System

Abstract

An object of the system according to the embodiment is to propose a special experience or a gift according to an age of a user and celebrate a milestone of life.SOLUTION: A system according to an embodiment includes a proposal unit, an event planning unit, and a roadmap generation unit. The suggestion unit suggests a special experience or a gift according to the age of the user. The event planning unit plans an event that celebrates a milestone of the user's life. The roadmap generator provides a roadmap of the user's personal growth and enjoyment.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to adequately propose special experiences or gifts appropriate to the user's age and celebrate milestones in life.

[0005] The system according to the embodiment aims to propose special experiences and gifts according to the user's age and celebrate milestones in life. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, an event planning unit, and a roadmap generation unit. The suggestion unit suggests special experiences and gifts according to the user's age. The event planning unit plans events to celebrate milestones in the user's life. The roadmap generation unit provides the user with a roadmap for personal growth and enjoyment. [Effects of the Invention]

[0007] The system according to the embodiment can suggest special experiences and gifts according to the user's age and celebrate milestones in life. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The personalized anniversary planner according to an embodiment of the present invention is a system that suggests special experiences, gifts, and events tailored to a user's age, providing a roadmap for personal growth and enjoyment, allowing the user to enjoy aging and actively celebrate life's milestones.

[0029] A personalized anniversary planner according to an embodiment includes a suggestion unit, an event planning unit, and a roadmap generation unit. The suggestion unit suggests special experiences and gifts based on the user's age. For example, the generation AI suggests special experiences and gifts based on the user's age and preferences. Input to the generation AI is prompts including information such as the user's age, preferences, and past experiences, and the generation AI generates optimal suggestions based on the prompts. The event planning unit plans events to celebrate milestones in the user's life. For example, the generation AI plans events tailored to milestones in the user's life. Input to the generation AI is prompts including information such as the user's age, preferences, and past event data, and the generation AI generates optimal event plans based on the prompts. The roadmap generation unit provides a roadmap for the user's personal growth and enjoyment. For example, the generation AI provides a roadmap for personal growth and enjoyment based on the user's age. Input to the generation AI is prompts including information such as the user's age, goals, and interests, and the generation AI generates an optimal roadmap based on the prompts. This allows the personalized anniversary planner according to an embodiment to help the user enjoy aging and actively celebrate milestones in life. For example, you can enjoy a special experience on your 30th birthday and an event with friends and family on your 40th birthday. Through a roadmap of personal growth and fun, you can set goals for the future and develop a positive attitude towards aging.

[0030] The suggestion unit can analyze the user's past experience data and suggest activities that the user has not experienced before. The suggestion unit, for example, analyzes the user's past travel history and activity participation history and suggests activities that the user has not experienced before. For example, it suggests special experiences in countries or regions the user has not visited before. The suggestion unit also analyzes the user's past hobby activities and suggests new hobbies and activities. For example, it suggests sports or art workshops that the user has not tried yet. The suggestion unit also analyzes the user's past event participation history and suggests events that the user has not experienced before. For example, it suggests festivals or concerts that the user has not attended yet. In this way, by analyzing the user's past experience data and suggesting activities that the user has not experienced before, it is possible to provide new experiences.

[0031] The suggestion unit can customize an appropriate experience by taking into account the user's health condition and fitness level. The suggestion unit, for example, analyzes the user's health condition and fitness level and suggests an appropriate experience. For example, it suggests a relaxation spa or fitness retreat based on the user's health condition. The suggestion unit also analyzes the user's fitness data and suggests an appropriate exercise program. For example, it suggests yoga classes or personal training tailored to the user's physical strength. The suggestion unit also analyzes the user's health checkup results and suggests experiences that are useful for health management. For example, it suggests nutritional counseling or health seminars based on the health checkup results. In this way, it is possible to support the user's health by providing an appropriate experience by taking into account the user's health condition and fitness level.

[0032] The suggestion unit can suggest relevant online courses and workshops based on the user's hobbies and interests. For example, the suggestion unit analyzes the user's hobbies and interests and suggests relevant online courses. For example, it suggests specialized online courses in fields that interest the user. The suggestion unit also analyzes the user's hobby activities and suggests relevant workshops. For example, it suggests art and craft workshops that interest the user. The suggestion unit also analyzes the user's interests and suggests online courses for discovering new hobbies. For example, it suggests online courses in fields that the user has not yet tried. This makes it possible to increase the user's motivation to learn by providing relevant online courses and workshops based on the user's hobbies and interests.

[0033] The suggestion unit can generate an experience plan that incorporates the opinions of the user's family and friends. For example, the suggestion unit collects the opinions of the user's family and friends and generates an experience plan based on them. For example, it suggests activities recommended by family and friends. The suggestion unit also customizes the experience plan based on feedback from the user's family and friends. For example, it suggests new experiences based on experiences that family and friends have enjoyed in the past. The suggestion unit also suggests joint experiences with the user's family and friends. For example, it suggests events and travel plans that can be enjoyed together with family and friends. In this way, by providing an experience plan that incorporates the opinions of the user's family and friends, it is possible to improve user satisfaction.

[0034] The event planning unit can analyze the user's past event data and incorporate elements of successful events. For example, the event planning unit analyzes the user's past event data and proposes a plan that incorporates elements of successful events. For example, it incorporates elements of parties that have been successful in the past. The event planning unit also analyzes the user's past event participation history and incorporates elements of successful events. For example, it proposes a new event plan based on the user's evaluations of events that they have participated in in the past. The event planning unit also proposes a customized plan that incorporates elements of successful events based on the user's past event data. For example, it incorporates elements of events that the user has enjoyed in the past. In this way, by analyzing the user's past event data and incorporating elements of successful events, the success rate of the event can be increased.

[0035] The event planning unit can propose an event plan that takes into account the user's cultural background and traditions. For example, the event planning unit considers the user's cultural background and traditions and proposes an event plan based on them. For example, it may incorporate traditional rituals and festivals rooted in the user's culture. The event planning unit also analyzes the user's cultural background and proposes an event plan based on it. For example, it may incorporate festivals and religious ceremonies in the user's region. The event planning unit also considers the user's traditions and proposes an event plan based on them. For example, it may incorporate traditional events of the user's family. In this way, by providing an event plan that takes into account the user's cultural background and traditions, it is possible to improve user satisfaction.

[0036] The event planning unit can suggest events at the user's workplace or community to strengthen social connections. The event planning unit, for example, suggests events at the user's workplace or community to strengthen social connections. For example, it suggests an event to which work colleagues or community members are invited. The event planning unit also plans events at the user's workplace or community to strengthen social connections. For example, it suggests team building events or local gatherings. The event planning unit also customizes events at the user's workplace or community to strengthen social connections. For example, it suggests events tailored to the specific needs of the user's workplace or community. In this way, it is possible to strengthen social connections by providing events at the user's workplace or community.

[0037] The event planning unit can plan themed events related to the user's pets or hobbies. The event planning unit, for example, plans themed events related to the user's pets. For example, it proposes pet birthday parties and events to enjoy with pets. The event planning unit also plans themed events related to the user's hobbies. For example, it proposes hobby exhibitions and workshops. The event planning unit also customizes themed events related to the user's pets or hobbies. For example, it proposes special events tailored to the user's pets or hobbies. In this way, by providing themed events related to the user's pets or hobbies, it is possible to improve user satisfaction.

[0038] The roadmap generation unit can analyze the user's career goals and skill set and propose a specific growth plan. The roadmap generation unit, for example, analyzes the user's career goals and skill set and proposes a specific growth plan. For example, it proposes training for career advancement and courses for skill development. The roadmap generation unit also analyzes the user's career goals and proposes a growth plan based on them. For example, it proposes a plan for the user to acquire the skills necessary for the career they are aiming for. The roadmap generation unit also analyzes the user's skill set and proposes a growth plan based on them. For example, it proposes a plan for further developing the skills the user has. In this way, it is possible to support the user's growth by analyzing the user's career goals and skill set and providing a specific growth plan.

[0039] The roadmap generation unit can provide a feasible fun plan by taking into account the user's lifestyle and daily habits. The roadmap generation unit, for example, provides a feasible fun plan by taking into account the user's lifestyle and daily habits. For example, it suggests hobbies and activities that can be incorporated into daily routines. The roadmap generation unit also analyzes the user's lifestyle habits and proposes a fun plan based on them. For example, it suggests a fun plan that matches the user's eating habits and exercise habits. The roadmap generation unit also analyzes the user's lifestyle and proposes a fun plan based on them. For example, it suggests a fun plan that matches the user's work and home schedules. In this way, by providing a feasible fun plan by taking into account the user's lifestyle and daily habits, it is possible to improve user satisfaction.

[0040] The roadmap generation unit can suggest a collaborative growth plan that can be shared with the user's family and friends. The roadmap generation unit, for example, suggests a collaborative growth plan that can be shared with the user's family and friends. For example, it suggests a fitness program that the whole family can participate in or an online course that can be learned together with friends. The roadmap generation unit also suggests collaborative projects with the user's family and friends. For example, it suggests a hobby project to work on together with family and friends. The roadmap generation unit also suggests goal setting with the user's family and friends. For example, it suggests a plan to set goals to be achieved together with family and friends. This makes it possible to strengthen the user's growth and social connections by providing a collaborative growth plan that can be shared with the user's family and friends.

[0041] The roadmap generation unit can provide a new skill acquisition plan based on the user's hobbies and interests. The roadmap generation unit provides, for example, a new skill acquisition plan based on the user's hobbies and interests. For example, it proposes a plan for acquiring specialized skills in a field in which the user is interested. The roadmap generation unit also analyzes the user's hobby activities and proposes a new skill acquisition plan based on the analysis. For example, it proposes a plan for acquiring hobby skills that the user has not yet tried. The roadmap generation unit also analyzes the user's interests and proposes a new skill acquisition plan based on the analysis. For example, it proposes a plan for acquiring new skills in a field in which the user is interested. In this way, it is possible to support the user's growth by providing a new skill acquisition plan based on the user's hobbies and interests.

[0042] The suggestion unit can analyze the user's past successful experiences and suggest new challenges based on them. The suggestion unit, for example, analyzes the user's past successful experiences and suggests new challenges based on them. For example, it suggests new challenges based on past successful projects or goals that have been achieved. The suggestion unit also analyzes the user's past successful experiences and suggests a plan to set a new goal based on them. For example, it sets a new goal based on goals that have been achieved in the past. The suggestion unit also analyzes the user's past successful experiences and suggests a new skill acquisition plan based on them. For example, it suggests a plan to acquire a new skill based on skills that have been acquired in the past. In this way, it is possible to support the user's growth by analyzing the user's past successful experiences and providing new challenges based on them.

[0043] The suggestion unit can provide positive stories about milestones in the user's life to increase motivation. The suggestion unit, for example, provides positive stories about milestones in the user's life to increase motivation. For example, it shares success stories and inspiring stories of other users. The suggestion unit also provides positive episodes about milestones in the user's life to increase motivation. For example, the user sets new goals based on goals and successful experiences that they have achieved in the past. The suggestion unit also provides positive messages about milestones in the user's life to increase motivation. For example, it provides new messages based on words of encouragement or messages of gratitude that the user has received in the past. In this way, the user's motivation can be increased by providing positive stories about milestones in their life.

[0044] The suggestion unit can provide positive statistical data and research results related to the user's age. For example, the suggestion unit provides positive statistical data related to the user's age. For example, it provides data related to improvements in health and happiness as one ages. The suggestion unit also provides positive research results related to the user's age. For example, it provides research results related to increases in knowledge and experience as one ages. The suggestion unit also suggests a plan for setting new goals based on the positive statistical data and research results related to the user's age. For example, it suggests a plan for setting new challenges and goals as one ages. In this way, by providing positive statistical data and research results related to the user's age, it is possible to foster a positive attitude toward aging in the user.

[0045] The suggestion unit can introduce success stories of people of the same age as the user to encourage empathy. The suggestion unit, for example, introduces success stories of people of the same age as the user to encourage empathy. For example, users of the same age share goals and successful experiences that they have achieved. The suggestion unit also proposes a plan to set new goals based on success stories of people of the same age as the user. For example, it sets new goals based on goals that users of the same age have achieved. The suggestion unit also proposes new challenges based on success stories of people of the same age as the user. For example, it proposes new challenges based on successful projects by users of the same age. In this way, by introducing success stories of people of the same age as the user, it is possible to encourage empathy and increase motivation in the user.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The suggestion unit can analyze the user's past successful experiences and suggest new challenges based on them. For example, it can suggest new challenges based on past successful projects or goals that have been achieved. The suggestion unit can also analyze the user's past successful experiences and suggest a plan to set a new goal based on them. For example, it can set a new goal based on goals that have been achieved in the past. The suggestion unit can also analyze the user's past successful experiences and suggest a new skill acquisition plan based on them. For example, it can suggest a plan to acquire a new skill based on skills that have been acquired in the past. In this way, it is possible to support the user's growth by analyzing the user's past successful experiences and providing new challenges based on them.

[0048] The suggestion unit can increase motivation by providing positive stories about milestones in the user's life. For example, it can share success stories or inspiring stories of other users. The suggestion unit can also increase motivation by providing positive episodes about milestones in the user's life. For example, it can set new goals based on goals the user has achieved in the past or successful experiences the user has had. The suggestion unit can also increase motivation by providing positive messages about milestones in the user's life. For example, it can provide new messages based on words of encouragement or messages of gratitude the user has received in the past. In this way, it is possible to increase motivation by providing positive stories about milestones in the user's life.

[0049] The suggestion unit can provide positive statistical data and research results related to the user's age. For example, it provides positive statistical data related to the user's age. For example, it provides data related to improvements in health and happiness as one ages. The suggestion unit also provides positive research results related to the user's age. For example, it provides research results related to increases in knowledge and experience as one ages. The suggestion unit also suggests a plan for setting new goals based on the positive statistical data and research results related to the user's age. For example, it suggests a plan for setting new challenges and goals as one ages. In this way, by providing positive statistical data and research results related to the user's age, it is possible to foster a positive attitude toward aging in the user.

[0050] The suggestion unit can introduce success stories of people of the same age as the user to encourage empathy. For example, users of the same age can share goals and successful experiences that they have achieved. The suggestion unit also proposes a plan to set new goals based on success stories of the user's same age. For example, it can set new goals based on goals that users of the same age have achieved. The suggestion unit also proposes new challenges based on success stories of the user's same age. For example, it can propose new challenges based on successful projects by users of the same age. In this way, by introducing success stories of the user's same age, it is possible to encourage empathy and increase motivation in the user.

[0051] The suggestion unit can analyze the user's past experience data and suggest activities that the user has not experienced before. For example, it can analyze the user's past travel history and activity participation history to suggest activities that the user has not experienced before. For example, it can suggest special experiences in countries or regions the user has not visited before. The suggestion unit can also analyze the user's past hobby activities and suggest new hobbies and activities. For example, it can suggest sports or art workshops that the user has not tried yet. The suggestion unit can also analyze the user's past event participation history and suggest events that the user has not experienced before. For example, it can suggest festivals or concerts that the user has not attended yet. In this way, it is possible to provide new experiences by analyzing the user's past experience data and suggesting activities that the user has not experienced before.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The suggestion unit suggests special experiences and gifts based on the user's age. For example, the generation AI suggests special experiences and gifts based on the user's age and preferences. The input to the generation AI is a prompt containing information such as the user's age, preferences, and past experiences, and the generation AI generates optimal suggestions based on the prompt. Step 2: The event planning department plans an event to celebrate a milestone in the user's life. For example, the generation AI plans an event that coincides with a milestone in the user's life. The input to the generation AI is a prompt that includes the user's age, preferences, and data on past events, and the generation AI generates an optimal event plan based on the prompt. Step 3: The roadmap generator provides the user with a roadmap for personal growth and enjoyment. For example, the generation AI provides a roadmap for personal growth and enjoyment appropriate to the user's age. The input to the generation AI is a prompt containing information such as the user's age, goals, and interests, and the generation AI generates an optimal roadmap based on the prompt.

[0054] (Example 2) The personalized anniversary planner according to an embodiment of the present invention is a system that suggests special experiences, gifts, and events tailored to a user's age, providing a roadmap for personal growth and enjoyment, allowing the user to enjoy aging and actively celebrate life's milestones.

[0055] A personalized anniversary planner according to an embodiment includes a suggestion unit, an event planning unit, and a roadmap generation unit. The suggestion unit suggests special experiences and gifts based on the user's age. For example, the generation AI suggests special experiences and gifts based on the user's age and preferences. Input to the generation AI is prompts including information such as the user's age, preferences, and past experiences, and the generation AI generates optimal suggestions based on the prompts. The event planning unit plans events to celebrate milestones in the user's life. For example, the generation AI plans events tailored to milestones in the user's life. Input to the generation AI is prompts including information such as the user's age, preferences, and past event data, and the generation AI generates optimal event plans based on the prompts. The roadmap generation unit provides a roadmap for the user's personal growth and enjoyment. For example, the generation AI provides a roadmap for personal growth and enjoyment based on the user's age. Input to the generation AI is prompts including information such as the user's age, goals, and interests, and the generation AI generates an optimal roadmap based on the prompts. This allows the personalized anniversary planner according to an embodiment to help the user enjoy aging and actively celebrate milestones in life. For example, you can enjoy a special experience on your 30th birthday and an event with friends and family on your 40th birthday. Through a roadmap of personal growth and fun, you can set goals for the future and develop a positive attitude towards aging.

[0056] The suggestion unit can analyze the user's past experience data and suggest activities that the user has not experienced before. The suggestion unit, for example, analyzes the user's past travel history and activity participation history and suggests activities that the user has not experienced before. For example, it suggests special experiences in countries or regions the user has not visited before. The suggestion unit also analyzes the user's past hobby activities and suggests new hobbies and activities. For example, it suggests sports or art workshops that the user has not tried yet. The suggestion unit also analyzes the user's past event participation history and suggests events that the user has not experienced before. For example, it suggests festivals or concerts that the user has not attended yet. In this way, by analyzing the user's past experience data and suggesting activities that the user has not experienced before, it is possible to provide new experiences.

[0057] The suggestion unit can customize an appropriate experience by taking into account the user's health condition and fitness level. The suggestion unit, for example, analyzes the user's health condition and fitness level and suggests an appropriate experience. For example, it suggests a relaxation spa or fitness retreat based on the user's health condition. The suggestion unit also analyzes the user's fitness data and suggests an appropriate exercise program. For example, it suggests yoga classes or personal training tailored to the user's physical strength. The suggestion unit also analyzes the user's health checkup results and suggests experiences that are useful for health management. For example, it suggests nutritional counseling or health seminars based on the health checkup results. In this way, it is possible to support the user's health by providing an appropriate experience by taking into account the user's health condition and fitness level.

[0058] The suggestion unit can use the emotion estimation function to suggest an experience that will bring the user the most enjoyment. The suggestion unit, for example, uses the emotion estimation function to suggest an experience that the user is likely to enjoy the most. For example, the suggestion unit analyzes the user's past emotion data to suggest an experience that evokes a strong emotion of joy. The suggestion unit also analyzes the user's facial expression data to suggest an experience that expresses the emotion of joy. For example, the suggestion unit suggests a new experience based on an experience that made the user smile. The suggestion unit also analyzes the user's voice data to suggest an experience that expresses the emotion of joy. For example, the suggestion unit suggests a new experience based on an experience in which the user spoke happily. In this way, the emotion estimation function can be used to provide an experience that the user is likely to enjoy the most, thereby improving user satisfaction.

[0059] The suggestion unit can suggest relevant online courses and workshops based on the user's hobbies and interests. For example, the suggestion unit analyzes the user's hobbies and interests and suggests relevant online courses. For example, it suggests specialized online courses in fields that interest the user. The suggestion unit also analyzes the user's hobby activities and suggests relevant workshops. For example, it suggests art and craft workshops that interest the user. The suggestion unit also analyzes the user's interests and suggests online courses for discovering new hobbies. For example, it suggests online courses in fields that the user has not yet tried. This makes it possible to increase the user's motivation to learn by providing relevant online courses and workshops based on the user's hobbies and interests.

[0060] The suggestion unit can generate an experience plan that incorporates the opinions of the user's family and friends. For example, the suggestion unit collects the opinions of the user's family and friends and generates an experience plan based on them. For example, it suggests activities recommended by family and friends. The suggestion unit also customizes the experience plan based on feedback from the user's family and friends. For example, it suggests new experiences based on experiences that family and friends have enjoyed in the past. The suggestion unit also suggests joint experiences with the user's family and friends. For example, it suggests events and travel plans that can be enjoyed together with family and friends. In this way, by providing an experience plan that incorporates the opinions of the user's family and friends, it is possible to improve user satisfaction.

[0061] The suggestion unit can use the emotion estimation function to suggest gifts based on the user's emotions. The suggestion unit, for example, uses the emotion estimation function to suggest gifts based on the user's emotions. For example, the suggestion unit analyzes the user's emotion data and suggests gifts that reflect a strong emotion of joy. The suggestion unit also analyzes the user's facial expression data and suggests gifts that express the emotion of joy. For example, the suggestion unit suggests a new gift based on gifts that make the user smile. The suggestion unit also analyzes the user's voice data and suggests gifts that express the emotion of joy. For example, the suggestion unit suggests a new gift based on gifts that the user talked about happily. In this way, by using the emotion estimation function to provide gifts based on the user's emotions, user satisfaction can be improved.

[0062] The event planning unit can analyze the user's past event data and incorporate elements of successful events. For example, the event planning unit analyzes the user's past event data and proposes a plan that incorporates elements of successful events. For example, it incorporates elements of parties that have been successful in the past. The event planning unit also analyzes the user's past event participation history and incorporates elements of successful events. For example, it proposes a new event plan based on the user's evaluations of events that they have participated in in the past. The event planning unit also proposes a customized plan that incorporates elements of successful events based on the user's past event data. For example, it incorporates elements of events that the user has enjoyed in the past. In this way, by analyzing the user's past event data and incorporating elements of successful events, the success rate of the event can be increased.

[0063] The event planning unit can propose an event plan that takes into account the user's cultural background and traditions. For example, the event planning unit considers the user's cultural background and traditions and proposes an event plan based on them. For example, it may incorporate traditional rituals and festivals rooted in the user's culture. The event planning unit also analyzes the user's cultural background and proposes an event plan based on it. For example, it may incorporate festivals and religious ceremonies in the user's region. The event planning unit also considers the user's traditions and proposes an event plan based on them. For example, it may incorporate traditional events of the user's family. In this way, by providing an event plan that takes into account the user's cultural background and traditions, it is possible to improve user satisfaction.

[0064] The event planning unit can use the emotion estimation function to plan an event that will most move the user. The event planning unit, for example, uses the emotion estimation function to plan an event that will most move the user. For example, the event planning unit analyzes the user's emotion data and suggests an event that will evoke a strong emotion. The event planning unit also analyzes the user's facial expression data and plans an event that will express an emotion. For example, the event planning unit suggests a new event based on an event that made the user cry. The event planning unit also analyzes the user's voice data and plans an event that will express an emotion. For example, the event planning unit suggests a new event based on an event that the user talked about with emotion. In this way, the emotion estimation function can be used to provide an event that will most move the user, thereby improving user satisfaction.

[0065] The event planning unit can suggest events at the user's workplace or community to strengthen social connections. The event planning unit, for example, suggests events at the user's workplace or community to strengthen social connections. For example, it suggests an event to which work colleagues or community members are invited. The event planning unit also plans events at the user's workplace or community to strengthen social connections. For example, it suggests team building events or local gatherings. The event planning unit also customizes events at the user's workplace or community to strengthen social connections. For example, it suggests events tailored to the specific needs of the user's workplace or community. In this way, it is possible to strengthen social connections by providing events at the user's workplace or community.

[0066] The event planning unit can plan themed events related to the user's pets or hobbies. The event planning unit, for example, plans themed events related to the user's pets. For example, it proposes pet birthday parties and events to enjoy with pets. The event planning unit also plans themed events related to the user's hobbies. For example, it proposes hobby exhibitions and workshops. The event planning unit also customizes themed events related to the user's pets or hobbies. For example, it proposes special events tailored to the user's pets or hobbies. In this way, by providing themed events related to the user's pets or hobbies, it is possible to improve user satisfaction.

[0067] The event planning unit can use the emotion estimation function to suggest an event theme based on the user's emotion. The event planning unit, for example, uses the emotion estimation function to suggest an event theme based on the user's emotion. For example, the event planning unit analyzes the user's emotion data and suggests a theme that expresses a strong emotion of joy. The event planning unit also analyzes the user's facial expression data and suggests a theme that expresses a joyful emotion. For example, the event planning unit suggests a new event theme based on a theme that makes the user smile. The event planning unit also analyzes the user's voice data and suggests a theme that expresses a joyful emotion. For example, the event planning unit suggests a new event theme based on a theme that the user talked about happily. In this way, by using the emotion estimation function to provide an event theme based on the user's emotion, user satisfaction can be improved.

[0068] The roadmap generation unit can analyze the user's career goals and skill set and propose a specific growth plan. The roadmap generation unit, for example, analyzes the user's career goals and skill set and proposes a specific growth plan. For example, it proposes training for career advancement and courses for skill development. The roadmap generation unit also analyzes the user's career goals and proposes a growth plan based on them. For example, it proposes a plan for the user to acquire the skills necessary for the career they are aiming for. The roadmap generation unit also analyzes the user's skill set and proposes a growth plan based on them. For example, it proposes a plan for further developing the skills the user has. In this way, it is possible to support the user's growth by analyzing the user's career goals and skill set and providing a specific growth plan.

[0069] The roadmap generation unit can provide a feasible fun plan by taking into account the user's lifestyle and daily habits. The roadmap generation unit, for example, provides a feasible fun plan by taking into account the user's lifestyle and daily habits. For example, it suggests hobbies and activities that can be incorporated into daily routines. The roadmap generation unit also analyzes the user's lifestyle habits and proposes a fun plan based on them. For example, it suggests a fun plan that matches the user's eating habits and exercise habits. The roadmap generation unit also analyzes the user's lifestyle and proposes a fun plan based on them. For example, it suggests a fun plan that matches the user's work and home schedules. In this way, by providing a feasible fun plan by taking into account the user's lifestyle and daily habits, it is possible to improve user satisfaction.

[0070] The roadmap generation unit can use the emotion estimation function to propose a growth plan that will most motivate the user. The roadmap generation unit, for example, uses the emotion estimation function to propose a growth plan that will most motivate the user. For example, the roadmap generation unit analyzes the user's emotion data to propose a plan that will increase motivation. The roadmap generation unit also analyzes the user's facial expression data to propose a plan that will increase motivation. For example, the roadmap generation unit proposes a new growth plan based on a plan that motivated the user. The roadmap generation unit also analyzes the user's voice data to propose a plan that will increase motivation. For example, the roadmap generation unit proposes a new growth plan based on a plan that the user talked about enthusiastically. In this way, the emotion estimation function can be used to support the user's growth by providing a growth plan that will most motivate the user.

[0071] The roadmap generation unit can suggest a collaborative growth plan that can be shared with the user's family and friends. The roadmap generation unit, for example, suggests a collaborative growth plan that can be shared with the user's family and friends. For example, it suggests a fitness program that the whole family can participate in or an online course that can be learned together with friends. The roadmap generation unit also suggests collaborative projects with the user's family and friends. For example, it suggests a hobby project to work on together with family and friends. The roadmap generation unit also suggests goal setting with the user's family and friends. For example, it suggests a plan to set goals to be achieved together with family and friends. This makes it possible to strengthen the user's growth and social connections by providing a collaborative growth plan that can be shared with the user's family and friends.

[0072] The roadmap generation unit can provide a new skill acquisition plan based on the user's hobbies and interests. The roadmap generation unit provides, for example, a new skill acquisition plan based on the user's hobbies and interests. For example, it proposes a plan for acquiring specialized skills in a field in which the user is interested. The roadmap generation unit also analyzes the user's hobby activities and proposes a new skill acquisition plan based on the analysis. For example, it proposes a plan for acquiring hobby skills that the user has not yet tried. The roadmap generation unit also analyzes the user's interests and proposes a new skill acquisition plan based on the analysis. For example, it proposes a plan for acquiring new skills in a field in which the user is interested. In this way, it is possible to support the user's growth by providing a new skill acquisition plan based on the user's hobbies and interests.

[0073] The roadmap generation unit can use the emotion estimation function to propose fun plans based on the user's emotions. The roadmap generation unit, for example, uses the emotion estimation function to propose fun plans based on the user's emotions. For example, it analyzes the user's emotion data and proposes plans that include a strong emotion of joy. The roadmap generation unit also analyzes the user's facial expression data and proposes plans that express the emotion of joy. For example, it proposes a new fun plan based on a plan that made the user smile. The roadmap generation unit also analyzes the user's voice data and proposes plans that express the emotion of joy. For example, it proposes a new fun plan based on a plan that the user talked about happily. In this way, by using the emotion estimation function to provide fun plans based on the user's emotions, it is possible to improve user satisfaction.

[0074] The suggestion unit can analyze the user's past successful experiences and suggest new challenges based on them. The suggestion unit, for example, analyzes the user's past successful experiences and suggests new challenges based on them. For example, it suggests new challenges based on past successful projects or goals that have been achieved. The suggestion unit also analyzes the user's past successful experiences and suggests a plan to set a new goal based on them. For example, it sets a new goal based on goals that have been achieved in the past. The suggestion unit also analyzes the user's past successful experiences and suggests a new skill acquisition plan based on them. For example, it suggests a plan to acquire a new skill based on skills that have been acquired in the past. In this way, it is possible to support the user's growth by analyzing the user's past successful experiences and providing new challenges based on them.

[0075] The suggestion unit can provide positive stories about milestones in the user's life to increase motivation. The suggestion unit, for example, provides positive stories about milestones in the user's life to increase motivation. For example, it shares success stories and inspiring stories of other users. The suggestion unit also provides positive episodes about milestones in the user's life to increase motivation. For example, the user sets new goals based on goals and successful experiences that they have achieved in the past. The suggestion unit also provides positive messages about milestones in the user's life to increase motivation. For example, it provides new messages based on words of encouragement or messages of gratitude that the user has received in the past. In this way, the user's motivation can be increased by providing positive stories about milestones in their life.

[0076] The suggestion unit can use the emotion estimation function to generate a message that evokes the most positive emotion for the user. The suggestion unit, for example, uses the emotion estimation function to generate a message that evokes the most positive emotion for the user. For example, the suggestion unit analyzes the user's emotion data to generate a message that evokes a strong positive emotion. The suggestion unit also analyzes the user's facial expression data to generate a message that expresses a positive emotion. For example, the suggestion unit generates a new message based on a message that made the user smile. The suggestion unit also analyzes the user's voice data to generate a message that expresses a positive emotion. For example, the suggestion unit generates a new message based on a message that the user spoke in a happy manner. In this way, by using the emotion estimation function to provide a message that evokes the most positive emotion for the user, it is possible to improve user satisfaction.

[0077] The suggestion unit can provide positive statistical data and research results related to the user's age. For example, the suggestion unit provides positive statistical data related to the user's age. For example, it provides data related to improvements in health and happiness as one ages. The suggestion unit also provides positive research results related to the user's age. For example, it provides research results related to increases in knowledge and experience as one ages. The suggestion unit also suggests a plan for setting new goals based on the positive statistical data and research results related to the user's age. For example, it suggests a plan for setting new challenges and goals as one ages. In this way, by providing positive statistical data and research results related to the user's age, it is possible to foster a positive attitude toward aging in the user.

[0078] The suggestion unit can introduce success stories of people of the same age as the user to encourage empathy. The suggestion unit, for example, introduces success stories of people of the same age as the user to encourage empathy. For example, users of the same age share goals and successful experiences that they have achieved. The suggestion unit also proposes a plan to set new goals based on success stories of people of the same age as the user. For example, it sets new goals based on goals that users of the same age have achieved. The suggestion unit also proposes new challenges based on success stories of people of the same age as the user. For example, it proposes new challenges based on successful projects by users of the same age. In this way, by introducing success stories of people of the same age as the user, it is possible to encourage empathy and increase motivation in the user.

[0079] The suggestion unit can use the emotion estimation function to provide a positive message based on the user's emotion. The suggestion unit, for example, uses the emotion estimation function to provide a positive message based on the user's emotion. For example, the suggestion unit analyzes the user's emotion data and provides a message with a strong positive emotion. The suggestion unit also analyzes the user's facial expression data and provides a message that expresses a positive emotion. For example, a new message is provided based on a message that made the user smile. The suggestion unit also analyzes the user's voice data and provides a message that expresses a positive emotion. For example, a new message is provided based on a message that the user spoke in a happy manner. In this way, by using the emotion estimation function to provide a positive message based on the user's emotion, it is possible to improve user satisfaction.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The suggestion unit can analyze the user's past successful experiences and suggest new challenges based on them. For example, it can suggest new challenges based on past successful projects or goals that have been achieved. The suggestion unit can also analyze the user's past successful experiences and suggest a plan to set a new goal based on them. For example, it can set a new goal based on goals that have been achieved in the past. The suggestion unit can also analyze the user's past successful experiences and suggest a new skill acquisition plan based on them. For example, it can suggest a plan to acquire a new skill based on skills that have been acquired in the past. In this way, it is possible to support the user's growth by analyzing the user's past successful experiences and providing new challenges based on them.

[0082] The suggestion unit can increase motivation by providing positive stories about milestones in the user's life. For example, it can share success stories or inspiring stories of other users. The suggestion unit can also increase motivation by providing positive episodes about milestones in the user's life. For example, it can set new goals based on goals the user has achieved in the past or successful experiences the user has had. The suggestion unit can also increase motivation by providing positive messages about milestones in the user's life. For example, it can provide new messages based on words of encouragement or messages of gratitude the user has received in the past. In this way, it is possible to increase motivation by providing positive stories about milestones in the user's life.

[0083] The suggestion unit can provide positive statistical data and research results related to the user's age. For example, it provides positive statistical data related to the user's age. For example, it provides data related to improvements in health and happiness as one ages. The suggestion unit also provides positive research results related to the user's age. For example, it provides research results related to increases in knowledge and experience as one ages. The suggestion unit also suggests a plan for setting new goals based on the positive statistical data and research results related to the user's age. For example, it suggests a plan for setting new challenges and goals as one ages. In this way, by providing positive statistical data and research results related to the user's age, it is possible to foster a positive attitude toward aging in the user.

[0084] The suggestion unit can introduce success stories of people of the same age as the user to encourage empathy. For example, users of the same age can share goals and successful experiences that they have achieved. The suggestion unit also proposes a plan to set new goals based on success stories of the user's same age. For example, it can set new goals based on goals that users of the same age have achieved. The suggestion unit also proposes new challenges based on success stories of the user's same age. For example, it can propose new challenges based on successful projects by users of the same age. In this way, by introducing success stories of the user's same age, it is possible to encourage empathy and increase motivation in the user.

[0085] The suggestion unit can analyze the user's past experience data and suggest activities that the user has not experienced before. For example, it can analyze the user's past travel history and activity participation history to suggest activities that the user has not experienced before. For example, it can suggest special experiences in countries or regions the user has not visited before. The suggestion unit can also analyze the user's past hobby activities and suggest new hobbies and activities. For example, it can suggest sports or art workshops that the user has not tried yet. The suggestion unit can also analyze the user's past event participation history and suggest events that the user has not experienced before. For example, it can suggest festivals or concerts that the user has not attended yet. In this way, it is possible to provide new experiences by analyzing the user's past experience data and suggesting activities that the user has not experienced before.

[0086] The suggestion unit can use the emotion estimation function to suggest experiences that will bring the user the most enjoyment. For example, the suggestion unit analyzes the user's past emotion data and suggests experiences that evoke a strong emotion of joy. The suggestion unit also analyzes the user's facial expression data and suggests experiences that express the emotion of joy. For example, the suggestion unit suggests a new experience based on an experience that made the user smile. The suggestion unit also analyzes the user's voice data and suggests experiences that express the emotion of joy. For example, the suggestion unit suggests a new experience based on an experience in which the user spoke happily. In this way, the emotion estimation function can be used to provide the user with the experience that is likely to bring the user the most enjoyment, thereby improving user satisfaction.

[0087] The suggestion unit can use the emotion estimation function to suggest gifts based on the user's emotions. For example, the suggestion unit analyzes the user's emotion data and suggests gifts that reflect a strong emotion of joy. The suggestion unit also analyzes the user's facial expression data and suggests gifts that reflect a joyful emotion. For example, the suggestion unit suggests a new gift based on gifts that make the user smile. The suggestion unit also analyzes the user's voice data and suggests gifts that reflect a joyful emotion. For example, the suggestion unit suggests a new gift based on gifts that the user talks about happily. In this way, by using the emotion estimation function to provide gifts based on the user's emotions, user satisfaction can be improved.

[0088] The event planning unit can use the emotion estimation function to plan an event that will most move the user. For example, the event planning unit analyzes the user's emotion data and suggests an event that will evoke a strong emotion. The event planning unit also analyzes the user's facial expression data and plans an event that will express the user's emotion. For example, the event planning unit suggests a new event based on an event that made the user cry. The event planning unit also analyzes the user's voice data and plans an event that will express the user's emotion. For example, the event planning unit suggests a new event based on an event that the user talked about with emotion. In this way, the emotion estimation function can be used to provide the user with the most moving event, thereby improving user satisfaction.

[0089] The roadmap generation unit can use the emotion estimation function to propose a growth plan that will most motivate the user. For example, it analyzes the user's emotion data and proposes a plan that will increase motivation. The roadmap generation unit can also analyze the user's facial expression data and propose a plan that will increase motivation. For example, it can propose a new growth plan based on a plan that the user found motivating. The roadmap generation unit can also analyze the user's voice data and propose a plan that will increase motivation. For example, it can propose a new growth plan based on a plan that the user spoke about enthusiastically. In this way, it is possible to support the user's growth by using the emotion estimation function to provide a growth plan that will most motivate the user.

[0090] The suggestion unit can use the emotion estimation function to generate a message that evokes the most positive emotion for the user. For example, the suggestion unit analyzes the user's emotion data and generates a message that evokes a strong positive emotion. The suggestion unit also analyzes the user's facial expression data and generates a message that expresses a positive emotion. For example, the suggestion unit generates a new message based on a message that made the user smile. The suggestion unit also analyzes the user's voice data and generates a message that expresses a positive emotion. For example, the suggestion unit generates a new message based on a message that the user spoke in a happy manner. In this way, the emotion estimation function can be used to provide a message that evokes the most positive emotion for the user, thereby improving user satisfaction.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The suggestion unit suggests special experiences and gifts based on the user's age. For example, the generation AI suggests special experiences and gifts based on the user's age and preferences. The input to the generation AI is a prompt containing information such as the user's age, preferences, and past experiences, and the generation AI generates optimal suggestions based on the prompt. Step 2: The event planning department plans an event to celebrate a milestone in the user's life. For example, the generation AI plans an event that coincides with a milestone in the user's life. The input to the generation AI is a prompt that includes the user's age, preferences, and data on past events, and the generation AI generates an optimal event plan based on the prompt. Step 3: The roadmap generator provides the user with a roadmap for personal growth and enjoyment. For example, the generation AI provides a roadmap for personal growth and enjoyment appropriate to the user's age. The input to the generation AI is a prompt containing information such as the user's age, goals, and interests, and the generation AI generates an optimal roadmap based on the prompt.

[0093] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a 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.

[0128] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A proposal department that proposes special experiences and gifts according to the user's age, an event planning unit that plans an event to celebrate a milestone in the user's life; a roadmap generator that provides a roadmap for the user's personal growth and enjoyment. A system characterized by:

2. The proposal unit Using emotion estimation function, suggest the experience that the user will enjoy most 2. The system of claim 1.

3. The event planning department Using the emotion estimation function, an event that will impress the user the most is planned.

2. The system of claim 1.

4. The roadmap generation unit Using emotion estimation function, we propose a growth plan that the user feels most motivated to use.

2. The system of claim 1.

5. The proposal unit Generate an experience plan incorporating the opinions of the user's family and friends 2. The system of claim 1.

6. The event planning department Propose an event plan that takes into account the user's cultural background and traditions 2. The system of claim 1.

7. The roadmap generation unit Analyze the user's career goals and skill set and propose a specific growth plan 2. The system of claim 1.

8. The proposal unit Analyze the user's past successes and propose new challenges based on them 2. The system of claim 1.

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