system

Abstract

The system according to this embodiment aims to respond to users' concerns and inquiries, and to optimize consumer behavior and perform activities on their behalf according to their individual needs. [Solution] The system according to the embodiment comprises a response unit, a collection unit, an optimization unit, and an agency unit. The response unit responds to the user's concerns and inquiries. The collection unit collects the user's personal data based on the information obtained by the response unit. The optimization unit optimizes consumer behavior based on the data collected by the collection unit. The agency unit performs activities instructed by the user based on the optimization results obtained by the optimization unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a system for dealing with users' troubles and consultations is not sufficiently developed, and it is difficult to optimize consumption behavior and substitute activities according to individual needs.

[0005] The system according to the embodiment aims to deal with users' troubles and consultations and to optimize consumption behavior and substitute activities according to individual needs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a response unit, a data collection unit, an optimization unit, and an agency unit. The response unit responds to the user's concerns and inquiries. The data collection unit collects the user's personal data based on the information obtained by the response unit. The optimization unit optimizes consumer behavior based on the data collected by the data collection unit. The agency unit performs activities instructed by the user based on the optimization results obtained by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can respond to users' concerns and inquiries, and optimize their consumption behavior and perform activities on their behalf according to their individual needs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The agent AI system according to an embodiment of the present invention is a system that uses individually customized agent AI to optimize consumer behavior by utilizing personal data, starting with the alleviation of feelings of loneliness, and performing instructed activities on behalf of the user. The agent AI system alleviates feelings of loneliness by responding to the user's worries and consultations and acting as a conversation partner. Next, the agent AI system collects the user's personal data and optimizes consumer behavior based on this data. Furthermore, the agent AI system performs activities instructed by the user. This makes the user's life more convenient and enables the business to scale. For example, the agent AI system provides appropriate advice in response to the user's worries and consultations, such as agent AI for health, childcare, learning, and fashion advisors. This allows the user to alleviate feelings of loneliness. Next, the agent AI system collects data such as the user's health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. This allows the agent AI system to obtain information to optimize the user's consumer behavior. Furthermore, the agent AI system performs activities instructed by the user. For example, it can remind the user of anniversaries with their partner or suggest and arrange surprise events that will be appreciated. Furthermore, it can manage health parameters and purchase daily necessities on behalf of parents. This makes users' lives more convenient and allows the business to scale. This not only alleviates feelings of loneliness and makes life more convenient for users, but also allows the business to scale. For example, for children, it can provide support and assistance tailored to their goals, not just their studies. For grandparents and older generations, it can provide health, medication / medication reminders, lifestyle guidance, and monitoring functions. In this way, the agent AI system can respond to users' worries and consultations, collect personal data to optimize consumption behavior, and perform instructed activities on their behalf, thereby making users' lives more convenient.

[0029] The agent AI system according to this embodiment comprises a response unit, a collection unit, an optimization unit, and an agency unit. The response unit responds to the user's concerns and consultations. The response unit provides appropriate advice to the user's concerns and consultations using agent AI such as health, childcare, learning, and fashion advisors. For example, the response unit can use a health advisor AI to provide advice to the user regarding their health concerns. The response unit can also use a childcare advisor AI to provide advice to the user regarding their childcare concerns. Furthermore, the response unit can use a learning advisor AI to provide advice to the user regarding their learning concerns. The collection unit collects the user's personal data based on the information obtained by the response unit. The collection unit collects data such as health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. For example, the collection unit can collect health parameters such as the user's body temperature, blood pressure, and heart rate. The collection unit can also collect lifestyle data such as the user's diet and exercise frequency. Furthermore, the collection unit can also collect the user's purchase history of daily necessities and luxury goods. The optimization unit optimizes consumer behavior based on data collected by the data collection unit. For example, the optimization unit analyzes the collected data and makes suggestions to optimize the user's consumer behavior. For example, the optimization unit can analyze the user's purchase patterns and suggest the most suitable products. It can also suggest the most suitable products based on the user's preferences. Furthermore, it can suggest the most suitable products based on the user's lifestyle. The proxy unit acts on behalf of the user, performing activities instructed by the user based on the optimization results obtained by the optimization unit. For example, the proxy unit can remind the user of anniversaries with their partner and suggest and arrange surprise events that will please them. For example, the proxy unit can send notifications to remind the user of anniversaries with their partner. It can also suggest and arrange surprise events that will please the partner. Furthermore, the proxy unit can also act on behalf of parents, managing their health parameters and purchasing daily necessities. For example, the proxy unit can manage the parents' health parameters and purchase necessary daily necessities.As a result, the agent AI system according to this embodiment can make the user's life more convenient by responding to the user's concerns and inquiries, collecting personal data to optimize consumption behavior, and performing instructed activities on their behalf.

[0030] The support department addresses users' concerns and inquiries. For example, it uses agent AIs such as health, childcare, learning, and fashion advisors to provide appropriate advice based on users' concerns and inquiries. Specifically, the health advisor AI provides advice on diet, exercise, sleep, etc., based on information about the user's health status and lifestyle. For example, if a user complains of feeling unwell, the health advisor AI can analyze the user's past health data and current symptoms to recommend appropriate countermeasures or a visit to a medical institution. The childcare advisor AI provides advice based on expert knowledge regarding concerns and questions about childcare. For example, if a user is troubled by their child's nighttime crying, the childcare advisor AI will suggest causes and solutions to the crying, supporting the user so they can raise their child with peace of mind. The learning advisor AI suggests effective learning methods and materials according to the user's learning progress and goals. For example, if a user is struggling with a particular subject, the learning advisor AI will analyze the user's learning history and understanding to provide an optimal learning plan. The fashion advisor AI suggests outfits and items based on the user's preferences and trends. For example, if a user is struggling to decide what to wear to a particular event, the fashion advisor AI will suggest outfits that match the event's theme and the user's preferences, supporting the user so they can participate with confidence. This allows the support team to provide expert and accurate advice to users regarding their diverse concerns and questions, thereby improving their quality of life.

[0031] The data collection unit collects users' personal data based on information obtained by the response unit. For example, the collection unit collects data such as health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. Specifically, it utilizes wearable devices and smartphone sensors to collect health parameters such as the user's body temperature, blood pressure, and heart rate. This allows for real-time monitoring of the user's health status and rapid response if an abnormality is detected. Furthermore, it uses meal logging apps and fitness trackers to collect lifestyle data such as the user's diet and exercise frequency. This allows for a detailed understanding of the user's lifestyle and provides data for health management and lifestyle improvement. Additionally, it utilizes data from online shopping sites and loyalty cards to collect the user's purchase history of daily necessities and luxury goods. This allows for analysis of the user's consumption behavior and preferences, enabling the suggestion of optimal products and services. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the optimization and proxy units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0032] The optimization unit optimizes consumer behavior based on data collected by the data collection unit. For example, the optimization unit analyzes the collected data and makes suggestions to optimize the user's consumer behavior. Specifically, it analyzes the user's purchase patterns and suggests the most suitable products. For example, based on data on daily necessities and luxury goods that users regularly purchase, it provides optimal purchase timing and information on advantageous campaigns. It also suggests the most suitable products based on the user's preferences. For example, if a user prefers to purchase products from a specific brand or category, it suggests new products or related products that match those preferences. Furthermore, it suggests the most suitable products based on the user's lifestyle. For example, if a user is health-conscious, it suggests healthy foods and supplements to support the user's health management. The optimization unit uses AI to analyze this data and simulate multiple scenarios to make the most effective suggestions. This allows the optimization unit to optimize the user's consumer behavior with high accuracy and improve user satisfaction. In addition, the optimization unit can continuously modify its suggestions based on real-time updated data to respond to the latest situations. For example, if a user's preferences or lifestyle change, the optimization unit immediately incorporates the new data and updates its suggestions. Furthermore, the optimization unit can make more accurate suggestions by taking into account regional characteristics and past consumption data. As a result, the optimization unit can always provide highly accurate suggestions based on the latest information, optimizing users' consumption behavior.

[0033] The proxy service unit performs activities instructed by the user based on the optimization results obtained by the optimization unit. For example, the proxy service unit can remind users of anniversaries with their partners and propose and arrange surprise events that will be appreciated. Specifically, it can send notifications to remind users of anniversaries with their partners. For example, it can link with the user's calendar app and send a reminder notification a few days before the anniversary. The proxy service unit can also propose and arrange surprise events that will be appreciated by the partner. For example, it can propose the most suitable surprise event based on the user's preferences and past successful surprises, and make restaurant reservations and gift arrangements. Furthermore, the proxy service unit can also manage the health parameters of parents and purchase daily necessities on their behalf. For example, it can manage the health parameters of parents and send notifications recommending medical consultation if an abnormality is detected. It can also purchase daily necessities for parents and arrange for the regular delivery of necessary items. In this way, the proxy service unit can make the user's life more convenient and support the management of important events and daily life. Furthermore, the proxy service department can collect user feedback and continuously improve the accuracy and effectiveness of its services. For example, it can review and improve its services based on feedback from users who have used the proxy service. In addition, the proxy service department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. In this way, the proxy service department can provide services to users quickly and reliably, making their lives more convenient.

[0034] The support unit can provide appropriate advice to users in response to their concerns and questions using agent AI such as health, childcare, learning, and fashion advisors. For example, the support unit can use a health advisor AI to provide advice to users regarding their health concerns. For example, the support unit can use a health advisor AI to monitor the user's health status and suggest appropriate health management methods. The support unit can also use a childcare advisor AI to provide advice to users regarding childcare concerns. For example, the support unit can use a childcare advisor AI to suggest appropriate childcare methods according to the child's development. Furthermore, the support unit can use a learning advisor AI to provide advice to users regarding learning concerns. For example, the support unit can use a learning advisor AI to suggest the optimal learning method according to the user's learning style. In this way, by using agent AI, appropriate advice can be provided in response to users' concerns and questions.

[0035] The data collection unit can collect data such as health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. For example, the data collection unit can collect health parameters such as the user's body temperature, blood pressure, and heart rate. For example, the data collection unit can periodically measure the user's body temperature and record the data. It can also measure the user's blood pressure and record the data. Furthermore, the data collection unit can measure the user's heart rate and record the data. The data collection unit can also collect lifestyle data such as the user's diet and exercise frequency. For example, the data collection unit can record the user's diet and analyze the data. It can also record the user's exercise frequency and analyze the data. Furthermore, the data collection unit can collect the user's purchase history of daily necessities and luxury goods. For example, the data collection unit can record the user's purchase history of daily necessities and analyze the data. It can also record the user's purchase history of luxury goods and analyze the data. By collecting data such as the user's health parameters, lifestyle habits, and purchase history, it is possible to obtain information necessary for optimizing consumer behavior. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health parameters into the AI, which can then analyze the data.

[0036] The optimization unit can optimize consumer behavior based on collected data. For example, the optimization unit can analyze collected data and make suggestions to optimize the user's consumer behavior. For example, the optimization unit can analyze the user's purchase patterns and suggest the most suitable products. For example, the optimization unit can analyze the user's purchase history and suggest products similar to those the user has purchased in the past. The optimization unit can also suggest the most suitable products based on the user's preferences. For example, the optimization unit can analyze user preference data and suggest products that the user will like. Furthermore, the optimization unit can suggest the most suitable products based on the user's lifestyle. For example, the optimization unit can analyze user lifestyle data and suggest products that suit the user's lifestyle. In this way, by optimizing consumer behavior based on collected data, the user's consumer behavior can be efficiently optimized. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input collected data into AI, which can analyze the data and suggest the most suitable products.

[0037] The proxy service can remind users of anniversaries with their partners and suggest and arrange surprise events that will be appreciated. For example, the proxy service can send notifications to remind users of anniversaries with their partners. For example, the proxy service can send notifications a few days before the anniversary to remind the user not to forget. The proxy service can also suggest and arrange surprise events that will be appreciated by the partner. For example, the proxy service can suggest and arrange surprise events that will please the partner. For example, the proxy service can suggest gifts that suit the partner's preferences and arrange their purchase. In this way, the user's life can be made more convenient by reminding users of anniversaries with their partners and suggesting and arranging surprise events. Some or all of the above processes in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can input a notification to remind users of anniversaries with their partners into a generation AI, and the generation AI can create the notification.

[0038] The proxy unit can manage health parameters and purchase daily necessities on behalf of the parent. For example, the proxy unit manages the parent's health parameters. For instance, the proxy unit periodically measures and records the parent's health parameters such as body temperature, blood pressure, and heart rate. The proxy unit can also purchase daily necessities on behalf of the parent. For example, the proxy unit periodically purchases the daily necessities the parent needs and delivers them to the parent. In this way, the user's life can be made more convenient by having the proxy unit manage health parameters and purchase daily necessities on behalf of the parent. Some or all of the above processes in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the parent's health parameters into a generating AI, which can analyze the data and make health management suggestions.

[0039] The proxy unit can provide health management, medication reminders, lifestyle management, and monitoring functions to generations older than grandparents. For example, the proxy unit manages the health of grandparents. For example, it regularly measures and records health parameters such as the grandparents' body temperature, blood pressure, and heart rate. The proxy unit can also provide medication reminders for grandparents. For example, it sends notifications to remind grandparents to take their medication. Furthermore, the proxy unit can manage the grandparents' lifestyle habits and provide monitoring functions. For example, it collects lifestyle data from grandparents and sends notifications if there are any abnormalities. In this way, by providing health management and monitoring functions to generations older than grandparents, the user's life can be made more convenient. Some or all of the above processes in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the grandparents' health parameters into a generating AI, which can analyze the data and make health management suggestions.

[0040] The support unit can analyze the user's past consultation history and select the optimal response method. For example, the support unit can suggest solutions to similar problems based on the content of past consultations. For example, the support unit can input the user's past consultation history into AI, and the AI ​​will suggest solutions to similar problems. The support unit can also identify specific patterns from the user's past consultation history and provide appropriate advice. For example, the support unit can input the user's past consultation history into AI, and the AI ​​will identify specific patterns and provide appropriate advice. Furthermore, the support unit can analyze the effectiveness of advice the user has received in the past and select the optimal response method. For example, the support unit can input the user's past consultation history into AI, and the AI ​​will analyze the effectiveness of the advice and select the optimal response method. In this way, the optimal response method can be selected by analyzing the user's past consultation history.

[0041] The support unit can filter responses to problems and inquiries based on the user's current living situation and areas of interest. For example, the support unit can consider the user's current living situation and provide appropriate advice. For instance, the support unit inputs the user's current living situation into the AI, which then provides appropriate advice. The support unit can also provide relevant information based on the user's areas of interest. For example, the support unit inputs the user's areas of interest into the AI, which then provides relevant information. Furthermore, the support unit can introduce appropriate resources based on the user's living situation and areas of interest. For example, the support unit inputs the user's living situation and areas of interest into the AI, which then introduces appropriate resources. This allows for more appropriate advice to be provided by filtering responses based on the user's living situation and areas of interest.

[0042] The support unit can prioritize providing highly relevant information by considering the user's geographical location when responding to concerns or inquiries. For example, if the user lives in a specific region, the support unit will provide information related to that region. For instance, the support unit inputs the user's geographical location into the AI, and the AI ​​provides information related to that region. Furthermore, if the user is traveling, the support unit can provide information related to their travel destination. For example, the support unit inputs the user's geographical location into the AI, and the AI ​​provides information related to their travel destination. In addition, if the user is considering moving, the support unit can provide information related to their new region. For example, the support unit inputs the user's geographical location into the AI, and the AI ​​provides information related to their new region. In this way, by considering the user's geographical location, the support unit can prioritize providing highly relevant information.

[0043] The support unit can analyze a user's social media activity and provide relevant information when addressing concerns or seeking advice. For example, the support unit can provide appropriate advice based on information shared by the user on social media. For instance, the support unit inputs the user's social media activity into an AI, which then provides appropriate advice. The support unit can also identify areas of interest from the user's social media activity and provide relevant information. For example, the support unit inputs the user's social media activity into an AI, which identifies areas of interest and provides relevant information. Furthermore, the support unit can analyze the user's social media activity and recommend appropriate resources. For example, the support unit inputs the user's social media activity into an AI, which then recommends appropriate resources. In this way, by analyzing the user's social media activity, relevant information can be provided.

[0044] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can propose the optimal collection method based on the data the user has collected in the past. For example, the data collection unit can input the user's past data collection history into an AI, which will then propose the optimal collection method. The data collection unit can also select an effective collection method from the user's past data collection history. For example, the data collection unit can input the user's past data collection history into an AI, which will then select an effective collection method. Furthermore, the data collection unit can prioritize suggesting collection methods that the user has used in the past. For example, the data collection unit can input the user's past data collection history into an AI, which will then prioritize suggesting collection methods that the user has used in the past. In this way, the optimal collection method can be selected by analyzing the user's past data collection history.

[0045] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the data collection process. For example, the data collection unit considers the user's current lifestyle and collects appropriate data. For instance, the data collection unit inputs the user's current lifestyle into the AI, which then collects the appropriate data. The data collection unit can also collect relevant data based on the user's areas of interest. For example, the data collection unit inputs the user's areas of interest into the AI, which then collects the relevant data. Furthermore, the data collection unit can recommend appropriate resources based on the user's lifestyle and areas of interest. For example, the data collection unit inputs the user's lifestyle and areas of interest into the AI, which then recommends appropriate resources. This allows for the collection of more relevant data by filtering based on the user's lifestyle and areas of interest.

[0046] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user lives in a specific region, the data collection unit will collect data related to that region. For instance, the data collection unit inputs the user's geographical location information into the AI, and the AI ​​collects data related to that region. The data collection unit can also collect data related to the user's travel destination if the user is traveling. For example, the data collection unit inputs the user's geographical location information into the AI, and the AI ​​collects data related to the travel destination. Furthermore, if the user is considering moving, the data collection unit can collect data related to the new region. For example, the data collection unit inputs the user's geographical location information into the AI, and the AI ​​collects data related to the new region. In this way, by considering the user's geographical location information, the data collection unit can prioritize the collection of highly relevant data.

[0047] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect appropriate data based on information shared by the user on social media. For example, the data collection unit can input the user's social media activity into an AI, which then collects the appropriate data. The data collection unit can also identify areas of interest from the user's social media activity and collect relevant data. For example, the data collection unit can input the user's social media activity into an AI, which then identifies areas of interest and collects relevant data. Furthermore, the data collection unit can analyze the user's social media activity and recommend appropriate resources. For example, the data collection unit can input the user's social media activity into an AI, which then recommends appropriate resources. In this way, relevant data can be collected by analyzing the user's social media activity.

[0048] The optimization unit can analyze the user's past consumption behavior during optimization to select the optimal optimization method. For example, the optimization unit can propose the optimal optimization method based on the user's past consumption behavior. For example, the optimization unit can input the user's past consumption behavior into the AI, which will then propose the optimal optimization method. The optimization unit can also select an effective optimization method from the user's past consumption behavior. For example, the optimization unit can input the user's past consumption behavior into the AI, which will then select an effective optimization method. Furthermore, the optimization unit can prioritize suggesting optimization methods that the user has used in the past. For example, the optimization unit can input the user's past consumption behavior into the AI, which will then prioritize suggesting optimization methods that the user has used in the past. In this way, the optimal optimization method can be selected by analyzing the user's past consumption behavior.

[0049] The optimization unit can customize the optimization methods based on the user's current living situation during the optimization process. For example, the optimization unit can consider the user's current living situation and propose appropriate optimization methods. For instance, the optimization unit inputs the user's current living situation into the AI, which then proposes appropriate optimization methods. The optimization unit can also propose relevant optimization methods based on the user's areas of interest. For example, the optimization unit inputs the user's areas of interest into the AI, which then proposes relevant optimization methods. Furthermore, the optimization unit can introduce appropriate resources based on the user's living situation and areas of interest. For example, the optimization unit inputs the user's living situation and areas of interest into the AI, which then introduces appropriate resources. By customizing the optimization methods based on the user's current living situation, more effective optimization becomes possible.

[0050] The optimization unit can select the optimal optimization method by considering the user's geographical location information during the optimization process. For example, if the user lives in a specific region, the optimization unit will suggest an optimization method relevant to that region. For instance, the optimization unit inputs the user's geographical location information into the AI, and the AI ​​proposes an optimization method relevant to that region. Furthermore, if the user is traveling, the optimization unit can suggest an optimization method relevant to their travel destination. For example, the optimization unit inputs the user's geographical location information into the AI, and the AI ​​proposes an optimization method relevant to their travel destination. In addition, if the user is considering moving, the optimization unit can suggest an optimization method relevant to their new region. For example, the optimization unit inputs the user's geographical location information into the AI, and the AI ​​proposes an optimization method relevant to their new region. This allows for the selection of a highly relevant optimization method by considering the user's geographical location information.

[0051] The optimization unit can analyze the user's social media activity during optimization and propose optimization methods. For example, the optimization unit can propose appropriate optimization methods based on information shared by the user on social media. For example, the optimization unit inputs the user's social media activity into an AI, which then proposes appropriate optimization methods. The optimization unit can also identify areas of interest from the user's social media activity and propose related optimization methods. For example, the optimization unit inputs the user's social media activity into an AI, which identifies areas of interest and proposes related optimization methods. Furthermore, the optimization unit can analyze the user's social media activity and introduce appropriate resources. For example, the optimization unit inputs the user's social media activity into an AI, which then introduces appropriate resources. In this way, by analyzing the user's social media activity, it is possible to propose relevant optimization methods.

[0052] The proxy service can analyze the user's past instruction history to select the optimal proxy service method. For example, the proxy service can suggest a similar proxy service method based on the user's past instructions. For example, the proxy service can input the user's past instruction history into the AI, which will then suggest a similar proxy service method. The proxy service can also select an effective proxy service method from the user's past instruction history. For example, the proxy service can input the user's past instruction history into the AI, which will then select an effective proxy service method. Furthermore, the proxy service can prioritize suggesting proxy service methods that the user has used in the past. For example, the proxy service can input the user's past instruction history into the AI, which will then prioritize suggesting proxy service methods that the user has used in the past. In this way, the optimal proxy service method can be selected by analyzing the user's past instruction history.

[0053] The proxy service can customize the means of proxy service based on the user's current living situation. For example, the proxy service can consider the user's current living situation and propose an appropriate proxy service. For example, the proxy service can input the user's current living situation into the AI, and the AI ​​will propose an appropriate proxy service. The proxy service can also propose relevant proxy services based on the user's areas of interest. For example, the proxy service can input the user's areas of interest into the AI, and the AI ​​will propose relevant proxy services. Furthermore, the proxy service can introduce appropriate resources based on the user's living situation and areas of interest. For example, the proxy service can input the user's living situation and areas of interest into the AI, and the AI ​​will introduce appropriate resources. This allows for more appropriate proxy service by customizing the means of proxy service based on the user's current living situation.

[0054] The proxy service can select the most suitable proxy service method by considering the user's geographical location information during the proxy service. For example, if the user lives in a specific region, the proxy service can suggest a proxy service method relevant to that region. For instance, the proxy service can input the user's geographical location information into the AI, and the AI ​​will suggest a proxy service method relevant to that region. Furthermore, if the user is traveling, the proxy service can suggest a proxy service method relevant to their travel destination. For example, the proxy service can input the user's geographical location information into the AI, and the AI ​​will suggest a proxy service method relevant to their travel destination. In addition, if the user is considering moving, the proxy service can suggest a proxy service method relevant to their new region. For example, the proxy service can input the user's geographical location information into the AI, and the AI ​​will suggest a proxy service method relevant to their new region. This allows the system to select a highly relevant proxy service method by considering the user's geographical location information.

[0055] The proxy service can analyze the user's social media activity and propose appropriate proxy methods during the proxy service process. For example, the proxy service can propose appropriate proxy methods based on information shared by the user on social media. For example, the proxy service can input the user's social media activity into an AI, which then proposes appropriate proxy methods. The proxy service can also identify areas of interest from the user's social media activity and propose relevant proxy methods. For example, the proxy service can input the user's social media activity into an AI, which then identifies areas of interest and proposes relevant proxy methods. Furthermore, the proxy service can analyze the user's social media activity and introduce appropriate resources. For example, the proxy service can input the user's social media activity into an AI, which then introduces appropriate resources. In this way, by analyzing the user's social media activity, relevant proxy methods can be proposed.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on data the user has collected in the past. Furthermore, the data collection unit can select an effective collection method from the user's past data collection history. In addition, the data collection unit can prioritize suggesting collection methods the user has used in the past. This allows the optimal collection method to be selected by analyzing the user's past data collection history.

[0058] The optimization unit can analyze the user's past consumption behavior during optimization to select the optimal optimization method. For example, the optimization unit can propose the optimal optimization method based on the user's past consumption behavior. Furthermore, the optimization unit can select an effective optimization method from the user's past consumption behavior. In addition, the optimization unit can prioritize suggesting optimization methods that the user has used in the past. This allows for the selection of the optimal optimization method by analyzing the user's past consumption behavior.

[0059] The proxy service can analyze the user's past instruction history to select the most suitable proxy service method during the proxy service process. For example, the proxy service can propose a similar proxy service method based on the user's past instructions. Furthermore, the proxy service can select an effective proxy service method from the user's past instruction history. In addition, the proxy service can prioritize suggesting proxy service methods that the user has used in the past. This allows the proxy service to select the most suitable method by analyzing the user's past instruction history.

[0060] The support unit can prioritize providing highly relevant information when responding to user inquiries or questions, taking into account the user's geographical location. For example, if a user lives in a specific region, it can provide information related to that region. If a user is traveling, it can provide information related to their travel destination. Furthermore, if a user is considering moving, it can provide information related to their new area. In this way, by considering the user's geographical location, it can prioritize providing highly relevant information.

[0061] The data collection unit can analyze users' social media activity and collect relevant data during the data collection process. For example, the data collection unit can collect appropriate data based on information shared by users on social media. Furthermore, the data collection unit can identify areas of interest from users' social media activity and collect relevant data. In addition, the data collection unit can analyze users' social media activity and recommend appropriate resources. This allows for the collection of relevant data by analyzing users' social media activity.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The support unit responds to users' concerns and inquiries. For example, it uses agent AI such as health, childcare, learning, and fashion advisors to provide appropriate advice according to the user's concerns and inquiries. Specifically, it uses a health advisor AI to provide advice on health-related concerns, and a childcare advisor AI to provide advice on childcare-related concerns. Step 2: The collection unit collects the user's personal data based on the information obtained by the response unit. For example, it collects data such as health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. Specifically, it collects health parameters such as the user's body temperature, blood pressure, and heart rate, lifestyle data such as diet and exercise frequency, and purchase history of daily necessities and luxury goods. Step 3: The optimization unit optimizes consumer behavior based on the data collected by the data collection unit. For example, it analyzes the collected data, analyzes the user's purchasing patterns, and suggests the most suitable products. It also suggests the most suitable products based on the user's preferences and lifestyle. Step 4: The proxy unit performs the activities instructed by the user based on the optimization results obtained by the optimization unit. For example, it may send reminders for anniversaries with a partner, or suggest and arrange surprise events that will be appreciated. Specifically, it may send anniversary reminder notifications, suggest and arrange surprise events, or manage parents' health parameters and purchase daily necessities on their behalf.

[0064] (Example of form 2) The agent AI system according to an embodiment of the present invention is a system that uses individually customized agent AI to optimize consumer behavior by utilizing personal data, starting with the alleviation of feelings of loneliness, and performing instructed activities on behalf of the user. The agent AI system alleviates feelings of loneliness by responding to the user's worries and consultations and acting as a conversation partner. Next, the agent AI system collects the user's personal data and optimizes consumer behavior based on this data. Furthermore, the agent AI system performs activities instructed by the user. This makes the user's life more convenient and enables the business to scale. For example, the agent AI system provides appropriate advice in response to the user's worries and consultations, such as agent AI for health, childcare, learning, and fashion advisors. This allows the user to alleviate feelings of loneliness. Next, the agent AI system collects data such as the user's health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. This allows the agent AI system to obtain information to optimize the user's consumer behavior. Furthermore, the agent AI system performs activities instructed by the user. For example, it can remind the user of anniversaries with their partner or suggest and arrange surprise events that will be appreciated. Furthermore, it can manage health parameters and purchase daily necessities on behalf of parents. This makes users' lives more convenient and allows the business to scale. This not only alleviates feelings of loneliness and makes life more convenient for users, but also allows the business to scale. For example, for children, it can provide support and assistance tailored to their goals, not just their studies. For grandparents and older generations, it can provide health, medication / medication reminders, lifestyle guidance, and monitoring functions. In this way, the agent AI system can respond to users' worries and consultations, collect personal data to optimize consumption behavior, and perform instructed activities on their behalf, thereby making users' lives more convenient.

[0065] The agent AI system according to this embodiment comprises a response unit, a collection unit, an optimization unit, and an agency unit. The response unit responds to the user's concerns and consultations. The response unit provides appropriate advice to the user's concerns and consultations using agent AI such as health, childcare, learning, and fashion advisors. For example, the response unit can use a health advisor AI to provide advice to the user regarding their health concerns. The response unit can also use a childcare advisor AI to provide advice to the user regarding their childcare concerns. Furthermore, the response unit can use a learning advisor AI to provide advice to the user regarding their learning concerns. The collection unit collects the user's personal data based on the information obtained by the response unit. The collection unit collects data such as health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. For example, the collection unit can collect health parameters such as the user's body temperature, blood pressure, and heart rate. The collection unit can also collect lifestyle data such as the user's diet and exercise frequency. Furthermore, the collection unit can also collect the user's purchase history of daily necessities and luxury goods. The optimization unit optimizes consumer behavior based on data collected by the data collection unit. For example, the optimization unit analyzes the collected data and makes suggestions to optimize the user's consumer behavior. For example, the optimization unit can analyze the user's purchase patterns and suggest the most suitable products. It can also suggest the most suitable products based on the user's preferences. Furthermore, it can suggest the most suitable products based on the user's lifestyle. The proxy unit acts on behalf of the user, performing activities instructed by the user based on the optimization results obtained by the optimization unit. For example, the proxy unit can remind the user of anniversaries with their partner and suggest and arrange surprise events that will please them. For example, the proxy unit can send notifications to remind the user of anniversaries with their partner. It can also suggest and arrange surprise events that will please the partner. Furthermore, the proxy unit can also act on behalf of parents, managing their health parameters and purchasing daily necessities. For example, the proxy unit can manage the parents' health parameters and purchase necessary daily necessities.As a result, the agent AI system according to this embodiment can make the user's life more convenient by responding to the user's concerns and inquiries, collecting personal data to optimize consumption behavior, and performing instructed activities on their behalf.

[0066] The support department addresses users' concerns and inquiries. For example, it uses agent AIs such as health, childcare, learning, and fashion advisors to provide appropriate advice based on users' concerns and inquiries. Specifically, the health advisor AI provides advice on diet, exercise, sleep, etc., based on information about the user's health status and lifestyle. For example, if a user complains of feeling unwell, the health advisor AI can analyze the user's past health data and current symptoms to recommend appropriate countermeasures or a visit to a medical institution. The childcare advisor AI provides advice based on expert knowledge regarding concerns and questions about childcare. For example, if a user is troubled by their child's nighttime crying, the childcare advisor AI will suggest causes and solutions to the crying, supporting the user so they can raise their child with peace of mind. The learning advisor AI suggests effective learning methods and materials according to the user's learning progress and goals. For example, if a user is struggling with a particular subject, the learning advisor AI will analyze the user's learning history and understanding to provide an optimal learning plan. The fashion advisor AI suggests outfits and items based on the user's preferences and trends. For example, if a user is struggling to decide what to wear to a particular event, the fashion advisor AI will suggest outfits that match the event's theme and the user's preferences, supporting the user so they can participate with confidence. This allows the support team to provide expert and accurate advice to users regarding their diverse concerns and questions, thereby improving their quality of life.

[0067] The data collection unit collects users' personal data based on information obtained by the response unit. For example, the collection unit collects data such as health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. Specifically, it utilizes wearable devices and smartphone sensors to collect health parameters such as the user's body temperature, blood pressure, and heart rate. This allows for real-time monitoring of the user's health status and rapid response if an abnormality is detected. Furthermore, it uses meal logging apps and fitness trackers to collect lifestyle data such as the user's diet and exercise frequency. This allows for a detailed understanding of the user's lifestyle and provides data for health management and lifestyle improvement. Additionally, it utilizes data from online shopping sites and loyalty cards to collect the user's purchase history of daily necessities and luxury goods. This allows for analysis of the user's consumption behavior and preferences, enabling the suggestion of optimal products and services. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the optimization and proxy units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The optimization unit optimizes consumer behavior based on data collected by the data collection unit. For example, the optimization unit analyzes the collected data and makes suggestions to optimize the user's consumer behavior. Specifically, it analyzes the user's purchase patterns and suggests the most suitable products. For example, based on data on daily necessities and luxury goods that users regularly purchase, it provides optimal purchase timing and information on advantageous campaigns. It also suggests the most suitable products based on the user's preferences. For example, if a user prefers to purchase products from a specific brand or category, it suggests new products or related products that match those preferences. Furthermore, it suggests the most suitable products based on the user's lifestyle. For example, if a user is health-conscious, it suggests healthy foods and supplements to support the user's health management. The optimization unit uses AI to analyze this data and simulate multiple scenarios to make the most effective suggestions. This allows the optimization unit to optimize the user's consumer behavior with high accuracy and improve user satisfaction. In addition, the optimization unit can continuously modify its suggestions based on real-time updated data to respond to the latest situations. For example, if a user's preferences or lifestyle change, the optimization unit immediately incorporates the new data and updates its suggestions. Furthermore, the optimization unit can make more accurate suggestions by taking into account regional characteristics and past consumption data. As a result, the optimization unit can always provide highly accurate suggestions based on the latest information, optimizing users' consumption behavior.

[0069] The proxy service unit performs activities instructed by the user based on the optimization results obtained by the optimization unit. For example, the proxy service unit can remind users of anniversaries with their partners and propose and arrange surprise events that will be appreciated. Specifically, it can send notifications to remind users of anniversaries with their partners. For example, it can link with the user's calendar app and send a reminder notification a few days before the anniversary. The proxy service unit can also propose and arrange surprise events that will be appreciated by the partner. For example, it can propose the most suitable surprise event based on the user's preferences and past successful surprises, and make restaurant reservations and gift arrangements. Furthermore, the proxy service unit can also manage the health parameters of parents and purchase daily necessities on their behalf. For example, it can manage the health parameters of parents and send notifications recommending medical consultation if an abnormality is detected. It can also purchase daily necessities for parents and arrange for the regular delivery of necessary items. In this way, the proxy service unit can make the user's life more convenient and support the management of important events and daily life. Furthermore, the proxy service department can collect user feedback and continuously improve the accuracy and effectiveness of its services. For example, it can review and improve its services based on feedback from users who have used the proxy service. In addition, the proxy service department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. In this way, the proxy service department can provide services to users quickly and reliably, making their lives more convenient.

[0070] The support unit can provide appropriate advice to users in response to their concerns and questions using agent AI such as health, childcare, learning, and fashion advisors. For example, the support unit can use a health advisor AI to provide advice to users regarding their health concerns. For example, the support unit can use a health advisor AI to monitor the user's health status and suggest appropriate health management methods. The support unit can also use a childcare advisor AI to provide advice to users regarding childcare concerns. For example, the support unit can use a childcare advisor AI to suggest appropriate childcare methods according to the child's development. Furthermore, the support unit can use a learning advisor AI to provide advice to users regarding learning concerns. For example, the support unit can use a learning advisor AI to suggest the optimal learning method according to the user's learning style. In this way, by using agent AI, appropriate advice can be provided in response to users' concerns and questions.

[0071] The data collection unit can collect data such as health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. For example, the data collection unit can collect health parameters such as the user's body temperature, blood pressure, and heart rate. For example, the data collection unit can periodically measure the user's body temperature and record the data. It can also measure the user's blood pressure and record the data. Furthermore, the data collection unit can measure the user's heart rate and record the data. The data collection unit can also collect lifestyle data such as the user's diet and exercise frequency. For example, the data collection unit can record the user's diet and analyze the data. It can also record the user's exercise frequency and analyze the data. Furthermore, the data collection unit can collect the user's purchase history of daily necessities and luxury goods. For example, the data collection unit can record the user's purchase history of daily necessities and analyze the data. It can also record the user's purchase history of luxury goods and analyze the data. By collecting data such as the user's health parameters, lifestyle habits, and purchase history, it is possible to obtain information necessary for optimizing consumer behavior. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health parameters into the AI, which can then analyze the data.

[0072] The optimization unit can optimize consumer behavior based on collected data. For example, the optimization unit can analyze collected data and make suggestions to optimize the user's consumer behavior. For example, the optimization unit can analyze the user's purchase patterns and suggest the most suitable products. For example, the optimization unit can analyze the user's purchase history and suggest products similar to those the user has purchased in the past. The optimization unit can also suggest the most suitable products based on the user's preferences. For example, the optimization unit can analyze user preference data and suggest products that the user will like. Furthermore, the optimization unit can suggest the most suitable products based on the user's lifestyle. For example, the optimization unit can analyze user lifestyle data and suggest products that suit the user's lifestyle. In this way, by optimizing consumer behavior based on collected data, the user's consumer behavior can be efficiently optimized. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input collected data into AI, which can analyze the data and suggest the most suitable products.

[0073] The proxy service can remind users of anniversaries with their partners and suggest and arrange surprise events that will be appreciated. For example, the proxy service can send notifications to remind users of anniversaries with their partners. For example, the proxy service can send notifications a few days before the anniversary to remind the user not to forget. The proxy service can also suggest and arrange surprise events that will be appreciated by the partner. For example, the proxy service can suggest and arrange surprise events that will please the partner. For example, the proxy service can suggest gifts that suit the partner's preferences and arrange their purchase. In this way, the user's life can be made more convenient by reminding users of anniversaries with their partners and suggesting and arranging surprise events. Some or all of the above processes in the proxy service may be performed using AI, for example, or not using AI. For example, the proxy service can input a notification to remind users of anniversaries with their partners into a generation AI, and the generation AI can create the notification.

[0074] The proxy unit can manage health parameters and purchase daily necessities on behalf of the parent. For example, the proxy unit manages the parent's health parameters. For instance, the proxy unit periodically measures and records the parent's health parameters such as body temperature, blood pressure, and heart rate. The proxy unit can also purchase daily necessities on behalf of the parent. For example, the proxy unit periodically purchases the daily necessities the parent needs and delivers them to the parent. In this way, the user's life can be made more convenient by having the proxy unit manage health parameters and purchase daily necessities on behalf of the parent. Some or all of the above processes in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the parent's health parameters into a generating AI, which can analyze the data and make health management suggestions.

[0075] The proxy unit can provide health management, medication reminders, lifestyle management, and monitoring functions to generations older than grandparents. For example, the proxy unit manages the health of grandparents. For example, it regularly measures and records health parameters such as the grandparents' body temperature, blood pressure, and heart rate. The proxy unit can also provide medication reminders for grandparents. For example, it sends notifications to remind grandparents to take their medication. Furthermore, the proxy unit can manage the grandparents' lifestyle habits and provide monitoring functions. For example, it collects lifestyle data from grandparents and sends notifications if there are any abnormalities. In this way, by providing health management and monitoring functions to generations older than grandparents, the user's life can be made more convenient. Some or all of the above processes in the proxy unit may be performed using AI, for example, or not using AI. For example, the proxy unit can input the grandparents' health parameters into a generating AI, which can analyze the data and make health management suggestions.

[0076] The response unit can estimate the user's emotions and adjust its response method to address concerns and consultations based on the estimated emotions. For example, if the user is stressed, the response unit can respond using gentle language to help them relax. For example, the response unit can estimate that the user is stressed using an emotion engine or generative AI and respond using gentle language to help them relax. The response unit can also respond in an empathetic and encouraging manner if the user is sad. For example, the response unit can estimate that the user is sad using an emotion engine or generative AI and respond in an empathetic and encouraging manner. Furthermore, if the user is agitated, the response unit can listen calmly and respond in a way that calms them down. For example, the response unit can estimate that the user is agitated using an emotion engine or generative AI and respond in a way that calms them down. In this way, by adjusting the response method based on the user's emotions, a more appropriate response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0077] The support unit can analyze the user's past consultation history and select the optimal response method. For example, the support unit can suggest solutions to similar problems based on the content of past consultations. For example, the support unit can input the user's past consultation history into AI, and the AI ​​will suggest solutions to similar problems. The support unit can also identify specific patterns from the user's past consultation history and provide appropriate advice. For example, the support unit can input the user's past consultation history into AI, and the AI ​​will identify specific patterns and provide appropriate advice. Furthermore, the support unit can analyze the effectiveness of advice the user has received in the past and select the optimal response method. For example, the support unit can input the user's past consultation history into AI, and the AI ​​will analyze the effectiveness of the advice and select the optimal response method. In this way, the optimal response method can be selected by analyzing the user's past consultation history.

[0078] The support unit can filter responses to problems and inquiries based on the user's current living situation and areas of interest. For example, the support unit can consider the user's current living situation and provide appropriate advice. For instance, the support unit inputs the user's current living situation into the AI, which then provides appropriate advice. The support unit can also provide relevant information based on the user's areas of interest. For example, the support unit inputs the user's areas of interest into the AI, which then provides relevant information. Furthermore, the support unit can introduce appropriate resources based on the user's living situation and areas of interest. For example, the support unit inputs the user's living situation and areas of interest into the AI, which then introduces appropriate resources. This allows for more appropriate advice to be provided by filtering responses based on the user's living situation and areas of interest.

[0079] The response unit can estimate the user's emotions and determine the priority of consultations based on the estimated emotions. For example, if the user has an urgent consultation, the response unit will prioritize it. For example, the response unit can estimate that the user has an urgent consultation using an emotion engine or generative AI and prioritize it. The response unit can also respond quickly if the user has a serious problem. For example, the response unit can estimate that the user has a serious problem using an emotion engine or generative AI and respond quickly. Furthermore, if the user has a minor consultation, the response unit can prioritize other important consultations. For example, the response unit can estimate that the user has a minor consultation using an emotion engine or generative AI and prioritize other important consultations. In this way, by determining the priority of consultations based on the user's emotions, more important consultations can be addressed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] The support unit can prioritize providing highly relevant information by considering the user's geographical location when responding to concerns or inquiries. For example, if the user lives in a specific region, the support unit will provide information related to that region. For instance, the support unit inputs the user's geographical location into the AI, and the AI ​​provides information related to that region. Furthermore, if the user is traveling, the support unit can provide information related to their travel destination. For example, the support unit inputs the user's geographical location into the AI, and the AI ​​provides information related to their travel destination. In addition, if the user is considering moving, the support unit can provide information related to their new region. For example, the support unit inputs the user's geographical location into the AI, and the AI ​​provides information related to their new region. In this way, by considering the user's geographical location, the support unit can prioritize providing highly relevant information.

[0081] The support unit can analyze a user's social media activity and provide relevant information when addressing concerns or seeking advice. For example, the support unit can provide appropriate advice based on information shared by the user on social media. For instance, the support unit inputs the user's social media activity into an AI, which then provides appropriate advice. The support unit can also identify areas of interest from the user's social media activity and provide relevant information. For example, the support unit inputs the user's social media activity into an AI, which identifies areas of interest and provides relevant information. Furthermore, the support unit can analyze the user's social media activity and recommend appropriate resources. For example, the support unit inputs the user's social media activity into an AI, which then recommends appropriate resources. In this way, by analyzing the user's social media activity, relevant information can be provided.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can select a time to collect data if the user is relaxed. For example, the data collection unit can estimate that the user is relaxed using an emotion engine or generative AI and select a time to collect data. The data collection unit can also postpone data collection if the user is busy. For example, the data collection unit can estimate that the user is busy using an emotion engine or generative AI and postpone data collection. Furthermore, the data collection unit can refrain from collecting data if the user is stressed. For example, the data collection unit can estimate that the user is stressed using an emotion engine or generative AI and refrain from collecting data. By adjusting the timing of data collection based on the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can propose the optimal collection method based on the data the user has collected in the past. For example, the data collection unit can input the user's past data collection history into an AI, which will then propose the optimal collection method. The data collection unit can also select an effective collection method from the user's past data collection history. For example, the data collection unit can input the user's past data collection history into an AI, which will then select an effective collection method. Furthermore, the data collection unit can prioritize suggesting collection methods that the user has used in the past. For example, the data collection unit can input the user's past data collection history into an AI, which will then prioritize suggesting collection methods that the user has used in the past. In this way, the optimal collection method can be selected by analyzing the user's past data collection history.

[0084] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the data collection process. For example, the data collection unit considers the user's current lifestyle and collects appropriate data. For instance, the data collection unit inputs the user's current lifestyle into the AI, which then collects the appropriate data. The data collection unit can also collect relevant data based on the user's areas of interest. For example, the data collection unit inputs the user's areas of interest into the AI, which then collects the relevant data. Furthermore, the data collection unit can recommend appropriate resources based on the user's lifestyle and areas of interest. For example, the data collection unit inputs the user's lifestyle and areas of interest into the AI, which then recommends appropriate resources. This allows for the collection of more relevant data by filtering based on the user's lifestyle and areas of interest.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user needs urgent data, the data collection unit will prioritize its collection. For example, the data collection unit will estimate that the user needs urgent data using an emotion engine or generative AI and prioritize its collection. The data collection unit can also collect important data quickly if the user needs important data. For example, the data collection unit will estimate that the user needs important data using an emotion engine or generative AI and prioritize its collection. Furthermore, if the user needs lighter data, the data collection unit can prioritize other important data. For example, the data collection unit will estimate that the user needs lighter data using an emotion engine or generative AI and prioritize other important data. This allows for the priority collection of more important data by determining data priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user lives in a specific region, the data collection unit will collect data related to that region. For instance, the data collection unit inputs the user's geographical location information into the AI, and the AI ​​collects data related to that region. The data collection unit can also collect data related to the user's travel destination if the user is traveling. For example, the data collection unit inputs the user's geographical location information into the AI, and the AI ​​collects data related to the travel destination. Furthermore, if the user is considering moving, the data collection unit can collect data related to the new region. For example, the data collection unit inputs the user's geographical location information into the AI, and the AI ​​collects data related to the new region. In this way, by considering the user's geographical location information, the data collection unit can prioritize the collection of highly relevant data.

[0087] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect appropriate data based on information shared by the user on social media. For example, the data collection unit can input the user's social media activity into an AI, which then collects the appropriate data. The data collection unit can also identify areas of interest from the user's social media activity and collect relevant data. For example, the data collection unit can input the user's social media activity into an AI, which then identifies areas of interest and collects relevant data. Furthermore, the data collection unit can analyze the user's social media activity and recommend appropriate resources. For example, the data collection unit can input the user's social media activity into an AI, which then recommends appropriate resources. In this way, relevant data can be collected by analyzing the user's social media activity.

[0088] The optimization unit can estimate the user's emotions and adjust the optimization method for consumer behavior based on the estimated emotions. For example, if the user is relaxed, the optimization unit can propose an optimization method that proceeds at a relaxed pace. For example, the optimization unit can estimate that the user is relaxed using an emotion engine or generative AI and propose an optimization method that proceeds at a relaxed pace. The optimization unit can also propose a method to quickly optimize consumer behavior if the user is in a hurry. For example, the optimization unit can estimate that the user is in a hurry using an emotion engine or generative AI and propose a method to quickly optimize consumer behavior. Furthermore, if the user is excited, the optimization unit can propose an optimization method that adds visually stimulating effects. For example, the optimization unit can estimate that the user is excited using an emotion engine or generative AI and propose an optimization method that adds visually stimulating effects. This allows for more appropriate optimization by adjusting the optimization method for consumer behavior based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The optimization unit can analyze the user's past consumption behavior during optimization to select the optimal optimization method. For example, the optimization unit can propose the optimal optimization method based on the user's past consumption behavior. For example, the optimization unit can input the user's past consumption behavior into the AI, which will then propose the optimal optimization method. The optimization unit can also select an effective optimization method from the user's past consumption behavior. For example, the optimization unit can input the user's past consumption behavior into the AI, which will then select an effective optimization method. Furthermore, the optimization unit can prioritize suggesting optimization methods that the user has used in the past. For example, the optimization unit can input the user's past consumption behavior into the AI, which will then prioritize suggesting optimization methods that the user has used in the past. In this way, the optimal optimization method can be selected by analyzing the user's past consumption behavior.

[0090] The optimization unit can customize the optimization methods based on the user's current living situation during the optimization process. For example, the optimization unit can consider the user's current living situation and propose appropriate optimization methods. For instance, the optimization unit inputs the user's current living situation into the AI, which then proposes appropriate optimization methods. The optimization unit can also propose relevant optimization methods based on the user's areas of interest. For example, the optimization unit inputs the user's areas of interest into the AI, which then proposes relevant optimization methods. Furthermore, the optimization unit can introduce appropriate resources based on the user's living situation and areas of interest. For example, the optimization unit inputs the user's living situation and areas of interest into the AI, which then introduces appropriate resources. By customizing the optimization methods based on the user's current living situation, more effective optimization becomes possible.

[0091] The optimization unit can estimate the user's emotions and determine optimization priorities based on those emotions. For example, if the user requires urgent optimization, the optimization unit will prioritize it. For instance, the optimization unit can estimate that the user requires urgent optimization using an emotion engine or generative AI and prioritize it. The optimization unit can also respond quickly if the user requires important optimization. For example, the optimization unit can estimate that the user requires important optimization using an emotion engine or generative AI and respond quickly. Furthermore, if the user requires minor optimization, the optimization unit can prioritize other important optimizations. For example, the optimization unit can estimate that the user requires minor optimization using an emotion engine or generative AI and prioritize other important optimizations. This allows for prioritizing optimizations based on the user's emotions, thereby prioritizing more important optimizations. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The optimization unit can select the optimal optimization method by considering the user's geographical location information during the optimization process. For example, if the user lives in a specific region, the optimization unit will suggest an optimization method relevant to that region. For instance, the optimization unit inputs the user's geographical location information into the AI, and the AI ​​proposes an optimization method relevant to that region. Furthermore, if the user is traveling, the optimization unit can suggest an optimization method relevant to their travel destination. For example, the optimization unit inputs the user's geographical location information into the AI, and the AI ​​proposes an optimization method relevant to their travel destination. In addition, if the user is considering moving, the optimization unit can suggest an optimization method relevant to their new region. For example, the optimization unit inputs the user's geographical location information into the AI, and the AI ​​proposes an optimization method relevant to their new region. This allows for the selection of a highly relevant optimization method by considering the user's geographical location information.

[0093] The optimization unit can analyze the user's social media activity during optimization and propose optimization methods. For example, the optimization unit can propose appropriate optimization methods based on information shared by the user on social media. For example, the optimization unit inputs the user's social media activity into an AI, which then proposes appropriate optimization methods. The optimization unit can also identify areas of interest from the user's social media activity and propose related optimization methods. For example, the optimization unit inputs the user's social media activity into an AI, which identifies areas of interest and proposes related optimization methods. Furthermore, the optimization unit can analyze the user's social media activity and introduce appropriate resources. For example, the optimization unit inputs the user's social media activity into an AI, which then introduces appropriate resources. In this way, by analyzing the user's social media activity, it is possible to propose relevant optimization methods.

[0094] The proxy unit can estimate the user's emotions and adjust the way it performs the proxy activity based on the estimated emotions. For example, if the user is relaxed, the proxy unit will perform the proxy activity at a relaxed pace. For example, the proxy unit can estimate that the user is relaxed using an emotion engine or generative AI and perform the proxy activity at a relaxed pace. The proxy unit can also perform the proxy activity quickly if the user is in a hurry. For example, the proxy unit can estimate that the user is in a hurry using an emotion engine or generative AI and perform the proxy activity quickly. Furthermore, if the user is excited, the proxy unit can perform the proxy activity with visually stimulating effects. For example, the proxy unit can estimate that the user is excited using an emotion engine or generative AI and perform the proxy activity with visually stimulating effects. This allows for more appropriate proxy performance by adjusting the way the proxy activity is performed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0095] The proxy service can analyze the user's past instruction history to select the optimal proxy service method. For example, the proxy service can suggest a similar proxy service method based on the user's past instructions. For example, the proxy service can input the user's past instruction history into the AI, which will then suggest a similar proxy service method. The proxy service can also select an effective proxy service method from the user's past instruction history. For example, the proxy service can input the user's past instruction history into the AI, which will then select an effective proxy service method. Furthermore, the proxy service can prioritize suggesting proxy service methods that the user has used in the past. For example, the proxy service can input the user's past instruction history into the AI, which will then prioritize suggesting proxy service methods that the user has used in the past. In this way, the optimal proxy service method can be selected by analyzing the user's past instruction history.

[0096] The proxy service can customize the means of proxy service based on the user's current living situation. For example, the proxy service can consider the user's current living situation and propose an appropriate proxy service. For example, the proxy service can input the user's current living situation into the AI, and the AI ​​will propose an appropriate proxy service. The proxy service can also propose relevant proxy services based on the user's areas of interest. For example, the proxy service can input the user's areas of interest into the AI, and the AI ​​will propose relevant proxy services. Furthermore, the proxy service can introduce appropriate resources based on the user's living situation and areas of interest. For example, the proxy service can input the user's living situation and areas of interest into the AI, and the AI ​​will introduce appropriate resources. This allows for more appropriate proxy service by customizing the means of proxy service based on the user's current living situation.

[0097] The proxy function can estimate the user's emotions and determine the priority of the activities to be performed based on those estimated emotions. For example, if the user requires urgent assistance, the proxy function will prioritize that request. For instance, the proxy function can estimate that the user requires urgent assistance using an emotion engine or generative AI and prioritize that request. The proxy function can also respond quickly if the user requires important assistance. For example, the proxy function can estimate that the user requires important assistance using an emotion engine or generative AI and respond quickly. Furthermore, if the user requires minor assistance, the proxy function can prioritize other important tasks. For example, the proxy function can estimate that the user requires minor assistance using an emotion engine or generative AI and prioritize other important tasks. This allows for prioritizing more important tasks by determining the priority of activities to be performed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0098] The proxy service can select the most suitable proxy service method by considering the user's geographical location information during the proxy service. For example, if the user lives in a specific region, the proxy service can suggest a proxy service method relevant to that region. For instance, the proxy service can input the user's geographical location information into the AI, and the AI ​​will suggest a proxy service method relevant to that region. Furthermore, if the user is traveling, the proxy service can suggest a proxy service method relevant to their travel destination. For example, the proxy service can input the user's geographical location information into the AI, and the AI ​​will suggest a proxy service method relevant to their travel destination. In addition, if the user is considering moving, the proxy service can suggest a proxy service method relevant to their new region. For example, the proxy service can input the user's geographical location information into the AI, and the AI ​​will suggest a proxy service method relevant to their new region. This allows the system to select a highly relevant proxy service method by considering the user's geographical location information.

[0099] The proxy service can analyze the user's social media activity and propose appropriate proxy methods during the proxy service process. For example, the proxy service can propose appropriate proxy methods based on information shared by the user on social media. For example, the proxy service can input the user's social media activity into an AI, which then proposes appropriate proxy methods. The proxy service can also identify areas of interest from the user's social media activity and propose relevant proxy methods. For example, the proxy service can input the user's social media activity into an AI, which then identifies areas of interest and proposes relevant proxy methods. Furthermore, the proxy service can analyze the user's social media activity and introduce appropriate resources. For example, the proxy service can input the user's social media activity into an AI, which then introduces appropriate resources. In this way, by analyzing the user's social media activity, relevant proxy methods can be proposed.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The support unit can estimate the user's emotions and adjust its approach to addressing the user's concerns and questions based on those estimates. For example, if the user is stressed, the support unit can offer advice to help them relax. If the user is sad, the support unit can take an empathetic and encouraging approach. Furthermore, if the user is agitated, the support unit can calmly listen and provide a soothing response. This enables appropriate responses tailored to the user's emotions.

[0102] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is relaxed, the unit can select a suitable time to collect data. If the user is busy, the unit can postpone data collection. Furthermore, if the user is stressed, the unit can refrain from collecting data. By adjusting the timing of data collection based on the user's emotions, data can be collected at a more appropriate time.

[0103] The optimization unit can estimate the user's emotions and adjust the method of optimizing consumer behavior based on those emotions. For example, if the user is relaxed, the optimization unit can suggest an optimization method that proceeds at a leisurely pace. If the user is in a hurry, the optimization unit can suggest a method that optimizes consumer behavior quickly. Furthermore, if the user is excited, the optimization unit can suggest an optimization method that incorporates visually stimulating effects. By adjusting the method of optimizing consumer behavior based on the user's emotions, more appropriate optimization becomes possible.

[0104] The proxy unit can estimate the user's emotions and adjust the way it performs the proxy activity based on those emotions. For example, if the user is relaxed, the proxy unit can perform the proxy activity at a leisurely pace. If the user is in a hurry, the proxy unit can perform the proxy activity quickly. Furthermore, if the user is excited, the proxy unit can perform the proxy activity with visually stimulating effects. By adjusting the way the proxy activity is performed based on the user's emotions, more appropriate proxy services become possible.

[0105] The response unit can estimate the user's emotions and determine the priority of the consultation based on those emotions. For example, if the user has an urgent consultation, the response unit can prioritize it. Also, if the user has a serious problem, the response unit can respond promptly. Furthermore, if the user has a minor consultation, the response unit can prioritize other important consultations. In this way, by determining the priority of consultations based on the user's emotions, it is possible to prioritize and respond to more important consultations.

[0106] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on data the user has collected in the past. Furthermore, the data collection unit can select an effective collection method from the user's past data collection history. In addition, the data collection unit can prioritize suggesting collection methods the user has used in the past. This allows the optimal collection method to be selected by analyzing the user's past data collection history.

[0107] The optimization unit can analyze the user's past consumption behavior during optimization to select the optimal optimization method. For example, the optimization unit can propose the optimal optimization method based on the user's past consumption behavior. Furthermore, the optimization unit can select an effective optimization method from the user's past consumption behavior. In addition, the optimization unit can prioritize suggesting optimization methods that the user has used in the past. This allows for the selection of the optimal optimization method by analyzing the user's past consumption behavior.

[0108] The proxy service can analyze the user's past instruction history to select the most suitable proxy service method during the proxy service process. For example, the proxy service can propose a similar proxy service method based on the user's past instructions. Furthermore, the proxy service can select an effective proxy service method from the user's past instruction history. In addition, the proxy service can prioritize suggesting proxy service methods that the user has used in the past. This allows the proxy service to select the most suitable method by analyzing the user's past instruction history.

[0109] The support unit can prioritize providing highly relevant information when responding to user inquiries or questions, taking into account the user's geographical location. For example, if a user lives in a specific region, it can provide information related to that region. If a user is traveling, it can provide information related to their travel destination. Furthermore, if a user is considering moving, it can provide information related to their new area. In this way, by considering the user's geographical location, it can prioritize providing highly relevant information.

[0110] The data collection unit can analyze users' social media activity and collect relevant data during the data collection process. For example, the data collection unit can collect appropriate data based on information shared by users on social media. Furthermore, the data collection unit can identify areas of interest from users' social media activity and collect relevant data. In addition, the data collection unit can analyze users' social media activity and recommend appropriate resources. This allows for the collection of relevant data by analyzing users' social media activity.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The support unit responds to users' concerns and inquiries. For example, it uses agent AI such as health, childcare, learning, and fashion advisors to provide appropriate advice according to the user's concerns and inquiries. Specifically, it uses a health advisor AI to provide advice on health-related concerns, and a childcare advisor AI to provide advice on childcare-related concerns. Step 2: The collection unit collects the user's personal data based on the information obtained by the response unit. For example, it collects data such as health parameters, lifestyle habits, and purchase history of daily necessities and luxury goods. Specifically, it collects health parameters such as the user's body temperature, blood pressure, and heart rate, lifestyle data such as diet and exercise frequency, and purchase history of daily necessities and luxury goods. Step 3: The optimization unit optimizes consumer behavior based on the data collected by the data collection unit. For example, it analyzes the collected data, analyzes the user's purchasing patterns, and suggests the most suitable products. It also suggests the most suitable products based on the user's preferences and lifestyle. Step 4: The proxy unit performs the activities instructed by the user based on the optimization results obtained by the optimization unit. For example, it may send reminders for anniversaries with a partner, or suggest and arrange surprise events that will be appreciated. Specifically, it may send anniversary reminder notifications, suggest and arrange surprise events, or manage parents' health parameters and purchase daily necessities on their behalf.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the response unit, collection unit, optimization unit, and proxy unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the response unit is implemented by the control unit 46A of the smart device 14 and provides appropriate advice in response to the user's concerns and inquiries. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects the user's personal data. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes consumption behavior based on the collected data. The proxy unit is implemented by the control unit 46A of the smart device 14 and performs activities instructed by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the response unit, collection unit, optimization unit, and proxy unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the response unit is implemented by the control unit 46A of the smart glasses 214 and provides appropriate advice in response to the user's concerns and inquiries. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects the user's personal data. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes consumer behavior based on the collected data. The proxy unit is implemented by the control unit 46A of the smart glasses 214 and performs activities instructed by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the response unit, collection unit, optimization unit, and proxy unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the response unit is implemented by the control unit 46A of the headset terminal 314 and provides appropriate advice in response to the user's concerns and inquiries. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects the user's personal data. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes consumption behavior based on the collected data. The proxy unit is implemented by the control unit 46A of the headset terminal 314 and performs activities instructed by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the response unit, collection unit, optimization unit, and proxy unit, is implemented in at least one of the robot 414 and the data processing device 12. For example, the response unit is implemented by the control unit 46A of the robot 414 and provides appropriate advice in response to the user's concerns and inquiries. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects the user's personal data. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and optimizes consumption behavior based on the collected data. The proxy unit is implemented by the control unit 46A of the robot 414 and performs activities instructed by the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A support department that handles user concerns and inquiries, A collection unit collects the user's personal data based on the information obtained by the aforementioned correspondence unit, An optimization unit that optimizes consumer behavior based on the data collected by the aforementioned collection unit, A proxy unit that performs activities instructed by the user based on the optimization results obtained by the optimization unit, Equipped with A system characterized by the following features. (Note 2) The corresponding part is, Using AI agents in areas such as health, childcare, learning, and fashion, we provide appropriate advice based on users' concerns and questions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We collect data such as health parameters, lifestyle habits, and purchase history of everyday items and luxury goods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, Optimize consumer behavior based on collected data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned agency unit, We provide reminders for anniversaries with your partner and offer suggestions and arrangements for surprise events that will be appreciated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned agency unit, They manage health parameters and purchase daily necessities on behalf of their parents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned agency unit, Providing health, medication / medication reminders, lifestyle guidance, and monitoring functions for generations older than grandparents. The system described in Appendix 1, characterized by the features described herein. (Note 8) The corresponding part is, The system estimates the user's emotions and adjusts how it responds to their concerns and questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The corresponding part is, Analyze the user's past consultation history and select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The corresponding part is, When responding to concerns and inquiries, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The corresponding part is, The system estimates the user's emotions and determines the priority of consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The corresponding part is, When addressing concerns or providing advice, the system prioritizes providing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The corresponding part is, When addressing concerns or providing advice, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is When collecting data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, It estimates user emotions and adjusts the method of optimizing consumer behavior based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, During optimization, the system analyzes users' past consumption behavior to select the most suitable optimization method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, the optimization methods are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, During optimization, the optimal optimization method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, During optimization, we analyze users' social media activity and propose optimization methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned agency unit, It estimates the user's emotions and adjusts the way it performs actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned agency unit, When performing a task on behalf of someone else, the system analyzes the user's past instruction history to select the most suitable method of assistance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned agency unit, When providing assistance, the method of assistance is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned agency unit, It estimates the user's emotions and determines the priority of activities to perform based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned agency unit, When performing the task on behalf of the user, the optimal method of assistance will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned agency unit, During the proxy service, we analyze the user's social media activity and propose the appropriate methods for handling it. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A support department that handles user concerns and inquiries, A collection unit collects the user's personal data based on the information obtained by the aforementioned correspondence unit, An optimization unit that optimizes consumer behavior based on the data collected by the aforementioned collection unit, A proxy unit that performs activities instructed by the user based on the optimization results obtained by the optimization unit, Equipped with A system characterized by the following features.

2. The corresponding part is, Using AI agents in areas such as health, childcare, learning, and fashion, we provide appropriate advice based on users' concerns and questions. The system according to feature 1.

3. The aforementioned collection unit is We collect data such as health parameters, lifestyle habits, and purchase history of everyday items and luxury goods. The system according to feature 1.

4. The optimization unit, Optimize consumer behavior based on collected data. The system according to feature 1.

5. The aforementioned agency unit, We provide reminders for anniversaries with your partner and offer suggestions and arrangements for surprise events that will be appreciated. The system according to feature 1.

6. The aforementioned agency unit, They manage health parameters and purchase daily necessities on behalf of their parents. The system according to feature 1.

7. The aforementioned agency unit, Providing health, medication / medication reminders, lifestyle guidance, and monitoring functions for generations older than grandparents. The system according to feature 1.

8. The corresponding part is, The system estimates the user's emotions and adjusts how it responds to their concerns and questions based on those estimated emotions. The system according to feature 1.

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