system

Abstract

The system according to this embodiment aims to propose an optimal end-of-life plan that takes into account the user's values ​​and considerations for the environment. [Solution] The system according to the embodiment comprises a learning unit, a proposal unit, and an optimization unit. The learning unit learns the user's values. The proposal unit proposes eco-friendly options based on the values ​​learned by the learning unit. The optimization unit proposes an optimal end-of-life plan based on the options proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a funeral plan considering the user's values and environmental considerations has not been sufficiently proposed, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal funeral plan considering the user's values and environmental considerations.

Means for Solving the Problems

[0006] The system according to the embodiment includes a learning unit, a proposal unit, and an optimization unit. The learning unit learns the user's values. The proposal unit proposes eco-friendly options based on the values learned by the learning unit. The optimization unit proposes an optimal funeral plan based on the options proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal end-of-life plan that takes into account the user's values ​​and considerations for the environment. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 system according to an embodiment of the present invention learns the user's values ​​and environmental concerns and proposes eco-friendly funerals, upcycling of belongings, and donations. This system respects individual wishes while creating a new style of end-of-life planning that incorporates social contribution and environmental protection, contributing to the realization of a sustainable society. Specifically, the system consists of the following steps. First, the system learns the user's values ​​and environmental concerns. Next, the system proposes options such as eco-friendly funerals, upcycling of belongings, and donations. This allows the user to select an end-of-life plan that matches their values. Furthermore, the system proposes the optimal end-of-life plan based on the user's selection. This system achieves a balance between the user's values ​​and environmental considerations, contributing to the realization of a sustainable society. As a specific process, the system uses an interactive interface with natural language processing as a method for learning the user's values. The system also proposes natural burials, tree burials, and bio-urun as concrete examples of eco-friendly funerals. The system also proposes concrete examples of upcycling of belongings and donations. As a result, the system can propose eco-friendly options based on the user's values ​​and provide the optimal end-of-life plan.

[0029] The system according to this embodiment comprises a learning unit, a suggestion unit, and an optimization unit. The learning unit learns the user's values. The learning unit can learn values ​​through dialogue with the user, for example, using natural language processing. The learning unit can also learn values ​​by analyzing the user's past behavioral history and social media activity. The learning unit can also learn more comprehensive values ​​by incorporating the opinions of the user's family and friends. The suggestion unit proposes eco-friendly options based on the values ​​learned by the learning unit. The suggestion unit can propose eco-friendly funeral options such as natural burial, tree burial, and bio-urun. The suggestion unit can also propose options such as upcycling, donating, and recycling of belongings. The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated emotions. The optimization unit proposes the optimal end-of-life plan based on the options proposed by the suggestion unit. The optimization unit can propose the optimal end-of-life plan based on the user's choices. The optimization unit can also estimate the user's emotions and adjust the optimization criteria based on the estimated emotions. As a result, the system according to this embodiment can propose eco-friendly options based on the user's values ​​and provide an optimal end-of-life plan.

[0030] The learning unit learns the user's values. For example, the learning unit can learn values ​​through dialogue with the user using natural language processing. Specifically, it uses natural language processing technology to analyze the content of the dialogue with the user and extract the user's values ​​and preferences. For example, it can understand through dialogue whether the user prefers an eco-friendly lifestyle or what kind of environmental protection activities they are interested in. The learning unit can also learn values ​​by analyzing the user's past behavior history and social media activity. For example, it can analyze events the user has participated in, products they have purchased, and social media posts to identify the user's values ​​and interests. Furthermore, the learning unit can learn more comprehensive values ​​by incorporating the opinions of the user's family and friends. For example, it can collect the content of conversations with the user's family and friends and the opinions they have expressed about the user to gain a deeper understanding of the user's values. This allows the learning unit to learn the user's values ​​from multiple perspectives and build a foundation for making optimal suggestions to the user. In addition, the learning unit can store the learned values ​​in a database and update it as needed, enabling it to always make suggestions based on the latest information. This allows the learning unit to accurately and comprehensively learn the user's values, improving the overall accuracy and reliability of the system.

[0031] The proposal department proposes eco-friendly options based on values ​​learned by the learning department. Specifically, it can propose eco-friendly funeral options such as natural burials, tree burials, and bio-urun (a type of burial service). Based on the user's values, the proposal department selects the most suitable eco-friendly funeral option and provides detailed information. For example, it explains the advantages and disadvantages, costs, and procedures of natural burials in detail to support the user in making an informed decision. The proposal department can also propose options such as upcycling, donating, and recycling of belongings. For example, it can provide specific suggestions on how to upcycle belongings, which organizations to donate to, and recycling methods, enabling the user to make environmentally conscious choices. Furthermore, the proposal department can estimate the user's emotions and adjust the expression of its suggestions based on those emotions. For example, if the user is feeling sadness or anxiety, it will use gentle language and reassuring expressions in its suggestions. Conversely, if the user is feeling positive, it will use positive expressions in its suggestions. In this way, the proposal department can provide suggestions that are sensitive to the user's emotions and support the user in making informed decisions. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to provide users with the most optimal eco-friendly options and improve the overall reliability and satisfaction of the system.

[0032] The Optimization Department proposes the optimal end-of-life plan based on the options suggested by the Proposal Department. Specifically, it can propose the optimal end-of-life plan based on the user's choices. For example, if a user chooses a natural burial, the Optimization Department will provide a detailed plan including the necessary procedures, costs, and location selection. Also, if a user wishes to upcycle their belongings, the Optimization Department will propose a specific plan, including how to upcycle the belongings and which company to entrust the work to. Furthermore, the Optimization Department can estimate the user's emotions and adjust the optimization criteria based on those emotions. For example, if a user is feeling anxious, it will propose a plan that provides reassurance, and if the user is feeling positive, it will propose a positive plan. In this way, the Optimization Department can provide the optimal end-of-life plan that is sensitive to the user's emotions. In addition, the Optimization Department can collect user feedback and continuously improve the optimization criteria and plan content. This allows the Optimization Department to provide users with the optimal end-of-life plan and improve the overall reliability and satisfaction of the system. Furthermore, the Optimization Department can work in conjunction with the Proposal Department to constantly explore new eco-friendly options to broaden user choices and promote the evolution of the system. This allows the optimization unit to always provide users with the latest and most optimal end-of-life planning plan, maximizing user satisfaction.

[0033] The learning unit can be equipped with an interactive interface using natural language processing. For example, the learning unit can interact with the user using natural language processing technology and learn their values. The learning unit can analyze the user's utterances using morphological analysis and extract their values. The learning unit can analyze the structure of the user's utterances using grammatical analysis and understand their values. The learning unit can analyze the meaning of the user's utterances using semantic analysis and grasp their values. As a result, the learning unit can effectively learn the user's values ​​through an interactive interface using natural language processing.

[0034] The proposal department can suggest eco-friendly funeral options such as natural burial, tree burial, and BioUrn. For example, the proposal department can suggest natural burial, which is carried out by methods such as earth burial or scattering of ashes at sea. The proposal department can also suggest tree burial, which involves burying the remains at the base of a tree. The proposal department can also suggest BioUrn, which is a urn made from biodegradable materials. By offering eco-friendly funeral options, the department can contribute to environmental protection.

[0035] The proposal department can suggest options such as upcycling, donating, and recycling of inherited items. For example, the proposal department might suggest upcycling inherited items. Upcycling inherited items is a method of reusing inherited items in new products. The proposal department could also suggest donating inherited items. Donating inherited items is a method of donating inherited items to charities. The proposal department could also suggest recycling inherited items. Recycling inherited items is a method of separating inherited items by material and reusing them. In this way, by suggesting upcycling or donating inherited items, it is possible to promote social contribution and environmental protection.

[0036] The optimization unit can propose the optimal end-of-life plan based on the user's choices. For example, it can propose the optimal end-of-life plan based on the user's chosen eco-friendly funeral options. It can also propose the optimal end-of-life plan based on the user's chosen options for upcycling or donating personal belongings. Based on the user's choices, the optimization unit can also propose the optimal end-of-life plan considering cost-effectiveness and environmental impact. In this way, by proposing the optimal end-of-life plan based on the user's choices, individual wishes can be respected.

[0037] The learning unit can analyze a user's past behavioral history and learn about changes in their values ​​in real time. For example, it can analyze the eco-friendly options a user has previously selected and learn about changes in their values. It can also analyze a user's past donation history and learn about changes in their values ​​regarding social contribution. Furthermore, it can analyze a user's past consumption behavior and learn about changes in their values ​​regarding environmental considerations. In this way, by analyzing a user's past behavioral history, it can learn about changes in their values ​​in real time.

[0038] The learning unit can learn a more comprehensive set of values ​​by incorporating the opinions of the user's family and friends. For example, the learning unit can learn the user's values ​​based on information provided by the user's family. The learning unit can learn the user's values ​​based on the opinions provided by the user's friends. The learning unit can also learn values ​​by analyzing the history of conversations with the user's family and friends. In this way, by incorporating the opinions of the user's family and friends, it can learn a more comprehensive set of values.

[0039] The learning unit can analyze users' social media activity and reflect this in learning their values. For example, the learning unit can analyze articles that users share on social media and learn their values. The learning unit can analyze accounts that users follow on social media and learn their values. The learning unit can also analyze groups that users participate in on social media and learn their values. In this way, by analyzing users' social media activity, it is possible to reflect this in learning their values.

[0040] The learning unit can analyze users' purchase history and learn their values ​​regarding environmental considerations. For example, the learning unit can analyze eco-friendly products purchased by users and learn their values. The learning unit can analyze recycled products purchased by users and learn their values. The learning unit can also analyze organic products purchased by users and learn their values. In this way, by analyzing users' purchase history, it is possible to learn their values ​​regarding environmental considerations.

[0041] The suggestion function can refer to the user's past selection history and propose more appropriate eco-friendly options. For example, the suggestion function makes suggestions based on the eco-friendly options the user has previously selected. The suggestion function can propose the most suitable eco-friendly option from the user's past selection history. The suggestion function can also analyze the user's past selection history and propose eco-friendly options that align with their values. This allows the suggestion function to propose more appropriate eco-friendly options by referring to the user's past selection history.

[0042] The proposal team can suggest the most suitable eco-friendly options, taking into account the user's local environmental regulations and culture. For example, the proposal team can suggest appropriate eco-friendly options based on the user's local environmental regulations. The proposal team can also suggest eco-friendly options that are easily accepted, taking into account the user's local culture. Furthermore, the proposal team can suggest the most suitable eco-friendly options by referencing the user's local environmental protection activities. This allows the proposal to suggest the most suitable eco-friendly options by considering the user's local environmental regulations and culture.

[0043] The proposal department can suggest the most suitable eco-friendly option, taking into account the user's health condition. For example, if the user is in good health, the proposal department can suggest an active eco-friendly option. If the user is in poor health, the proposal department can suggest an eco-friendly option that places less burden on the user. The proposal department can also suggest an appropriate eco-friendly option depending on the user's health condition. In this way, by considering the user's health condition, the proposal department can suggest the most suitable eco-friendly option.

[0044] The proposal department can propose cost-effective and eco-friendly options, taking into account the user's economic situation. For example, the proposal department can propose the optimal eco-friendly option according to the user's budget. The proposal department can propose cost-effective and eco-friendly options, taking into account the user's economic situation. The proposal department can also propose cost-effective and eco-friendly options based on the user's economic situation. This allows for the proposal of cost-effective and eco-friendly options by considering the user's economic situation.

[0045] The optimization unit can refer to the user's past selection history and update the optimal end-of-life plan in real time. For example, the optimization unit updates the optimal end-of-life plan based on the user's past selection of eco-friendly options. The optimization unit can update the most suitable end-of-life plan in real time based on the user's past selection history. The optimization unit can also analyze the user's past selection history and update the end-of-life plan in real time to match the user's values. This allows the optimal end-of-life plan to be updated in real time by referring to the user's past selection history.

[0046] The optimization department can propose a more comprehensive end-of-life plan by incorporating the opinions of the user's family and friends. For example, the optimization department can propose the optimal end-of-life plan based on information provided by the user's family. The optimization department can propose the optimal end-of-life plan based on the opinions provided by the user's friends. The optimization department can also propose the optimal end-of-life plan by analyzing the history of conversations with the user's family and friends. In this way, by incorporating the opinions of the user's family and friends, a more comprehensive end-of-life plan can be proposed.

[0047] The optimization unit can propose an optimal end-of-life plan, taking into account the user's local environmental regulations and culture. For example, the optimization unit proposes a suitable end-of-life plan based on the user's local environmental regulations. The optimization unit can also propose an end-of-life plan that is easily accepted, taking into account the user's local culture. Furthermore, the optimization unit can propose an optimal end-of-life plan by referencing the user's local environmental protection activities. In this way, by considering the user's local environmental regulations and culture, it can propose an optimal end-of-life plan.

[0048] The optimization unit can propose an optimal end-of-life plan, taking into account the user's health condition. For example, if the user is in good health, the optimization unit will propose an active end-of-life plan. If the user is in poor health, the optimization unit can propose an end-of-life plan that is less burdensome. The optimization unit can also propose an appropriate end-of-life plan according to the user's health condition. In this way, by taking the user's health condition into consideration, it can propose an optimal end-of-life plan.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The learning function can consider the user's hobbies and interests when learning their values. For example, if a user is interested in nature conservation, the learning function can suggest eco-friendly options based on that information. If a user is interested in art and crafts, the learning function can suggest art projects or craft projects as ideas for upcycling belongings. Furthermore, if a user is interested in a particular charity, it can suggest donations to that organization. This allows for suggestions based on the user's hobbies and interests, providing a more personalized end-of-life plan.

[0051] The learning unit can consider the user's life events when learning their values. For example, if a user experiences life events such as marriage or childbirth, the unit can learn their values ​​based on those experiences. If a user experiences life events such as changing jobs or moving, the unit can learn how their values ​​have changed based on those experiences. Furthermore, if a user experiences difficult situations such as illness or accidents, the unit can learn their values ​​based on those experiences. This makes it possible to learn values ​​based on the user's life events, enabling the provision of more appropriate end-of-life planning.

[0052] The proposal department can suggest not only eco-friendly options but also options that consider the user's health and well-being, based on the user's values. For example, if the user is interested in health, the proposal department can suggest healthy eating and exercise plans. If the user is interested in mental health, the proposal department can suggest stress management and relaxation methods. Also, if the user values ​​social connections, the proposal department can suggest community activities and volunteer work. This allows for the provision of a comprehensive end-of-life plan that takes the user's health and well-being into consideration.

[0053] The Proposal Department can propose not only eco-friendly options but also options that take into account the user's culture and religion based on the user's values. For example, if the user believes in a specific religion, funeral options based on the doctrines of that religion can be proposed. If the user belongs to a specific culture, funeral and relic handling methods suitable for that culture can be proposed. Also, if the user has a multicultural background, options that take into account multiple cultures can be proposed. This enables the provision of an end-of-life plan that takes into account the user's culture and religion.

[0054] The Optimization Department can propose not only eco-friendly options but also options that take into account the user's economic situation based on the user's values. For example, if the user is planning for end-of-life with a limited budget, the Optimization Department proposes eco-friendly options with high cost performance. If the user has economic surplus, the Optimization Department can propose more expensive eco-friendly options. Also, if the user needs economic support, the Optimization Department can provide information on support programs and subsidies. This enables the provision of an end-of-life plan that takes into account the user's economic situation.

[0055] The processing flow of Form Example 1 will be briefly described below.

[0056] Step 1: The Learning Department learns the user's values. The Learning Department can learn values through conversations with the user using natural language processing. Also, the values can be learned by analyzing the user's past behavior history and social media activities. Furthermore, the opinions of the user's family and friends can be incorporated to learn a more comprehensive set of values. Step 2: The suggestion unit proposes eco-friendly options based on the values ​​learned by the learning unit. The suggestion unit can propose eco-friendly funeral options such as natural burial, tree burial, and bio-urn. It can also propose options such as upcycling, donating, and recycling of belongings. Furthermore, the suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on those estimated emotions. Step 3: The optimization unit proposes the optimal end-of-life plan based on the options suggested by the proposal unit. The optimization unit can propose the optimal end-of-life plan based on the user's choices. The optimization unit can also estimate the user's emotions and adjust the optimization criteria based on the estimated emotions.

[0057] (Example of form 2) The system according to an embodiment of the present invention learns the user's values ​​and environmental concerns and proposes eco-friendly funerals, upcycling of belongings, and donations. This system respects individual wishes while creating a new style of end-of-life planning that incorporates social contribution and environmental protection, contributing to the realization of a sustainable society. Specifically, the system consists of the following steps. First, the system learns the user's values ​​and environmental concerns. Next, the system proposes options such as eco-friendly funerals, upcycling of belongings, and donations. This allows the user to select an end-of-life plan that matches their values. Furthermore, the system proposes the optimal end-of-life plan based on the user's selection. This system achieves a balance between the user's values ​​and environmental considerations, contributing to the realization of a sustainable society. As a specific process, the system uses an interactive interface with natural language processing as a method for learning the user's values. The system also proposes natural burials, tree burials, and bio-urun as concrete examples of eco-friendly funerals. The system also proposes concrete examples of upcycling of belongings and donations. As a result, the system can propose eco-friendly options based on the user's values ​​and provide the optimal end-of-life plan.

[0058] The system according to this embodiment comprises a learning unit, a suggestion unit, and an optimization unit. The learning unit learns the user's values. The learning unit can learn values ​​through dialogue with the user, for example, using natural language processing. The learning unit can also learn values ​​by analyzing the user's past behavioral history and social media activity. The learning unit can also learn more comprehensive values ​​by incorporating the opinions of the user's family and friends. The suggestion unit proposes eco-friendly options based on the values ​​learned by the learning unit. The suggestion unit can propose eco-friendly funeral options such as natural burial, tree burial, and bio-urun. The suggestion unit can also propose options such as upcycling, donating, and recycling of belongings. The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated emotions. The optimization unit proposes the optimal end-of-life plan based on the options proposed by the suggestion unit. The optimization unit can propose the optimal end-of-life plan based on the user's choices. The optimization unit can also estimate the user's emotions and adjust the optimization criteria based on the estimated emotions. As a result, the system according to this embodiment can propose eco-friendly options based on the user's values ​​and provide an optimal end-of-life plan.

[0059] The learning unit learns the user's values. For example, the learning unit can learn values ​​through dialogue with the user using natural language processing. Specifically, it uses natural language processing technology to analyze the content of the dialogue with the user and extract the user's values ​​and preferences. For example, it can understand through dialogue whether the user prefers an eco-friendly lifestyle or what kind of environmental protection activities they are interested in. The learning unit can also learn values ​​by analyzing the user's past behavior history and social media activity. For example, it can analyze events the user has participated in, products they have purchased, and social media posts to identify the user's values ​​and interests. Furthermore, the learning unit can learn more comprehensive values ​​by incorporating the opinions of the user's family and friends. For example, it can collect the content of conversations with the user's family and friends and the opinions they have expressed about the user to gain a deeper understanding of the user's values. This allows the learning unit to learn the user's values ​​from multiple perspectives and build a foundation for making optimal suggestions to the user. In addition, the learning unit can store the learned values ​​in a database and update it as needed, enabling it to always make suggestions based on the latest information. This allows the learning unit to accurately and comprehensively learn the user's values, improving the overall accuracy and reliability of the system.

[0060] The proposal department proposes eco-friendly options based on values ​​learned by the learning department. Specifically, it can propose eco-friendly funeral options such as natural burials, tree burials, and bio-urun (a type of burial service). Based on the user's values, the proposal department selects the most suitable eco-friendly funeral option and provides detailed information. For example, it explains the advantages and disadvantages, costs, and procedures of natural burials in detail to support the user in making an informed decision. The proposal department can also propose options such as upcycling, donating, and recycling of belongings. For example, it can provide specific suggestions on how to upcycle belongings, which organizations to donate to, and recycling methods, enabling the user to make environmentally conscious choices. Furthermore, the proposal department can estimate the user's emotions and adjust the expression of its suggestions based on those emotions. For example, if the user is feeling sadness or anxiety, it will use gentle language and reassuring expressions in its suggestions. Conversely, if the user is feeling positive, it will use positive expressions in its suggestions. In this way, the proposal department can provide suggestions that are sensitive to the user's emotions and support the user in making informed decisions. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to provide users with the most optimal eco-friendly options and improve the overall reliability and satisfaction of the system.

[0061] The Optimization Department proposes the optimal end-of-life plan based on the options suggested by the Proposal Department. Specifically, it can propose the optimal end-of-life plan based on the user's choices. For example, if a user chooses a natural burial, the Optimization Department will provide a detailed plan including the necessary procedures, costs, and location selection. Also, if a user wishes to upcycle their belongings, the Optimization Department will propose a specific plan, including how to upcycle the belongings and which company to entrust the work to. Furthermore, the Optimization Department can estimate the user's emotions and adjust the optimization criteria based on those emotions. For example, if a user is feeling anxious, it will propose a plan that provides reassurance, and if the user is feeling positive, it will propose a positive plan. In this way, the Optimization Department can provide the optimal end-of-life plan that is sensitive to the user's emotions. In addition, the Optimization Department can collect user feedback and continuously improve the optimization criteria and plan content. This allows the Optimization Department to provide users with the optimal end-of-life plan and improve the overall reliability and satisfaction of the system. Furthermore, the Optimization Department can work in conjunction with the Proposal Department to constantly explore new eco-friendly options to broaden user choices and promote the evolution of the system. This allows the optimization unit to always provide users with the latest and most optimal end-of-life planning plan, maximizing user satisfaction.

[0062] The learning unit can be equipped with an interactive interface using natural language processing. For example, the learning unit can interact with the user using natural language processing technology and learn their values. The learning unit can analyze the user's utterances using morphological analysis and extract their values. The learning unit can analyze the structure of the user's utterances using grammatical analysis and understand their values. The learning unit can analyze the meaning of the user's utterances using semantic analysis and grasp their values. As a result, the learning unit can effectively learn the user's values ​​through an interactive interface using natural language processing.

[0063] The proposal department can suggest eco-friendly funeral options such as natural burial, tree burial, and BioUrn. For example, the proposal department can suggest natural burial, which is carried out by methods such as earth burial or scattering of ashes at sea. The proposal department can also suggest tree burial, which involves burying the remains at the base of a tree. The proposal department can also suggest BioUrn, which is a urn made from biodegradable materials. By offering eco-friendly funeral options, the department can contribute to environmental protection.

[0064] The proposal department can suggest options such as upcycling, donating, and recycling of inherited items. For example, the proposal department might suggest upcycling inherited items. Upcycling inherited items is a method of reusing inherited items in new products. The proposal department could also suggest donating inherited items. Donating inherited items is a method of donating inherited items to charities. The proposal department could also suggest recycling inherited items. Recycling inherited items is a method of separating inherited items by material and reusing them. In this way, by suggesting upcycling or donating inherited items, it is possible to promote social contribution and environmental protection.

[0065] The optimization unit can propose the optimal end-of-life plan based on the user's choices. For example, it can propose the optimal end-of-life plan based on the user's chosen eco-friendly funeral options. It can also propose the optimal end-of-life plan based on the user's chosen options for upcycling or donating personal belongings. Based on the user's choices, the optimization unit can also propose the optimal end-of-life plan considering cost-effectiveness and environmental impact. In this way, by proposing the optimal end-of-life plan based on the user's choices, individual wishes can be respected.

[0066] The learning unit can estimate the user's emotions and adjust the value learning method based on the estimated user emotions. For example, if the user is sad, the learning unit can engage in dialogue using gentle words to alleviate the emotions and advance the value learning process. If the user is agitated, the learning unit can engage in dialogue in a calm tone to help the user calmly organize their values ​​and advance the value learning process. If the user is feeling anxious, the learning unit can also advance the value learning process by providing concrete examples to reassure them. By adjusting the value learning method according to the user's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The learning unit can analyze a user's past behavioral history and learn about changes in their values ​​in real time. For example, it can analyze the eco-friendly options a user has previously selected and learn about changes in their values. It can also analyze a user's past donation history and learn about changes in their values ​​regarding social contribution. Furthermore, it can analyze a user's past consumption behavior and learn about changes in their values ​​regarding environmental considerations. In this way, by analyzing a user's past behavioral history, it can learn about changes in their values ​​in real time.

[0068] The learning unit can learn a more comprehensive set of values ​​by incorporating the opinions of the user's family and friends. For example, the learning unit can learn the user's values ​​based on information provided by the user's family. The learning unit can learn the user's values ​​based on the opinions provided by the user's friends. The learning unit can also learn values ​​by analyzing the history of conversations with the user's family and friends. In this way, by incorporating the opinions of the user's family and friends, it can learn a more comprehensive set of values.

[0069] The learning unit can estimate the user's emotions and determine learning priorities based on those estimated emotions. For example, if the user is stressed, the learning unit can start learning with relaxing content. If the user is excited, the learning unit can start learning with interesting content. If the user is anxious, the learning unit can also start learning with reassuring content. This allows for more effective learning by determining learning priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The learning unit can analyze users' social media activity and reflect this in learning their values. For example, the learning unit can analyze articles that users share on social media and learn their values. The learning unit can analyze accounts that users follow on social media and learn their values. The learning unit can also analyze groups that users participate in on social media and learn their values. In this way, by analyzing users' social media activity, it is possible to reflect this in learning their values.

[0071] The learning unit can analyze users' purchase history and learn their values ​​regarding environmental considerations. For example, the learning unit can analyze eco-friendly products purchased by users and learn their values. The learning unit can analyze recycled products purchased by users and learn their values. The learning unit can also analyze organic products purchased by users and learn their values. In this way, by analyzing users' purchase history, it is possible to learn their values ​​regarding environmental considerations.

[0072] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is sad, the suggestion function can suggest eco-friendly options using gentle language. If the user is excited, the suggestion function can suggest eco-friendly options using specific examples. If the user is feeling anxious, the suggestion function can also suggest eco-friendly options with detailed explanations to provide reassurance. By adjusting the way suggestions are presented according to the user's emotions, more effective suggestions become possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The suggestion function can refer to the user's past selection history and propose more appropriate eco-friendly options. For example, the suggestion function makes suggestions based on the eco-friendly options the user has previously selected. The suggestion function can propose the most suitable eco-friendly option from the user's past selection history. The suggestion function can also analyze the user's past selection history and propose eco-friendly options that align with their values. This allows the suggestion function to propose more appropriate eco-friendly options by referring to the user's past selection history.

[0074] The proposal team can suggest the most suitable eco-friendly options, taking into account the user's local environmental regulations and culture. For example, the proposal team can suggest appropriate eco-friendly options based on the user's local environmental regulations. The proposal team can also suggest eco-friendly options that are easily accepted, taking into account the user's local culture. Furthermore, the proposal team can suggest the most suitable eco-friendly options by referencing the user's local environmental protection activities. This allows the proposal to suggest the most suitable eco-friendly options by considering the user's local environmental regulations and culture.

[0075] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function will prioritize suggesting options that promote relaxation. If the user is excited, the suggestion function can prioritize suggesting options that pique their interest. If the user is anxious, the suggestion function can also prioritize suggesting options that provide a sense of security. By prioritizing suggestions according to the user's emotions, more effective suggestions become possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The proposal department can suggest the most suitable eco-friendly option, taking into account the user's health condition. For example, if the user is in good health, the proposal department can suggest an active eco-friendly option. If the user is in poor health, the proposal department can suggest an eco-friendly option that places less burden on the user. The proposal department can also suggest an appropriate eco-friendly option depending on the user's health condition. In this way, by considering the user's health condition, the proposal department can suggest the most suitable eco-friendly option.

[0077] The proposal department can propose cost-effective and eco-friendly options, taking into account the user's economic situation. For example, the proposal department can propose the optimal eco-friendly option according to the user's budget. The proposal department can propose cost-effective and eco-friendly options, taking into account the user's economic situation. The proposal department can also propose cost-effective and eco-friendly options based on the user's economic situation. This allows for the proposal of cost-effective and eco-friendly options by considering the user's economic situation.

[0078] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on the estimated emotions. For example, if the user is sad, the optimization unit can use gentle words to alleviate the emotions. If the user is excited, the optimization unit can use a calm tone to help the user calmly organize their values. If the user is anxious, the optimization unit can also use concrete examples to provide reassurance. By adjusting the optimization criteria according to the user's emotions, more effective optimization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The optimization unit can refer to the user's past selection history and update the optimal end-of-life plan in real time. For example, the optimization unit updates the optimal end-of-life plan based on the user's past selection of eco-friendly options. The optimization unit can update the most suitable end-of-life plan in real time based on the user's past selection history. The optimization unit can also analyze the user's past selection history and update the end-of-life plan in real time to match the user's values. This allows the optimal end-of-life plan to be updated in real time by referring to the user's past selection history.

[0080] The optimization department can propose a more comprehensive end-of-life plan by incorporating the opinions of the user's family and friends. For example, the optimization department can propose the optimal end-of-life plan based on information provided by the user's family. The optimization department can propose the optimal end-of-life plan based on the opinions provided by the user's friends. The optimization department can also propose the optimal end-of-life plan by analyzing the history of conversations with the user's family and friends. In this way, by incorporating the opinions of the user's family and friends, a more comprehensive end-of-life plan can be proposed.

[0081] The optimization unit can estimate the user's emotions and determine optimization priorities based on those estimated emotions. For example, if the user is stressed, the optimization unit can start optimizing with relaxing content. If the user is excited, the optimization unit can start optimizing with interesting content. If the user is anxious, the optimization unit can also start optimizing with reassuring content. This allows for more effective optimization by determining optimization priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The optimization unit can propose an optimal end-of-life plan, taking into account the user's local environmental regulations and culture. For example, the optimization unit proposes a suitable end-of-life plan based on the user's local environmental regulations. The optimization unit can also propose an end-of-life plan that is easily accepted, taking into account the user's local culture. Furthermore, the optimization unit can propose an optimal end-of-life plan by referencing the user's local environmental protection activities. In this way, by considering the user's local environmental regulations and culture, it can propose an optimal end-of-life plan.

[0083] The optimization unit can propose an optimal end-of-life plan, taking into account the user's health condition. For example, if the user is in good health, the optimization unit will propose an active end-of-life plan. If the user is in poor health, the optimization unit can propose an end-of-life plan that is less burdensome. The optimization unit can also propose an appropriate end-of-life plan according to the user's health condition. In this way, by taking the user's health condition into consideration, it can propose an optimal end-of-life plan.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The learning function can consider the user's hobbies and interests when learning their values. For example, if a user is interested in nature conservation, the learning function can suggest eco-friendly options based on that information. If a user is interested in art and crafts, the learning function can suggest art projects or craft projects as ideas for upcycling belongings. Furthermore, if a user is interested in a particular charity, it can suggest donations to that organization. This allows for suggestions based on the user's hobbies and interests, providing a more personalized end-of-life plan.

[0086] The learning unit can consider the user's life events when learning their values. For example, if a user experiences life events such as marriage or childbirth, the unit can learn their values ​​based on those experiences. If a user experiences life events such as changing jobs or moving, the unit can learn how their values ​​have changed based on those experiences. Furthermore, if a user experiences difficult situations such as illness or accidents, the unit can learn their values ​​based on those experiences. This makes it possible to learn values ​​based on the user's life events, enabling the provision of more appropriate end-of-life planning.

[0087] The proposal department can suggest not only eco-friendly options but also options that consider the user's health and well-being, based on the user's values. For example, if the user is interested in health, the proposal department can suggest healthy eating and exercise plans. If the user is interested in mental health, the proposal department can suggest stress management and relaxation methods. Also, if the user values ​​social connections, the proposal department can suggest community activities and volunteer work. This allows for the provision of a comprehensive end-of-life plan that takes the user's health and well-being into consideration.

[0088] Based on the user's values, the Proposal Department can not only propose eco-friendly options but also options that take into account the user's culture and religion. For example, if the user believes in a specific religion, funeral options based on the doctrines of that religion are proposed. If the user belongs to a specific culture, funeral and heritage disposal methods suitable for that culture can be proposed. Also, if the user has a multicultural background, options that consider multiple cultures can be proposed. This enables the provision of an end-of-life plan that takes into account the user's culture and religion.

[0089] Based on the user's values, the Optimization Department can not only propose eco-friendly options but also options that take into account the user's economic situation. For example, if the user is planning for end-of-life with a limited budget, the Optimization Department proposes eco-friendly options with high cost performance. If the user has economic surplus, the Optimization Department can propose more expensive eco-friendly options. Also, if the user needs economic support, the Optimization Department can provide information on support programs and subsidies. This enables the provision of an end-of-life plan that takes into account the user's economic situation.

[0090] The Learning Department can estimate the user's emotions and adjust the value learning method based on the estimated emotions of the user. For example, if the user is sad, a conversation using kind words is carried out to soothe the emotions and the value learning is advanced. If the user is excited, a calm tone is used to carry out a conversation to organize the values calmly and the value learning can be advanced. If the user is feeling anxious, the value learning can also be advanced while showing specific examples to give a sense of security. By adjusting the value learning method according to the user's emotions, more effective learning becomes possible.

[0091] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is sad, it can suggest eco-friendly options using gentle language. If the user is excited, it can suggest eco-friendly options using specific examples. If the user is feeling anxious, it can suggest eco-friendly options with detailed explanations to provide reassurance. By adjusting the way suggestions are presented according to the user's emotions, more effective suggestions become possible.

[0092] The optimization unit can estimate the user's emotions and adjust the optimization criteria based on those emotions. For example, if the user is sad, it can optimize using gentle words to alleviate their feelings. If the user is excited, it can optimize in a calm tone to help them calmly organize their values. If the user is anxious, it can optimize by showing concrete examples to provide reassurance. By adjusting the optimization criteria according to the user's emotions, more effective optimization becomes possible.

[0093] The learning unit can estimate the user's emotions and determine learning priorities based on those estimates. For example, if the user is stressed, learning can begin with relaxing content. If the user is excited, learning can begin with interesting content. If the user is anxious, learning can begin with reassuring content. By prioritizing learning according to the user's emotions, more effective learning becomes possible.

[0094] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, it can prioritize suggesting options that promote relaxation. If the user is excited, it can prioritize suggesting options that pique their interest. If the user is anxious, it can prioritize suggesting options that provide a sense of security. By prioritizing suggestions according to the user's emotions, more effective suggestions can be made.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The learning unit learns the user's values. The learning unit can learn values ​​through interaction with the user using natural language processing. It can also learn values ​​by analyzing the user's past behavior history and social media activity. Furthermore, it can learn more comprehensive values ​​by incorporating the opinions of the user's family and friends. Step 2: The suggestion unit proposes eco-friendly options based on the values ​​learned by the learning unit. The suggestion unit can propose eco-friendly funeral options such as natural burial, tree burial, and bio-urn. It can also propose options such as upcycling, donating, and recycling of belongings. Furthermore, the suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on those estimated emotions. Step 3: The optimization unit proposes the optimal end-of-life plan based on the options suggested by the proposal unit. The optimization unit can propose the optimal end-of-life plan based on the user's choices. The optimization unit can also estimate the user's emotions and adjust the optimization criteria based on the estimated emotions.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the learning unit, proposal unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns values ​​through interaction with the user using natural language processing. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes eco-friendly options based on the learned values. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal end-of-life plan based on the proposed options. 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.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the learning unit, proposal unit, and optimization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns values ​​through interaction with the user using natural language processing. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes eco-friendly options based on the learned values. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal end-of-life plan based on the proposed options. 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.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the learning unit, proposal unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns values ​​through interaction with the user using natural language processing. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes eco-friendly options based on the learned values. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal end-of-life plan based on the proposed options. 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.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the learning unit, proposal unit, and optimization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns values ​​through interaction with the user using natural language processing. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes eco-friendly options based on the learned values. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes an optimal end-of-life plan based on the proposed options. 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A learning unit that learns the user's values, Based on the values ​​learned by the aforementioned learning unit, the proposal unit proposes eco-friendly options, An optimization unit proposes the optimal end-of-life plan based on the options proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned learning unit, It features an interactive interface that uses natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We offer eco-friendly funeral options such as natural burials, tree burials, and bio-urn services. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose options such as upcycling, donating, and recycling inherited items. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, We propose the optimal end-of-life planning based on the user's choices. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, It estimates the user's emotions and adjusts the value learning method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, Analyze the user's past behavior history and learn about changes in their values ​​in real time. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, We incorporate the opinions of users' family and friends to learn a more inclusive set of values. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, It estimates the user's emotions and determines learning priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, Analyze users' social media activity and use that information to learn their values. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, Analyze users' purchase history to learn their values ​​regarding environmental considerations. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, By referring to the user's past selection history, we suggest more appropriate and eco-friendly options. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, We propose the most suitable eco-friendly options, taking into account the user's local environmental regulations and culture. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, We propose the most eco-friendly option, taking into account the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, We propose cost-effective and eco-friendly options, taking into account the user's financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, It estimates user sentiment and adjusts optimization criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, Referencing the user's past selection history, the system updates the optimal end-of-life planning in real time. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, We incorporate the opinions of the user's family and friends to propose a more comprehensive end-of-life planning solution. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, We propose the optimal end-of-life planning solution, taking into account the user's local environmental regulations and culture. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, We propose the optimal end-of-life plan, taking into account the user's health condition. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 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 learning unit that learns the user's values, Based on the values ​​learned by the aforementioned learning unit, the proposal unit proposes eco-friendly options, An optimization unit proposes the optimal end-of-life plan based on the options proposed by the aforementioned proposal unit, Equipped with A system characterized by the following features.

2. The aforementioned learning unit, It features an interactive interface that uses natural language processing. The system according to feature 1.

3. The aforementioned proposal section is, We offer eco-friendly funeral options such as natural burials, tree burials, and bio-urn services. The system according to feature 1.

4. The aforementioned proposal section is, We propose options such as upcycling, donating, and recycling inherited items. The system according to feature 1.

5. The optimization unit, We propose the optimal end-of-life planning based on the user's choices. The system according to feature 1.

6. The aforementioned learning unit, It estimates the user's emotions and adjusts the value learning method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned learning unit, Analyze the user's past behavior history and learn about changes in their values ​​in real time. The system according to feature 1.

8. The aforementioned learning unit, We incorporate the opinions of users' family and friends to learn a more inclusive set of values. The system according to feature 1.

Images