereaved family. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recognition unit may be performed using a generative AI, for example, or without a generative AI. For example, the recognition unit can input the emotional data of the bereaved family into a generating AI, which can then perform the task of determining the priority of the deceased's belongings.

[0083] The recognition unit can improve recognition accuracy by considering the storage location and method of the belongings when recognizing them. The recognition unit can, for example, improve recognition accuracy by considering the storage location and method of the belongings when recognizing them. The recognition unit can, for example, improve recognition accuracy by considering the storage location and method of the belongings when recognizing them. The recognition unit can, for example, improve recognition accuracy by considering the storage location and method of the belongings when recognizing them. As a result, recognition accuracy is improved by considering the storage location and method of the belongings. Some or all of the above processing in the recognition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recognition unit can input data on the storage location and method of the belongings into the generating AI and have the generating AI perform the improvement of recognition accuracy.

[0084] The recognition unit can improve recognition accuracy by referring to related literature and historical background when recognizing the belongings. The recognition unit can, for example, improve recognition accuracy by referring to related literature and historical background when recognizing the belongings. The recognition unit can, for example, improve recognition accuracy by referring to related literature and historical background when recognizing the belongings. The recognition unit can, for example, improve recognition accuracy by referring to related literature and historical background when recognizing the belongings. As a result, recognition accuracy is improved by referring to related literature and historical background of the belongings. Some or all of the above processing in the recognition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the recognition unit can input data on related literature and historical background of the belongings into a generating AI and have the generating AI perform the improvement of recognition accuracy.

[0085] The evaluation unit can estimate the emotions of the bereaved family and adjust the evaluation criteria for the belongings based on the estimated emotions of the bereaved family. The evaluation unit can estimate the emotions of the bereaved family and adjust the evaluation criteria for the belongings based on the estimated emotions of the bereaved family. The evaluation unit can estimate the emotions of the bereaved family and adjust the evaluation criteria for the belongings based on the estimated emotions of the bereaved family. The evaluation unit can estimate the emotions of the bereaved family and adjust the evaluation criteria for the belongings based on the estimated emotions of the bereaved family. This ensures that the belongings are properly evaluated by adjusting the evaluation criteria according to the emotions of the bereaved family. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evaluation unit may be performed using a generative AI, for example, or without a generative AI. For example, the evaluation unit can input the emotions of the bereaved family into a generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0086] The evaluation unit can improve the accuracy of its evaluations by considering the frequency and condition of use of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering the frequency and condition of use of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering the frequency and condition of use of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering the frequency and condition of use of the deceased's belongings when analyzing past photographs and videos. As a result, the evaluation accuracy is improved by considering the frequency and condition of use of the deceased's belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past photograph and video data into a generating AI and have the generating AI perform an evaluation of the frequency and condition of use.

[0087] The evaluation unit can perform evaluations by considering the attribute information of the owner of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can perform evaluations by considering the attribute information of the owner of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can perform evaluations by considering the attribute information of the owner of the deceased's belongings when analyzing past photographs and videos. The evaluation unit can perform evaluations by considering the attribute information of the owner of the deceased's belongings when analyzing past photographs and videos. This ensures that the evaluation is performed appropriately by considering the attribute information of the owner of the deceased's belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past photograph and video data into a generating AI and have the generating AI perform an evaluation that takes the owner's attribute information into account.

[0088] The evaluation unit can estimate the emotions of the bereaved family and adjust the display method of the evaluation results based on the estimated emotions of the bereaved family. The evaluation unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the evaluation results based on the estimated emotions of the bereaved family. The evaluation unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the evaluation results based on the estimated emotions of the bereaved family. The evaluation unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the evaluation results based on the estimated emotions of the bereaved family. This provides a display method of evaluation results that is appropriate to the emotions of the bereaved family. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evaluation unit may be performed using a generative AI, for example, or without a generative AI. For example, the evaluation unit can input the emotions of the bereaved family into a generative AI and have the generative AI perform the adjustment of the display method of the evaluation results.

[0089] The evaluation unit can improve the accuracy of its evaluations by considering events and incidents related to the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering events and incidents related to the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering events and incidents related to the deceased's belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by considering events and incidents related to the deceased's belongings when analyzing past photographs and videos. As a result, the evaluation accuracy is improved by considering events and incidents related to the deceased's belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past photograph and video data into a generating AI and have the generating AI perform an evaluation of related events and incidents.

[0090] The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and historical background of the belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and historical background of the belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and historical background of the belongings when analyzing past photographs and videos. The evaluation unit can improve the accuracy of its evaluations by referring to relevant literature and historical background of the belongings when analyzing past photographs and videos. As a result, the accuracy of the evaluation is improved by referring to relevant literature and historical background of the belongings. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past photograph and video data into a generating AI and have the generating AI perform an evaluation of the relevant literature and historical background.

[0091] The emotion analysis unit can estimate the emotions of the bereaved family and adjust the accuracy of the emotion analysis based on the estimated emotions of the bereaved family. The emotion analysis unit can, for example, estimate the emotions of the bereaved family and adjust the accuracy of the emotion analysis based on the estimated emotions of the bereaved family. The emotion analysis unit can, for example, estimate the emotions of the bereaved family and adjust the accuracy of the emotion analysis based on the estimated emotions of the bereaved family. The emotion analysis unit can, for example, estimate the emotions of the bereaved family and adjust the accuracy of the emotion analysis based on the estimated emotions of the bereaved family. This ensures that the emotion analysis is performed appropriately by adjusting the accuracy of the emotion analysis according to the emotions of the bereaved family. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the emotion analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the emotion analysis unit can input the bereaved family's emotion data into a generative AI and have the generative AI perform the adjustment of the accuracy of the emotion analysis.

[0092] The emotion analysis unit can improve the accuracy of its analysis by referring to the bereaved family's past emotional history when analyzing their voice and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by referring to the bereaved family's past emotional history when analyzing their voice and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by referring to the bereaved family's past emotional history when analyzing their voice and facial expressions. This improves the accuracy of the emotion analysis by referring to the bereaved family's past emotional history. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input data on the bereaved family's past emotional history into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0093] The emotion analysis unit can perform emotion analysis while considering the attribute information of the bereaved family when analyzing the voice and facial expressions of the bereaved family. For example, the emotion analysis unit can perform emotion analysis while considering the attribute information of the bereaved family when analyzing the voice and facial expressions of the bereaved family. For example, the emotion analysis unit can perform emotion analysis while considering the attribute information of the bereaved family when analyzing the voice and facial expressions of the bereaved family. For example, the emotion analysis unit can perform emotion analysis while considering the attribute information of the bereaved family when analyzing the voice and facial expressions of the bereaved family. This ensures that emotion analysis is performed appropriately by considering the attribute information of the bereaved family. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the emotion analysis unit can input data on the attribute information of the bereaved family into a generating AI and have the generating AI perform emotion analysis.

[0094] The emotion analysis unit can estimate the emotions of the bereaved family and adjust the display method of the emotion analysis results based on the estimated emotions of the bereaved family. The emotion analysis unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the emotion analysis results based on the estimated emotions of the bereaved family. The emotion analysis unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the emotion analysis results based on the estimated emotions of the bereaved family. The emotion analysis unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the emotion analysis results based on the estimated emotions of the bereaved family. This provides a display method of the emotion analysis results that corresponds to the emotions of the bereaved family. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the emotion analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the emotion analysis unit can input the bereaved family's emotion data into a generative AI and have the generative AI perform the adjustment of the display method of the emotion analysis results.

[0095] The emotion analysis unit can improve the accuracy of its analysis by considering the living situation and environment of the bereaved family when analyzing their voices and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by considering the living situation and environment of the bereaved family when analyzing their voices and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by considering the living situation and environment of the bereaved family when analyzing their voices and facial expressions. This improves the accuracy of the emotion analysis by considering the living situation and environment of the bereaved family. Some or all of the above-described processes in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input data on the bereaved family's living situation and environment into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0096] The emotion analysis unit can improve the accuracy of its analysis by referring to relevant literature and past events related to the bereaved family when analyzing their voices and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by referring to relevant literature and past events related to the bereaved family when analyzing their voices and facial expressions. For example, the emotion analysis unit can improve the accuracy of its analysis by referring to relevant literature and past events related to the bereaved family when analyzing their voices and facial expressions. This improves the accuracy of the emotion analysis by referring to relevant literature and past events related to the bereaved family. Some or all of the above processing in the emotion analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the emotion analysis unit can input data on relevant literature and past events related to the bereaved family into a generative AI and have the generative AI perform the improvement of the analysis accuracy.

[0097] The proposal unit can estimate the emotions of the bereaved family and adjust the proposal content based on the estimated emotions. The proposal unit can, for example, estimate the emotions of the bereaved family and adjust the proposal content based on the estimated emotions. The proposal unit can, for example, estimate the emotions of the bereaved family and adjust the proposal content based on the estimated emotions. The proposal unit can, for example, estimate the emotions of the bereaved family and adjust the proposal content based on the estimated emotions. This provides proposal content that is appropriate to the emotions of the bereaved family. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the proposal unit may be performed using a generative AI, for example, or without a generative AI. For example, the proposal unit can input the emotions of the bereaved family into a generative AI and have the generative AI perform the adjustment of the proposal content.

[0098] The proposal unit can improve the accuracy of its suggestions by considering the market value and rarity of the items when evaluating their physical and emotional value. For example, the proposal unit can improve the accuracy of its suggestions by considering the market value and rarity of the items when evaluating their physical and emotional value. For example, the proposal unit can improve the accuracy of its suggestions by considering the market value and rarity of the items when evaluating their physical and emotional value. For example, the proposal unit can improve the accuracy of its suggestions by considering the market value and rarity of the items when evaluating their physical and emotional value. This improves the accuracy of suggestions by considering the market value and rarity of the items. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input data on the market value and rarity of the items into a generative AI and have the generative AI perform the improvement of the suggestion accuracy.

[0099] The proposal department can make suggestions when evaluating the physical and emotional value of the belongings, taking into account the methods of preservation and storage environment. The proposal department can make suggestions when evaluating the physical and emotional value of the belongings, taking into account the methods of preservation and storage environment. The proposal department can make suggestions when evaluating the physical and emotional value of the belongings, taking into account the methods of preservation and storage environment. The proposal department can make suggestions when evaluating the physical and emotional value of the belongings, taking into account the methods of preservation and storage environment. This ensures that appropriate suggestions are made by considering the methods of preservation and storage environment of the belongings. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input data on the methods of preservation and storage environment of the belongings into a generative AI and have the generative AI execute the suggestions.

[0100] The proposal unit can estimate the emotions of the bereaved family and adjust the display method of the proposal results based on the estimated emotions of the bereaved family. The proposal unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the proposal results based on the estimated emotions of the bereaved family. The proposal unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the proposal results based on the estimated emotions of the bereaved family. The proposal unit can, for example, estimate the emotions of the bereaved family and adjust the display method of the proposal results based on the estimated emotions of the bereaved family. This provides a display method of the proposal results that is appropriate to the emotions of the bereaved family. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the proposal unit may be performed using a generative AI, for example, or without a generative AI. For example, the proposal unit can input the emotions of the bereaved family into a generative AI and have the generative AI perform the adjustment of the display method of the proposal results.

[0101] The suggestion unit can improve the accuracy of its suggestions by considering events and incidents related to the belongings when evaluating their physical and emotional value. For example, the suggestion unit can improve the accuracy of its suggestions by considering events and incidents related to the belongings when evaluating their physical and emotional value. For example, the suggestion unit can improve the accuracy of its suggestions by considering events and incidents related to the belongings when evaluating their physical and emotional value. For example, the suggestion unit can improve the accuracy of its suggestions by considering events and incidents related to the belongings when evaluating their physical and emotional value. This improves the accuracy of suggestions by considering events and incidents related to the belongings. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input data on events and incidents related to the belongings into a generative AI and have the generative AI perform the improvement of suggestion accuracy.

[0102] The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature and historical background when evaluating the physical and emotional value of the belongings. The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature and historical background when evaluating the physical and emotional value of the belongings. The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature and historical background when evaluating the physical and emotional value of the belongings. The suggestion unit can improve the accuracy of its suggestions by referring to relevant literature and historical background when evaluating the physical and emotional value of the belongings. As a result, the accuracy of the suggestions is improved by referring to relevant literature and historical background of the belongings. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input data on relevant literature and historical background of the belongings into a generative AI and have the generative AI perform the improvement of the suggestion accuracy.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal disposal and preservation methods for heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0105] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal disposal and preservation methods for heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0106] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal disposal and preservation methods for heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0107] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0108] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0109] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to grasp their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0110] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to understand their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0111] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to understand their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0112] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the types and conditions of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family members to understand their emotional states. The proposal unit can comprehensively evaluate the physical value, emotional value of heirlooms, and the emotional state of the bereaved family members, and propose the optimal methods for disposing of and preserving heirlooms. Thereby, it can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting. "

[0113] The heirloom sorting system can support efficient and considerate heirloom sorting through the recognition, evaluation, sentiment analysis, and proposal of heirlooms. For example, the recognition unit can analyze the shape and characteristics of heirlooms to identify the type and condition of heirlooms. The evaluation unit can analyze past photos and videos to evaluate the memories and values of heirlooms. The sentiment analysis unit can analyze the voices and expressions of the bereaved family to understand their emotional state. The proposal unit can comprehensively evaluate the physical value and emotional value of heirlooms and the emotional state of the bereaved family, and propose the optimal disposal and preservation methods for heirlooms. This can support efficient and considerate heirloom sorting while reducing the emotional burden of heirloom sorting.

[0114] The processing flow of Form Example 2 will be briefly described below.

[0115] Step 1: The recognition unit recognizes the heirloom. The recognition unit recognizes the heirloom through, for example, a camera and identifies the type and condition of the heirloom. The shape and characteristics of the heirloom can be analyzed using image recognition technology to identify the type and condition of the heirloom. For example, heirlooms such as old photo albums, letters, and accessories are photographed with a camera, and the image recognition technology identifies them. Step 2: The evaluation unit evaluates the memories and values of the heirloom recognized by the recognition unit. The evaluation unit uses generative AI to evaluate the memories and values of the heirloom from past photos and videos. For example, it analyzes the memories of the family shown in the photo album and the content of the message written in the letter to evaluate the emotional value of the heirloom. Step 3: The sentiment analysis unit reads the sentiment of the bereaved family towards the heirloom evaluated by the evaluation unit. The sentiment analysis unit uses generative AI to read the sentiment from the voices and expressions of the bereaved family. For example, it analyzes the voice and expression of the bereaved family while looking at the heirloom to understand their emotional state. Step 4: The proposal department proposes the optimal disposal or preservation method for the deceased's belongings based on the emotions read by the emotion analysis department. The proposal department uses generative AI to comprehensively evaluate the physical and emotional value of the belongings, as well as the emotional state of the bereaved family, and proposes the optimal disposal or preservation method. For example, it might propose preserving belongings with high emotional value, or selling belongings that have high physical value but low emotional value.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the recognition unit, evaluation unit, emotion analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the smart device 14 to photograph the belongings and the control unit 46A performs image recognition technology. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze past photos and videos to evaluate the memories and value of the belongings. The emotion analysis unit detects the voices and facial expressions of the bereaved family using the microphone 38B and camera 42 of the smart device 14 and the control unit 46A analyzes their emotions. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively evaluates the physical and emotional value of the belongings and the emotional state of the bereaved family, and proposes the optimal method for disposing of or preserving the belongings. 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.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the recognition unit, evaluation unit, emotion analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the smart glasses 214 to photograph the belongings and the control unit 46A performs image recognition technology. The evaluation unit is implemented in the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze past photos and videos to evaluate the memories and value of the belongings. The emotion analysis unit detects the voices and facial expressions of the bereaved family using the microphone 238 and camera 42 of the smart glasses 214 and the control unit 46A analyzes their emotions. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and comprehensively evaluates the physical and emotional value of the belongings and the emotional state of the bereaved family, and proposes the optimal method for disposing of or preserving the belongings. 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.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the recognition unit, evaluation unit, emotion analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the headset terminal 314 to photograph the belongings and the control unit 46A performs image recognition technology. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze past photos and videos to evaluate the memories and value of the belongings. The emotion analysis unit detects the voices and facial expressions of the bereaved family using the microphone 238 and camera 42 of the headset terminal 314 and the control unit 46A analyzes their emotions. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively evaluates the physical and emotional value of the belongings and the emotional state of the bereaved family, and proposes the optimal method for disposing of or preserving the belongings. 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.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the recognition unit, evaluation unit, emotion analysis unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recognition unit uses the camera 42 of the robot 414 to photograph the belongings and the control unit 46A performs image recognition technology. The evaluation unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses generating AI to analyze past photos and videos to evaluate the memories and value of the belongings. The emotion analysis unit detects the voices and facial expressions of the bereaved family using the microphone 238 and camera 42 of the robot 414 and the control unit 46A analyzes their emotions. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and comprehensively evaluates the physical and emotional value of the belongings and the emotional state of the bereaved family, and proposes the optimal method for disposing of or preserving the belongings. 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A recognition unit that recognizes the belongings of the deceased, An evaluation unit that evaluates the memories and value of the belongings recognized by the recognition unit, An emotion analysis unit that reads the feelings of the bereaved family towards the belongings evaluated by the aforementioned evaluation unit, Based on the emotions read by the aforementioned emotion analysis unit, the proposal unit proposes the most suitable method for disposing of or preserving the belongings, Equipped with A system characterized by the following features. (Note 2) The aforementioned recognition unit, The camera recognizes the belongings of the deceased and identifies their type and condition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, We analyze past photos and videos to assess the memories and value of the deceased's belongings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned emotion analysis unit, Analyze the voices and facial expressions of the bereaved family to understand their emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We comprehensively evaluate the physical and emotional value of the deceased's belongings, as well as the emotional state of the bereaved family, to propose the most suitable method for disposal or preservation of the items. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, I suggest preserving heirlooms with high emotional value, and selling those with high physical value but low emotional value. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recognition unit, The system estimates the emotions of the bereaved family and adjusts the accuracy of recognizing the belongings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recognition unit, When identifying personal belongings, we improve recognition accuracy by taking into account the deterioration and damage of the items over time. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recognition unit, When identifying personal belongings, we obtain additional information to determine the material and manufacturing date of the items. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recognition unit, We estimate the feelings of the bereaved family and determine the priority of the belongings to be recognized based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recognition unit, When identifying personal belongings, we improve recognition accuracy by considering the storage location and method of the belongings. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recognition unit, When identifying personal belongings, we improve the accuracy of the identification process by referring to related literature and historical context. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, We estimate the feelings of the bereaved family and adjust the evaluation criteria for the belongings based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, When analyzing past photos and videos, we improve evaluation accuracy by considering the frequency and condition of use of the deceased's belongings. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, When analyzing past photos and videos, the evaluation takes into account the attribute information of the owner of the deceased's belongings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, The system estimates the feelings of the bereaved family and adjusts the display method of the evaluation results based on the estimated feelings of the bereaved family. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, When analyzing past photos and videos, we improve the accuracy of the evaluation by considering events and incidents related to the deceased's belongings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, When analyzing past photographs and videos, we improve the accuracy of the evaluation by referring to related documents and historical background of the personal belongings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned emotion analysis unit, The system estimates the emotions of the bereaved family and adjusts the accuracy of the emotion analysis based on the estimated emotions of the bereaved family. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned emotion analysis unit, When analyzing the voices and facial expressions of bereaved family members, we improve the accuracy of the analysis by referring to their past emotional history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned emotion analysis unit, When analyzing the voices and facial expressions of bereaved family members, emotional analysis is performed while taking into account the attribute information of the bereaved family members. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned emotion analysis unit, The system estimates the emotions of the bereaved family and adjusts the display method of the emotion analysis results based on the estimated emotions of the bereaved family. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned emotion analysis unit, When analyzing the voices and facial expressions of bereaved family members, we improve the accuracy of the analysis by taking into account their living situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned emotion analysis unit, When analyzing the voices and expressions of bereaved family members, we improve the accuracy of the analysis by referring to relevant literature and past events related to the bereaved family. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We estimate the feelings of the bereaved family and adjust the proposal based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When evaluating the physical and emotional value of inherited items, we improve the accuracy of our recommendations by taking into account the market value and rarity of the items. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When evaluating the physical and emotional value of a deceased person's belongings, we will make suggestions while taking into consideration the methods of preservation and storage environment for those items. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, The system estimates the feelings of the bereaved family and adjusts the display method of the proposed results based on the estimated feelings of the bereaved family. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When evaluating the physical and emotional value of a person's belongings, we improve the accuracy of our recommendations by considering events and circumstances related to those belongings. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When evaluating the physical and emotional value of personal belongings, we improve the accuracy of our recommendations by referring to relevant literature and historical context. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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 recognition unit that recognizes the belongings of the deceased, An evaluation unit that evaluates the memories and value of the belongings recognized by the recognition unit, An emotion analysis unit that reads the feelings of the bereaved family towards the belongings evaluated by the aforementioned evaluation unit, Based on the emotions read by the aforementioned emotion analysis unit, the proposal unit proposes the most suitable method for disposing of or preserving the belongings, Equipped with A system characterized by the following features.

2. The aforementioned recognition unit, The camera recognizes the belongings of the deceased and identifies their type and condition. The system according to feature 1.

3. The evaluation unit described above, We analyze past photos and videos to assess the memories and value of the deceased's belongings. The system according to feature 1.

4. The aforementioned emotion analysis unit, Analyze the voices and facial expressions of the bereaved family to understand their emotional state. The system according to feature 1.

5. The aforementioned proposal section is, We comprehensively evaluate the physical and emotional value of the deceased's belongings, as well as the emotional state of the bereaved family, to propose the most suitable method for disposal or preservation of the items. The system according to feature 1.

6. The aforementioned proposal section is, I suggest preserving heirlooms with high emotional value, and selling those with high physical value but low emotional value. The system according to feature 1.

7. The aforementioned recognition unit, The system estimates the emotions of the bereaved family and adjusts the accuracy of recognizing the belongings based on those estimated emotions. The system according to feature 1.

8. The aforementioned recognition unit, When identifying personal belongings, we improve recognition accuracy by taking into account the deterioration and damage of the items over time. The system according to feature 1.

9. The aforementioned recognition unit, When identifying personal belongings, we obtain additional information to determine the material and manufacturing date of the items. The system according to feature 1.

10. The aforementioned recognition unit, We estimate the feelings of the bereaved family and determine the priority of the belongings to be recognized based on those estimated feelings. The system according to feature 1.

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